SYMBOL INDEX (8050 symbols across 743 files) FILE: CI/batch/cancel-job.py function main (line 29) | def main(): FILE: CI/batch/submit-job.py function printLogs (line 94) | def printLogs(logGroupName, logStreamName, startTime): function nowInMillis (line 117) | def nowInMillis(): function main (line 122) | def main(): FILE: CI/bench/evaluate.py function process_results (line 9) | def process_results(eval_flag: bool): function main (line 91) | def main(): FILE: CI/bench/tabular/amlb_user_dir/setup_hf_cache.py function setup_hf_cache (line 11) | def setup_hf_cache(): FILE: common/src/autogluon/common/dataset.py class TabularDataset (line 13) | class TabularDataset: method __new__ (line 38) | def __new__(cls, data, **kwargs) -> pd.DataFrame: method load (line 44) | def load(cls, path: str | Path, **kwargs) -> pd.DataFrame: method save (line 48) | def save(cls, path: str | Path, df: pd.DataFrame, **kwargs): FILE: common/src/autogluon/common/features/feature_metadata.py class FeatureMetadata (line 13) | class FeatureMetadata: method __init__ (line 38) | def __init__( method __eq__ (line 59) | def __eq__(self, other) -> bool: method _validate (line 73) | def _validate(self): method get_features (line 90) | def get_features( method get_feature_type_raw (line 203) | def get_feature_type_raw(self, feature: str) -> str: method get_feature_types_special (line 206) | def get_feature_types_special(self, feature: str) -> list: method get_type_map_special (line 211) | def get_type_map_special(self) -> dict: method get_type_group_map_special_from_type_map_special (line 215) | def get_type_group_map_special_from_type_map_special(type_map_special:... method get_type_group_map_raw (line 222) | def get_type_group_map_raw(self): method remove_features (line 228) | def remove_features(self, features: list, inplace=False): method keep_features (line 243) | def keep_features(self, features: list, inplace=False): method add_special_types (line 253) | def add_special_types(self, type_map_special: Dict[str, List[str]], in... method _remove_features_from_type_group_map (line 291) | def _remove_features_from_type_group_map(d, features): method _remove_features_from_type_map (line 296) | def _remove_features_from_type_map(d, features): method rename_features (line 301) | def rename_features(self, rename_map: dict, inplace=False): method join_metadata (line 321) | def join_metadata(self, metadata, shared_raw_features="error"): method _add_type_group_map_special (line 372) | def _add_type_group_map_special(type_group_map_special_lst: List[dict]... method _get_feature_types (line 388) | def _get_feature_types(feature: str, feature_types_dict: dict) -> list: method join_metadatas (line 398) | def join_metadatas(metadata_list, shared_raw_features="error"): method to_dict (line 404) | def to_dict(self, inverse=False) -> dict: method print_feature_metadata_full (line 423) | def print_feature_metadata_full( method from_df (line 475) | def from_df(cls, df: pd.DataFrame): method verify_data (line 492) | def verify_data(self, df: pd.DataFrame) -> bool: method verify_data_subset (line 502) | def verify_data_subset(self, df: pd.DataFrame) -> bool: method verify_data_superset (line 512) | def verify_data_superset(self, df: pd.DataFrame) -> bool: method _verify_data_type_raw (line 524) | def _verify_data_type_raw(self, type_map_raw: dict) -> bool: method __str__ (line 531) | def __str__(self): FILE: common/src/autogluon/common/features/infer_types.py function get_type_family_raw (line 12) | def get_type_family_raw(dtype) -> str: function get_type_map_real (line 43) | def get_type_map_real(df: DataFrame) -> dict: function get_type_map_raw (line 49) | def get_type_map_raw(df: DataFrame) -> dict: function get_type_map_special (line 54) | def get_type_map_special(X: DataFrame) -> dict: function get_types_special (line 63) | def get_types_special(X: Series) -> List[str]: function get_type_group_map (line 74) | def get_type_group_map(type_map: dict) -> defaultdict: function get_type_group_map_real (line 85) | def get_type_group_map_real(df: DataFrame) -> defaultdict: function get_type_group_map_raw (line 90) | def get_type_group_map_raw(df: DataFrame) -> defaultdict: function get_type_group_map_special (line 95) | def get_type_group_map_special(df: DataFrame) -> defaultdict: function check_if_datetime_as_object_feature (line 103) | def check_if_datetime_as_object_feature(X: Series) -> bool: function check_if_nlp_feature (line 131) | def check_if_nlp_feature(X: Series) -> bool: function get_bool_true_val (line 154) | def get_bool_true_val(uniques): FILE: common/src/autogluon/common/loaders/_utils.py function replace_file (line 25) | def replace_file(src, dst): function _str_to_unicode (line 55) | def _str_to_unicode(x): function _handle_errors (line 61) | def _handle_errors(rv, src): function replace_file (line 73) | def replace_file(src, dst): function sha1sum (line 90) | def sha1sum(filename): function download (line 108) | def download( function path_expander (line 248) | def path_expander(path, base_folder): function protected_zip_extraction (line 253) | def protected_zip_extraction(zipfile_path, sha1_hash, folder): FILE: common/src/autogluon/common/loaders/load_json.py function load (line 11) | def load(path: str, *, verbose=True) -> dict | list: FILE: common/src/autogluon/common/loaders/load_pd.py function load (line 20) | def load( function _load_multipart_child (line 152) | def _load_multipart_child(chunk): function _load_multipart (line 186) | def _load_multipart( function _load_multipart_s3 (line 246) | def _load_multipart_s3( FILE: common/src/autogluon/common/loaders/load_pkl.py function load (line 16) | def load(path: str, format: str | None = None, verbose: bool = True, **k... function _is_web_url (line 80) | def _is_web_url(path: str) -> bool: function _load_pickle_from_url (line 88) | def _load_pickle_from_url(url: str): function load_with_fn (line 94) | def load_with_fn(path, pickle_fn, format=None, verbose=True): FILE: common/src/autogluon/common/loaders/load_s3.py function list_bucket_s3 (line 9) | def list_bucket_s3(bucket): function download (line 23) | def download(input_bucket, input_prefix, local_path): function list_bucket_prefix_suffix_contains_s3 (line 39) | def list_bucket_prefix_suffix_contains_s3( FILE: common/src/autogluon/common/loaders/load_str.py function load (line 8) | def load(path: str) -> str: FILE: common/src/autogluon/common/loaders/load_zip.py function unzip (line 7) | def unzip(path, sha1sum=None, unzip_dir=None): FILE: common/src/autogluon/common/model_filter/_model_filter.py class ModelFilter (line 7) | class ModelFilter: method include_models (line 11) | def include_models( method exclude_models (line 48) | def exclude_models( method filter_models (line 79) | def filter_models( FILE: common/src/autogluon/common/savers/save_json.py function save (line 12) | def save(path, obj, sanitize=True): function sanitize_object_to_primitives (line 32) | def sanitize_object_to_primitives(obj): FILE: common/src/autogluon/common/savers/save_pd.py function save (line 24) | def save( function _save_multipart_child (line 150) | def _save_multipart_child(chunk): function _save_multipart (line 164) | def _save_multipart(path, df, index=False, verbose=True, type=None, sep=... FILE: common/src/autogluon/common/savers/save_pkl.py function save (line 15) | def save(path, object, format=None, verbose=True, **kwargs): function save_with_fn (line 40) | def save_with_fn(path, object, pickle_fn, format=None, verbose=True, com... function save_s3 (line 60) | def save_s3(path: str, obj, pickle_fn, verbose=True): FILE: common/src/autogluon/common/savers/save_str.py function save (line 9) | def save(path, data: str, verbose=True): FILE: common/src/autogluon/common/space.py class Space (line 4) | class Space(object): method default (line 8) | def default(self): class SimpleSpace (line 13) | class SimpleSpace(Space): method __init__ (line 14) | def __init__(self, default): method __repr__ (line 19) | def __repr__(self): method default (line 28) | def default(self): method default (line 33) | def default(self, value): class DiscreteSpace (line 38) | class DiscreteSpace(SimpleSpace): method __len__ (line 43) | def __len__(self) -> int: class Categorical (line 48) | class Categorical(DiscreteSpace): method __init__ (line 62) | def __init__(self, *data): method __iter__ (line 66) | def __iter__(self): method __getitem__ (line 70) | def __getitem__(self, index): method __setitem__ (line 73) | def __setitem__(self, index, data): method __len__ (line 76) | def __len__(self): method convert_to_sklearn (line 79) | def convert_to_sklearn(self): method __repr__ (line 82) | def __repr__(self): class Real (line 87) | class Real(SimpleSpace): method __init__ (line 107) | def __init__(self, lower, upper, default=None, log=False): method convert_to_sklearn (line 119) | def convert_to_sklearn(self): class Int (line 129) | class Int(DiscreteSpace): method __init__ (line 147) | def __init__(self, lower, upper, default=None): method convert_to_sklearn (line 154) | def convert_to_sklearn(self): method __len__ (line 159) | def __len__(self): class Bool (line 163) | class Bool(Int): method __init__ (line 172) | def __init__(self): FILE: common/src/autogluon/common/utils/cache_presets_to_yaml.py function _is_s3_uri (line 8) | def _is_s3_uri(path: str) -> bool: function _parse_s3_uri (line 12) | def _parse_s3_uri(uri: str) -> tuple[str, str]: function _s3_put_text (line 23) | def _s3_put_text( function presets_to_yaml_files (line 47) | def presets_to_yaml_files( FILE: common/src/autogluon/common/utils/compression_utils.py function _gzip_open (line 1) | def _gzip_open(*args, **kwargs): function _bz2_open (line 7) | def _bz2_open(*args, **kwargs): function _lzma_open (line 15) | def _lzma_open(*args, **kwargs): function get_validated_path (line 41) | def get_validated_path(filename, compression_fn=None): function get_compression_map (line 49) | def get_compression_map(): FILE: common/src/autogluon/common/utils/context.py function set_torch_num_threads (line 5) | def set_torch_num_threads(num_cpus): FILE: common/src/autogluon/common/utils/cpu_utils.py function get_available_cpu_count (line 15) | def get_available_cpu_count(only_physical_cores: bool = False) -> int: FILE: common/src/autogluon/common/utils/cv_splitter.py class CVSplitter (line 23) | class CVSplitter: method __init__ (line 24) | def __init__( method _get_splitter_cls (line 77) | def _get_splitter_cls(self): method _get_splitter (line 93) | def _get_splitter(self, splitter_cls) -> BaseCrossValidator: method split (line 104) | def split(self, X: pd.DataFrame | None, y: pd.Series | np.ndarray) -> ... FILE: common/src/autogluon/common/utils/decorators.py function unpack (line 11) | def unpack(g, *other_args): function _looks_like_preset_location (line 41) | def _looks_like_preset_location(s: str) -> bool: function _resolve_preset_str (line 62) | def _resolve_preset_str( function _apply_presets (line 97) | def _apply_presets(preset_dict: Dict[str, dict], presets_alias: Dict[str... function apply_presets (line 151) | def apply_presets(preset_dict: Dict[str, dict], presets_alias: Dict[str,... FILE: common/src/autogluon/common/utils/deprecated_utils.py function _deprecation_warning (line 11) | def _deprecation_warning( function Deprecated (line 34) | def Deprecated( function _rename_kwargs (line 137) | def _rename_kwargs( function Deprecated_args (line 165) | def Deprecated_args( function construct_deprecated_wrapper (line 242) | def construct_deprecated_wrapper(ag_version) -> Callable: function construct_deprecated_args_wrapper (line 251) | def construct_deprecated_args_wrapper(ag_version) -> Callable: FILE: common/src/autogluon/common/utils/distribute_utils.py class DistributedContext (line 4) | class DistributedContext: method get_util_path (line 28) | def get_util_path() -> str: method get_model_sync_path (line 33) | def get_model_sync_path() -> str: method is_distributed_mode (line 38) | def is_distributed_mode() -> bool: method is_shared_network_file_system (line 43) | def is_shared_network_file_system() -> bool: FILE: common/src/autogluon/common/utils/file_utils.py function get_directory_size (line 6) | def get_directory_size(path: str) -> int: function get_directory_size_per_file (line 20) | def get_directory_size_per_file(path: str, *, sort_by: str = "size", inc... FILE: common/src/autogluon/common/utils/hyperparameter_utils.py function is_advanced_hyperparameter_format (line 1) | def is_advanced_hyperparameter_format(hyperparameters: dict) -> bool: function get_hyperparameter_str_deprecation_msg (line 36) | def get_hyperparameter_str_deprecation_msg() -> str: function get_deprecated_lightgbm_large_hyperparameters (line 53) | def get_deprecated_lightgbm_large_hyperparameters() -> dict: FILE: common/src/autogluon/common/utils/lite.py function disable_if_lite_mode (line 4) | def disable_if_lite_mode(ret=None): FILE: common/src/autogluon/common/utils/log_utils.py class DuplicateFilter (line 7) | class DuplicateFilter(object): method __init__ (line 22) | def __init__(self, filter_targets=None): method filter (line 28) | def filter(self, record): method attach_filter_targets (line 34) | def attach_filter_targets(self, filter_targets): method clear_filter_targets (line 40) | def clear_filter_targets(self): function verbosity2loglevel (line 45) | def verbosity2loglevel(verbosity): function set_logger_verbosity (line 62) | def set_logger_verbosity(verbosity: int, logger=None): function add_log_to_file (line 72) | def add_log_to_file(file_path: str, logger: Optional[logging.Logger] = N... function _check_if_kaggle (line 90) | def _check_if_kaggle() -> bool: function _add_stream_handler (line 101) | def _add_stream_handler(): function fix_logging_if_kaggle (line 118) | def fix_logging_if_kaggle(): function fix_sklearnex_logging_if_kaggle (line 130) | def fix_sklearnex_logging_if_kaggle(): function convert_time_in_s_to_log_friendly (line 147) | def convert_time_in_s_to_log_friendly(time_in_sec: float, min_value: flo... function reset_logger_for_remote_call (line 179) | def reset_logger_for_remote_call(verbosity: int): function warn_if_mlflow_autologging_is_enabled (line 200) | def warn_if_mlflow_autologging_is_enabled(logger: Optional[logging.Logge... FILE: common/src/autogluon/common/utils/multiprocessing_utils.py function execute_multiprocessing (line 9) | def execute_multiprocessing(workers_count, transformer, chunks, multipro... FILE: common/src/autogluon/common/utils/nvutil.py function cudaInit (line 20) | def cudaInit(): function cudaDeviceGetCount (line 43) | def cudaDeviceGetCount(): function _LoadNvmlLibrary (line 51) | def _LoadNvmlLibrary(): function cudaSystemGetNVMLVersion (line 88) | def cudaSystemGetNVMLVersion(): function _cudaGetFunctionPointer (line 100) | def _cudaGetFunctionPointer(name): function _cudaCheckReturn (line 121) | def _cudaCheckReturn(ret): class NVMLError (line 127) | class NVMLError(Exception): method __new__ (line 135) | def __new__(typ, value): method __str__ (line 146) | def __str__(self): method __eq__ (line 154) | def __eq__(self, other): function cudaShutdown (line 158) | def cudaShutdown(): FILE: common/src/autogluon/common/utils/pandas_utils.py function _suspend_logging_for_package (line 13) | def _suspend_logging_for_package(package_name): function get_approximate_df_mem_usage (line 34) | def get_approximate_df_mem_usage(df: DataFrame, sample_ratio=0.2): FILE: common/src/autogluon/common/utils/path_converter.py class PathConverter (line 6) | class PathConverter: method _is_windows (line 10) | def _is_windows(): method _is_absolute (line 14) | def _is_absolute(path: str) -> bool: method _validate_path (line 18) | def _validate_path(path: str): method to_windows (line 24) | def to_windows(path: str) -> str: method _to_windows (line 29) | def _to_windows(path: str) -> PureWindowsPath: method to_posix (line 33) | def to_posix(path: str) -> str: method _to_posix (line 38) | def _to_posix(path: str) -> PurePosixPath: method to_current (line 42) | def to_current(path: str) -> str: method os_path_sep (line 46) | def os_path_sep() -> str: method to_relative (line 52) | def to_relative(path: str) -> str: method to_absolute (line 65) | def to_absolute(path: str) -> str: FILE: common/src/autogluon/common/utils/presets_io.py function _read_bytes_from_s3 (line 8) | def _read_bytes_from_s3(uri: str) -> bytes: function _read_bytes_from_http (line 21) | def _read_bytes_from_http(uri: str, *, timeout_s: float = 30.0) -> bytes: function load_preset_dict_from_location (line 29) | def load_preset_dict_from_location(location: str) -> dict: FILE: common/src/autogluon/common/utils/resource_utils.py class ResourceManager (line 17) | class ResourceManager: method get_cpu_count (line 21) | def get_cpu_count(only_physical_cores: bool = False) -> int: method get_cpu_count_psutil (line 41) | def get_cpu_count_psutil(logical=True): method get_gpu_count (line 48) | def get_gpu_count() -> int: method get_gpu_count_torch (line 55) | def get_gpu_count_torch(cuda_only: bool = False) -> int: method get_gpu_free_memory (line 91) | def get_gpu_free_memory(): method get_memory_size (line 110) | def get_memory_size(format: str = "B") -> float: method get_memory_rss (line 126) | def get_memory_rss(format: str = "B") -> float: method get_available_virtual_mem (line 131) | def get_available_virtual_mem(format: str = "B") -> float: method bytes_converter (line 136) | def bytes_converter(value: float, format_in: str, format_out: str) -> ... method get_process (line 167) | def get_process(pid=None): method get_available_disk_size (line 173) | def get_available_disk_size(): method get_disk_usage (line 185) | def get_disk_usage(path: str): method _get_gpu_count_cuda (line 194) | def _get_gpu_count_cuda(): method _get_custom_memory_size (line 206) | def _get_custom_memory_size(): method _get_memory_size (line 217) | def _get_memory_size() -> float: method _get_memory_rss (line 227) | def _get_memory_rss() -> float: method _get_available_virtual_mem (line 232) | def _get_available_virtual_mem() -> float: class RayResourceManager (line 243) | class RayResourceManager: method _init_ray (line 247) | def _init_ray(): method _get_cluster_resources (line 260) | def _get_cluster_resources(key: str, default_val: Union[int, float] = 0): method get_cpu_count (line 278) | def get_cpu_count() -> int: method get_gpu_count (line 283) | def get_gpu_count() -> int: method get_available_virtual_mem (line 288) | def get_available_virtual_mem(format: str = "B") -> float: function get_resource_manager (line 293) | def get_resource_manager(): FILE: common/src/autogluon/common/utils/s3_utils.py function is_s3_url (line 14) | def is_s3_url(path: str) -> bool: function s3_path_to_bucket_prefix (line 32) | def s3_path_to_bucket_prefix(s3_path: str) -> Tuple[str, str]: function s3_bucket_prefix_to_path (line 54) | def s3_bucket_prefix_to_path(bucket: str, prefix: str, version: str = "s... function delete_s3_prefix (line 60) | def delete_s3_prefix(bucket: str, prefix: str): function upload_file (line 73) | def upload_file(*, file_name: str, bucket: str, prefix: Optional[str] = ... function upload_s3_folder (line 104) | def upload_s3_folder( function download_s3_folder (line 173) | def download_s3_folder( function get_s3_to_local_tuple_list_from_s3_folder (line 249) | def get_s3_to_local_tuple_list_from_s3_folder( function get_s3_to_local_tuple_list (line 286) | def get_s3_to_local_tuple_list( function download_s3_file (line 320) | def download_s3_file( function copy_s3_file (line 383) | def copy_s3_file(origin_path: str, destination_path: str) -> None: function download_s3_files (line 405) | def download_s3_files(*, s3_to_local_tuple_list: List[Tuple[str, str]], ... function _get_local_objs_to_upload_and_s3_prefix (line 417) | def _get_local_objs_to_upload_and_s3_prefix(folder_to_upload: str) -> Li... function _get_local_path_to_download_objs (line 440) | def _get_local_path_to_download_objs(s3_objs: List[str], prefix: str, lo... FILE: common/src/autogluon/common/utils/simulation_utils.py function _recursive_dd (line 8) | def _recursive_dd(): function _dd_to_dict (line 12) | def _dd_to_dict(dd): function convert_simulation_artifacts_to_tabular_predictions_dict (line 20) | def convert_simulation_artifacts_to_tabular_predictions_dict( FILE: common/src/autogluon/common/utils/system_info.py function get_ag_system_info_disk_space (line 9) | def get_ag_system_info_disk_space(path: str) -> Tuple[str, int]: function get_ag_system_info (line 43) | def get_ag_system_info(*, path: str = None, include_gpu_count=False, inc... FILE: common/src/autogluon/common/utils/try_import.py function try_import_mxboard (line 26) | def try_import_mxboard(): function try_import_ray (line 33) | def try_import_ray() -> ModuleType: function try_import_catboost (line 66) | def try_import_catboost(): function try_import_lightgbm (line 89) | def try_import_lightgbm(): function try_import_xgboost (line 104) | def try_import_xgboost(): function try_import_interpret (line 121) | def try_import_interpret(): function try_import_faiss (line 131) | def try_import_faiss(): function try_import_fastai (line 138) | def try_import_fastai(): function try_import_torch (line 156) | def try_import_torch(): function try_import_autogluon_multimodal (line 167) | def try_import_autogluon_multimodal(): function try_import_rapids_cuml (line 177) | def try_import_rapids_cuml(): function try_import_imodels (line 190) | def try_import_imodels(): FILE: common/src/autogluon/common/utils/utils.py function setup_outputdir (line 28) | def setup_outputdir( function get_python_version (line 124) | def get_python_version(include_micro=True) -> str: function get_package_versions (line 131) | def get_package_versions(*, strict: bool = False) -> tuple[dict[str, str... function get_autogluon_metadata (line 182) | def get_autogluon_metadata() -> dict[str, Any]: function compare_autogluon_metadata (line 196) | def compare_autogluon_metadata(*, original: dict, current: dict, check_p... function bytes_to_mega_bytes (line 238) | def bytes_to_mega_bytes(memory_amount: int) -> int: function check_saved_predictor_version (line 243) | def check_saved_predictor_version( function hash_pandas_df (line 275) | def hash_pandas_df(df: Optional[pd.DataFrame]) -> str: function seed_everything (line 288) | def seed_everything(seed: int) -> None: FILE: common/src/autogluon/common/utils/warning_filter.py class warning_filter (line 6) | class warning_filter(warnings.catch_warnings): method __enter__ (line 7) | def __enter__(self): FILE: common/tests/conftest.py function pytest_addoption (line 4) | def pytest_addoption(parser): function pytest_configure (line 8) | def pytest_configure(config): function pytest_collection_modifyitems (line 14) | def pytest_collection_modifyitems(config, items): FILE: common/tests/test_check_style.py function test_check_style (line 8) | def test_check_style(): FILE: common/tests/unittests/test_compression_utils.py function test_get_validated_path_no_compression_fn (line 4) | def test_get_validated_path_no_compression_fn(): function test_get_validated_path_with_compression_fn (line 15) | def test_get_validated_path_with_compression_fn(): function test_get_compression_map (line 72) | def test_get_compression_map(): FILE: common/tests/unittests/test_dataset.py function test_tabular_dataset (line 6) | def test_tabular_dataset(): FILE: common/tests/unittests/test_deprecated_utils.py function test_should_deprecate_warning (line 13) | def test_should_deprecate_warning(mock_version): function test_should_raise_deprecate_error (line 51) | def test_should_raise_deprecate_error(mock_version): function test_should_not_deprecate (line 83) | def test_should_not_deprecate(mock_version): function test_should_raise_if_both_new_and_deprecated_args_passed (line 106) | def test_should_raise_if_both_new_and_deprecated_args_passed(): FILE: common/tests/unittests/test_hash_pandas_df.py function _get_pandas_df (line 12) | def _get_pandas_df(num_rows=20): function test_when_df_saved_and_loaded_from_disk_then_hash_is_unchanged (line 22) | def test_when_df_saved_and_loaded_from_disk_then_hash_is_unchanged(): function test_when_df_copied_then_hash_is_unchanged (line 31) | def test_when_df_copied_then_hash_is_unchanged(): function test_when_df_columns_permuted_then_hash_is_unchanged (line 38) | def test_when_df_columns_permuted_then_hash_is_unchanged(): FILE: common/tests/unittests/test_import_version.py function test_import_version (line 4) | def test_import_version(): FILE: common/tests/unittests/test_infer_types.py function test_when_sortable_uniques_then_returns_sorted_second_value (line 17) | def test_when_sortable_uniques_then_returns_sorted_second_value(uniques,... function test_when_numpy_array_with_nan_then_nan_not_chosen_as_true (line 21) | def test_when_numpy_array_with_nan_then_nan_not_chosen_as_true(): function test_when_pandas_categorical_then_no_attribute_error (line 33) | def test_when_pandas_categorical_then_no_attribute_error(series): FILE: common/tests/unittests/test_log_utils.py function test_convert_time_in_s_to_log_friendly (line 9) | def test_convert_time_in_s_to_log_friendly(): function test_when_mlflow_autolog_is_disabled_then_no_warning_is_logged (line 74) | def test_when_mlflow_autolog_is_disabled_then_no_warning_is_logged(caplog): FILE: common/tests/unittests/test_memory_limit.py function get_and_assert_max_memory (line 5) | def get_and_assert_max_memory(): function test_memory_mocking (line 32) | def test_memory_mocking(): function test_custom_memory_soft_limit_envar (line 47) | def test_custom_memory_soft_limit_envar(get_and_assert_max_memory): FILE: common/tests/unittests/test_model_filter.py function test_filter_model (line 36) | def test_filter_model(models, included_model_types, excluded_model_types... FILE: common/tests/unittests/test_path_converter.py function test_to_windows (line 9) | def test_to_windows(og_path, expected_path): function test_to_posix (line 20) | def test_to_posix(og_path, expected_path): function test_to_current (line 33) | def test_to_current(og_path, expected_path, mock_system): function test_should_raise_on_absolute_path (line 39) | def test_should_raise_on_absolute_path(path): FILE: common/tests/unittests/test_s3_utils.py function test_get_local_path_to_download_objs (line 31) | def test_get_local_path_to_download_objs(s3_objs, prefix, local_path, ex... function test_get_s3_to_local_tuple_list (line 75) | def test_get_s3_to_local_tuple_list(s3_bucket, s3_prefix, local_path, s3... function test_get_s3_to_local_tuple_list_raises (line 90) | def test_get_s3_to_local_tuple_list_raises(s3_bucket, s3_prefix, local_p... function test_get_local_objs_to_upload_and_s3_prefix (line 97) | def test_get_local_objs_to_upload_and_s3_prefix(): function test_get_local_objs_to_upload_and_s3_prefix_empty (line 125) | def test_get_local_objs_to_upload_and_s3_prefix_empty(): FILE: common/tests/unittests/test_setup_outputdir.py class SetupOutputDirTestCase (line 14) | class SetupOutputDirTestCase(unittest.TestCase): method test_os_path (line 15) | def test_os_path(self): method test_s3_path (line 54) | def test_s3_path(self): FILE: common/tests/unittests/test_version.py function test_version_has_major_minor_micro (line 6) | def test_version_has_major_minor_micro(): FILE: common/tests/unittests/utils/test_compare_autogluon_metadata.py class CompareAutoGluonMetadataTestCase (line 7) | class CompareAutoGluonMetadataTestCase(unittest.TestCase): method test_no_warnings (line 8) | def test_no_warnings(self): method test_version_mismatch (line 13) | def test_version_mismatch(self): method test_py_version_mismatch (line 20) | def test_py_version_mismatch(self): method test_py_version_micro_mismatch (line 27) | def test_py_version_micro_mismatch(self): method test_py_version_both_mismatch (line 34) | def test_py_version_both_mismatch(self): method test_system_mismatch (line 42) | def test_system_mismatch(self): method test_combined_mismatch (line 49) | def test_combined_mismatch(self): method test_new_key (line 58) | def test_new_key(self): method test_package_mismatch (line 65) | def test_package_mismatch(self): FILE: common/tests/unittests/utils/test_cpu_detection.py function test_get_cpu_count_matches_available_count (line 8) | def test_get_cpu_count_matches_available_count(): function test_get_cpu_count_matches_available_count_physical_cores (line 13) | def test_get_cpu_count_matches_available_count_physical_cores(): function test_ag_cpu_count_environment_variable (line 19) | def test_ag_cpu_count_environment_variable(): function test_ag_cpu_count_environment_variable_overrides_physical_cores (line 25) | def test_ag_cpu_count_environment_variable_overrides_physical_cores(): function test_slurm_cpus_per_task_environment_variable (line 31) | def test_slurm_cpus_per_task_environment_variable(): function test_ag_cpu_count_takes_precedence (line 37) | def test_ag_cpu_count_takes_precedence(): function test_loky_logical_cores_detection (line 44) | def test_loky_logical_cores_detection(mock_loky_cpu_count): function test_loky_physical_cores_detection (line 56) | def test_loky_physical_cores_detection(mock_loky_cpu_count): function test_minimum_cpu_count_is_one (line 68) | def test_minimum_cpu_count_is_one(mock_loky_cpu_count): function test_normal_operation (line 78) | def test_normal_operation(): FILE: common/tests/unittests/utils/test_get_package_versions.py class _FakeDist (line 8) | class _FakeDist: method __init__ (line 9) | def __init__(self, *, name=None, version="1.0", metadata=None, raise_o... method metadata (line 16) | def metadata(self): function test_get_package_versions_happy_path (line 29) | def test_get_package_versions_happy_path(monkeypatch): function test_get_package_versions_name_is_none_falls_back_to_dist_name (line 43) | def test_get_package_versions_name_is_none_falls_back_to_dist_name(monke... function test_get_package_versions_missing_name_and_no_dist_name_is_skipped (line 56) | def test_get_package_versions_missing_name_and_no_dist_name_is_skipped(m... function test_get_package_versions_weird_metadata_does_not_crash (line 70) | def test_get_package_versions_weird_metadata_does_not_crash(monkeypatch): function test_get_package_versions_strict_raises (line 85) | def test_get_package_versions_strict_raises(monkeypatch): FILE: common/tests/unittests/utils/test_pandas_utils.py function test_sample_ratio_ge_1_returns_deep_memory_usage (line 10) | def test_sample_ratio_ge_1_returns_deep_memory_usage(monkeypatch): function test_numeric_columns_sampling_returns_shallow_memory_usage (line 30) | def test_numeric_columns_sampling_returns_shallow_memory_usage(monkeypat... function test_category_column_estimate_matches_formula (line 50) | def test_category_column_estimate_matches_formula(monkeypatch): function test_inexact_object_column_uses_head_deep_scaled (line 80) | def test_inexact_object_column_uses_head_deep_scaled(monkeypatch): FILE: common/tests/unittests/utils/test_presets_yaml_loading.py class _FakeHTTPResponse (line 9) | class _FakeHTTPResponse: method __init__ (line 10) | def __init__(self, payload: bytes): method read (line 13) | def read(self) -> bytes: method __enter__ (line 16) | def __enter__(self): method __exit__ (line 19) | def __exit__(self, exc_type, exc, tb): function test_load_https_with_fragment_selects_named_preset (line 26) | def test_load_https_with_fragment_selects_named_preset(monkeypatch): function test_load_https_without_fragment_returns_top_level_dict (line 52) | def test_load_https_without_fragment_returns_top_level_dict(monkeypatch): function test_load_fragment_missing_key_raises (line 75) | def test_load_fragment_missing_key_raises(monkeypatch): function test_unknown_preset_name_raises_original_valid_presets_error (line 96) | def test_unknown_preset_name_raises_original_valid_presets_error(monkeyp... function test_unknown_yaml_like_string_attempts_loading (line 128) | def test_unknown_yaml_like_string_attempts_loading(monkeypatch): FILE: core/src/autogluon/core/_setup_utils.py function load_version_file (line 57) | def load_version_file(): function get_dependency_version_ranges (line 63) | def get_dependency_version_ranges(packages: list) -> list: function update_version (line 67) | def update_version(version, use_file_if_exists=True, create_file=False): function create_version_file (line 93) | def create_version_file(*, version, submodule): function default_setup_args (line 105) | def default_setup_args(*, version, submodule): FILE: core/src/autogluon/core/augmentation/distill_utils.py function format_distillation_labels (line 17) | def format_distillation_labels(y, problem_type, num_classes=None, eps_la... function augment_data (line 33) | def augment_data( function postprocess_augmented (line 61) | def postprocess_augmented(X_aug, X): function spunge_augment (line 70) | def spunge_augment( function munge_augment (line 158) | def munge_augment( FILE: core/src/autogluon/core/calibrate/_decision_threshold.py function calibrate_decision_threshold (line 18) | def calibrate_decision_threshold( FILE: core/src/autogluon/core/calibrate/conformity_score.py function compute_conformity_score (line 5) | def compute_conformity_score(y_val_pred: np.ndarray, y_val: np.ndarray, ... FILE: core/src/autogluon/core/calibrate/temperature_scaling.py function tune_temperature_scaling (line 9) | def tune_temperature_scaling( function custom_softmax (line 78) | def custom_softmax(logits: np.ndarray) -> np.ndarray: function apply_temperature_scaling (line 85) | def apply_temperature_scaling( FILE: core/src/autogluon/core/callbacks/_abstract_callback.py class AbstractCallback (line 15) | class AbstractCallback(object, metaclass=ABCMeta): method __init__ (line 47) | def __init__(self): method before_trainer_fit (line 50) | def before_trainer_fit(self, trainer: AbstractTrainer, **kwargs): method after_trainer_fit (line 68) | def after_trainer_fit(self, trainer: AbstractTrainer): method before_model_fit (line 76) | def before_model_fit( method _before_model_fit (line 131) | def _before_model_fit( method after_model_fit (line 146) | def after_model_fit( method _after_model_fit (line 197) | def _after_model_fit( FILE: core/src/autogluon/core/callbacks/_early_stopping_callback.py class EarlyStoppingCallback (line 13) | class EarlyStoppingCallback(AbstractCallback): method __init__ (line 37) | def __init__(self, patience: int = 10, patience_per_level: bool = True... method before_trainer_fit (line 51) | def before_trainer_fit(self, trainer: AbstractTrainer, **kwargs): method _before_model_fit (line 54) | def _before_model_fit( method _after_model_fit (line 78) | def _after_model_fit(self, trainer: AbstractTrainer, **kwargs) -> bool: method calc_new_best (line 91) | def calc_new_best(self, trainer: AbstractTrainer, **kwargs): method _calc_new_best (line 97) | def _calc_new_best(self, trainer: AbstractTrainer): method _cur_best (line 108) | def _cur_best(self, trainer: AbstractTrainer) -> tuple[str, float]: method _early_stop (line 134) | def _early_stop(self): method _log (line 140) | def _log(self, logger: Logger, level, msg: str): FILE: core/src/autogluon/core/callbacks/_early_stopping_count_callback.py class EarlyStoppingCountCallback (line 14) | class EarlyStoppingCountCallback(AbstractCallback): method __init__ (line 34) | def __init__(self, patience: int | list | None = 10, patience_per_leve... method before_trainer_fit (line 43) | def before_trainer_fit(self, trainer: AbstractTrainer, **kwargs): method _before_model_fit (line 57) | def _before_model_fit( method _after_model_fit (line 78) | def _after_model_fit( method _early_stop (line 90) | def _early_stop(self): method _log (line 96) | def _log(self, logger: Logger, level, msg: str): FILE: core/src/autogluon/core/callbacks/_early_stopping_ensemble_callback.py class EarlyStoppingEnsembleCallback (line 12) | class EarlyStoppingEnsembleCallback(EarlyStoppingCallback): method __init__ (line 18) | def __init__(self, **kwargs): method before_trainer_fit (line 24) | def before_trainer_fit(self, trainer: AbstractTrainer, **kwargs): method calc_new_best (line 28) | def calc_new_best(self, trainer: AbstractTrainer, **kwargs): method _fit_weighted_ensemble (line 34) | def _fit_weighted_ensemble(self, trainer: AbstractTrainer): FILE: core/src/autogluon/core/callbacks/_example_callback.py class ExampleCallback (line 16) | class ExampleCallback(AbstractCallback): method _before_model_fit (line 21) | def _before_model_fit( method _after_model_fit (line 57) | def _after_model_fit( method before_trainer_fit (line 66) | def before_trainer_fit(self, trainer: AbstractTrainer, **kwargs): method after_trainer_fit (line 71) | def after_trainer_fit(self, trainer: AbstractTrainer): FILE: core/src/autogluon/core/callbacks/_smooth_count.py function _interp_loglog (line 10) | def _interp_loglog(n: float, n0: float, y0: float, n1: float, y1: float)... function _parse_points_spec (line 26) | def _parse_points_spec(points: PointsSpec) -> tuple[list[Point], bool]: function max_models_from_num_samples_val (line 60) | def max_models_from_num_samples_val( FILE: core/src/autogluon/core/data/cleaner.py class Cleaner (line 11) | class Cleaner: method construct (line 13) | def construct(problem_type: str, label: str, threshold: int): method fit (line 23) | def fit(self, X: DataFrame) -> DataFrame: method fit_transform (line 26) | def fit_transform(self, X: DataFrame) -> DataFrame: method transform (line 30) | def transform(self, X: DataFrame) -> DataFrame: class CleanerDummy (line 34) | class CleanerDummy(Cleaner): method __init__ (line 35) | def __init__(self): method fit (line 38) | def fit(self, X: DataFrame) -> DataFrame: method transform (line 41) | def transform(self, X: DataFrame) -> DataFrame: class CleanerMulticlass (line 45) | class CleanerMulticlass(Cleaner): method __init__ (line 46) | def __init__(self, label: str, threshold: int): method fit (line 51) | def fit(self, X: DataFrame): method transform (line 54) | def transform(self, X: DataFrame) -> DataFrame: method get_valid_classes (line 58) | def get_valid_classes(X, label, threshold): method remove_classes (line 81) | def remove_classes(X, label, valid_classes): FILE: core/src/autogluon/core/data/label_cleaner.py class LabelCleaner (line 15) | class LabelCleaner: method __init__ (line 23) | def __init__(self, y: Union[Series, np.ndarray, list, DataFrame]): method construct (line 28) | def construct( method transformed_dtype (line 55) | def transformed_dtype(self): method to_transformed_dtype (line 61) | def to_transformed_dtype(self, y: Union[Series, np.ndarray, list]) -> ... method to_original_dtype (line 66) | def to_original_dtype(self, y: Union[Series, np.ndarray, list]) -> Ser... method transform (line 71) | def transform(self, y: Union[Series, np.ndarray, list]) -> Series: method inverse_transform (line 76) | def inverse_transform(self, y: Union[Series, np.ndarray, list]) -> Ser... method _transform (line 81) | def _transform(self, y: Series) -> Series: method _inverse_transform (line 84) | def _inverse_transform(self, y: Series) -> Series: method transform_proba (line 87) | def transform_proba(self, y: Union[DataFrame, Series, np.ndarray], as_... method inverse_transform_proba (line 90) | def inverse_transform_proba(self, y, as_pandas=False, as_pred=False): method _convert_to_valid_series (line 94) | def _convert_to_valid_series(y: Union[Series, np.ndarray, list]) -> Se... class LabelCleanerMulticlass (line 102) | class LabelCleanerMulticlass(LabelCleaner): method __init__ (line 103) | def __init__(self, y: Series, y_uncleaned: Series): method _transform (line 125) | def _transform(self, y: Series) -> Series: method _inverse_transform (line 129) | def _inverse_transform(self, y: Series) -> Series: method transform_pred_uncleaned (line 133) | def transform_pred_uncleaned(self, y: Union[Series, np.ndarray]) -> Se... method inverse_transform_pred_uncleaned (line 141) | def inverse_transform_pred_uncleaned(self, y: Union[Series, np.ndarray... method transform_proba (line 150) | def transform_proba(self, y: Union[DataFrame, np.ndarray], as_pandas=F... method inverse_transform_proba (line 168) | def inverse_transform_proba(self, y, as_pandas=False, as_pred=False): method _generate_categorical_mapping (line 191) | def _generate_categorical_mapping(y: Series) -> dict: class LabelCleanerBinary (line 198) | class LabelCleanerBinary(LabelCleaner): method __init__ (line 199) | def __init__(self, y: Series, positive_class=None): method inverse_transform_proba (line 248) | def inverse_transform_proba(self, y, as_pandas=False, as_pred=False): method _transform (line 269) | def _transform(self, y: Series) -> Series: method _inverse_transform (line 273) | def _inverse_transform(self, y: Series) -> Series: method transform_proba (line 276) | def transform_proba(self, y: Union[DataFrame, Series, np.ndarray], as_... class LabelCleanerMulticlassToBinary (line 285) | class LabelCleanerMulticlassToBinary(LabelCleanerMulticlass): method __init__ (line 286) | def __init__(self, y: Series, y_uncleaned: Series): method _transform (line 291) | def _transform(self, y: Series) -> Series: method inverse_transform_proba (line 296) | def inverse_transform_proba(self, y, as_pandas=False, as_pred=False): method convert_binary_proba_to_multiclass_proba (line 302) | def convert_binary_proba_to_multiclass_proba(y, as_pandas=False): class LabelCleanerSoftclass (line 315) | class LabelCleanerSoftclass(LabelCleaner): method __init__ (line 316) | def __init__(self, y: DataFrame): method transform (line 320) | def transform(self, y: Union[Series, np.ndarray, list]) -> Series: method inverse_transform (line 324) | def inverse_transform(self, y: Union[Series, np.ndarray, list]) -> Ser... method _transform (line 328) | def _transform(self, y: DataFrame) -> DataFrame: method _inverse_transform (line 331) | def _inverse_transform(self, y: DataFrame) -> DataFrame: method _convert_to_valid_series (line 335) | def _convert_to_valid_series(y: DataFrame) -> DataFrame: class LabelCleanerDummy (line 339) | class LabelCleanerDummy(LabelCleaner): method __init__ (line 340) | def __init__(self, problem_type=REGRESSION): method transform (line 343) | def transform(self, y: Union[Series, np.ndarray, list]) -> Series: method inverse_transform (line 347) | def inverse_transform(self, y: Union[Series, np.ndarray, list]) -> Ser... method _transform (line 351) | def _transform(self, y: Union[Series, DataFrame]) -> Union[Series, Dat... method _inverse_transform (line 354) | def _inverse_transform(self, y: Union[Series, DataFrame]) -> Union[Ser... method to_transformed_dtype (line 357) | def to_transformed_dtype(self, y: Union[Series, np.ndarray, list]) -> ... method to_original_dtype (line 360) | def to_original_dtype(self, y: Union[Series, np.ndarray, list]) -> Ser... FILE: core/src/autogluon/core/hpo/exceptions.py class EmptySearchSpace (line 1) | class EmptySearchSpace(Exception): FILE: core/src/autogluon/core/hpo/executors.py class HpoExecutor (line 29) | class HpoExecutor(ABC): method __init__ (line 32) | def __init__(self): method executor_type (line 40) | def executor_type(self): method time_limit (line 45) | def time_limit(self): method time_limit (line 50) | def time_limit(self, value): method initialize (line 54) | def initialize( method register_resources (line 75) | def register_resources( method validate_search_space (line 272) | def validate_search_space( method prepare_data (line 289) | def prepare_data( method execute (line 331) | def execute(self, **kwargs): method report (line 336) | def report(self, reporter: "LocalReporter", **kwargs): method get_hpo_results (line 341) | def get_hpo_results(self, model_name: str, model_path_root: str, **kwa... class RayHpoExecutor (line 355) | class RayHpoExecutor(HpoExecutor): method __init__ (line 375) | def __init__(self): method executor_type (line 380) | def executor_type(self): method initialize (line 383) | def initialize(self, hyperparameter_tune_kwargs, default_num_trials=No... method validate_search_space (line 399) | def validate_search_space(self, search_space, model_name): method execute (line 414) | def execute( method report (line 485) | def report(self, reporter, **kwargs): method get_hpo_results (line 490) | def get_hpo_results(self, model_name, model_path_root, **kwargs): class CustomHpoExecutor (line 514) | class CustomHpoExecutor(HpoExecutor): method __init__ (line 517) | def __init__(self): method executor_type (line 523) | def executor_type(self): method time_limit (line 527) | def time_limit(self): method time_limit (line 531) | def time_limit(self, value): method initialize (line 536) | def initialize(self, hyperparameter_tune_kwargs, default_num_trials=No... method register_resources (line 556) | def register_resources(self, initialized_model, **kwargs): method validate_search_space (line 566) | def validate_search_space(self, search_space, model_name): method execute (line 579) | def execute(self, model_trial, train_fn_kwargs, **kwargs): method report (line 593) | def report(self, reporter, **kwargs): method get_hpo_results (line 597) | def get_hpo_results(self, model_name, model_path_root, time_start, **k... class HpoExecutorFactory (line 639) | class HpoExecutorFactory: method get_hpo_executor (line 648) | def get_hpo_executor(hpo_executor: str) -> HpoExecutor: FILE: core/src/autogluon/core/hpo/ray_hpo.py class RayTuneAdapter (line 34) | class RayTuneAdapter(ABC): method __init__ (line 48) | def __init__(self): method adapter_type (line 56) | def adapter_type(self): method get_supported_searchers (line 59) | def get_supported_searchers(self) -> list: method get_supported_schedulers (line 67) | def get_supported_schedulers(self) -> list: method check_user_provided_resources_per_trial (line 75) | def check_user_provided_resources_per_trial(self, resources_per_trial:... method get_resource_calculator (line 80) | def get_resource_calculator(self, **kwargs) -> ResourceCalculator: method update_resource_info (line 84) | def update_resource_info(self, resources_info: dict): method get_resources_per_trial (line 91) | def get_resources_per_trial( method trainable_args_update_method (line 136) | def trainable_args_update_method(trainable_args: dict) -> dict: function run (line 144) | def run( function cleanup_trials (line 302) | def cleanup_trials(save_dir: str, trials_to_keep: Optional[List[str]]): function cleanup_checkpoints (line 322) | def cleanup_checkpoints(save_dir): function _trial_name_creator (line 341) | def _trial_name_creator(trial): function _trial_dirname_creator (line 345) | def _trial_dirname_creator(trial): function _validate_resources_per_trial (line 349) | def _validate_resources_per_trial(resources_per_trial): function _convert_search_space (line 358) | def _convert_search_space(search_space: dict): function _get_searcher (line 377) | def _get_searcher( function _get_scheduler (line 413) | def _get_scheduler(hyperparameter_tune_kwargs: dict, supported_scheduler... class TabularRayTuneAdapter (line 442) | class TabularRayTuneAdapter(RayTuneAdapter): method adapter_type (line 447) | def adapter_type(self): method get_resource_calculator (line 450) | def get_resource_calculator(self, num_gpus, **kwargs) -> ResourceCalcu... method trainable_args_update_method (line 453) | def trainable_args_update_method(self, trainable_args: dict) -> dict: class AutommRayTuneAdapter (line 465) | class AutommRayTuneAdapter(RayTuneAdapter): method __init__ (line 469) | def __init__(self): method adapter_type (line 473) | def adapter_type(self): method check_user_provided_resources_per_trial (line 476) | def check_user_provided_resources_per_trial(self, resources_per_trial:... method get_resource_calculator (line 485) | def get_resource_calculator(self, num_gpus: float): method trainable_args_update_method (line 490) | def trainable_args_update_method(self, trainable_args: dict) -> dict: class TimeSeriesRayTuneAdapter (line 497) | class TimeSeriesRayTuneAdapter(TabularRayTuneAdapter): method adapter_type (line 502) | def adapter_type(self): class RayTuneAdapterFactory (line 506) | class RayTuneAdapterFactory: method get_adapter (line 516) | def get_adapter(adapter_type: str) -> RayTuneAdapter: FILE: core/src/autogluon/core/hpo/ray_tune_scheduler.py class AvgEarlyStopFIFOScheduler (line 6) | class AvgEarlyStopFIFOScheduler(FIFOScheduler): method __init__ (line 7) | def __init__(self, time_limit=None, **kwargs): method on_trial_complete (line 14) | def on_trial_complete(self, trial_runner, trial, result): method choose_trial_to_run (line 20) | def choose_trial_to_run(self, trial_runner): method avg_time (line 29) | def avg_time(self): FILE: core/src/autogluon/core/hpo/ray_tune_scheduler_factory.py class SchedulerFactory (line 4) | class SchedulerFactory: method get_scheduler (line 29) | def get_scheduler(scheduler_name: str, user_init_args, **kwargs): FILE: core/src/autogluon/core/hpo/ray_tune_searcher_factory.py class SearcherFactory (line 5) | class SearcherFactory: method get_searcher (line 27) | def get_searcher(searcher_name: str, user_init_args, **kwargs): FILE: core/src/autogluon/core/hpo/space_converter.py class RaySpaceConverter (line 8) | class RaySpaceConverter(ABC): method space_type (line 11) | def space_type(self): method convert (line 17) | def convert(space): class RayCategoricalSpaceConverter (line 22) | class RayCategoricalSpaceConverter(RaySpaceConverter): method space_type (line 24) | def space_type(self): method convert (line 28) | def convert(space): class RayRealSpaceConverter (line 33) | class RayRealSpaceConverter(RaySpaceConverter): method space_type (line 35) | def space_type(self): method convert (line 39) | def convert(space): class RayIntSpaceConverter (line 48) | class RayIntSpaceConverter(RaySpaceConverter): method space_type (line 50) | def space_type(self): method convert (line 54) | def convert(space): class RayBoolSpaceConverter (line 59) | class RayBoolSpaceConverter(RayIntSpaceConverter): method space_type (line 61) | def space_type(self): class RaySpaceConverterFactory (line 65) | class RaySpaceConverterFactory: method get_space_converter (line 76) | def get_space_converter(converter_type: str) -> RaySpaceConverter: FILE: core/src/autogluon/core/learner/abstract_learner.py class AbstractLearner (line 11) | class AbstractLearner: method __init__ (line 23) | def __init__(self, path_context: str, random_state: int = 0, **kwargs): method create_contexts (line 47) | def create_contexts(self, path_context: str): method set_contexts (line 59) | def set_contexts(self, path_context: str): method is_fit (line 65) | def is_fit(self): method fit (line 68) | def fit(self, *args, **kwargs): method predict (line 71) | def predict(self, *args, **kwargs): method score (line 74) | def score(self, *args, **kwargs): method leaderboard (line 77) | def leaderboard(self, *args, **kwargs): method save (line 80) | def save(self): method load (line 91) | def load(cls, path_context, reset_paths=True): method save_trainer (line 105) | def save_trainer(self, trainer): method load_trainer (line 113) | def load_trainer(self) -> AbstractTrainer: method load_info (line 127) | def load_info(cls, path, reset_paths=True, load_model_if_required=True): method save_info (line 138) | def save_info(self, include_model_info=False): method get_info (line 145) | def get_info(self, **kwargs): FILE: core/src/autogluon/core/learning_curves/plot_curves.py function plot_curves (line 10) | def plot_curves(learning_curves: tuple[dict, dict], model: str, metric: ... FILE: core/src/autogluon/core/metrics/__init__.py class Scorer (line 23) | class Scorer(object, metaclass=ABCMeta): method __init__ (line 56) | def __init__( method __call__ (line 78) | def __call__(self, y_true, y_pred, sample_weight=None, **kwargs) -> fl... method error (line 113) | def error(self, *args, **kwargs) -> float: method convert_score_to_error (line 123) | def convert_score_to_error(self, score: float) -> float: method convert_error_to_score (line 131) | def convert_error_to_score(self, error: float) -> float: method optimum (line 140) | def optimum(self) -> float: method _score (line 147) | def _score(self, y_true, y_pred, **kwargs) -> float: method add_alias (line 151) | def add_alias(self, alias): method greater_is_better (line 159) | def greater_is_better(self) -> bool: method greater_is_better_internal (line 172) | def greater_is_better_internal(self) -> bool: method convert_score_to_original (line 186) | def convert_score_to_original(self, score: float) -> float: method _preprocess (line 191) | def _preprocess(self, y_true, y_pred, **kwargs): method __repr__ (line 194) | def __repr__(self) -> str: method needs_pred (line 199) | def needs_pred(self) -> bool: method needs_proba (line 205) | def needs_proba(self) -> bool: method needs_class (line 211) | def needs_class(self) -> bool: method needs_threshold (line 217) | def needs_threshold(self) -> bool: method needs_quantile (line 223) | def needs_quantile(self) -> bool: method needs_pos_label (line 228) | def needs_pos_label(self) -> bool: class _PredictScorer (line 239) | class _PredictScorer(Scorer): method _preprocess (line 240) | def _preprocess(self, y_true, y_pred, **kwargs): method needs_pred (line 262) | def needs_pred(self): method needs_proba (line 266) | def needs_proba(self): method needs_class (line 270) | def needs_class(self) -> bool: method needs_threshold (line 274) | def needs_threshold(self): method needs_quantile (line 278) | def needs_quantile(self): class _ClassScorer (line 282) | class _ClassScorer(Scorer): method _preprocess (line 283) | def _preprocess(self, y_true, y_pred, **kwargs): method needs_pred (line 303) | def needs_pred(self): method needs_proba (line 307) | def needs_proba(self): method needs_class (line 311) | def needs_class(self) -> bool: method needs_threshold (line 315) | def needs_threshold(self): method needs_quantile (line 319) | def needs_quantile(self): class _ProbaScorer (line 323) | class _ProbaScorer(Scorer): method _preprocess (line 324) | def _preprocess(self, y_true, y_pred, **kwargs): method needs_pred (line 328) | def needs_pred(self): method needs_proba (line 332) | def needs_proba(self): method needs_class (line 336) | def needs_class(self) -> bool: method needs_threshold (line 340) | def needs_threshold(self): method needs_quantile (line 344) | def needs_quantile(self): class _ThresholdScorer (line 348) | class _ThresholdScorer(Scorer): method _preprocess (line 349) | def _preprocess(self, y_true, y_pred, **kwargs): method needs_pred (line 366) | def needs_pred(self): method needs_proba (line 370) | def needs_proba(self): method needs_class (line 374) | def needs_class(self) -> bool: method needs_threshold (line 378) | def needs_threshold(self): method needs_quantile (line 382) | def needs_quantile(self): class _QuantileScorer (line 386) | class _QuantileScorer(Scorer): method _preprocess (line 387) | def _preprocess(self, y_true, y_pred, **kwargs): method needs_pred (line 406) | def needs_pred(self): method needs_proba (line 410) | def needs_proba(self): method needs_class (line 414) | def needs_class(self) -> bool: method needs_threshold (line 418) | def needs_threshold(self): method needs_quantile (line 422) | def needs_quantile(self): function _add_scorer_to_metric_dict (line 426) | def _add_scorer_to_metric_dict(metric_dict, scorer): function make_scorer (line 441) | def make_scorer( function smape_func (line 597) | def smape_func(y_true, y_pred) -> float: function local_spearmanr (line 608) | def local_spearmanr(y_true, y_pred): function local_pearsonr (line 615) | def local_pearsonr(y_true, y_pred): function rmse_func (line 622) | def rmse_func(y_true, y_pred, **kwargs): function customized_log_loss (line 662) | def customized_log_loss(y_true, y_pred, eps=1e-15): function customized_roc_auc (line 693) | def customized_roc_auc(y_true, y_pred, **kwargs): function _get_valid_metric_problem_types (line 793) | def _get_valid_metric_problem_types(metric: str): function get_metric (line 801) | def get_metric(metric, problem_type: str = None, metric_type: str = None... FILE: core/src/autogluon/core/metrics/classification_metrics.py function balanced_accuracy (line 27) | def balanced_accuracy(solution, prediction): function pac (line 87) | def pac(solution, prediction): function confusion_matrix (line 274) | def confusion_matrix(solution, prediction, labels=None, weights=None, no... function quadratic_kappa (line 351) | def quadratic_kappa(y_true, y_pred): function customized_binary_roc_auc_score (line 390) | def customized_binary_roc_auc_score( FILE: core/src/autogluon/core/metrics/quantile_metrics.py function pinball_loss (line 10) | def pinball_loss(target_value, quantile_values, quantile_levels, sample_... FILE: core/src/autogluon/core/metrics/score_func.py function compute_metric (line 15) | def compute_metric( FILE: core/src/autogluon/core/metrics/softclass_metrics.py function _soft_log_loss (line 14) | def _soft_log_loss(true_probs, predicted_probs): FILE: core/src/autogluon/core/models/_utils.py function get_early_stopping_rounds (line 5) | def get_early_stopping_rounds( FILE: core/src/autogluon/core/models/abstract/abstract_model.py class Taggable (line 72) | class Taggable(ABC): method _class_tags (line 74) | def _class_tags(cls) -> dict: method _more_tags (line 77) | def _more_tags(self) -> dict: method _get_tags (line 80) | def _get_tags(self) -> dict: method _get_class_tags (line 98) | def _get_class_tags(cls) -> dict: class Tunable (line 117) | class Tunable(ABC): method estimate_memory_usage (line 118) | def estimate_memory_usage(self, *args, **kwargs) -> float | None: method get_minimum_resources (line 124) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method _get_model_base (line 131) | def _get_model_base(self) -> "Tunable": method get_params (line 135) | def get_params(self) -> dict: method hyperparameter_tune (line 142) | def hyperparameter_tune(self, *args, **kwargs) -> tuple: class ModelBase (line 146) | class ModelBase(Taggable, ABC): method __init__ (line 148) | def __init__( method rename (line 158) | def rename(self, name: str) -> None: method get_info (line 162) | def get_info(self, *args, **kwargs) -> dict[str, Any]: method fit (line 166) | def fit(self, *args, **kwargs) -> Self: method predict (line 170) | def predict(self, *args, **kwargs) -> Any: method save (line 174) | def save(self, path: str | None = None, verbose: bool = True) -> str: method load (line 179) | def load(cls, path: str, reset_paths: bool = True) -> Self: class AbstractModel (line 184) | class AbstractModel(ModelBase, Tunable): method __init__ (line 240) | def __init__( method _init_user_params (line 324) | def _init_user_params( method _init_params (line 395) | def _init_params(self): method _init_params_aux (line 408) | def _init_params_aux(self): method _get_params_aux (line 417) | def _get_params_aux(self) -> dict: method _validate_params (line 426) | def _validate_params(self): method _validate_params_aux (line 432) | def _validate_params_aux(self): method path_suffix (line 442) | def path_suffix(self) -> str: method is_valid (line 445) | def is_valid(self) -> bool: method is_initialized (line 452) | def is_initialized(self) -> bool: method can_infer (line 460) | def can_infer(self) -> bool: method is_fit (line 464) | def is_fit(self) -> bool: method can_fit (line 468) | def can_fit(self) -> bool: method can_predict_proba (line 472) | def can_predict_proba(self) -> bool: method can_estimate_memory_usage_static (line 477) | def can_estimate_memory_usage_static(self) -> bool: method can_estimate_memory_usage_static_child (line 484) | def can_estimate_memory_usage_static_child(self) -> bool: method can_estimate_memory_usage_static_lite (line 491) | def can_estimate_memory_usage_static_lite(self) -> bool: method _set_default_params (line 499) | def _set_default_params(self): method _set_default_auxiliary_params (line 502) | def _set_default_auxiliary_params(self): method _get_default_auxiliary_params (line 513) | def _get_default_auxiliary_params(self) -> dict: method _set_default_param_value (line 546) | def _set_default_param_value(self, param_name, param_value, params=None): method _get_default_searchspace (line 552) | def _get_default_searchspace(self) -> dict: method _get_search_space (line 562) | def _get_search_space(self): method set_contexts (line 575) | def set_contexts(self, path_context): method create_contexts (line 580) | def create_contexts(path_context: str) -> str: method rename (line 584) | def rename(self, name: str): method preprocess (line 592) | def preprocess(self, X, preprocess_nonadaptive: bool = True, preproces... method _preprocess_align_features (line 607) | def _preprocess_align_features(self, X: pd.DataFrame, **kwargs): method _preprocess_model_specific (line 613) | def _preprocess_model_specific(self, X: pd.DataFrame, **kwargs) -> pd.... method _preprocess (line 648) | def _preprocess(self, X: pd.DataFrame, **kwargs): method _preprocess_nonadaptive (line 663) | def _preprocess_nonadaptive(self, X: pd.DataFrame, **kwargs) -> pd.Dat... method _preprocess_set_features (line 677) | def _preprocess_set_features(self, X: pd.DataFrame, feature_metadata: ... method _preprocess_set_features_internal (line 732) | def _preprocess_set_features_internal(self, X: pd.DataFrame, feature_m... method _get_valid_features (line 766) | def _get_valid_features(self, feature_metadata: FeatureMetadata = None... method _update_feature_metadata (line 792) | def _update_feature_metadata(self, X: pd.DataFrame, feature_metadata: ... method _infer_feature_metadata (line 799) | def _infer_feature_metadata(self, X: pd.DataFrame) -> FeatureMetadata: method _preprocess_fit_args (line 802) | def _preprocess_fit_args(self, **kwargs) -> dict: method initialize (line 837) | def initialize(self, **kwargs) -> dict: method _infer_problem_type (line 848) | def _infer_problem_type(cls, *, y: pd.Series, silent: bool = True) -> ... method _infer_num_classes (line 853) | def _infer_num_classes(cls, *, y: pd.Series, problem_type: str = None)... method _initialize (line 860) | def _initialize(self, X=None, y=None, feature_metadata=None, num_class... method _init_misc (line 881) | def _init_misc(self, **kwargs): method _process_user_provided_resource_requirement_to_calculate_total_resource_when_ensemble (line 905) | def _process_user_provided_resource_requirement_to_calculate_total_res... method _calculate_total_resources (line 939) | def _calculate_total_resources( method _preprocess_fit_resources (line 1086) | def _preprocess_fit_resources( method _register_fit_metadata (line 1139) | def _register_fit_metadata(self, **kwargs): method _compute_fit_metadata (line 1147) | def _compute_fit_metadata( method get_fit_metadata (line 1165) | def get_fit_metadata(self) -> dict: method _get_child_aux_val (line 1176) | def _get_child_aux_val(self, key: str, default=None): method fit (line 1184) | def fit( method validate_fit_args (line 1332) | def validate_fit_args(self, X: pd.DataFrame, feature_metadata: Feature... method _post_fit (line 1399) | def _post_fit(self, **kwargs): method get_features (line 1435) | def get_features(self) -> list[str]: method _fit (line 1442) | def _fit( method init_random_seed (line 1475) | def init_random_seed(self, random_seed: int | None | str, hyperparamet... method _get_random_seed_from_hyperparameters (line 1520) | def _get_random_seed_from_hyperparameters(self, hyperparameters: dict)... method _apply_temperature_scaling (line 1548) | def _apply_temperature_scaling(self, y_pred_proba: np.ndarray) -> np.n... method _apply_conformalization (line 1555) | def _apply_conformalization(self, y_pred: np.ndarray) -> np.ndarray: method predict (line 1564) | def predict(self, X, **kwargs) -> np.ndarray: method predict_proba (line 1574) | def predict_proba( method _predict_proba_batch (line 1617) | def _predict_proba_batch( method _predict_proba_internal (line 1637) | def _predict_proba_internal(self, X, *, normalize: bool | None = None,... method predict_from_proba (line 1646) | def predict_from_proba(self, y_pred_proba: np.ndarray) -> np.ndarray: method _predict_proba (line 1670) | def _predict_proba(self, X, **kwargs) -> np.ndarray: method _convert_proba_to_unified_form (line 1682) | def _convert_proba_to_unified_form(self, y_pred_proba: np.ndarray) -> ... method score (line 1706) | def score( method score_with_y_pred_proba (line 1735) | def score_with_y_pred_proba( method save (line 1760) | def save(self, path: str | None = None, verbose: bool = True) -> str: method load (line 1796) | def load(cls, path: str, reset_paths: bool = True, verbose: bool = True): method save_learning_curves (line 1827) | def save_learning_curves( method _make_learning_curves (line 1901) | def _make_learning_curves( method load_learning_curves (line 1939) | def load_learning_curves(cls, path: str) -> list: method compute_feature_importance (line 1969) | def compute_feature_importance( method _compute_permutation_importance (line 2041) | def _compute_permutation_importance( method can_compile (line 2073) | def can_compile(self, compiler_configs: dict = None) -> bool: method compile (line 2099) | def compile(self, compiler_configs: dict = None): method _compile (line 2126) | def _compile(self, **kwargs): method _get_input_types (line 2133) | def _get_input_types(self, batch_size=None) -> list: method _default_compiler (line 2156) | def _default_compiler(cls): method _valid_compilers (line 2161) | def _valid_compilers(cls) -> list: method _get_compiler (line 2165) | def _get_compiler(self, compiler: str = None, compiler_fallback_to_nat... method get_compiler_name (line 2195) | def get_compiler_name(self) -> str: method get_trained_params (line 2202) | def get_trained_params(self) -> dict: method convert_to_refit_full_via_copy (line 2212) | def convert_to_refit_full_via_copy(self): method get_params (line 2224) | def get_params(self) -> dict: method get_hyperparameters_init (line 2242) | def get_hyperparameters_init(self) -> dict: method convert_to_template (line 2256) | def convert_to_template(self): method convert_to_refit_full_template (line 2267) | def convert_to_refit_full_template(self): method hyperparameter_tune (line 2293) | def hyperparameter_tune( method _hyperparameter_tune (line 2366) | def _hyperparameter_tune( method _get_hpo_backend (line 2457) | def _get_hpo_backend(self) -> str: method _get_default_hpo_executor (line 2463) | def _get_default_hpo_executor(self) -> HpoExecutor: method _path_v2 (line 2479) | def _path_v2(self) -> str: method reset_metrics (line 2484) | def reset_metrics(self): method disk_usage (line 2495) | def disk_usage(self) -> int: method get_memory_size (line 2503) | def get_memory_size(self, allow_exception: bool = False) -> int | None: method _get_memory_size (line 2533) | def _get_memory_size(self) -> int: method _estimate_dtypes_after_preprocessing_cheap (line 2538) | def _estimate_dtypes_after_preprocessing_cheap( method estimate_memory_usage (line 2567) | def estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method estimate_memory_usage_static_lite (line 2619) | def estimate_memory_usage_static_lite( method estimate_memory_usage_static (line 2638) | def estimate_memory_usage_static( method estimate_memory_usage_child (line 2683) | def estimate_memory_usage_child(self, X: pd.DataFrame, **kwargs) -> int: method estimate_memory_usage_static_child (line 2701) | def estimate_memory_usage_static_child( method validate_fit_resources (line 2742) | def validate_fit_resources(self, num_cpus="auto", num_gpus="auto", tot... method _validate_fit_resources (line 2752) | def _validate_fit_resources(self, **resources): method get_minimum_resources (line 2772) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method _estimate_memory_usage (line 2789) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 2810) | def _estimate_memory_usage_static( method _validate_fit_memory_usage (line 2821) | def _validate_fit_memory_usage( method reduce_memory_size (line 2926) | def reduce_memory_size( method delete_from_disk (line 2939) | def delete_from_disk(self, silent: bool = False): method get_info (line 2955) | def get_info(self, include_feature_metadata: bool = True) -> dict: method get_params_aux_info (line 2993) | def get_params_aux_info(self) -> dict: method load_info (line 3009) | def load_info(cls, path: str, load_model_if_required: bool = True) -> ... method save_info (line 3022) | def save_info(self) -> dict: method predict_n_size (line 3031) | def predict_n_size(self) -> int | None: method predict_n_time_per_row (line 3038) | def predict_n_time_per_row(self) -> float | None: method record_predict_info (line 3047) | def record_predict_info(self, X: pd.DataFrame): method _init_preprocessor (line 3059) | def _init_preprocessor( method _recursive_init_preprocessors (line 3082) | def _recursive_init_preprocessors(self, prep_param: tuple | list[list ... method get_preprocessor (line 3114) | def get_preprocessor(self, ag_params: dict | None = None) -> AbstractF... method _get_maximum_resources (line 3150) | def _get_maximum_resources(self) -> dict[str, int | float]: method _get_default_resources (line 3164) | def _get_default_resources(self) -> tuple[int, float]: method _get_default_ag_args (line 3176) | def _get_default_ag_args(cls) -> dict: method _get_default_ag_args_ensemble (line 3186) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method supported_problem_types (line 3194) | def supported_problem_types(cls) -> list[str] | None: method _get_default_stopping_metric (line 3202) | def _get_default_stopping_metric(self) -> Scorer: method _get_params (line 3218) | def _get_params(self) -> dict: method _get_ag_params (line 3222) | def _get_ag_params(self, params_aux: dict | None = None) -> dict: method _get_model_params (line 3237) | def _get_model_params(self, convert_search_spaces_to_default: bool = F... method _get_model_params_static (line 3258) | def _get_model_params_static(cls, hyperparameters: dict, convert_searc... method _ag_params (line 3283) | def _ag_params(self) -> set[str]: method _ag_params_common (line 3313) | def _ag_params_common(cls) -> set[str]: method _features (line 3344) | def _features(self) -> list[str]: method _get_model_base (line 3347) | def _get_model_base(self): method fit_num_cpus (line 3351) | def fit_num_cpus(self) -> int: method fit_num_gpus (line 3356) | def fit_num_gpus(self) -> float: method fit_num_cpus_child (line 3361) | def fit_num_cpus_child(self) -> int: method fit_num_gpus_child (line 3366) | def fit_num_gpus_child(self) -> float: method get_ag_priority (line 3371) | def get_ag_priority(cls, problem_type: str | None = None) -> int: method _class_tags (line 3382) | def _class_tags(cls) -> dict: FILE: core/src/autogluon/core/models/abstract/abstract_nn_model.py class AbstractNeuralNetworkModel (line 12) | class AbstractNeuralNetworkModel(AbstractModel): method __init__ (line 13) | def __init__(self, **kwargs): method _get_types_of_features (line 18) | def _get_types_of_features( FILE: core/src/autogluon/core/models/abstract/model_trial.py function model_trial (line 12) | def model_trial( function init_model (line 66) | def init_model(args, model_cls, init_params, backend, is_bagged_model=Fa... function fit_and_save_model (line 95) | def fit_and_save_model(model, fit_args, predict_proba_args, y_val, time_... function skip_hpo (line 130) | def skip_hpo(model, X, y, X_val, y_val, time_limit=None, **kwargs): FILE: core/src/autogluon/core/models/dummy/_dummy_quantile_regressor.py class DummyQuantileRegressor (line 4) | class DummyQuantileRegressor: method __init__ (line 7) | def __init__(self, quantile_levels: list): method fit (line 11) | def fit(self, X, y): method predict (line 19) | def predict(self, X): FILE: core/src/autogluon/core/models/dummy/dummy_model.py class DummyModel (line 10) | class DummyModel(AbstractModel): method _get_model_type (line 19) | def _get_model_type(self): method preprocess (line 31) | def preprocess(self, X: pd.DataFrame, **kwargs): method _fit (line 34) | def _fit(self, X, y, **kwargs): method supported_problem_types (line 48) | def supported_problem_types(cls) -> list[str] | None: FILE: core/src/autogluon/core/models/ensemble/bagged_ensemble_model.py class BaggedEnsembleModel (line 44) | class BaggedEnsembleModel(AbstractModel): method __init__ (line 66) | def __init__( method _set_default_params (line 114) | def _set_default_params(self): method _get_default_auxiliary_params (line 131) | def _get_default_auxiliary_params(self) -> dict: method is_valid (line 139) | def is_valid(self) -> bool: method can_infer (line 142) | def can_infer(self) -> bool: method is_stratified (line 145) | def is_stratified(self) -> bool: method is_binned (line 161) | def is_binned(self) -> bool: method is_fit (line 171) | def is_fit(self) -> bool: method can_fit (line 174) | def can_fit(self) -> bool: method can_estimate_memory_usage_static_child (line 185) | def can_estimate_memory_usage_static_child(self) -> bool: method n_children (line 193) | def n_children(self) -> int: method is_valid_oof (line 197) | def is_valid_oof(self) -> bool: method predict_proba_oof (line 200) | def predict_proba_oof(self, **kwargs) -> np.array: method _predict_proba_oof (line 205) | def _predict_proba_oof(oof_pred_proba, oof_pred_model_repeats, return_... method _init_misc (line 211) | def _init_misc(self, **kwargs): method preprocess (line 220) | def preprocess(self, X: pd.DataFrame, preprocess_nonadaptive: bool = T... method _get_cv_splitter (line 231) | def _get_cv_splitter(self, n_splits: int, n_repeats: int, groups=None)... method _fit (line 242) | def _fit( method validate_fit_args (line 442) | def validate_fit_args(self, X: pd.DataFrame, **kwargs): method _update_k_fold (line 447) | def _update_k_fold(self, k_fold: int, k_fold_end: int = None, verbose:... method _get_child_aux_val (line 462) | def _get_child_aux_val(self, key: str, default=None): method _validate_bag_kwargs (line 466) | def _validate_bag_kwargs( method predict_proba_children (line 539) | def predict_proba_children( method predict_children (line 583) | def predict_children( method _predict_proba_internal (line 627) | def _predict_proba_internal(self, X, *, normalize: bool | None = None,... method _predict_proba (line 637) | def _predict_proba(self, X, normalize=False, **kwargs) -> np.ndarray: method score_with_oof (line 640) | def score_with_oof(self, y, sample_weight=None): method _fit_single (line 649) | def _fit_single( method _set_n_repeat_single (line 770) | def _set_n_repeat_single(self): method _get_default_fold_fitting_strategy (line 777) | def _get_default_fold_fitting_strategy(self): method _get_fold_fitting_strategy (line 791) | def _get_fold_fitting_strategy(self, model_base, num_gpus): method _fit_folds (line 821) | def _fit_folds( method _generate_fold_configs (line 963) | def _generate_fold_configs( method estimate_memory_usage_child (line 1021) | def estimate_memory_usage_child(self, **kwargs) -> int: method estimate_memory_usage_static_child (line 1031) | def estimate_memory_usage_static_child(self, **kwargs) -> int: method compute_feature_importance (line 1043) | def compute_feature_importance( method get_features (line 1160) | def get_features(self) -> list[str]: method load_child (line 1164) | def load_child(self, model: AbstractModel | str, verbose: bool = False... method add_child (line 1171) | def add_child(self, model: AbstractModel | str, add_child_times: bool ... method save_child (line 1206) | def save_child(self, model: AbstractModel | str, path: str | None = No... method can_compile (line 1214) | def can_compile(self, compiler_configs=None): method compile (line 1220) | def compile(self, compiler_configs=None): method get_compiler_name (line 1228) | def get_compiler_name(self) -> str: method convert_to_refit_full_template (line 1233) | def convert_to_refit_full_template(self, name_suffix: str = REFIT_FULL... method convert_to_refit_full_template_child (line 1256) | def convert_to_refit_full_template_child(self) -> AbstractModel: method convert_to_refit_full_via_copy (line 1264) | def convert_to_refit_full_via_copy(self) -> AbstractModel: method get_params (line 1287) | def get_params(self) -> dict: method get_hyperparameters_init_child (line 1297) | def get_hyperparameters_init_child( method convert_to_template_child (line 1321) | def convert_to_template_child(self): method _get_compressed_params (line 1324) | def _get_compressed_params(self, model_params_list=None) -> dict: method _get_compressed_params_trained (line 1349) | def _get_compressed_params_trained(self): method _get_model_base (line 1353) | def _get_model_base(self) -> AbstractModel: method _add_child_times_to_bag (line 1359) | def _add_child_times_to_bag(self, model: AbstractModel): method _add_parallel_child_times (line 1366) | def _add_parallel_child_times(self, fit_time: float, predict_time: flo... method _add_predict_n_size (line 1382) | def _add_predict_n_size(self, predict_n_size_lst: list[float]): method _add_child_num_cpus (line 1387) | def _add_child_num_cpus(self, num_cpus: int): method _add_child_num_gpus (line 1392) | def _add_child_num_gpus(self, num_gpus: float): method fit_num_cpus_child (line 1398) | def fit_num_cpus_child(self) -> int: method fit_num_gpus_child (line 1406) | def fit_num_gpus_child(self) -> float: method predict_n_size (line 1414) | def predict_n_size(self) -> float | None: method load (line 1422) | def load( method load_oof (line 1433) | def load_oof(cls, path: str, verbose: bool = True) -> np.array: method _load_oof (line 1445) | def _load_oof(self): method persist_child_models (line 1453) | def persist_child_models(self, reset_paths: bool = True): method unpersist_child_models (line 1460) | def unpersist_child_models(self): method _get_child_model_names (line 1463) | def _get_child_model_names(self, models: list[str | AbstractModel]) ->... method load_model_base (line 1472) | def load_model_base(self) -> AbstractModel: method save_model_base (line 1475) | def save_model_base(self, model_base: AbstractModel): method save (line 1478) | def save(self, path: str = None, verbose: bool = True, save_oof: bool ... method reduce_memory_size (line 1506) | def reduce_memory_size( method _model_names (line 1545) | def _model_names(self) -> list[str]: method get_info (line 1548) | def get_info(self, include_feature_metadata: bool = True): method get_memory_size (line 1603) | def get_memory_size(self, allow_exception: bool = False) -> int | None: method validate_fit_resources (line 1610) | def validate_fit_resources(self, **kwargs): method get_minimum_resources (line 1613) | def get_minimum_resources(self, **kwargs) -> dict[str, int]: method _get_default_resources (line 1616) | def _get_default_resources(self): method _validate_fit_memory_usage (line 1619) | def _validate_fit_memory_usage(self, **kwargs) -> tuple[int | None, in... method _get_child_info (line 1623) | def _get_child_info(self, include_feature_metadata: bool = True) -> dict: method _construct_empty_oof (line 1635) | def _construct_empty_oof(self, X: pd.DataFrame, y: pd.Series) -> tuple... method _hyperparameter_tune (line 1647) | def _hyperparameter_tune( method _more_tags (line 1751) | def _more_tags(self) -> dict: method _get_tags_child (line 1757) | def _get_tags_child(self) -> dict: FILE: core/src/autogluon/core/models/ensemble/fold_fitting_strategy.py class AbstractFoldFittingStrategy (line 52) | class AbstractFoldFittingStrategy: method schedule_fold_model_fit (line 54) | def schedule_fold_model_fit(self, fold_ctx): method after_all_folds_scheduled (line 63) | def after_all_folds_scheduled(self): method _fit (line 71) | def _fit(self, model_base, time_start_fold, time_limit_fold, fold_ctx,... class FoldFittingStrategy (line 77) | class FoldFittingStrategy(AbstractFoldFittingStrategy): method __init__ (line 138) | def __init__( method schedule_fold_model_fit (line 182) | def schedule_fold_model_fit(self, fold_ctx): method after_all_folds_scheduled (line 185) | def after_all_folds_scheduled(self): method _validate_user_specified_resources (line 188) | def _validate_user_specified_resources(self): method _get_fold_time_limit (line 248) | def _get_fold_time_limit(self, fold_ctx): method _update_bagged_ensemble (line 266) | def _update_bagged_ensemble(self, fold_model, pred_proba, fold_ctx): method _predict_oof (line 281) | def _predict_oof(self, fold_model: AbstractModel, fold_ctx) -> Tuple[A... method _get_fold_properties (line 306) | def _get_fold_properties(fold_ctx): class SequentialLocalFoldFittingStrategy (line 322) | class SequentialLocalFoldFittingStrategy(FoldFittingStrategy): method __init__ (line 327) | def __init__(self, **kwargs): method schedule_fold_model_fit (line 374) | def schedule_fold_model_fit(self, fold_ctx): method after_all_folds_scheduled (line 377) | def after_all_folds_scheduled(self): method _fit_fold_model (line 381) | def _fit_fold_model(self, fold_ctx): method _fit (line 388) | def _fit(self, model_base, time_start_fold, time_limit_fold, fold_ctx,... function _ray_fit (line 442) | def _ray_fit( function _ray_predict_oof (line 563) | def _ray_predict_oof( class ParallelFoldFittingStrategy (line 578) | class ParallelFoldFittingStrategy(FoldFittingStrategy): method __init__ (line 617) | def __init__( method mem_est_proportion_per_fold (line 655) | def mem_est_proportion_per_fold(self): method folds_to_fit_in_parallel_with_mem (line 659) | def folds_to_fit_in_parallel_with_mem(self, user_specified_num_folds_p... method _estimate_data_memory_usage (line 689) | def _estimate_data_memory_usage(self): method schedule_fold_model_fit (line 694) | def schedule_fold_model_fit(self, fold_ctx): method _get_ray_init_args (line 697) | def _get_ray_init_args(self) -> Dict[str, Any]: method _process_fold_results (line 704) | def _process_fold_results(self, finished, unfinished, fold_ctx): method _update_bagged_ensemble_times (line 767) | def _update_bagged_ensemble_times(self): method _update_bagged_ensemble_child_resources (line 776) | def _update_bagged_ensemble_child_resources(self): method _run_parallel (line 782) | def _run_parallel(self, X, y, X_pseudo, y_pseudo, model_base_ref, time... method _run_pseudo_sequential (line 819) | def _run_pseudo_sequential(self, X, y, X_pseudo, y_pseudo, model_base_... method _calculate_gpu_assignment (line 857) | def _calculate_gpu_assignment(self, task_id: int, gpus_per_task: int |... method after_all_folds_scheduled (line 878) | def after_all_folds_scheduled(self): method terminate_all_unfinished_tasks (line 901) | def terminate_all_unfinished_tasks(self, unfinished_tasks): method _fit (line 918) | def _fit( method _update_bagged_ensemble (line 982) | def _update_bagged_ensemble( method _get_fold_time_limit (line 1022) | def _get_fold_time_limit(self): method _get_resource_suggestions (line 1034) | def _get_resource_suggestions( method _prepare_data (line 1100) | def _prepare_data(self, in_mem=True): method _parse_ray_error (line 1125) | def _parse_ray_error(self, e): method sync_model_artifact (line 1164) | def sync_model_artifact(self, local_path: str, model_sync_path: str): method _sync_model_artifact (line 1178) | def _sync_model_artifact(self, **kwargs): class ParallelLocalFoldFittingStrategy (line 1182) | class ParallelLocalFoldFittingStrategy(ParallelFoldFittingStrategy): method _get_ray_init_args (line 1183) | def _get_ray_init_args(self): class ParallelDistributedFoldFittingStrategy (line 1190) | class ParallelDistributedFoldFittingStrategy(ParallelFoldFittingStrategy): method __init__ (line 1191) | def __init__(self, **kwargs): method _sync_model_artifact (line 1200) | def _sync_model_artifact(self, local_path, model_sync_path): function _json_safe (line 1209) | def _json_safe(x: Any) -> Any: function encode_exception (line 1217) | def encode_exception(e: BaseException) -> dict[str, Any]: class UnknownRemoteException (line 1227) | class UnknownRemoteException(RuntimeError): method __init__ (line 1228) | def __init__(self, exc_type: str, message: str): function decode_exception (line 1249) | def decode_exception( FILE: core/src/autogluon/core/models/ensemble/ray_parallel_fold_fitting_strategy.py function model_fit_task_ray (line 12) | def model_fit_task_ray(X_fold, X_val_fold, fold_model, kwargs_fold, time... class RayParallelFitter (line 18) | class RayParallelFitter(SequentialLocalFoldFittingStrategy): method __init__ (line 19) | def __init__( method schedule_fold_model_fit (line 49) | def schedule_fold_model_fit(self, model_base, fold_ctx, kwargs): method wait_for_completion (line 60) | def wait_for_completion(self): FILE: core/src/autogluon/core/models/ensemble/stacker_ensemble_model.py class StackerEnsembleModel (line 27) | class StackerEnsembleModel(BaggedEnsembleModel): method __init__ (line 65) | def __init__( method _update_feature_metadata (line 95) | def _update_feature_metadata(self, X: pd.DataFrame, feature_metadata: ... method _validate_params (line 135) | def _validate_params(self): method _get_dynamic_max_base_models_per_type (line 160) | def _get_dynamic_max_base_models_per_type(self, X: pd.DataFrame): method _infer_feature_metadata (line 188) | def _infer_feature_metadata(self, X: pd.DataFrame) -> FeatureMetadata: method limit_models_per_type (line 206) | def limit_models_per_type(models, model_types, model_scores, max_base_... method limit_models (line 220) | def limit_models(self, models, model_scores, max_base_models): method _set_default_params (line 226) | def _set_default_params(self): method preprocess (line 237) | def preprocess(self, X, fit=False, compute_base_preds=True, infer=True... method pred_probas_to_df (line 277) | def pred_probas_to_df(self, pred_proba: list, index=None) -> pd.DataFr... method _fit (line 287) | def _fit(self, X, y, compute_base_preds=True, time_limit=None, **kwargs): method set_stack_columns (line 295) | def set_stack_columns(self, stack_column_prefix_lst): method _hyperparameter_tune (line 315) | def _hyperparameter_tune(self, X, y, k_fold, hpo_executor, compute_bas... method get_params (line 324) | def get_params(self): method load_base_model (line 336) | def load_base_model(self, model_name): method get_info (line 345) | def get_info(self, **kwargs): method _remove_unused_stack_in_feature_metadata (line 356) | def _remove_unused_stack_in_feature_metadata(self, feature_metadata: F... FILE: core/src/autogluon/core/models/ensemble/weighted_ensemble_model.py class WeightedEnsembleModel (line 17) | class WeightedEnsembleModel(StackerEnsembleModel): method __init__ (line 24) | def __init__(self, **kwargs): method _fit (line 28) | def _fit(self, X, y, **kwargs): method _get_model_weights (line 53) | def _get_model_weights(self) -> dict: method compute_feature_importance (line 66) | def compute_feature_importance(self, X, y, features=None, is_oof=True,... method _set_default_params (line 87) | def _set_default_params(self): method _more_tags (line 93) | def _more_tags(self): FILE: core/src/autogluon/core/models/greedy_ensemble/ensemble_selection.py class AbstractWeightedEnsemble (line 18) | class AbstractWeightedEnsemble: method predict (line 19) | def predict(self, X): method predict_proba (line 23) | def predict_proba(self, X): method weight_pred_probas (line 27) | def weight_pred_probas(pred_probas, weights): class EnsembleSelection (line 33) | class EnsembleSelection(AbstractWeightedEnsemble): method __init__ (line 34) | def __init__( method fit (line 62) | def fit( method _fit (line 80) | def _fit(self, predictions: List[np.ndarray], labels: np.ndarray, time... method _calculate_regret (line 217) | def _calculate_regret( method _calculate_weights (line 236) | def _calculate_weights(self): class SimpleWeightedEnsemble (line 249) | class SimpleWeightedEnsemble(AbstractWeightedEnsemble): method __init__ (line 252) | def __init__(self, weights, problem_type, **kwargs): method ensemble_size (line 257) | def ensemble_size(self): FILE: core/src/autogluon/core/models/greedy_ensemble/greedy_weighted_ensemble_model.py class GreedyWeightedEnsembleModel (line 14) | class GreedyWeightedEnsembleModel(AbstractModel): method __init__ (line 18) | def __init__(self, base_model_names=None, model_base=EnsembleSelection... method _set_default_params (line 25) | def _set_default_params(self): method _get_default_auxiliary_params (line 33) | def _get_default_auxiliary_params(self) -> dict: method _preprocess_nonadaptive (line 42) | def _preprocess_nonadaptive(self, X, **kwargs): method _initialize (line 47) | def _initialize(self, **kwargs): method _fit (line 59) | def _fit(self, X, y, X_val=None, y_val=None, time_limit=None, sample_w... method convert_pred_probas_df_to_list (line 77) | def convert_pred_probas_df_to_list(self, pred_probas_df) -> list: method remove_zero_weight_models (line 90) | def remove_zero_weight_models(base_model_names, base_model_weights): method _set_stack_columns (line 99) | def _set_stack_columns(self, base_model_names): method _infer_base_model_names (line 112) | def _infer_base_model_names(self): method _get_model_weights (line 127) | def _get_model_weights(self) -> dict: method get_info (line 132) | def get_info(self, **kwargs): method _get_default_ag_args (line 138) | def _get_default_ag_args(cls) -> dict: method supported_problem_types (line 145) | def supported_problem_types(cls) -> list[str] | None: method _get_default_stopping_metric (line 148) | def _get_default_stopping_metric(self): class SimpleWeightedEnsembleModel (line 152) | class SimpleWeightedEnsembleModel(GreedyWeightedEnsembleModel): method __init__ (line 156) | def __init__(self, model_base=SimpleWeightedEnsemble, **kwargs): method _fit (line 159) | def _fit(self, **kwargs): FILE: core/src/autogluon/core/problem_type.py class ProblemType (line 9) | class ProblemType: method __init__ (line 26) | def __init__(self, can_predict: bool, can_predict_proba: bool, is_clas... class ProblemTypeInfo (line 32) | class ProblemTypeInfo: method __init__ (line 35) | def __init__(self, problem_type_dict: Dict[str, ProblemType]): method list_problem_types (line 38) | def list_problem_types(self): method can_predict (line 41) | def can_predict(self, problem_type: str) -> bool: method can_predict_proba (line 44) | def can_predict_proba(self, problem_type: str) -> bool: method is_classification (line 47) | def is_classification(self, problem_type: str) -> bool: method _get_problem_type (line 50) | def _get_problem_type(self, problem_type: str) -> ProblemType: method list_classification (line 53) | def list_classification(self) -> List[str]: FILE: core/src/autogluon/core/pseudolabeling/pseudolabeling.py function sample_bins_uniformly (line 11) | def sample_bins_uniformly(y_pred_proba: pd.DataFrame, df_indexes): function filter_pseudo (line 58) | def filter_pseudo( function filter_ensemble_pseudo (line 119) | def filter_ensemble_pseudo(predictor, unlabeled_data: pd.DataFrame, num_... function filter_pseudo_std_regression (line 158) | def filter_pseudo_std_regression( function filter_ensemble_classification (line 212) | def filter_ensemble_classification( function assert_pseudo_column_match (line 260) | def assert_pseudo_column_match(X: pd.DataFrame, X_pseudo: pd.DataFrame): FILE: core/src/autogluon/core/ray/distributed_jobs_managers.py class ModelResources (line 21) | class ModelResources: class ParallelFitManager (line 32) | class ParallelFitManager: method __init__ (line 72) | def __init__( method available_num_cpus_virtual (line 157) | def available_num_cpus_virtual(self) -> int: method total_num_cpus_virtual (line 161) | def total_num_cpus_virtual(self) -> int: method num_children_model (line 164) | def num_children_model(self, model: AbstractModel) -> int: method max_mem_per_core (line 171) | def max_mem_per_core(self): method total_mem_per_core (line 175) | def total_mem_per_core(self): method schedule_jobs (line 184) | def schedule_jobs(self, *, models_to_fit: list[AbstractModel] | list[s... method check_sufficient_resources (line 434) | def check_sufficient_resources(self, *, resources: ModelResources) -> ... method get_resources_for_model (line 448) | def get_resources_for_model(self, *, model: AbstractModel | str) -> Mo... method get_memory_estimate_for_model_child (line 458) | def get_memory_estimate_for_model_child(self, *, model: AbstractModel)... method get_memory_estimate_for_model (line 500) | def get_memory_estimate_for_model( method get_resources_for_model_refit (line 517) | def get_resources_for_model_refit(self, model: str) -> ModelResources: method get_resources_for_model_fit (line 547) | def get_resources_for_model_fit(self, *, model: AbstractModel) -> Mode... method allocate_resources (line 591) | def allocate_resources( method deallocate_resources (line 603) | def deallocate_resources(self, *, job_ref: str) -> None: method clean_unfinished_job_refs (line 614) | def clean_unfinished_job_refs(self, *, unfinished_job_refs: list[str] ... method clean_job_state (line 624) | def clean_job_state(self, *, unfinished_job_refs: list[str] | None = N... method clean_up_ray (line 636) | def clean_up_ray(self, *, unfinished_job_refs: list[str] | None = None... function prepare_model_resources_for_fit (line 648) | def prepare_model_resources_for_fit( FILE: core/src/autogluon/core/ray/resources_calculator.py class ResourceCalculator (line 10) | class ResourceCalculator(ABC): method calc_type (line 13) | def calc_type(self): method get_resources_per_job (line 18) | def get_resources_per_job(self, **kwargs) -> dict: method wrap_resources_per_job_into_placement_group (line 22) | def wrap_resources_per_job_into_placement_group(self, resources_per_job): class CpuResourceCalculator (line 40) | class CpuResourceCalculator(ResourceCalculator): method calc_type (line 42) | def calc_type(self): method get_resources_per_job (line 45) | def get_resources_per_job( class GpuResourceCalculator (line 106) | class GpuResourceCalculator(ResourceCalculator): method calc_type (line 108) | def calc_type(self): method get_resources_per_job (line 111) | def get_resources_per_job( class NonParallelGpuResourceCalculator (line 173) | class NonParallelGpuResourceCalculator(ResourceCalculator): method calc_type (line 179) | def calc_type(self): method get_resources_per_job (line 182) | def get_resources_per_job( class ResourceCalculatorFactory (line 226) | class ResourceCalculatorFactory: method get_resource_calculator (line 235) | def get_resource_calculator(calculator_type: str) -> ResourceCalculator: FILE: core/src/autogluon/core/scheduler/reporter.py class FakeReporter (line 8) | class FakeReporter(object): method __call__ (line 11) | def __call__(self, **kwargs): FILE: core/src/autogluon/core/scheduler/scheduler_factory.py function compile_scheduler_options (line 21) | def compile_scheduler_options( function scheduler_factory (line 138) | def scheduler_factory( function get_scheduler_from_preset (line 230) | def get_scheduler_from_preset(scheduler_cls): function get_hyperparameter_tune_kwargs_preset (line 241) | def get_hyperparameter_tune_kwargs_preset(preset: str): FILE: core/src/autogluon/core/scheduler/seq_scheduler.py class LocalReporter (line 18) | class LocalReporter: method __init__ (line 23) | def __init__(self, trial, searcher_config, training_history: dict, con... method __call__ (line 33) | def __call__(self, *args, **kwargs): method terminate (line 52) | def terminate(self): class LocalSequentialScheduler (line 56) | class LocalSequentialScheduler(object): method __init__ (line 89) | def __init__( method init_limits_ (line 116) | def init_limits_(self, kwargs): method get_searcher_ (line 124) | def get_searcher_(self, searcher, train_fn, search_space, **kwargs) ->... method run (line 149) | def run(self, **kwargs): method has_enough_time_for_trial_ (line 207) | def has_enough_time_for_trial_( method get_average_trial_time_ (line 242) | def get_average_trial_time_(cls, i, avg_trial_run_time, trial_start_ti... method run_trial (line 250) | def run_trial(self, task_id=0) -> Tuple[bool, dict]: method run_job_ (line 275) | def run_job_(self, task_id, searcher_config, reporter): method run_with_config (line 303) | def run_with_config(self, config): method join_jobs (line 313) | def join_jobs(self, timeout=None): method get_best_config (line 316) | def get_best_config(self): method get_best_reward (line 323) | def get_best_reward(self): method get_training_curves (line 327) | def get_training_curves(self, filename=None, plot=False, use_legend=Tr... method __get_training_history_metric (line 354) | def __get_training_history_metric(self, metric, default=None): method get_best_task_id (line 360) | def get_best_task_id(self): FILE: core/src/autogluon/core/searcher/dummy_searcher.py class DummySearcher (line 11) | class DummySearcher(LocalSearcher): method __init__ (line 14) | def __init__(self, **kwargs): method get_config (line 18) | def get_config(self, **kwargs) -> dict: FILE: core/src/autogluon/core/searcher/exceptions.py class ExhaustedSearchSpaceError (line 1) | class ExhaustedSearchSpaceError(Exception): FILE: core/src/autogluon/core/searcher/local_grid_searcher.py class LocalGridSearcher (line 16) | class LocalGridSearcher(LocalSearcher): method __init__ (line 21) | def __init__(self, grid_numeric_spaces_points_number=4, grid_num_sampl... method _get_params_space (line 39) | def _get_params_space(self) -> dict: method _get_samples_number (line 58) | def _get_samples_number(self, key): method __len__ (line 64) | def __len__(self): method get_config (line 67) | def get_config(self): FILE: core/src/autogluon/core/searcher/local_random_searcher.py class LocalRandomSearcher (line 16) | class LocalRandomSearcher(LocalSearcher): method __init__ (line 21) | def __init__(self, *, first_is_default=True, random_seed=0, **kwargs): method _get_params_space (line 29) | def _get_params_space(self) -> dict: method _get_num_configs (line 37) | def _get_num_configs(self) -> int: method _sample_config (line 48) | def _sample_config(self) -> dict: method get_config (line 57) | def get_config(self, **kwargs) -> dict: FILE: core/src/autogluon/core/searcher/local_searcher.py class LocalSearcher (line 12) | class LocalSearcher(object): method __init__ (line 22) | def __init__(self, search_space: dict, reward_attribute: str = "reward... method configure_scheduler (line 38) | def configure_scheduler(self, scheduler): method _reward_while_pending (line 58) | def _reward_while_pending(): method get_config (line 62) | def get_config(self, **kwargs): method update (line 75) | def update(self, config: dict, **kwargs): method register_pending (line 84) | def register_pending(self, config, milestone=None): method evaluation_failed (line 97) | def evaluation_failed(self, config, **kwargs): method get_best_reward (line 105) | def get_best_reward(self): method get_reward (line 113) | def get_reward(self, config): method get_best_config (line 119) | def get_best_config(self): method get_results (line 127) | def get_results(self, sort=True) -> list: method _get_params_static (line 145) | def _get_params_static(self) -> dict: method _get_params_default (line 155) | def _get_params_default(self, params_static: dict) -> dict: method _get_params_cat_dict (line 166) | def _get_params_cat_dict(self) -> dict: method _add_result (line 183) | def _add_result(self, config: dict, result: float): method _pickle_config (line 190) | def _pickle_config(self, config: dict) -> bytes: method _unpickle_config (line 221) | def _unpickle_config(self, config_pkl: bytes) -> dict: FILE: core/src/autogluon/core/searcher/searcher_factory.py function searcher_factory (line 16) | def searcher_factory(searcher_name, **kwargs): FILE: core/src/autogluon/core/stacked_overfitting/utils.py function get_affected_stacked_overfitting_model_names (line 9) | def get_affected_stacked_overfitting_model_names(leaderboard: pd.DataFra... function get_best_val_models (line 44) | def get_best_val_models(leaderboard: pd.DataFrame) -> Tuple[str, str, bo... function _check_stacked_overfitting_for_models (line 83) | def _check_stacked_overfitting_for_models( function check_stacked_overfitting_from_leaderboard (line 120) | def check_stacked_overfitting_from_leaderboard(leaderboard: pd.DataFrame... FILE: core/src/autogluon/core/testing/global_context_snapshot.py class GlobalContextSnapshot (line 14) | class GlobalContextSnapshot: method capture (line 67) | def capture(cls) -> "GlobalContextSnapshot": method assert_unchanged (line 152) | def assert_unchanged(self, other: "GlobalContextSnapshot") -> None: FILE: core/src/autogluon/core/trainer/abstract_trainer.py class AbstractTrainer (line 16) | class AbstractTrainer(Generic[ModelTypeT]): method __init__ (line 21) | def __init__(self, path: str, *, low_memory: bool, save_data: bool): method _get_banned_model_names (line 39) | def _get_banned_model_names(self) -> list[str]: method path_root (line 46) | def path_root(self) -> str: method path_utils (line 51) | def path_utils(self) -> str: method path_data (line 55) | def path_data(self) -> str: method set_contexts (line 58) | def set_contexts(self, path_context: str) -> None: method create_contexts (line 61) | def create_contexts(self, path_context: str) -> str: method save_model (line 65) | def save_model(self, model: ModelTypeT) -> None: method get_models_attribute_dict (line 70) | def get_models_attribute_dict(self, attribute: str, models: list[str] ... method get_model_attribute (line 73) | def get_model_attribute(self, model: str | ModelTypeT, attribute: str,... method set_model_attribute (line 91) | def set_model_attribute(self, model: str | ModelTypeT, attribute: str,... method get_minimum_model_set (line 96) | def get_minimum_model_set(self, model: str | ModelTypeT, include_self:... method get_model_info (line 107) | def get_model_info(self, model: str | ModelTypeT) -> dict[str, Any]: method get_model_names (line 119) | def get_model_names(self) -> list[str]: method get_models_info (line 123) | def get_models_info(self, models: list[str | ModelTypeT] | None = None... method load_model (line 132) | def load_model( method load_info (line 151) | def load_info(cls, path: str, reset_paths: bool = False, load_model_if... method save_info (line 162) | def save_info(self, include_model_info: bool = False) -> dict[str, Any]: method get_model_best (line 169) | def get_model_best(self) -> str: method get_info (line 172) | def get_info(self, include_model_info: bool = False) -> dict[str, Any]: method save (line 175) | def save(self) -> None: method load (line 179) | def load(cls, path: str, reset_paths: bool = False) -> Self: method fit (line 189) | def fit(self, *args, **kwargs): method predict (line 192) | def predict(self, *args, **kwargs) -> Any: FILE: core/src/autogluon/core/trainer/utils.py function process_hyperparameters (line 7) | def process_hyperparameters(hyperparameters: dict) -> dict: FILE: core/src/autogluon/core/utils/early_stopping.py class AbstractES (line 4) | class AbstractES: method update (line 9) | def update(self, cur_round, is_best: bool = False) -> bool: method early_stop (line 12) | def early_stop(self, cur_round, is_best: bool = False) -> bool: class NoES (line 16) | class NoES(AbstractES): method update (line 21) | def update(self, cur_round: int, is_best: bool = False) -> bool: method early_stop (line 24) | def early_stop(self, cur_round: int, is_best: bool = False) -> bool: class SimpleES (line 28) | class SimpleES(AbstractES): method __init__ (line 38) | def __init__(self, patience: int = 10): method update (line 42) | def update(self, cur_round: int, is_best: bool = False) -> bool: method early_stop (line 47) | def early_stop(self, cur_round: int, is_best: bool = False) -> bool: class AdaptiveES (line 56) | class AdaptiveES(AbstractES): method __init__ (line 94) | def __init__( method update (line 108) | def update(self, cur_round: int, is_best: bool = False) -> bool: method early_stop (line 119) | def early_stop(self, cur_round: int, is_best: bool = False) -> bool: method _update_patience (line 128) | def _update_patience(self, best_round: int) -> int: FILE: core/src/autogluon/core/utils/exceptions.py class AutoGluonException (line 1) | class AutoGluonException(Exception): class InsufficientTime (line 10) | class InsufficientTime(AutoGluonException): class TimeLimitExceeded (line 19) | class TimeLimitExceeded(InsufficientTime): class NotEnoughMemoryError (line 27) | class NotEnoughMemoryError(AutoGluonException): class NoGPUError (line 31) | class NoGPUError(AutoGluonException): class NotEnoughCudaMemoryError (line 35) | class NotEnoughCudaMemoryError(AutoGluonException): class NoValidFeatures (line 39) | class NoValidFeatures(AutoGluonException): class NoStackFeatures (line 43) | class NoStackFeatures(NoValidFeatures): class NotValidStacker (line 47) | class NotValidStacker(AutoGluonException): FILE: core/src/autogluon/core/utils/feature_selection.py function add_noise_column (line 24) | def add_noise_column( function merge_importance_dfs (line 39) | def merge_importance_dfs(df_old: pd.DataFrame, df_new: pd.DataFrame, usi... function sort_features_by_priority (line 94) | def sort_features_by_priority( class FeatureSelector (line 125) | class FeatureSelector: method __init__ (line 126) | def __init__( method select_features (line 169) | def select_features( method compute_next_candidate (line 364) | def compute_next_candidate( method compute_next_candidate_round (line 402) | def compute_next_candidate_round( method compute_next_candidate_given_fi (line 503) | def compute_next_candidate_given_fi( method compute_expected_fi_time_single (line 550) | def compute_expected_fi_time_single( method compute_time_budget_fi (line 562) | def compute_time_budget_fi(self, X_fi: pd.DataFrame, n_subsample: int,... method fit_score_model (line 579) | def fit_score_model( method get_extra_fn_args (line 614) | def get_extra_fn_args(self, **kwargs) -> dict: method setup (line 621) | def setup( method get_random_state (line 700) | def get_random_state(self) -> int: FILE: core/src/autogluon/core/utils/files.py function unzip (line 17) | def unzip(zip_file_path, root=os.path.expanduser("./")): function download (line 30) | def download(url, path=None, overwrite=False, sha1_hash=None): function check_sha1 (line 99) | def check_sha1(filename, sha1_hash): function make_temp_directory (line 126) | def make_temp_directory(): FILE: core/src/autogluon/core/utils/infer_utils.py function get_model_true_infer_speed_per_row_batch (line 8) | def get_model_true_infer_speed_per_row_batch( function get_model_true_infer_speed_per_row_batch_bulk (line 103) | def get_model_true_infer_speed_per_row_batch_bulk( FILE: core/src/autogluon/core/utils/plots.py function plot_performance_vs_trials (line 16) | def plot_performance_vs_trials( function plot_summary_of_models (line 51) | def plot_summary_of_models( function plot_tabular_models (line 110) | def plot_tabular_models( function _formatDict (line 157) | def _formatDict(d): function mousover_plot (line 166) | def mousover_plot( FILE: core/src/autogluon/core/utils/time.py function time_func (line 11) | def time_func(f, args: list = None, kwargs: dict = None, time_limit: flo... function sample_df_for_time_func (line 54) | def sample_df_for_time_func(df: DataFrame, sample_size: int, max_sample_... FILE: core/src/autogluon/core/utils/utils.py function setup_compute (line 37) | def setup_compute(nthreads_per_trial, ngpus_per_trial): function setup_trial_limits (line 51) | def setup_trial_limits(time_limit, num_trials, hyperparameters): function get_leaderboard_pareto_frontier (line 69) | def get_leaderboard_pareto_frontier( function shuffle_df_rows (line 98) | def shuffle_df_rows(X: DataFrame, seed=0, reset_index=True): function normalize_binary_probas (line 109) | def normalize_binary_probas(y_predprob, eps): function normalize_multi_probas (line 123) | def normalize_multi_probas(y_predprob, eps): function default_holdout_frac (line 135) | def default_holdout_frac(num_train_rows, hyperparameter_tune: bool = Fal... function augment_rare_classes (line 152) | def augment_rare_classes(X, label, threshold): function get_pred_from_proba_df (line 218) | def get_pred_from_proba_df( function get_pred_from_proba (line 241) | def get_pred_from_proba( function convert_pred_probas_to_df (line 267) | def convert_pred_probas_to_df( function extract_label (line 299) | def extract_label(data: DataFrame, label: str) -> (DataFrame, Series): function generate_train_test_split_combined (line 323) | def generate_train_test_split_combined( function generate_train_test_split (line 393) | def generate_train_test_split( function normalize_pred_probas (line 592) | def normalize_pred_probas(y_predprob, problem_type, eps=1e-7): function infer_problem_type (line 618) | def infer_problem_type(y: Series, silent=False) -> str: function infer_eval_metric (line 698) | def infer_eval_metric(problem_type: str) -> Scorer: function extract_column (line 710) | def extract_column(X, col_name): function compute_weighted_metric (line 720) | def compute_weighted_metric( function compute_permutation_feature_importance (line 756) | def compute_permutation_feature_importance( function _validate_features (line 1013) | def _validate_features(features: list, valid_features: list): function _compute_fi_with_stddev (line 1043) | def _compute_fi_with_stddev(fi_list_dict: dict, importance_as_list=False... function _compute_mean_stddev_and_p_value (line 1068) | def _compute_mean_stddev_and_p_value(values: list): function _get_safe_fi_batch_count (line 1087) | def _get_safe_fi_batch_count(X, num_features, X_transformed=None, max_me... function unevaluated_fi_df_template (line 1119) | def unevaluated_fi_df_template(features: List[str]) -> pd.DataFrame: FILE: core/src/autogluon/core/utils/version_utils.py function _get_autogluon_versions (line 13) | def _get_autogluon_versions(): function _get_dependency_versions (line 28) | def _get_dependency_versions(package): function _get_sys_info (line 49) | def _get_sys_info(): function show_versions (line 77) | def show_versions(): FILE: core/tests/conftest.py function pytest_addoption (line 4) | def pytest_addoption(parser): function pytest_configure (line 9) | def pytest_configure(config): function pytest_collection_modifyitems (line 16) | def pytest_collection_modifyitems(config, items): FILE: core/tests/test_check_style.py function test_check_style (line 6) | def test_check_style(): FILE: core/tests/unittests/calibrate/test_decision_threshold.py function _get_sample_data (line 8) | def _get_sample_data(): function test_calibrate_decision_threshold (line 32) | def test_calibrate_decision_threshold(): function test_calibrate_decision_threshold_select_closer_to_0_5 (line 74) | def test_calibrate_decision_threshold_select_closer_to_0_5(): function test_calibrate_decision_threshold_proba_metric_0_5 (line 96) | def test_calibrate_decision_threshold_proba_metric_0_5(): function test_calibrate_decision_threshold_out_of_bounds (line 114) | def test_calibrate_decision_threshold_out_of_bounds(): function test_calibrate_decision_threshold_invalid_args (line 132) | def test_calibrate_decision_threshold_invalid_args(): FILE: core/tests/unittests/core/scheduler/test_scheduler_factory.py function test_scheduler_factory__can_construct_valid_config_with_str_scheduler (line 7) | def test_scheduler_factory__can_construct_valid_config_with_str_schedule... function test_scheduler_factory__can_construct_valid_config_with_class_scheduler (line 33) | def test_scheduler_factory__can_construct_valid_config_with_class_schedu... function test_scheduler_factory__reaises_exception_on_missing_scheduler (line 40) | def test_scheduler_factory__reaises_exception_on_missing_scheduler(): function test_scheduler_factory__reaises_exception_on_unknown_str_scheduler (line 45) | def test_scheduler_factory__reaises_exception_on_unknown_str_scheduler(): function test_scheduler_factory__reaises_exception_on_missing_searcher (line 52) | def test_scheduler_factory__reaises_exception_on_missing_searcher(): function test_get_hyperparameter_tune_kwargs_preset__preset_exists (line 57) | def test_get_hyperparameter_tune_kwargs_preset__preset_exists(): function test_get_hyperparameter_tune_kwargs_preset__preset_missing (line 61) | def test_get_hyperparameter_tune_kwargs_preset__preset_missing(): FILE: core/tests/unittests/hpo/test_ray_hpo.py class DummyAdapter (line 15) | class DummyAdapter(RayTuneAdapter): method adapter_type (line 20) | def adapter_type(self): method get_resource_calculator (line 23) | def get_resource_calculator(self, **kwargs): method get_resources_per_trial (line 26) | def get_resources_per_trial(self, total_resources, num_samples, **kwar... method trainable_args_update_method (line 29) | def trainable_args_update_method(self, trainable_args): function _dummy_objective (line 36) | def _dummy_objective(x, a, b): function _dummy_trainable (line 40) | def _dummy_trainable(config): function test_invalid_searcher (line 47) | def test_invalid_searcher(): function test_invalid_scheduler (line 67) | def test_invalid_scheduler(): function test_invalid_preset (line 87) | def test_invalid_preset(): function test_empty_search_space (line 103) | def test_empty_search_space(): function test_run (line 126) | def test_run(searcher, scheduler): FILE: core/tests/unittests/hpo/test_space_converter.py function test_space_converter (line 24) | def test_space_converter(space, expected_space): FILE: core/tests/unittests/metrics/test_classification_metrics.py function test_confusion_matrix_with_valid_inputs_without_labels_and_weights (line 9) | def test_confusion_matrix_with_valid_inputs_without_labels_and_weights(): function test_confusion_matrix_with_valid_inputs_with_labels_and_without_weights (line 22) | def test_confusion_matrix_with_valid_inputs_with_labels_and_without_weig... function test_confusion_matrix_with_valid_inputs_with_labels_and_with_weights (line 36) | def test_confusion_matrix_with_valid_inputs_with_labels_and_with_weights(): function test_confusion_matrix_with_valid_inputs_with_lesser_number_of_labels_and_without_weights (line 51) | def test_confusion_matrix_with_valid_inputs_with_lesser_number_of_labels... function test_confusion_matrix_with_unequal_samples (line 65) | def test_confusion_matrix_with_unequal_samples(): function test_confusion_matrix_with_multioutput_samples (line 75) | def test_confusion_matrix_with_multioutput_samples(): function test_confusion_matrix_with_empty_labels (line 85) | def test_confusion_matrix_with_empty_labels(): function test_confusion_matrix_with_multiDimensional_labels (line 96) | def test_confusion_matrix_with_multiDimensional_labels(): function test_confusion_matrix_with_invalid_weights (line 107) | def test_confusion_matrix_with_invalid_weights(): function test_confusion_matrix_with_empty_inputs (line 118) | def test_confusion_matrix_with_empty_inputs(): function test_log_loss (line 139) | def test_log_loss(gt, probs): function test_soft_log_loss (line 156) | def test_soft_log_loss(gt, probs): function test_log_loss_single_binary_class (line 164) | def test_log_loss_single_binary_class(): function test_log_loss_with_sklearn (line 178) | def test_log_loss_with_sklearn(gt, probs): function test_roc_auc_score_with_sklearn (line 191) | def test_roc_auc_score_with_sklearn(): function test_roc_auc_score_with_sklearn_single_raise (line 203) | def test_roc_auc_score_with_sklearn_single_raise(): function test_roc_auc_score_with_sklearn_zero_raise (line 225) | def test_roc_auc_score_with_sklearn_zero_raise(): function test_quadratic_kappa (line 234) | def test_quadratic_kappa(): FILE: core/tests/unittests/metrics/test_metric_kwargs.py function test_metric_kwargs (line 7) | def test_metric_kwargs(): function test_metric_kwargs_init (line 30) | def test_metric_kwargs_init(): FILE: core/tests/unittests/metrics/test_metrics.py function test_metric_exists (line 120) | def test_metric_exists(metrics: dict, expected_metrics_and_aliases: set): function test_metrics_perfect_binary (line 146) | def test_metrics_perfect_binary(metric: str): function test_metrics_perfect_multiclass (line 152) | def test_metrics_perfect_multiclass(metric: str): function test_metrics_perfect_raises_binary_single_sample (line 161) | def test_metrics_perfect_raises_binary_single_sample(metric: str): function test_metrics_perfect_binary_single_sample (line 169) | def test_metrics_perfect_binary_single_sample(metric: str): function test_metrics_perfect_multiclass_single_sample (line 175) | def test_metrics_perfect_multiclass_single_sample(metric: str): function test_metrics_perfect_str_multiclass (line 180) | def test_metrics_perfect_str_multiclass(metric: str): function test_metrics_perfect_str_binary (line 187) | def test_metrics_perfect_str_binary(metric: str): function test_metrics_perfect_proba_raises_str_binary (line 198) | def test_metrics_perfect_proba_raises_str_binary(metric: str): function test_metrics_perfect_proba_raises_str_multiclass (line 206) | def test_metrics_perfect_proba_raises_str_multiclass(metric: str): function test_metrics_perfect_regression (line 214) | def test_metrics_perfect_regression(metric: str): function test_metrics_imperfect_binary (line 220) | def test_metrics_imperfect_binary(metric: str): function test_metrics_imperfect_multiclass (line 225) | def test_metrics_imperfect_multiclass(metric: str): function test_metrics_imperfect_regression (line 230) | def test_metrics_imperfect_regression(metric: str): function test_metrics_imperfect_str_binary (line 235) | def test_metrics_imperfect_str_binary(metric: str): function test_metrics_imperfect_str_multiclass (line 245) | def test_metrics_imperfect_str_multiclass(metric: str): function _assert_valid_scorer_classifier (line 250) | def _assert_valid_scorer_classifier(scorer: Scorer): function _assert_valid_scorer_regressor (line 260) | def _assert_valid_scorer_regressor(scorer: Scorer): function _assert_valid_scorer (line 270) | def _assert_valid_scorer(scorer: Scorer): function _assert_perfect_score (line 278) | def _assert_perfect_score(scorer: Scorer, abs_tol=1e-5): function _assert_perfect_score_single_sample (line 288) | def _assert_perfect_score_single_sample(scorer: Scorer, abs_tol=1e-5): function _assert_perfect_score_generic (line 298) | def _assert_perfect_score_generic(scorer: Scorer, y_true, y_pred, abs_to... function _assert_perfect_score_str_binary (line 312) | def _assert_perfect_score_str_binary(scorer: Scorer, abs_tol=1e-5): function _assert_perfect_score_str_multiclass (line 322) | def _assert_perfect_score_str_multiclass(scorer: Scorer, abs_tol=1e-5): function _assert_imperfect_score (line 332) | def _assert_imperfect_score(scorer: Scorer, abs_tol: float = 1e-5) -> fl... function _assert_imperfect_score_str_binary (line 342) | def _assert_imperfect_score_str_binary(scorer: Scorer, abs_tol: float = ... function _assert_imperfect_score_str_multiclass (line 360) | def _assert_imperfect_score_str_multiclass(scorer: Scorer, abs_tol: floa... function _assert_imperfect_score_generic (line 378) | def _assert_imperfect_score_generic(scorer: Scorer, y_true, y_pred, abs_... function test_rmse_with_sklearn (line 396) | def test_rmse_with_sklearn(sample_weight): function test_invalid_scorer (line 414) | def test_invalid_scorer(): FILE: core/tests/unittests/metrics/test_quantile_metrics.py function test_invalid_quantile_values_shape_raises (line 7) | def test_invalid_quantile_values_shape_raises(): function test_mismatched_target_prediction_length_raises (line 18) | def test_mismatched_target_prediction_length_raises(): function test_mismatched_quantiles_raises (line 29) | def test_mismatched_quantiles_raises(): function test_single_prediction (line 40) | def test_single_prediction(): function test_multiple_predictions (line 54) | def test_multiple_predictions(): function test_multiple_predictions_with_weights (line 68) | def test_multiple_predictions_with_weights(): FILE: core/tests/unittests/models/abstract_model/test_init_user_params.py function _assert_init_user_params (line 7) | def _assert_init_user_params( function test_init_user_params_none (line 27) | def test_init_user_params_none(): function test_init_user_params_simple (line 36) | def test_init_user_params_simple(): function test_init_user_params_ag_args_fit_none (line 51) | def test_init_user_params_ag_args_fit_none(): function test_init_user_params_with_prefix (line 67) | def test_init_user_params_with_prefix(): function test_init_user_params_with_ag_args_fit (line 83) | def test_init_user_params_with_ag_args_fit(): function test_init_user_params_with_ag_args_fit_and_prefix (line 99) | def test_init_user_params_with_ag_args_fit_and_prefix(): function test_init_user_params_with_all (line 119) | def test_init_user_params_with_all(): function test_init_user_params_with_all_and_custom (line 140) | def test_init_user_params_with_all_and_custom(): FILE: core/tests/unittests/models/test_bagged_ensemble_model.py function test_generate_fold_configs (line 7) | def test_generate_fold_configs(): FILE: core/tests/unittests/ray/test_resource_calculator.py function test_cpu_calculator_no_bottleneck (line 12) | def test_cpu_calculator_no_bottleneck(): function test_cpu_calculator_mem_bottleneck (line 36) | def test_cpu_calculator_mem_bottleneck(): function test_gpu_calculator_no_bottleneck (line 65) | def test_gpu_calculator_no_bottleneck(): function test_gpu_calculator_cpu_bottleneck (line 93) | def test_gpu_calculator_cpu_bottleneck(): function test_non_parallel_gpu_calculator (line 121) | def test_non_parallel_gpu_calculator(): function test_resource_not_enough (line 150) | def test_resource_not_enough(calculator_type): FILE: core/tests/unittests/scheduler/test_scheduler.py function test_local_sequential_scheduler (line 10) | def test_local_sequential_scheduler(): function test_timeout_scheduler (line 30) | def test_timeout_scheduler(): FILE: core/tests/unittests/scheduler/test_seq_scheduler.py function test_get_average_trial_time_ (line 9) | def test_get_average_trial_time_(): function test_has_enough_time_for_trial__enough_time__no_avg_time (line 18) | def test_has_enough_time_for_trial__enough_time__no_avg_time(): function test_has_enough_time_for_trial__enough_time__avg_time_allows_trials (line 25) | def test_has_enough_time_for_trial__enough_time__avg_time_allows_trials(): function test_has_enough_time_for_trial__enough_time__avg_time_not_allows_trials (line 32) | def test_has_enough_time_for_trial__enough_time__avg_time_not_allows_tri... function test_has_enough_time_for_trial__time_exceeded_no_avg_time (line 39) | def test_has_enough_time_for_trial__time_exceeded_no_avg_time(): function test_has_enough_time_for_trial__avg_time (line 46) | def test_has_enough_time_for_trial__avg_time(): function test_has_enough_time_for_trial__enough_time__avg_time_not_allows_trials_by_fill_factor (line 53) | def test_has_enough_time_for_trial__enough_time__avg_time_not_allows_tri... function test_LocalSequentialScheduler_no_criteria (line 60) | def test_LocalSequentialScheduler_no_criteria(): function test_search_space (line 72) | def test_search_space(): function test_scheduler_can_handle_failing_jobs (line 104) | def test_scheduler_can_handle_failing_jobs(): FILE: core/tests/unittests/searcher/test_local_grid_searcher.py function test_local_grid_searcher_categorical (line 5) | def test_local_grid_searcher_categorical(): function test_local_grid_searcher_numeric (line 61) | def test_local_grid_searcher_numeric(): function test_local_grid_searcher_numeric_grid_settings (line 84) | def test_local_grid_searcher_numeric_grid_settings(): FILE: core/tests/unittests/searcher/test_local_random_searcher.py function dictsAlmostEqual (line 8) | def dictsAlmostEqual(dict1, dict2, rel_tol=1e-8): function test_local_random_searcher (line 25) | def test_local_random_searcher(): FILE: core/tests/unittests/searcher/test_local_searcher.py class TestLocalSearcher (line 7) | class TestLocalSearcher(unittest.TestCase): method test_local_searcher (line 8) | def test_local_searcher(self): method test_local_searcher_pickle (line 56) | def test_local_searcher_pickle(self): FILE: core/tests/unittests/test_feature_selection.py function evaluated_fi_df_template (line 10) | def evaluated_fi_df_template(features, importance=None, n=None): function sample_features (line 23) | def sample_features(): function sample_importance_df_1 (line 28) | def sample_importance_df_1(sample_features): function sample_importance_df_2 (line 33) | def sample_importance_df_2(sample_features): function test_add_noise_column_df (line 37) | def test_add_noise_column_df(): function test_merge_importance_dfs_base (line 46) | def test_merge_importance_dfs_base(sample_features): function test_merge_importance_dfs_same_model (line 52) | def test_merge_importance_dfs_same_model(sample_features, sample_importa... function test_merge_importance_dfs_different_model (line 66) | def test_merge_importance_dfs_different_model(sample_features, sample_im... function test_merge_importance_dfs_all (line 82) | def test_merge_importance_dfs_all(sample_features, sample_importance_df_... function test_sort_features_by_priority_base (line 98) | def test_sort_features_by_priority_base(sample_features): function test_sort_features_by_priority_same_model (line 106) | def test_sort_features_by_priority_same_model(sample_features): function test_sort_features_by_priority_different_model (line 115) | def test_sort_features_by_priority_different_model(sample_features): function test_sort_features_by_priority_all (line 132) | def test_sort_features_by_priority_all(sample_features): FILE: core/tests/unittests/test_import_version.py function test_import_version (line 4) | def test_import_version(): FILE: core/tests/unittests/test_parallel_local_folding.py class DummyBigModel (line 20) | class DummyBigModel(AbstractModel): method _estimate_memory_usage (line 21) | def _estimate_memory_usage(self, **kwargs): function _prepare_data (line 25) | def _prepare_data(): function _construct_dummy_fold_strategy (line 35) | def _construct_dummy_fold_strategy(num_jobs, model_base_cls=AbstractMode... function _test_resource_allocation_and_time_limit (line 64) | def _test_resource_allocation_and_time_limit(num_jobs, num_folds_paralle... function test_resource_allocation_and_time_limit (line 94) | def test_resource_allocation_and_time_limit(): function test_dynamic_resource_allocation (line 112) | def test_dynamic_resource_allocation(resource_cal, mock_get_mem): FILE: core/tests/unittests/test_search_space.py function test_search_space (line 5) | def test_search_space(): function test_search_space_dot_key (line 26) | def test_search_space_dot_key(): FILE: core/tests/unittests/utils/decorators/test_presets.py class TestPresets (line 6) | class TestPresets(unittest.TestCase): method test_presets (line 7) | def test_presets(self): FILE: core/tests/unittests/utils/test_augment_rare_classes.py function utils_log_records (line 11) | def utils_log_records(): function _messages (line 43) | def _messages(records: list[logging.LogRecord]) -> list[str]: function test_no_augmentation_returns_same_df_object (line 47) | def test_no_augmentation_returns_same_df_object(utils_log_records): function test_augment_single_rare_class_adds_expected_rows_and_meets_threshold (line 58) | def test_augment_single_rare_class_adds_expected_rows_and_meets_threshol... function test_augment_multiple_rare_classes_total_added_and_threshold_met (line 77) | def test_augment_multiple_rare_classes_total_added_and_threshold_met(): function test_augmented_indices_unique_and_start_after_max (line 95) | def test_augmented_indices_unique_and_start_after_max(): function test_missing_classes_zero_count_are_warned_and_ignored (line 109) | def test_missing_classes_zero_count_are_warned_and_ignored(utils_log_rec... FILE: core/tests/unittests/utils/test_time.py function test_sample_df_for_time_func (line 6) | def test_sample_df_for_time_func(): FILE: core/tests/unittests/utils/test_utils.py class TestInferProblemType (line 12) | class TestInferProblemType(unittest.TestCase): method test_infer_problem_type_empty (line 13) | def test_infer_problem_type_empty(self): method test_infer_problem_type_nan (line 17) | def test_infer_problem_type_nan(self): method test_infer_problem_type_inf (line 21) | def test_infer_problem_type_inf(self): method test_infer_problem_type_ninf (line 25) | def test_infer_problem_type_ninf(self): method test_infer_problem_type_binary (line 29) | def test_infer_problem_type_binary(self): method test_infer_problem_type_binary_with_nan (line 33) | def test_infer_problem_type_binary_with_nan(self): method test_infer_problem_type_str (line 37) | def test_infer_problem_type_str(self): method test_infer_problem_type_category (line 41) | def test_infer_problem_type_category(self): method test_infer_problem_type_object (line 45) | def test_infer_problem_type_object(self): method test_infer_problem_type_multiclass_with_nan (line 49) | def test_infer_problem_type_multiclass_with_nan(self): method test_infer_problem_type_big_float_data_regression (line 53) | def test_infer_problem_type_big_float_data_regression(self): method test_infer_problem_type_small_float_data_multiclass (line 58) | def test_infer_problem_type_small_float_data_multiclass(self): method test_infer_problem_type_small_float_data_regression (line 63) | def test_infer_problem_type_small_float_data_regression(self): method test_infer_problem_type_big_integer_data_regression (line 68) | def test_infer_problem_type_big_integer_data_regression(self): method test_infer_problem_type_small_integer_data_multiclass (line 73) | def test_infer_problem_type_small_integer_data_multiclass(self): method test_infer_problem_type_small_integer_data_regression (line 80) | def test_infer_problem_type_small_integer_data_regression(self): function _assert_equals_generate_train_test_split (line 86) | def _assert_equals_generate_train_test_split(X, y, test_size, problem_ty... function test_generate_train_test_split_edgecase (line 140) | def test_generate_train_test_split_edgecase(): FILE: core/tests/unittests/utils/test_version_utils.py function test_show_versions (line 4) | def test_show_versions(): FILE: eda/src/autogluon/eda/analysis/anomaly.py function _suod_silent_print (line 30) | def _suod_silent_print(silent=True): # pragma: no cover class AnomalyDetector (line 61) | class AnomalyDetector: method __init__ (line 97) | def __init__( method _get_default_detector_list (line 125) | def _get_default_detector_list(): method problem_type (line 137) | def problem_type(self): method fit_transform (line 140) | def fit_transform(self, train_data: pd.DataFrame) -> pd.Series: method transform (line 175) | def transform(self, x: pd.DataFrame): method predict (line 201) | def predict(self, x): class AnomalyDetectorAnalysis (line 208) | class AnomalyDetectorAnalysis(AbstractAnalysis): method __init__ (line 259) | def __init__( method can_handle (line 272) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 287) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... method _create_detector (line 305) | def _create_detector(self, args) -> AnomalyDetector: method explain_rows_fn (line 309) | def explain_rows_fn(args: AnalysisState, detector: AnomalyDetector, da... FILE: eda/src/autogluon/eda/analysis/base.py class AbstractAnalysis (line 14) | class AbstractAnalysis(ABC, StateCheckMixin): method __init__ (line 52) | def __init__( method _gather_args (line 67) | def _gather_args(self) -> AnalysisState: method available_datasets (line 77) | def available_datasets(args: AnalysisState) -> Generator[Tuple[str, Da... method _get_state_from_parent (line 97) | def _get_state_from_parent(self) -> AnalysisState: method can_handle (line 107) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 128) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... method fit (line 145) | def fit(self, **kwargs) -> AnalysisState: class BaseAnalysis (line 172) | class BaseAnalysis(AbstractAnalysis): method __init__ (line 190) | def __init__( method can_handle (line 195) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 198) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class Namespace (line 202) | class Namespace(AbstractAnalysis): method can_handle (line 243) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method __init__ (line 246) | def __init__( method fit (line 256) | def fit(self, **kwargs) -> AnalysisState: method _fit (line 262) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... method _get_state_from_parent (line 265) | def _get_state_from_parent(self) -> AnalysisState: class SaveArgsToState (line 272) | class SaveArgsToState(AbstractAnalysis): method __init__ (line 292) | def __init__( method can_handle (line 303) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 306) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... FILE: eda/src/autogluon/eda/analysis/dataset.py class Sampler (line 35) | class Sampler(AbstractAnalysis): method __init__ (line 67) | def __init__( method can_handle (line 79) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 82) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class ProblemTypeControl (line 96) | class ProblemTypeControl(AbstractAnalysis): method __init__ (line 112) | def __init__( method can_handle (line 124) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 127) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class TrainValidationSplit (line 134) | class TrainValidationSplit(AbstractAnalysis): method __init__ (line 190) | def __init__( method can_handle (line 202) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 205) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class DatasetSummary (line 213) | class DatasetSummary(AbstractAnalysis): method can_handle (line 237) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 240) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class RawTypesAnalysis (line 252) | class RawTypesAnalysis(AbstractAnalysis): method can_handle (line 276) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 279) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class VariableTypeAnalysis (line 285) | class VariableTypeAnalysis(AbstractAnalysis): method __init__ (line 322) | def __init__( method can_handle (line 332) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 335) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... method map_raw_type_to_feature_type (line 344) | def map_raw_type_to_feature_type( class SpecialTypesAnalysis (line 359) | class SpecialTypesAnalysis(AbstractAnalysis): method can_handle (line 383) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 386) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... method infer_special_types (line 392) | def infer_special_types(ds): class LabelInsightsAnalysis (line 405) | class LabelInsightsAnalysis(AbstractAnalysis): method __init__ (line 452) | def __init__( method can_handle (line 475) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 480) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... method _test_data_with_label_present (line 534) | def _test_data_with_label_present(self, args, label): FILE: eda/src/autogluon/eda/analysis/explain.py class _ShapAutoGluonWrapper (line 18) | class _ShapAutoGluonWrapper: method __init__ (line 19) | def __init__(self, predictor, feature_names, target_class=None): method predict_proba (line 26) | def predict_proba(self, X): class ShapAnalysis (line 41) | class ShapAnalysis(AbstractAnalysis): method __init__ (line 89) | def __init__( method can_handle (line 106) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 109) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... FILE: eda/src/autogluon/eda/analysis/interaction.py class Correlation (line 26) | class Correlation(AbstractAnalysis): method __init__ (line 83) | def __init__( method can_handle (line 98) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 101) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class CorrelationSignificance (line 130) | class CorrelationSignificance(AbstractAnalysis): method can_handle (line 167) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 170) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class FeatureInteraction (line 178) | class FeatureInteraction(AbstractAnalysis): method __init__ (line 229) | def __init__( method can_handle (line 245) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 248) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs): method _generate_key_if_not_provided (line 273) | def _generate_key_if_not_provided(self, key: Optional[str], cols: Dict... class DistributionFit (line 284) | class DistributionFit(AbstractAnalysis): method __init__ (line 358) | def __init__( method can_handle (line 392) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 395) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... method _fit_dist (line 408) | def _fit_dist(self, series, pvalue_min=0.01): method _list_parameters (line 434) | def _list_parameters(self, distribution): class FeatureDistanceAnalysis (line 454) | class FeatureDistanceAnalysis(AbstractAnalysis): method __init__ (line 505) | def __init__( method can_handle (line 515) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 518) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... method __get_linkage_clusters (line 536) | def __get_linkage_clusters(linkage, columns, threshold: float): FILE: eda/src/autogluon/eda/analysis/missing.py class MissingValuesAnalysis (line 8) | class MissingValuesAnalysis(AbstractAnalysis): method can_handle (line 34) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 37) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... FILE: eda/src/autogluon/eda/analysis/model.py class AutoGluonModelQuickFit (line 21) | class AutoGluonModelQuickFit(AbstractAnalysis): method __init__ (line 69) | def __init__( method can_handle (line 91) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 94) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class AutoGluonModelEvaluator (line 105) | class AutoGluonModelEvaluator(AbstractAnalysis): method __init__ (line 152) | def __init__( method can_handle (line 162) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 174) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs): method _predict (line 227) | def _predict(self, problem_type, predictor, val_data): FILE: eda/src/autogluon/eda/analysis/shift.py class XShiftDetector (line 19) | class XShiftDetector(AbstractAnalysis, StateCheckMixin): method __init__ (line 67) | def __init__( method can_handle (line 109) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 112) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... function post_fit (line 155) | def post_fit(func): class Classifier2ST (line 165) | class Classifier2ST: method __init__ (line 190) | def __init__( method _make_source_target_label (line 218) | def _make_source_target_label(data, sample_label): method fit (line 226) | def fit(self, data, **kwargs): method _pvalue_half_permutation (line 260) | def _pvalue_half_permutation(self, num_permutations=1000): method pvalue (line 274) | def pvalue(self, num_permutations: int = 1000): method feature_importance (line 290) | def feature_importance(self): FILE: eda/src/autogluon/eda/analysis/transform.py class ApplyFeatureGenerator (line 16) | class ApplyFeatureGenerator(AbstractAnalysis, StateCheckMixin): method __init__ (line 58) | def __init__( method can_handle (line 84) | def can_handle(self, state: AnalysisState, args: AnalysisState) -> bool: method _fit (line 87) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... FILE: eda/src/autogluon/eda/auto/simple.py function analyze (line 78) | def analyze( function analyze_interaction (line 189) | def analyze_interaction( function _is_single_numeric_variable (line 296) | def _is_single_numeric_variable(x, y, hue, x_type): function quick_fit (line 300) | def quick_fit( function dataset_overview (line 529) | def dataset_overview( function covariate_shift_detection (line 651) | def covariate_shift_detection( function _is_lightgbm_available (line 784) | def _is_lightgbm_available() -> bool: function get_default_estimator_if_not_specified (line 793) | def get_default_estimator_if_not_specified(fit_args, fit_bagging_folds: ... function target_analysis (line 809) | def target_analysis( function _render_features_highly_correlated_with_target (line 934) | def _render_features_highly_correlated_with_target( function _render_correlation_analysis (line 957) | def _render_correlation_analysis(state, train_data, label, sample, fig_a... function _render_distribution_fit_information_if_available (line 984) | def _render_distribution_fit_information_if_available(state, label) -> O... function missing_values_analysis (line 1006) | def missing_values_analysis( function explain_rows (line 1089) | def explain_rows( function partial_dependence_plots (line 1193) | def partial_dependence_plots( function _validate_and_normalize_pdp_args (line 1411) | def _validate_and_normalize_pdp_args( function _prepare_pdp_data (line 1436) | def _prepare_pdp_data( function detect_anomalies (line 1473) | def detect_anomalies( FILE: eda/src/autogluon/eda/state.py class AnalysisState (line 8) | class AnalysisState(dict): method __getattr__ (line 14) | def __getattr__(self, item) -> Any: # needed for mypy checks method __init__ (line 17) | def __init__(self, *args, **kwargs) -> None: method __setattr__ (line 26) | def __setattr__(self, name: str, value) -> None: method __setitem__ (line 31) | def __setitem__(self, key, value) -> None: method __dict__ (line 37) | def __dict__(self): class StateCheckMixin (line 41) | class StateCheckMixin: method at_least_one_key_must_be_present (line 44) | def at_least_one_key_must_be_present(self, state: AnalysisState, *keys... method all_keys_must_be_present (line 65) | def all_keys_must_be_present(self, state: AnalysisState, *keys) -> bool: function is_key_present_in_state (line 90) | def is_key_present_in_state(state: AnalysisState, key: str): FILE: eda/src/autogluon/eda/utils/common.py function get_empty_dict_if_none (line 6) | def get_empty_dict_if_none(value) -> dict: function expand_nested_args_into_nested_maps (line 12) | def expand_nested_args_into_nested_maps(args: Dict[str, Any]) -> Dict[st... FILE: eda/src/autogluon/eda/utils/defaults.py class QuickFitDefaults (line 1) | class QuickFitDefaults: FILE: eda/src/autogluon/eda/visualization/anomaly.py class AnomalyScoresVisualization (line 15) | class AnomalyScoresVisualization(AbstractVisualization, JupyterMixin): method __init__ (line 91) | def __init__( method can_handle (line 107) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 110) | def _render(self, state: AnalysisState) -> None: FILE: eda/src/autogluon/eda/visualization/base.py class AbstractVisualization (line 10) | class AbstractVisualization(ABC, StateCheckMixin): method __init__ (line 35) | def __init__(self, namespace: Optional[str] = None, **kwargs) -> None: method _get_namespace_state (line 42) | def _get_namespace_state(self, state): method can_handle (line 49) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 68) | def _render(self, state: AnalysisState) -> None: method render (line 84) | def render(self, state: AnalysisState) -> None: FILE: eda/src/autogluon/eda/visualization/dataset.py class DatasetStatistics (line 13) | class DatasetStatistics(AbstractVisualization, JupyterMixin): method __init__ (line 63) | def __init__( method can_handle (line 76) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 81) | def _render(self, state: AnalysisState) -> None: method _merge_analysis_facets (line 106) | def _merge_analysis_facets(ds: str, state: AnalysisState): method _fix_counts (line 124) | def _fix_counts(df: DataFrame, cols: List[str]) -> DataFrame: class DatasetTypeMismatch (line 131) | class DatasetTypeMismatch(AbstractVisualization, JupyterMixin): method __init__ (line 164) | def __init__(self, headers: bool = False, namespace: Optional[str] = N... method can_handle (line 168) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 171) | def _render(self, state: AnalysisState) -> None: class LabelInsightsVisualization (line 184) | class LabelInsightsVisualization(AbstractVisualization, JupyterMixin): method __init__ (line 220) | def __init__(self, headers: bool = False, namespace: Optional[str] = N... method can_handle (line 224) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 227) | def _render(self, state: AnalysisState) -> None: method _regression_add_out_of_domain_insights (line 241) | def _regression_add_out_of_domain_insights(insights: AnalysisState, md... method _classification_add_missing_classes_insights (line 252) | def _classification_add_missing_classes_insights(insights: AnalysisSta... method _classification_add_minority_class_imbalance_insights (line 261) | def _classification_add_minority_class_imbalance_insights(insights: An... method _classification_add_low_cardinality_classes_insights (line 279) | def _classification_add_low_cardinality_classes_insights(insights: Ana... FILE: eda/src/autogluon/eda/visualization/explain.py class _AbstractExplainPlot (line 14) | class _AbstractExplainPlot(AbstractVisualization, JupyterMixin, ABC): method __init__ (line 15) | def __init__(self, display_rows: bool = False, namespace: Optional[str... method can_handle (line 19) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 24) | def _render(self, state: AnalysisState) -> None: method _render_internal (line 38) | def _render_internal(self, expected_value, shap_values, features, expl... class ExplainForcePlot (line 42) | class ExplainForcePlot(_AbstractExplainPlot): method _render_internal (line 79) | def _render_internal(self, expected_value, shap_values, features, expl... class ExplainWaterfallPlot (line 84) | class ExplainWaterfallPlot(_AbstractExplainPlot): method _render_internal (line 121) | def _render_internal(self, expected_value, shap_values, features, expl... FILE: eda/src/autogluon/eda/visualization/interaction.py class _AbstractCorrelationChart (line 27) | class _AbstractCorrelationChart(AbstractVisualization, JupyterMixin, ABC): method __init__ (line 28) | def __init__( method _render_internal (line 41) | def _render_internal(self, state: AnalysisState, render_key: str, head... class CorrelationVisualization (line 76) | class CorrelationVisualization(_AbstractCorrelationChart): method can_handle (line 98) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 101) | def _render(self, state: AnalysisState) -> None: class _AbstractFeatureInteractionPlotRenderer (line 109) | class _AbstractFeatureInteractionPlotRenderer(ABC): method _render (line 111) | def _render(self, state, ds, params, param_types, ax, data, chart_args): method render (line 114) | def render(self, state, ds, params, param_types, data, fig_args, chart... class CorrelationSignificanceVisualization (line 120) | class CorrelationSignificanceVisualization(_AbstractCorrelationChart): method can_handle (line 144) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 147) | def _render(self, state: AnalysisState) -> None: class FeatureDistanceAnalysisVisualization (line 152) | class FeatureDistanceAnalysisVisualization(AbstractVisualization, Jupyte... method __init__ (line 170) | def __init__( method can_handle (line 181) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 184) | def _render(self, state: AnalysisState) -> None: class FeatureInteractionVisualization (line 211) | class FeatureInteractionVisualization(AbstractVisualization, JupyterMixin): method __init__ (line 240) | def __init__( method can_handle (line 259) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 262) | def _render(self, state: AnalysisState) -> None: method _prepare_chart_args (line 316) | def _prepare_chart_args(self, df, x, x_type, y, y_type, hue) -> Tuple[... method _convert_categoricals_to_objects (line 333) | def _convert_categoricals_to_objects(self, df, x, x_type, y, y_type, h... method _swap_y_and_hue_if_necessary (line 340) | def _swap_y_and_hue_if_necessary(self, x_type, y, y_type, hue, hue_type): method _get_value_and_type (line 347) | def _get_value_and_type( method _get_chart_renderer (line 356) | def _get_chart_renderer( method _map_raw_type_to_feature_type (line 380) | def _map_raw_type_to_feature_type( class _HistPlotRenderer (line 396) | class _HistPlotRenderer(_AbstractFeatureInteractionPlotRenderer): method _render (line 397) | def _render(self, state, ds, params, param_types, ax, data, chart_ar... class _KdePlotRenderer (line 424) | class _KdePlotRenderer(_AbstractFeatureInteractionPlotRenderer): method _render (line 425) | def _render(self, state, ds, params, param_types, ax, data, chart_ar... class _BoxPlotRenderer (line 430) | class _BoxPlotRenderer(_AbstractFeatureInteractionPlotRenderer): method _render (line 431) | def _render(self, state, ds, params, param_types, ax, data, chart_ar... class _CountPlotRenderer (line 435) | class _CountPlotRenderer(_AbstractFeatureInteractionPlotRenderer): method _render (line 436) | def _render(self, state, ds, params, param_types, ax, data, chart_ar... class _BarPlotRenderer (line 442) | class _BarPlotRenderer(_AbstractFeatureInteractionPlotRenderer): method _render (line 443) | def _render(self, state, ds, params, param_types, ax, data, chart_ar... class _ScatterPlotRenderer (line 448) | class _ScatterPlotRenderer(_AbstractFeatureInteractionPlotRenderer): method _render (line 449) | def _render(self, state, ds, params, param_types, ax, data, chart_ar... class _RegPlotRenderer (line 452) | class _RegPlotRenderer(_AbstractFeatureInteractionPlotRenderer): method _render (line 453) | def _render(self, state, ds, params, param_types, ax, data, chart_ar... class _LinePlotRenderer (line 456) | class _LinePlotRenderer(_AbstractFeatureInteractionPlotRenderer): method _render (line 457) | def _render(self, state, ds, params, param_types, ax, data, chart_ar... class PDPInteractions (line 461) | class PDPInteractions(AbstractVisualization, JupyterMixin): method __init__ (line 497) | def __init__( method can_handle (line 523) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 526) | def _render(self, state: AnalysisState) -> None: method _get_args (line 553) | def _get_args(self): class _SklearnAutoGluonWrapper (line 577) | class _SklearnAutoGluonWrapper: method __init__ (line 578) | def __init__(self, estimator): method _estimator_type (line 583) | def _estimator_type(self): method __sklearn_is_fitted__ (line 586) | def __sklearn_is_fitted__(self): method fit (line 589) | def fit(self, X, Y=None): method predict (line 592) | def predict(self, X): method predict_proba (line 595) | def predict_proba(self, X): method classes_ (line 599) | def classes_(self): FILE: eda/src/autogluon/eda/visualization/jupyter.py class JupyterMixin (line 4) | class JupyterMixin: method __init__ (line 5) | def __init__(self) -> None: method display_obj (line 9) | def display_obj(obj): method render_header_if_needed (line 12) | def render_header_if_needed(self, state, header_text, ds=""): method render_markdown (line 20) | def render_markdown(md): class JupyterTools (line 24) | class JupyterTools: method fix_tabs_scrolling (line 25) | def fix_tabs_scrolling(self): FILE: eda/src/autogluon/eda/visualization/layouts.py class SimpleVerticalLinearLayout (line 21) | class SimpleVerticalLinearLayout(AbstractVisualization): method __init__ (line 26) | def __init__( method can_handle (line 38) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 41) | def _render(self, state: AnalysisState) -> None: class SimpleHorizontalLayout (line 46) | class SimpleHorizontalLayout(SimpleVerticalLinearLayout): method _render (line 52) | def _render(self, state: AnalysisState) -> None: class TabLayout (line 61) | class TabLayout(SimpleVerticalLinearLayout): method __init__ (line 68) | def __init__(self, facets: Dict[str, AbstractVisualization], namespace... method _render (line 72) | def _render(self, state: AnalysisState) -> None: class MarkdownSectionComponent (line 83) | class MarkdownSectionComponent(AbstractVisualization, JupyterMixin): method __init__ (line 100) | def __init__( method can_handle (line 116) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 119) | def _render(self, state: AnalysisState) -> None: class PropertyRendererComponent (line 123) | class PropertyRendererComponent(AbstractVisualization, JupyterMixin): method __init__ (line 138) | def __init__( method can_handle (line 145) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 148) | def _render(self, state: AnalysisState) -> None: FILE: eda/src/autogluon/eda/visualization/missing.py class MissingValues (line 15) | class MissingValues(AbstractVisualization, JupyterMixin): method __init__ (line 60) | def __init__( method can_handle (line 70) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 80) | def _render(self, state: AnalysisState) -> None: method _has_too_many_variables_for_matrix (line 86) | def _has_too_many_variables_for_matrix(self, state: AnalysisState): method _internal_render (line 93) | def _internal_render(widget, data, **kwargs): method _get_operation (line 97) | def _get_operation(self, graph_type): FILE: eda/src/autogluon/eda/visualization/model.py class ConfusionMatrix (line 18) | class ConfusionMatrix(AbstractVisualization, JupyterMixin): method __init__ (line 54) | def __init__( method can_handle (line 68) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 71) | def _render(self, state: AnalysisState) -> None: class _YellowbrickAutoGluonWrapper (line 100) | class _YellowbrickAutoGluonWrapper(ContribEstimator): method score (line 103) | def score(self, y_pred, y_true, **kwargs): method predict (line 107) | def predict(self, y_pred, **kwargs): class RegressionEvaluation (line 112) | class RegressionEvaluation(AbstractVisualization, JupyterMixin): method __init__ (line 154) | def __init__( method can_handle (line 171) | def can_handle(self, state: AnalysisState) -> bool: method _get_plot_mode (line 174) | def _get_plot_mode(self): method _render (line 184) | def _render(self, state: AnalysisState) -> None: method _repack_parameters (line 204) | def _repack_parameters(ev): class FeatureImportance (line 212) | class FeatureImportance(AbstractVisualization, JupyterMixin): method __init__ (line 250) | def __init__( method can_handle (line 267) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 270) | def _render(self, state: AnalysisState) -> None: class ModelLeaderboard (line 285) | class ModelLeaderboard(AbstractVisualization, JupyterMixin): method __init__ (line 317) | def __init__(self, namespace: Optional[str] = None, headers: bool = Fa... method can_handle (line 321) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 324) | def _render(self, state: AnalysisState) -> None: FILE: eda/src/autogluon/eda/visualization/shift.py class XShiftSummary (line 10) | class XShiftSummary(AbstractVisualization, JupyterMixin): method __init__ (line 19) | def __init__(self, headers: bool = False, namespace: Optional[str] = N... method _summary (line 23) | def _summary(self, results: dict) -> str: method _render_feature_importance_if_needed (line 41) | def _render_feature_importance_if_needed(self, state): method can_handle (line 51) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 54) | def _render(self, state: AnalysisState) -> None: FILE: eda/tests/conftest.py function pytest_addoption (line 4) | def pytest_addoption(parser): function pytest_configure (line 8) | def pytest_configure(config): function pytest_collection_modifyitems (line 15) | def pytest_collection_modifyitems(config, items): FILE: eda/tests/test_check_style.py function test_check_style (line 6) | def test_check_style(): FILE: eda/tests/unittests/analysis/test_anomaly.py function test_AnomalyDetector (line 13) | def test_AnomalyDetector(): function _generate_dataset (line 24) | def _generate_dataset(seed=0): function test_AnomalyDetector__defaults_init (line 32) | def test_AnomalyDetector__defaults_init(): function test_AnomalyDetectorAnalysis (line 53) | def test_AnomalyDetectorAnalysis(monkeypatch): function test_AnomalyDetectorAnalysis__interpret (line 83) | def test_AnomalyDetectorAnalysis__interpret(monkeypatch): function test_AnomalyDetectorAnalysis__create_detector (line 115) | def test_AnomalyDetectorAnalysis__create_detector(): function test_AnomalyDetectorAnalysis__can_handle (line 123) | def test_AnomalyDetectorAnalysis__can_handle(): function test_AnomalyDetectorAnalysis__explain_rows_fn (line 143) | def test_AnomalyDetectorAnalysis__explain_rows_fn(dataset): FILE: eda/tests/unittests/analysis/test_base.py function test_abstractanalysis_parameter_shadowing (line 11) | def test_abstractanalysis_parameter_shadowing(): function test_abstractanalysis_available_datasets (line 32) | def test_abstractanalysis_available_datasets(): function test_abstractanalysis_available_datasets_some_present (line 48) | def test_abstractanalysis_available_datasets_some_present(): function test_abstractanalysis_fit_is_not_called_if_cannot_handle (line 61) | def test_abstractanalysis_fit_is_not_called_if_cannot_handle(): function test_abstractanalysis_fit_is_called_if_can_handle (line 69) | def test_abstractanalysis_fit_is_called_if_can_handle(): function test_abstractanalysis_fit_on_inner_before_outer_raises_exception (line 77) | def test_abstractanalysis_fit_on_inner_before_outer_raises_exception(): function test_abstractanalysis_fit_gathers_args (line 86) | def test_abstractanalysis_fit_gathers_args(): function test_namespaces (line 100) | def test_namespaces(): function test_SaveArgsToState (line 120) | def test_SaveArgsToState(): function test_SaveArgsToState__no_key (line 139) | def test_SaveArgsToState__no_key(): FILE: eda/tests/unittests/analysis/test_dataset.py class SomeAnalysis (line 20) | class SomeAnalysis(BaseAnalysis): method _fit (line 21) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... function test_Sampler (line 25) | def test_Sampler(): function test_Sampler__window_larger_than_dataset (line 60) | def test_Sampler__window_larger_than_dataset(): function test_Sampler_frac (line 80) | def test_Sampler_frac(): function test_TrainValidationSplit (line 96) | def test_TrainValidationSplit(): function __get_dataset_summary_test_datasets (line 131) | def __get_dataset_summary_test_datasets(): function test_DatasetSummary (line 146) | def test_DatasetSummary(): function test_RawTypesAnalysis (line 157) | def test_RawTypesAnalysis(): function test_VariableTypeAnalysis_can_handle (line 165) | def test_VariableTypeAnalysis_can_handle(): function test_VariableTypeAnalysis__map_raw_type_to_feature_type__special_cases (line 174) | def test_VariableTypeAnalysis__map_raw_type_to_feature_type__special_cas... function test_VariableTypeAnalysis__map_raw_type_to_feature_type__regular_cases (line 186) | def test_VariableTypeAnalysis__map_raw_type_to_feature_type__regular_cas... function test_VariableTypeAnalysis (line 193) | def test_VariableTypeAnalysis(): function test_SpecialTypesAnalysis (line 215) | def test_SpecialTypesAnalysis(): function test_LabelInsightsAnalysis__classification__low_cardinality_classes (line 227) | def test_LabelInsightsAnalysis__classification__low_cardinality_classes(... function test_LabelInsightsAnalysis__classification__class_imbalance (line 256) | def test_LabelInsightsAnalysis__classification__class_imbalance(n_c1, n_... function test_LabelInsightsAnalysis__classification__not_present_in_train (line 283) | def test_LabelInsightsAnalysis__classification__not_present_in_train(): function test_LabelInsightsAnalysis__regression__no_ood (line 306) | def test_LabelInsightsAnalysis__regression__no_ood(): function test_LabelInsightsAnalysis__regression__ood (line 325) | def test_LabelInsightsAnalysis__regression__ood(threshold, is_warning_ex... FILE: eda/tests/unittests/analysis/test_explain.py function test_ShapAnalysis (line 22) | def test_ShapAnalysis(label, expected_task_type, monkeypatch): FILE: eda/tests/unittests/analysis/test_interaction.py function load_adult_data (line 25) | def load_adult_data(): function test_Correlation_spearman (line 34) | def test_Correlation_spearman(): function test_Correlation_pearson (line 56) | def test_Correlation_pearson(): function test_Correlation_kendall (line 78) | def test_Correlation_kendall(): function test_Correlation_phik (line 100) | def test_Correlation_phik(): function test_Correlation_focus (line 124) | def test_Correlation_focus(): function test_CorrelationSignificance__can_handle (line 157) | def test_CorrelationSignificance__can_handle(): function test_CorrelationSignificance (line 177) | def test_CorrelationSignificance(): function __create_test_df (line 204) | def __create_test_df(): function __compare_outputs (line 218) | def __compare_outputs(expected: AnalysisState, actual: AnalysisState): function test_FeatureInteraction (line 225) | def test_FeatureInteraction(): function test_FeatureInteraction__key_provided (line 245) | def test_FeatureInteraction__key_provided(): function test_FeatureInteraction__generate_key_if_not_provided (line 265) | def test_FeatureInteraction__generate_key_if_not_provided(cols, expected): function test_FeatureInteraction__can_handle (line 269) | def test_FeatureInteraction__can_handle(): function test_DistributionFit__happy_path (line 275) | def test_DistributionFit__happy_path(): function test_DistributionFit__constructor_defaults (line 325) | def test_DistributionFit__constructor_defaults(): function test_DistributionFit__constructor_unsupported_dist (line 340) | def test_DistributionFit__constructor_unsupported_dist(): function test_DistributionFit__non_numeric_col (line 348) | def test_DistributionFit__non_numeric_col(): function test_FeatureDistanceAnalysis__happy_path (line 365) | def test_FeatureDistanceAnalysis__happy_path(): FILE: eda/tests/unittests/analysis/test_missing.py function test_MissingValuesAnalysis (line 8) | def test_MissingValuesAnalysis(): FILE: eda/tests/unittests/analysis/test_model.py function fit_model (line 15) | def fit_model(path, df_train, target, fit_args=None): function test_AutoGluonModelEvaluator_regression (line 26) | def test_AutoGluonModelEvaluator_regression(): function test_AutoGluonModelEvaluator_regression__with_test_data (line 56) | def test_AutoGluonModelEvaluator_regression__with_test_data(): function test_AutoGluonModelEvaluator_classification (line 86) | def test_AutoGluonModelEvaluator_classification(): function test_AutoGluonModelQuickFit (line 111) | def test_AutoGluonModelQuickFit(save_model_to_state): function test_AutoGluonModelQuickFit__constructor_defaults (line 154) | def test_AutoGluonModelQuickFit__constructor_defaults(): function _assert_importance_is_present (line 158) | def _assert_importance_is_present(state): FILE: eda/tests/unittests/analysis/test_transform.py class SomeAnalysis (line 12) | class SomeAnalysis(BaseAnalysis): method _fit (line 13) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... function __replace_ints (line 17) | def __replace_ints(values: List[str]) -> List[str]: function test_ApplyFeatureGenerator (line 23) | def test_ApplyFeatureGenerator(): FILE: eda/tests/unittests/auto/test_simple.py class SomeAnalysis (line 56) | class SomeAnalysis(BaseAnalysis): method _fit (line 57) | def _fit(self, state: AnalysisState, args: AnalysisState, **fit_kwargs... class SomeVisualization (line 61) | class SomeVisualization(AbstractVisualization): method can_handle (line 62) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 65) | def _render(self, state: AnalysisState) -> None: function test_analyze (line 69) | def test_analyze(): function test_analyze_return_state (line 103) | def test_analyze_return_state(): function test_analyze_None_state (line 109) | def test_analyze_None_state(): function test_analyze_state_dict_convert (line 114) | def test_analyze_state_dict_convert(): function test_analyze_state_no_AnalysisState_convert (line 123) | def test_analyze_state_no_AnalysisState_convert(): function test_quick_fit (line 132) | def test_quick_fit(monkeypatch): function test_dataset_overview (line 162) | def test_dataset_overview(monkeypatch): function test_covariate_shift_detection (line 194) | def test_covariate_shift_detection(monkeypatch): function _force_using_rf_if_on_mac (line 222) | def _force_using_rf_if_on_mac(m): function test_get_empty_dict_if_none (line 229) | def test_get_empty_dict_if_none(): function test_get_default_estimator_if_not_specified (line 247) | def test_get_default_estimator_if_not_specified(monkeypatch, hyperparame... function test_analyze_interaction__with_distribution (line 274) | def test_analyze_interaction__with_distribution(monkeypatch, fit_distrib... function test_analyze_interaction__do_not_fit (line 290) | def test_analyze_interaction__do_not_fit(monkeypatch): function test_analyze_interaction__is_single_numeric_variable (line 318) | def test_analyze_interaction__is_single_numeric_variable(x, y, hue, x_ty... function test_target_analysis__classification (line 322) | def test_target_analysis__classification(monkeypatch): function test_target_analysis__regression (line 370) | def test_target_analysis__regression(monkeypatch): function test_explain_rows (line 455) | def test_explain_rows(plot, monkeypatch): function test_partial_dependence_plots__prepare_pdp_data (line 498) | def test_partial_dependence_plots__prepare_pdp_data(): function test_partial_dependence_plots__prepare_pdp_data__features_specified (line 522) | def test_partial_dependence_plots__prepare_pdp_data__features_specified(): function test_partial_dependence_plots__validate_and_normalize_pdp_args__none_args (line 533) | def test_partial_dependence_plots__validate_and_normalize_pdp_args__none... function test_partial_dependence_plots__validate_and_normalize_pdp_args__single_feature (line 543) | def test_partial_dependence_plots__validate_and_normalize_pdp_args__sing... function test_partial_dependence_plots__validate_and_normalize_pdp_args__wrong_features (line 551) | def test_partial_dependence_plots__validate_and_normalize_pdp_args__wron... function test_partial_dependence_plots__validate_and_normalize_pdp_args (line 557) | def test_partial_dependence_plots__validate_and_normalize_pdp_args(): function test_partial_dependence_plots (line 570) | def test_partial_dependence_plots(monkeypatch): function test_detect_anomalies (line 619) | def test_detect_anomalies(monkeypatch, add_explainability): FILE: eda/tests/unittests/test_shift.py function load_adult_data (line 16) | def load_adult_data(): function sim_cov_shift (line 25) | def sim_cov_shift(train, test, p_nonmarr=0.75, val=False): function test_shift (line 45) | def test_shift(): FILE: eda/tests/unittests/test_state.py function test_analysis_state (line 7) | def test_analysis_state(): function test_analysis_state_ignore_non_dict_args (line 15) | def test_analysis_state_ignore_non_dict_args(): function test_analysis_state_nested (line 20) | def test_analysis_state_nested(): function test_analysis_state_nested_missing (line 25) | def test_analysis_state_nested_missing(): function test_analysis_state_mutation (line 30) | def test_analysis_state_mutation(): function test_analysis_state_nested_mutation (line 37) | def test_analysis_state_nested_mutation(): function test_statecheckmixin_at_least_one_key_must_be_present (line 45) | def test_statecheckmixin_at_least_one_key_must_be_present(): function test_statecheckmixin_all_keys_must_be_present (line 54) | def test_statecheckmixin_all_keys_must_be_present(): function test_expand_nested_args_into_nested_maps (line 63) | def test_expand_nested_args_into_nested_maps(): function test_expand_nested_args_into_nested_maps__namespaces_overlap (line 71) | def test_expand_nested_args_into_nested_maps__namespaces_overlap(): FILE: eda/tests/unittests/visualization/test_anomaly.py function test_AnomalyScoresVisualization__init (line 12) | def test_AnomalyScoresVisualization__init(): function test_AnomalyScoresVisualization (line 24) | def test_AnomalyScoresVisualization(monkeypatch): FILE: eda/tests/unittests/visualization/test_base.py class SomeVisualization (line 7) | class SomeVisualization(AbstractVisualization): method can_handle (line 8) | def can_handle(self, state: AnalysisState) -> bool: method _render (line 11) | def _render(self, state: AnalysisState) -> None: function test_AbstractVisualization_cannot_render (line 15) | def test_AbstractVisualization_cannot_render(): function test_AbstractVisualization_can_render (line 24) | def test_AbstractVisualization_can_render(): FILE: eda/tests/unittests/visualization/test_dataset.py function test_DatasetStatistics (line 18) | def test_DatasetStatistics(): function test_DatasetStatistics__can_handle (line 61) | def test_DatasetStatistics__can_handle(state_present, expected): function test__merge_analysis_facets__single_values (line 66) | def test__merge_analysis_facets__single_values(field): function test__merge_analysis_facets__single_values__missing_statistics (line 75) | def test__merge_analysis_facets__single_values__missing_statistics(): function test__merge_analysis_facets__multiple_values (line 85) | def test__merge_analysis_facets__multiple_values(): function test__fix_counts (line 105) | def test__fix_counts(): function test_DatasetTypeMismatch (line 118) | def test_DatasetTypeMismatch(): function test_DatasetTypeMismatch__no_warnings (line 136) | def test_DatasetTypeMismatch__no_warnings(): function test_LabelInsightsVisualization (line 148) | def test_LabelInsightsVisualization(): function test_LabelInsightsVisualization__no_state (line 191) | def test_LabelInsightsVisualization__no_state(): FILE: eda/tests/unittests/visualization/test_explain.py function test_ExplainForcePlot (line 20) | def test_ExplainForcePlot(display_rows, monkeypatch): function test_ExplainWaterfallPlot (line 64) | def test_ExplainWaterfallPlot(display_rows, monkeypatch): FILE: eda/tests/unittests/visualization/test_interaction.py function test_CorrelationVisualization_single_value (line 25) | def test_CorrelationVisualization_single_value(monkeypatch): function test_CorrelationVisualization (line 39) | def test_CorrelationVisualization(monkeypatch): function test_CorrelationVisualization_phik (line 45) | def test_CorrelationVisualization_phik(monkeypatch): function test_CorrelationVisualization_focus (line 59) | def test_CorrelationVisualization_focus(monkeypatch): function test_CorrelationVisualization__can_handle (line 81) | def test_CorrelationVisualization__can_handle(): function test_CorrelationSignificanceVisualization__can_handle (line 88) | def test_CorrelationSignificanceVisualization__can_handle(): function test_CorrelationSignificanceVisualization (line 98) | def test_CorrelationSignificanceVisualization(monkeypatch): function __get_train_data (line 115) | def __get_train_data(): function __test_internal (line 127) | def __test_internal(monkeypatch, render_key, state, heatmap_args, facet_... function test_FeatureInteractionVisualization__happy_path (line 165) | def test_FeatureInteractionVisualization__happy_path(monkeypatch): function test_FeatureInteractionVisualization__headers (line 195) | def test_FeatureInteractionVisualization__headers(monkeypatch, is_single... function test_FeatureInteractionVisualization__state_different_key (line 223) | def test_FeatureInteractionVisualization__state_different_key(monkeypatch): function test_FeatureInteractionVisualization__no_renderer (line 244) | def test_FeatureInteractionVisualization__no_renderer(monkeypatch): function test_FeatureInteractionVisualization__convert_categoricals_to_objects (line 261) | def test_FeatureInteractionVisualization__convert_categoricals_to_object... function test_FeatureInteractionVisualization__prepare_chart_args__single_var (line 313) | def test_FeatureInteractionVisualization__prepare_chart_args__single_var( function test_FeatureInteractionVisualization__fig_args (line 352) | def test_FeatureInteractionVisualization__fig_args(x_type, y_type, hue_t... function test_FeatureInteractionVisualization__map_raw_type_to_feature_type (line 383) | def test_FeatureInteractionVisualization__map_raw_type_to_feature_type( function __get_feature_interaction_state (line 395) | def __get_feature_interaction_state(): function test_FeatureInteractionVisualization__HistPlotRenderer (line 417) | def test_FeatureInteractionVisualization__HistPlotRenderer(monkeypatch, ... function test_FeatureDistanceAnalysisVisualization__happy_path (line 469) | def test_FeatureDistanceAnalysisVisualization__happy_path(monkeypatch, h... function test_FeatureInteractionVisualization__no_fig_args (line 554) | def test_FeatureInteractionVisualization__no_fig_args(): function __get_args__figargs (line 561) | def __get_args__figargs(figsize, ncols, nrows): function test_PDPInteractions__get_args (line 578) | def test_PDPInteractions__get_args(features, two_way, expected_result): function test_PDPInteractions (line 588) | def test_PDPInteractions(monkeypatch): function test_PDPInteractions__two_way (line 622) | def test_PDPInteractions__two_way(monkeypatch): FILE: eda/tests/unittests/visualization/test_layouts.py function test_PropertyRendererComponent (line 10) | def test_PropertyRendererComponent(with_transform_fn, expected): FILE: eda/tests/unittests/visualization/test_missing.py function test_MissingValues (line 12) | def test_MissingValues(): function test_MissingValues__no_headers (line 38) | def test_MissingValues__no_headers(): function test_get_operation (line 54) | def test_get_operation(input_type, expected): function test_has_too_many_variables_for_matrix (line 67) | def test_has_too_many_variables_for_matrix(cols_number, expected): function __prepare_test_data (line 80) | def __prepare_test_data(): FILE: eda/tests/unittests/visualization/test_model.py function test_ConfusionMatrix (line 14) | def test_ConfusionMatrix(monkeypatch, confusion_matrix_normalized, expec... function test_ConfusionMatrix__can_handle (line 64) | def test_ConfusionMatrix__can_handle(): function test_RegressionEvaluation (line 70) | def test_RegressionEvaluation(monkeypatch): function test_RegressionEvaluation__repack_parameters (line 124) | def test_RegressionEvaluation__repack_parameters(train_present, val_pres... function test_RegressionEvaluation__can_handle (line 140) | def test_RegressionEvaluation__can_handle(): function test_RegressionEvaluation__handle_None_fig_args (line 168) | def test_RegressionEvaluation__handle_None_fig_args(): function test_FeatureImportance__can_handle (line 174) | def test_FeatureImportance__can_handle(): function test_FeatureImportance__handle_None_fig_args (line 191) | def test_FeatureImportance__handle_None_fig_args(): function test_FeatureImportance (line 203) | def test_FeatureImportance(monkeypatch, show_barplots): function test_ModelLeaderboard (line 246) | def test_ModelLeaderboard(): FILE: examples/automm/Conv-LoRA/prepare_semantic_segmentation_datasets.py function get_data_home_dir (line 6) | def get_data_home_dir(): FILE: examples/automm/Conv-LoRA/run_semantic_segmentation.py function get_default_training_setting (line 9) | def get_default_training_setting(dataset_name): function expand_path (line 39) | def expand_path(df, dataset_dir): FILE: examples/automm/TCGA_cancer_survival/example_cancer_survival.py function get_parser (line 29) | def get_parser(): function data_loader (line 45) | def data_loader(path="./dataset/", ): function preprocess (line 62) | def preprocess(df, test_size, shuffle): function train (line 80) | def train(args): function set_seed (line 107) | def set_seed(seed): FILE: examples/automm/distillation/automm_distillation_glue.py function get_parser (line 23) | def get_parser(): function main (line 53) | def main(args): FILE: examples/automm/distillation/automm_distillation_pawsx.py function tasks_to_id (line 12) | def tasks_to_id(pawsx_tasks): function getDatasetSplits (line 19) | def getDatasetSplits(pawsx_tasks): function main (line 39) | def main(args): FILE: examples/automm/distillation/eval_pawsx.py function tasks_to_id (line 12) | def tasks_to_id(pawsx_tasks): function main (line 20) | def main(args): FILE: examples/automm/kaggle_california_house_price/example_kaggle_house.py function get_parser (line 11) | def get_parser(): function get_automm_hyperparameters (line 34) | def get_automm_hyperparameters(mode, text_backbone, cat_as_text): function preprocess (line 53) | def preprocess(df, with_tax_values=True, log_scale_lot=True, function set_seed (line 74) | def set_seed(seed): function train (line 81) | def train(args): FILE: examples/automm/kaggle_feedback_prize/kaggle_feedback_prize_preprocess.py function get_essay (line 6) | def get_essay(essay_id: str, input_dir: str, is_train: bool = True) -> str: function read_and_process_data (line 13) | def read_and_process_data(path: str, file: str, is_train: bool) -> pd.Da... FILE: examples/automm/kaggle_feedback_prize/kaggle_feedback_prize_train.py function get_args (line 13) | def get_args() -> argparse.ArgumentParser: function get_hparams (line 44) | def get_hparams(args: argparse.ArgumentParser) -> dict: function set_seed (line 55) | def set_seed(seed: int) -> None: function train (line 61) | def train( FILE: examples/automm/kaggle_pawpularity/kaggle_pawpularity_submit.py function load_data (line 43) | def load_data(data_path): FILE: examples/automm/kaggle_pawpularity/kaggle_pawpularity_train.py function get_args (line 11) | def get_args(): function load_data (line 100) | def load_data(data_path: str): FILE: examples/automm/memory_bank/memory_bank.py function get_args (line 19) | def get_args(): class AutoMMMemoryBank (line 62) | class AutoMMMemoryBank(nn.Module): method __init__ (line 68) | def __init__( method adapt_logits (line 113) | def adapt_logits(self, affinity, pure_logits, alpha, beta): method change_head_state (line 136) | def change_head_state(self, grad_state): method change_adapter_state (line 149) | def change_adapter_state(self, grad_state): method forward (line 161) | def forward(self, x, alpha=1, beta=1, pure_logits=None): function train_logits (line 205) | def train_logits( function run_memory_bank (line 301) | def run_memory_bank( function main (line 399) | def main(): FILE: examples/automm/memory_bank/utils.py function cls_acc (line 13) | def cls_acc(output, target, topk=1): function generate_image_df (line 20) | def generate_image_df(args, dataset): function generate_dataset (line 31) | def generate_dataset(args): function extract_embedding (line 57) | def extract_embedding(args, data, predictor, column_names): function generate_clip_weights (line 68) | def generate_clip_weights(args, classnames, template, predictor): function generate_bank_model (line 84) | def generate_bank_model(args, train_df, predictor): function extract_val_test (line 104) | def extract_val_test(args, predictor, val_df, test_df): function search_hp (line 112) | def search_hp( class Wrapper (line 163) | class Wrapper(TorchDataset): method __init__ (line 165) | def __init__(self, data_source, column_names=["image"], label_column="... method __len__ (line 170) | def __len__(self): method __getitem__ (line 173) | def __getitem__(self, idx): function build_data_loader (line 180) | def build_data_loader( FILE: examples/automm/object_detection/benchmarking.py function main (line 9) | def main(benchmark_root, dataset_name, presets, seed): FILE: examples/automm/object_detection/detection_eval.py function detection_evaluation (line 20) | def detection_evaluation( FILE: examples/automm/object_detection/detection_inference.py function test_inference (line 9) | def test_inference(dataset, checkpoint_name): function test_voc_inference (line 42) | def test_voc_inference(checkpoint_name="faster_rcnn_r50_fpn_1x_voc0712"): FILE: examples/automm/object_detection/detection_load.py function load_and_evaluate (line 7) | def load_and_evaluate( FILE: examples/automm/object_detection/detection_train.py function detection_train (line 35) | def detection_train( FILE: examples/automm/object_detection/eval_pretrained_coco_format.py function tutorial_script_for_eval_pretrained_coco_format (line 20) | def tutorial_script_for_eval_pretrained_coco_format(): function eval_pretrained_coco_format (line 38) | def eval_pretrained_coco_format( FILE: examples/automm/object_detection/eval_pretrained_voc_format.py function tutorial_script_for_eval_pretrained_voc_format (line 15) | def tutorial_script_for_eval_pretrained_voc_format(): function eval_pretrained_voc_format (line 35) | def eval_pretrained_voc_format( FILE: examples/automm/object_detection/finetune_coco_format.py function finetune_coco_format (line 11) | def finetune_coco_format( function main (line 49) | def main(): FILE: examples/automm/object_detection/finetune_on_pothole_dataset.py function download_pothole_dataset (line 14) | def download_pothole_dataset(): function main (line 27) | def main(): FILE: examples/automm/object_detection/inference_pretrained_coco_format.py function tutorial_script_for_eval_pretrained_coco_format (line 23) | def tutorial_script_for_eval_pretrained_coco_format(): function eval_pretrained_coco_format (line 42) | def eval_pretrained_coco_format( FILE: examples/automm/object_detection/inference_pretrained_voc_format.py function tutorial_script_for_eval_pretrained_voc_format (line 21) | def tutorial_script_for_eval_pretrained_voc_format(): function eval_pretrained_voc_format (line 40) | def eval_pretrained_voc_format( FILE: examples/automm/object_detection/load_predictor.py function tutorial_script_for_save_load_predictor (line 18) | def tutorial_script_for_save_load_predictor(): function detection_load_predictor_and_eval (line 32) | def detection_load_predictor_and_eval(test_path, load_path, num_gpus): function main (line 40) | def main(): FILE: examples/automm/object_detection/pretrain_objects365.py function main (line 6) | def main(): FILE: examples/automm/object_detection/quick_start_on_a_tiny_dataset.py function tutorial_script_for_quick_start (line 9) | def tutorial_script_for_quick_start(): FILE: examples/automm/object_detection/visualize_results.py function tutorial_script_for_visualize_detection_results (line 16) | def tutorial_script_for_visualize_detection_results(): function visualize_detection_results (line 46) | def visualize_detection_results( FILE: examples/automm/pipeline/feature_extraction_example.py function evaluate (line 18) | def evaluate(predictor, df, onnx_session=None): function main (line 49) | def main(args): FILE: examples/automm/production/onnx_text.py function eval_cosine (line 13) | def eval_cosine(predictor, df, onnx_session): FILE: examples/automm/tabular_dl/dataset.py class BaseTabularDataset (line 17) | class BaseTabularDataset(abc.ABC): method data (line 20) | def data(self): method label_column (line 25) | def label_column(self): method label_type (line 30) | def label_type(self): method metric (line 35) | def metric(self): method problem_type (line 40) | def problem_type(self): class AdultTabularDataset (line 44) | class AdultTabularDataset(BaseTabularDataset): method __init__ (line 60) | def __init__(self, split="train", path="./dataset/"): method data (line 72) | def data(self): method label_column (line 76) | def label_column(self): method label_type (line 80) | def label_type(self): method metric (line 84) | def metric(self): method problem_type (line 88) | def problem_type(self): class AloiTabularDataset (line 92) | class AloiTabularDataset(BaseTabularDataset): method __init__ (line 108) | def __init__(self, split="train", path="./dataset/"): method data (line 120) | def data(self): method label_column (line 124) | def label_column(self): method label_type (line 128) | def label_type(self): method metric (line 132) | def metric(self): method problem_type (line 136) | def problem_type(self): class CaliforniaHousingTabularDataset (line 140) | class CaliforniaHousingTabularDataset(BaseTabularDataset): method __init__ (line 156) | def __init__(self, split="train", path="./dataset/"): method data (line 168) | def data(self): method label_column (line 172) | def label_column(self): method label_type (line 176) | def label_type(self): method metric (line 180) | def metric(self): method problem_type (line 184) | def problem_type(self): class CovtypeTabularDataset (line 188) | class CovtypeTabularDataset(BaseTabularDataset): method __init__ (line 204) | def __init__(self, split="train", path="./dataset/"): method data (line 216) | def data(self): method label_column (line 220) | def label_column(self): method label_type (line 224) | def label_type(self): method metric (line 228) | def metric(self): method problem_type (line 232) | def problem_type(self): class EpsilonTabularDataset (line 236) | class EpsilonTabularDataset(BaseTabularDataset): method __init__ (line 252) | def __init__(self, split="train", path="./dataset/"): method data (line 264) | def data(self): method label_column (line 268) | def label_column(self): method label_type (line 272) | def label_type(self): method metric (line 276) | def metric(self): method problem_type (line 280) | def problem_type(self): class HelenaTabularDataset (line 284) | class HelenaTabularDataset(BaseTabularDataset): method __init__ (line 300) | def __init__(self, split="train", path="./dataset/"): method data (line 312) | def data(self): method label_column (line 316) | def label_column(self): method label_type (line 320) | def label_type(self): method metric (line 324) | def metric(self): method problem_type (line 328) | def problem_type(self): class HiggsSmallTabularDataset (line 332) | class HiggsSmallTabularDataset(BaseTabularDataset): method __init__ (line 348) | def __init__(self, split="train", path="./dataset/"): method data (line 360) | def data(self): method label_column (line 364) | def label_column(self): method label_type (line 368) | def label_type(self): method metric (line 372) | def metric(self): method problem_type (line 376) | def problem_type(self): class JannisTabularDataset (line 380) | class JannisTabularDataset(BaseTabularDataset): method __init__ (line 396) | def __init__(self, split="train", path="./dataset/"): method data (line 408) | def data(self): method label_column (line 412) | def label_column(self): method label_type (line 416) | def label_type(self): method metric (line 420) | def metric(self): method problem_type (line 424) | def problem_type(self): class MicrosoftTabularDataset (line 428) | class MicrosoftTabularDataset(BaseTabularDataset): method __init__ (line 444) | def __init__(self, split="train", path="./dataset/"): method data (line 456) | def data(self): method label_column (line 460) | def label_column(self): method label_type (line 464) | def label_type(self): method metric (line 468) | def metric(self): method problem_type (line 472) | def problem_type(self): class YahooTabularDataset (line 476) | class YahooTabularDataset(BaseTabularDataset): method __init__ (line 492) | def __init__(self, split="train", path="./dataset/"): method data (line 504) | def data(self): method label_column (line 508) | def label_column(self): method label_type (line 512) | def label_type(self): method metric (line 516) | def metric(self): method problem_type (line 520) | def problem_type(self): class YearTabularDataset (line 524) | class YearTabularDataset(BaseTabularDataset): method __init__ (line 540) | def __init__(self, split="train", path="./dataset/"): method data (line 552) | def data(self): method label_column (line 556) | def label_column(self): method label_type (line 560) | def label_type(self): method metric (line 564) | def metric(self): method problem_type (line 568) | def problem_type(self): FILE: examples/automm/tabular_dl/example_tabular.py function main (line 62) | def main(args): FILE: examples/automm/text_prediction/generate_submission.py function get_parser (line 8) | def get_parser(): function get_test_index (line 15) | def get_test_index(path): function main (line 24) | def main(args): FILE: examples/automm/text_prediction/prepare_glue.py function read_tsv_glue (line 48) | def read_tsv_glue(tsv_file, num_skip=1, keep_column_names=False): function read_jsonl_superglue (line 86) | def read_jsonl_superglue(jsonl_file): function read_cola (line 104) | def read_cola(dir_path): function read_sst (line 120) | def read_sst(dir_path): function read_mrpc (line 131) | def read_mrpc(dir_path): function read_qqp (line 146) | def read_qqp(dir_path): function read_sts (line 161) | def read_sts(dir_path): function read_mnli (line 183) | def read_mnli(dir_path): function read_snli (line 197) | def read_snli(dir_path): function read_qnli (line 221) | def read_qnli(dir_path): function read_rte (line 233) | def read_rte(dir_path): function read_wnli (line 246) | def read_wnli(dir_path): function read_glue_diagnostic (line 260) | def read_glue_diagnostic(dir_path): function read_cb (line 268) | def read_cb(dir_path): function read_copa (line 281) | def read_copa(dir_path): function read_multirc (line 295) | def read_multirc(dir_path): function read_rte_superglue (line 326) | def read_rte_superglue(dir_path): function read_wic (line 340) | def read_wic(dir_path): function read_wsc (line 376) | def read_wsc(dir_path): function read_boolq (line 417) | def read_boolq(dir_path): function read_record (line 426) | def read_record(dir_path): function read_winogender_diagnostic (line 458) | def read_winogender_diagnostic(dir_path): function read_broadcoverage_diagnostic (line 464) | def read_broadcoverage_diagnostic(dir_path): function format_mrpc (line 532) | def format_mrpc(data_dir): function get_tasks (line 575) | def get_tasks(benchmark, task_names): function get_parser (line 595) | def get_parser(): function main (line 614) | def main(args): function cli_main (line 696) | def cli_main(): FILE: examples/automm/text_prediction/run_competition.py function get_parser (line 12) | def get_parser(): function load_machine_hack_product_sentiment (line 52) | def load_machine_hack_product_sentiment(train_path, test_path): function load_price_of_books (line 62) | def load_price_of_books(train_path, test_path): function load_data_scientist_salary (line 80) | def load_data_scientist_salary(train_path, test_path): function load_mercari_price_prediction (line 88) | def load_mercari_price_prediction(train_path, test_path): function run (line 146) | def run(args): FILE: examples/automm/text_prediction/run_text_prediction.py function get_parser (line 28) | def get_parser(): function train (line 72) | def train(args): function predict (line 137) | def predict(args): FILE: examples/image_regression/demo.py function image_regression (line 4) | def image_regression(): FILE: examples/tabular/example_custom_feature_generator.py class PlusKFeatureGenerator (line 78) | class PlusKFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 79) | def __init__(self, k, **kwargs): method _fit_transform (line 83) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 90) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 96) | def get_default_infer_features_in_args() -> dict: FILE: examples/tabular/example_custom_model_tabular.py class NaiveBayesModel (line 15) | class NaiveBayesModel(AbstractModel): method _preprocess (line 18) | def _preprocess(self, X, **kwargs): method _fit (line 26) | def _fit(self, X, y, **kwargs): class AdvancedNaiveBayesModel (line 35) | class AdvancedNaiveBayesModel(AbstractModel): method _preprocess (line 36) | def _preprocess(self, X, **kwargs): method _fit (line 40) | def _fit(self, X, y, **kwargs): method _get_default_auxiliary_params (line 48) | def _get_default_auxiliary_params(self) -> dict: FILE: examples/tabular/example_mitra.py function test_mitra (line 15) | def test_mitra(): function run_bagging (line 28) | def run_bagging(task_id, fold, bagging=True, target_dataset="tabrepo10fo... FILE: features/src/autogluon/features/binning.py function bin_column (line 10) | def bin_column(series: Series, bins, dtype): function generate_bins (line 15) | def generate_bins(X_features: DataFrame, features_to_bin: list, ideal_bi... function get_bins (line 68) | def get_bins(bins: Series, bin_index: list, bin_epsilon: float) -> Inter... FILE: features/src/autogluon/features/generators/abstract.py class AbstractFeatureGenerator (line 25) | class AbstractFeatureGenerator: method __init__ (line 122) | def __init__( method fit (line 225) | def fit(self, X: DataFrame, **kwargs): method fit_transform (line 241) | def fit_transform( method _fit_passthrough (line 360) | def _fit_passthrough(self) -> tuple[FeatureMetadata, list[str]]: method _transform_passthrough (line 381) | def _transform_passthrough(self, X: DataFrame, X_out: DataFrame) -> Da... method transform (line 384) | def transform(self, X: DataFrame) -> DataFrame: method _fit_transform (line 438) | def _fit_transform(self, X: DataFrame, y: Series, **kwargs) -> (DataFr... method _transform (line 470) | def _transform(self, X: DataFrame) -> DataFrame: method _infer_features_in_full (line 490) | def _infer_features_in_full(self, X: DataFrame, feature_metadata_in: F... method _infer_features_in (line 527) | def _infer_features_in(self, X: DataFrame) -> list: method _infer_feature_metadata_in (line 547) | def _infer_feature_metadata_in(X: DataFrame) -> FeatureMetadata: method get_default_infer_features_in_args (line 567) | def get_default_infer_features_in_args() -> dict: method get_infer_features_in_args_to_drop (line 571) | def get_infer_features_in_args_to_drop() -> dict: method estimate_output_feature_metadata (line 587) | def estimate_output_feature_metadata(self, feature_metadata_in: Featur... method _fit_generators (line 591) | def _fit_generators( method _transform_generators (line 607) | def _transform_generators(X, generators: list["AbstractFeatureGenerato... method _merge_feature_metadata (line 618) | def _merge_feature_metadata( method _concat_features (line 632) | def _concat_features(cls, feature_df_list: list[DataFrame], index: pd.... method _keep_features_in (line 641) | def _keep_features_in(self, features: list): method _remove_features_in (line 646) | def _remove_features_in(self, features: list): method _remove_features_out (line 675) | def _remove_features_out(self, features: list): method _remove_unused_features (line 697) | def _remove_unused_features(self, feature_links_chain): method _rename_features_in (line 712) | def _rename_features_in(self, column_rename_map: dict): method _pre_fit_validate (line 718) | def _pre_fit_validate(self, X: DataFrame, y: Series, **kwargs): method _post_fit_cleanup (line 728) | def _post_fit_cleanup(self): method _ensure_no_duplicate_column_names (line 735) | def _ensure_no_duplicate_column_names(self, X: DataFrame): method _get_useless_features (line 750) | def _get_useless_features(X: DataFrame, columns_to_check: List[str] = ... method set_log_prefix (line 760) | def set_log_prefix(self, log_prefix, prepend=False): method set_verbosity (line 766) | def set_verbosity(self, verbosity: int): method _log (line 769) | def _log(self, level, msg, log_prefix=None, verb_min=None): method is_fit (line 777) | def is_fit(self): method is_valid_metadata_in (line 781) | def is_valid_metadata_in(self, feature_metadata_in: FeatureMetadata): method get_feature_links (line 796) | def get_feature_links(self) -> Dict[str, List[str]]: method _get_feature_links (line 800) | def _get_feature_links(self, features_in: List[str], features_out: Lis... method get_feature_links_chain (line 811) | def get_feature_links_chain(self) -> List[Dict[str, List[str]]]: method _get_feature_links_from_chain (line 829) | def _get_feature_links_from_chain(feature_links_chain: List[Dict[str, ... method _get_unused_features (line 849) | def _get_unused_features(self, feature_links_chain: List[Dict[str, Lis... method _get_unused_features_generic (line 864) | def _get_unused_features_generic( method print_generator_info (line 885) | def print_generator_info(self, log_level: int = 20): method print_feature_metadata_info (line 901) | def print_feature_metadata_info(self, log_level: int = 20): method save (line 920) | def save(self, path: str): method _more_tags (line 923) | def _more_tags(self) -> dict: method get_tags (line 940) | def get_tags(self) -> dict: function estimate_feature_metadata_after_generators (line 956) | def estimate_feature_metadata_after_generators( FILE: features/src/autogluon/features/generators/arithmetic/_numba_opt.py function eval_order_fused (line 6) | def eval_order_fused(X_base: np.ndarray, idx_mat: np.ndarray, op_mat: np... function _pearson_pairwise_nan (line 41) | def _pearson_pairwise_nan(A: np.ndarray) -> np.ndarray: FILE: features/src/autogluon/features/generators/arithmetic/canonical_key.py function _monom_from_var (line 18) | def _monom_from_var(var: str, exp: int = 1) -> Monom: function _monom_add (line 24) | def _monom_add(m: Monom, var: str, delta: int) -> Monom: function _poly_add (line 32) | def _poly_add(a: Poly, b: Poly, sign: int = 1) -> Poly: function _poly_mul (line 41) | def _poly_mul(a: Poly, b: Poly) -> Poly: function _is_single_monomial (line 57) | def _is_single_monomial(poly: Poly) -> Tuple[bool, Union[None, Monom], U... function _poly_div_by_monomial (line 65) | def _poly_div_by_monomial(num: Poly, denom_m: Monom, denom_c: int) -> Un... function _canonical_items_from_poly (line 97) | def _canonical_items_from_poly(poly: Poly) -> Tuple[Tuple[int, Tuple[Tup... function _structural_key (line 109) | def _structural_key(expr: Union[str, Operation]) -> Tuple: function _expr_to_canonical_key (line 125) | def _expr_to_canonical_key(expr: Union[str, Operation]) -> Tuple: function filter_canonical_expressions (line 167) | def filter_canonical_expressions(exprs: list[Operation]) -> np.ndarray: FILE: features/src/autogluon/features/generators/arithmetic/combinations.py function estimate_no_higher_interaction_features (line 13) | def estimate_no_higher_interaction_features(num_base_feats, num_new_feats): function _expr_to_canonical_key (line 20) | def _expr_to_canonical_key(expr: str) -> Tuple: function filter_canonical_expressions (line 84) | def filter_canonical_expressions(exprs: Iterable[str]) -> np.ndarray: function get_all_bivariate_interactions (line 115) | def get_all_bivariate_interactions( function add_higher_interaction (line 185) | def add_higher_interaction( FILE: features/src/autogluon/features/generators/arithmetic/combinations_lite.py function get_all_bivariate_interactions (line 11) | def get_all_bivariate_interactions( function add_higher_interaction (line 69) | def add_higher_interaction( FILE: features/src/autogluon/features/generators/arithmetic/filtering.py function remove_mostlynan_features (line 12) | def remove_mostlynan_features(X: pd.DataFrame, nan_threshold: float = 0.... function remove_imbalanced (line 16) | def remove_imbalanced(X: pd.DataFrame, mode_imbalance_threshold=0.99) ->... function mode_freq_fast (line 21) | def mode_freq_fast(s: pd.Series) -> float: function remove_same_range_features (line 26) | def remove_same_range_features(X: pd.DataFrame, x: pd.Series) -> float: function basic_filter (line 34) | def basic_filter( function fast_spearman (line 82) | def fast_spearman(X: pd.DataFrame) -> pd.DataFrame: function drop_high_corr (line 94) | def drop_high_corr(corr: pd.DataFrame, corr_threshold: float = 0.9) -> l... function filter_by_spearman (line 102) | def filter_by_spearman(X: pd.DataFrame, corr_threshold: float = 0.95) ->... function cross_spearman (line 110) | def cross_spearman(df1, df2): function filter_by_cross_correlation (line 160) | def filter_by_cross_correlation(X_base: pd.DataFrame, X_new: pd.DataFram... FILE: features/src/autogluon/features/generators/arithmetic/memory.py function dataset_magnitude (line 7) | def dataset_magnitude(X_in: pd.DataFrame, method: Literal["rms", "max", ... function global_scale_preserve_ops (line 26) | def global_scale_preserve_ops( function minimize_numeric_dtypes (line 46) | def minimize_numeric_dtypes(X): function reduce_memory_usage (line 77) | def reduce_memory_usage(X_in: pd.DataFrame, verbose: bool = True, rescal... FILE: features/src/autogluon/features/generators/arithmetic/operation.py class Operation (line 4) | class Operation: method __init__ (line 5) | def __init__(self, left, right, op: str): method _render (line 10) | def _render(self, x): method name (line 15) | def name(self) -> str: method __str__ (line 20) | def __str__(self): FILE: features/src/autogluon/features/generators/arithmetic/preprocessor.py class TimerLog (line 33) | class TimerLog: method __init__ (line 35) | def __init__(self): method block (line 39) | def block(self, name: str): method summary (line 47) | def summary(self, verbose: bool = False) -> dict: class ArithmeticFeatureGenerator (line 64) | class ArithmeticFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 109) | def __init__( method estimate_new_dtypes (line 168) | def estimate_new_dtypes(self, n_numeric, n_categorical, n_binary, **kw... method estimate_no_of_new_features (line 197) | def estimate_no_of_new_features(self, X: pd.DataFrame, **kwargs) -> int: method spearman_selection (line 233) | def spearman_selection(self, X: pd.DataFrame, y: pd.Series): method random_selection (line 335) | def random_selection(self, X: pd.DataFrame, y: pd.Series): method _fit (line 381) | def _fit(self, X: pd.DataFrame, y: pd.Series | None, **kwargs): method _fit_transform (line 513) | def _fit_transform(self, X: DataFrame, y: Series, **kwargs) -> Tuple[D... method _add_arithmetic_dag (line 521) | def _add_arithmetic_dag( method _transform (line 619) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 644) | def get_default_infer_features_in_args() -> dict: FILE: features/src/autogluon/features/generators/astype.py class AsTypeFeatureGenerator (line 17) | class AsTypeFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 44) | def __init__( method _fit_transform (line 79) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 138) | def _transform(self, X: DataFrame) -> DataFrame: method _log_invalid_dtypes (line 170) | def _log_invalid_dtypes(self, X: pd.DataFrame): method _convert_to_bool (line 199) | def _convert_to_bool(self, X: DataFrame) -> DataFrame: method _convert_to_bool_simple (line 205) | def _convert_to_bool_simple(self, X: DataFrame) -> DataFrame: method _convert_to_bool_fast (line 214) | def _convert_to_bool_fast(self, X: DataFrame) -> DataFrame: method _convert_to_bool_fast_batch (line 226) | def _convert_to_bool_fast_batch(self, X: DataFrame) -> DataFrame: method _convert_to_bool_fast_realtime (line 237) | def _convert_to_bool_fast_realtime(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 248) | def get_default_infer_features_in_args() -> dict: method _infer_features_in_full (line 251) | def _infer_features_in_full(self, X: DataFrame, feature_metadata_in: F... method _remove_features_in (line 259) | def _remove_features_in(self, features): method _set_bool_features_val (line 269) | def _set_bool_features_val(self): method print_feature_metadata_info (line 275) | def print_feature_metadata_info(self, log_level=20): method _more_tags (line 282) | def _more_tags(self): FILE: features/src/autogluon/features/generators/auto_ml_pipeline.py class AutoMLPipelineFeatureGenerator (line 29) | class AutoMLPipelineFeatureGenerator(PipelineFeatureGenerator): method __init__ (line 102) | def __init__( method _get_default_generators (line 142) | def _get_default_generators(self, vectorizer=None): method _get_category_feature_generator (line 196) | def _get_category_feature_generator(self): class AutoMLInterpretablePipelineFeatureGenerator (line 200) | class AutoMLInterpretablePipelineFeatureGenerator(AutoMLPipelineFeatureG... method _get_category_feature_generator (line 201) | def _get_category_feature_generator(self): FILE: features/src/autogluon/features/generators/binned.py class BinnedFeatureGenerator (line 17) | class BinnedFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 20) | def __init__(self, num_bins=10, **kwargs): method _fit_transform (line 24) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 35) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 39) | def get_default_infer_features_in_args() -> dict: method _get_bin_map (line 42) | def _get_bin_map(self, X: DataFrame) -> dict: method _transform_bin (line 45) | def _transform_bin(self, X: DataFrame): method _remove_features_in (line 54) | def _remove_features_in(self, features: list): method _more_tags (line 65) | def _more_tags(self): FILE: features/src/autogluon/features/generators/bulk.py class BulkFeatureGenerator (line 18) | class BulkFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 93) | def __init__( method _fit_transform (line 170) | def _fit_transform(self, X: DataFrame, **kwargs) -> tuple[DataFrame, d... method _fit_transform_stage (line 194) | def _fit_transform_stage( method _transform (line 242) | def _transform(self, X: DataFrame) -> DataFrame: method _transform_stage (line 254) | def _transform_stage( method get_feature_links_chain (line 269) | def get_feature_links_chain(self): method _remove_unused_features (line 283) | def _remove_unused_features(self, feature_links_chain): method _get_unused_features (line 316) | def _get_unused_features(self, feature_links_chain): method get_default_infer_features_in_args (line 336) | def get_default_infer_features_in_args() -> dict: FILE: features/src/autogluon/features/generators/cat_as_num.py class CatAsNumFeatureGenerator (line 15) | class CatAsNumFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 33) | def __init__( method estimate_no_of_new_features (line 47) | def estimate_no_of_new_features(self, X: pd.DataFrame, **kwargs) -> int: method _fit (line 54) | def _fit(self, X_in: pd.DataFrame, y_in=None): method _fit_transform (line 86) | def _fit_transform(self, X: pd.DataFrame, y: pd.Series, **kwargs) -> T... method _transform (line 91) | def _transform(self, X_in: pd.DataFrame, **kwargs) -> pd.DataFrame: method get_default_infer_features_in_args (line 114) | def get_default_infer_features_in_args() -> dict: FILE: features/src/autogluon/features/generators/cat_int.py class CategoricalInteractionFeatureGenerator (line 22) | class CategoricalInteractionFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 55) | def __init__( method get_default_infer_features_in_args (line 98) | def get_default_infer_features_in_args() -> dict: method estimate_new_dtypes (line 115) | def estimate_new_dtypes(self, n_numeric, n_categorical, n_binary, **kw... method estimate_no_of_new_features (line 133) | def estimate_no_of_new_features(self, X: pd.DataFrame, **kwargs) -> int: method group_feat_combs_by_order (line 164) | def group_feat_combs_by_order(self, new_col_set): method get_interaction_names_dict (line 171) | def get_interaction_names_dict(self, X: pd.DataFrame, max_order: int =... method combine_predefined (line 190) | def combine_predefined(self, X_in: pd.DataFrame, comb_lst: List[str], ... method _build_interactions (line 219) | def _build_interactions(self, X, fit_mode): method _fit_or_map (line 279) | def _fit_or_map(self, name, combined, fit_mode): method _vectorized_map (line 297) | def _vectorized_map(combined, uniques): method frequency_encode_new_features (line 327) | def frequency_encode_new_features(self, X: pd.DataFrame, y: pd.Series ... method _fit (line 345) | def _fit(self, X: pd.DataFrame, y: pd.Series, **kwargs): method _fit_transform (line 373) | def _fit_transform(self, X: pd.DataFrame, y: pd.Series, **kwargs) -> T... method _transform (line 382) | def _transform(self, X: pd.DataFrame, y: pd.Series = None, fit_mode: b... FILE: features/src/autogluon/features/generators/category.py class CategoryFeatureGenerator (line 27) | class CategoryFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 67) | def __init__( method _fit_transform (line 97) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 115) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 119) | def get_default_infer_features_in_args() -> dict: method _generate_features_category (line 125) | def _generate_features_category(self, X: DataFrame) -> DataFrame: method _generate_category_map (line 139) | def _generate_category_map(self, X: DataFrame) -> (DataFrame, dict): method _remove_features_in (line 183) | def _remove_features_in(self, features: list): method _more_tags (line 194) | def _more_tags(self): FILE: features/src/autogluon/features/generators/datetime.py class DatetimeFeatureGenerator (line 14) | class DatetimeFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 24) | def __init__(self, features: list = None, **kwargs): method _fit_transform (line 30) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 36) | def _transform(self, X: DataFrame, is_fit=False) -> DataFrame: method get_default_infer_features_in_args (line 40) | def get_default_infer_features_in_args() -> dict: method normalize_timeseries (line 43) | def normalize_timeseries(self, X: pd.DataFrame, feature: str, is_fit: ... method _generate_features_datetime (line 67) | def _generate_features_datetime(self, X: DataFrame, is_fit: bool) -> D... method _remove_features_in (line 78) | def _remove_features_in(self, features: list): FILE: features/src/autogluon/features/generators/drop_duplicates.py class DropDuplicatesFeatureGenerator (line 20) | class DropDuplicatesFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 40) | def __init__(self, sample_size_init=1000, sample_size_final=5000, **kw... method _fit_transform (line 45) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 64) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 68) | def get_default_infer_features_in_args() -> dict: method _drop_duplicate_features (line 72) | def _drop_duplicate_features( method _drop_duplicate_features_generic (line 102) | def _drop_duplicate_features_generic(cls, X: DataFrame, keep: Union[st... method _fingerprint_numeric_series_full (line 110) | def _fingerprint_numeric_series_full(s: pd.Series) -> bytes: method _drop_duplicate_features_numeric (line 149) | def _drop_duplicate_features_numeric( method _drop_duplicate_features_categorical (line 230) | def _drop_duplicate_features_categorical(cls, X: DataFrame, keep: Unio... method _more_tags (line 271) | def _more_tags(self): FILE: features/src/autogluon/features/generators/drop_unique.py class DropUniqueFeatureGenerator (line 14) | class DropUniqueFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 17) | def __init__(self, max_unique_ratio=0.99, **kwargs): method _fit_transform (line 21) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 29) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 33) | def get_default_infer_features_in_args() -> dict: method _drop_unique_features (line 38) | def _drop_unique_features(X: DataFrame, feature_metadata: FeatureMetad... method _more_tags (line 64) | def _more_tags(self): FILE: features/src/autogluon/features/generators/dummy.py class DummyFeatureGenerator (line 12) | class DummyFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 18) | def __init__(self, features_in="empty", feature_metadata_in="empty", *... method _fit_transform (line 25) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 29) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 33) | def get_default_infer_features_in_args() -> dict: method _generate_features_dummy (line 37) | def _generate_features_dummy(X: DataFrame): method is_valid_metadata_in (line 42) | def is_valid_metadata_in(self, feature_metadata_in: FeatureMetadata): FILE: features/src/autogluon/features/generators/fillna.py class FillNaFeatureGenerator (line 17) | class FillNaFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 37) | def __init__(self, fillna_map=None, fillna_default=np.nan, inplace=Fal... method _fit_transform (line 46) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 56) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 76) | def get_default_infer_features_in_args() -> dict: method _remove_features_in (line 79) | def _remove_features_in(self, features): method _more_tags (line 85) | def _more_tags(self): FILE: features/src/autogluon/features/generators/frequency.py class FrequencyFeatureGenerator (line 20) | class FrequencyFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 45) | def __init__( method estimate_no_of_new_features (line 70) | def estimate_no_of_new_features(self, X: pd.DataFrame, **kwargs) -> int: method filter_candidates_by_distinctiveness (line 79) | def filter_candidates_by_distinctiveness(cls, X: pd.DataFrame) -> list: method _fit (line 90) | def _fit(self, X_in: pd.DataFrame, y_in: pd.Series = None): method _fit_transform (line 108) | def _fit_transform(self, X: pd.DataFrame, y: pd.Series, **kwargs) -> T... method _transform (line 121) | def _transform(self, X_in, **kwargs): method get_default_infer_features_in_args (line 142) | def get_default_infer_features_in_args() -> dict: FILE: features/src/autogluon/features/generators/groupby.py function q25 (line 12) | def q25(series): function q75 (line 16) | def q75(series): function q10 (line 20) | def q10(series): function q90 (line 24) | def q90(series): function rank_categoricals_by_small_counts (line 44) | def rank_categoricals_by_small_counts( class GroupByFeatureGenerator (line 79) | class GroupByFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 89) | def __init__( method _to_dataframe (line 126) | def _to_dataframe(self, X): method _split_aggs (line 131) | def _split_aggs(self): method _relative_enabled (line 143) | def _relative_enabled(self) -> bool: method _drop_basic (line 146) | def _drop_basic(self) -> bool: method _features_per_pair (line 150) | def _features_per_pair(self): method _fit (line 170) | def _fit(self, X, y=None): method _transform (line 289) | def _transform(self, X): method _fit_transform (line 439) | def _fit_transform(self, X, y, **kwargs): method get_default_infer_features_in_args (line 444) | def get_default_infer_features_in_args() -> dict: FILE: features/src/autogluon/features/generators/identity.py class IdentityFeatureGenerator (line 12) | class IdentityFeatureGenerator(AbstractFeatureGenerator): method _fit_transform (line 15) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 19) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 23) | def get_default_infer_features_in_args() -> dict: method _more_tags (line 26) | def _more_tags(self): method estimate_output_feature_metadata (line 29) | def estimate_output_feature_metadata(self, feature_metadata_in: Featur... FILE: features/src/autogluon/features/generators/isnan.py class IsNanFeatureGenerator (line 14) | class IsNanFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 29) | def __init__(self, null_map=None, **kwargs): method _fit_transform (line 36) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 48) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 59) | def get_default_infer_features_in_args() -> dict: method _remove_features_in (line 62) | def _remove_features_in(self, features: list): method _more_tags (line 69) | def _more_tags(self): FILE: features/src/autogluon/features/generators/label_encoder.py class LabelEncoderFeatureGenerator (line 14) | class LabelEncoderFeatureGenerator(AbstractFeatureGenerator): method _fit_transform (line 17) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 24) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 28) | def get_default_infer_features_in_args() -> dict: method convert_category_to_int (line 32) | def convert_category_to_int(X: DataFrame) -> DataFrame: method _more_tags (line 37) | def _more_tags(self): FILE: features/src/autogluon/features/generators/memory_minimize.py class CategoryMemoryMinimizeFeatureGenerator (line 14) | class CategoryMemoryMinimizeFeatureGenerator(AbstractFeatureGenerator): method _fit_transform (line 20) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 26) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 30) | def get_default_infer_features_in_args() -> dict: method _get_category_map (line 33) | def _get_category_map(self, X: DataFrame) -> dict: method _minimize_categorical_memory_usage (line 41) | def _minimize_categorical_memory_usage(self, X: DataFrame): method _remove_features_in (line 50) | def _remove_features_in(self, features: list): method _more_tags (line 57) | def _more_tags(self): class NumericMemoryMinimizeFeatureGenerator (line 62) | class NumericMemoryMinimizeFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 73) | def __init__(self, dtype_out=np.uint8, **kwargs): method _fit_transform (line 77) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 81) | def _transform(self, X): method get_default_infer_features_in_args (line 85) | def get_default_infer_features_in_args() -> dict: method _get_dtype_clip_args (line 89) | def _get_dtype_clip_args(dtype) -> (np.dtype, int, int): method _minimize_numeric_memory_usage (line 96) | def _minimize_numeric_memory_usage(self, X: DataFrame): method _more_tags (line 99) | def _more_tags(self): FILE: features/src/autogluon/features/generators/one_hot_encoder.py class CatToInt (line 16) | class CatToInt: method __init__ (line 33) | def __init__(self, max_levels, fillna_val=None, infrequent_val="na"): method fit (line 41) | def fit(self, X: DataFrame): method transform (line 64) | def transform(self, X: DataFrame): method pd_to_np (line 72) | def pd_to_np(self, X: DataFrame) -> np.ndarray: method _get_dtype_and_fillna (line 83) | def _get_dtype_and_fillna(self, X: DataFrame, dtype_buffer=2): class OneHotEncoderFeatureGenerator (line 118) | class OneHotEncoderFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 134) | def __init__(self, max_levels=None, dtype=np.uint8, sparse=True, drop=... method _fit_transform (line 142) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 162) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 171) | def get_default_infer_features_in_args() -> dict: method transform_ohe (line 174) | def transform_ohe(self, X: DataFrame): method _more_tags (line 184) | def _more_tags(self): FILE: features/src/autogluon/features/generators/oof_target_encoder.py class OOFTargetEncodingFeatureGenerator (line 10) | class OOFTargetEncodingFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 40) | def __init__( method estimate_new_dtypes (line 58) | def estimate_new_dtypes(self, n_numeric, n_categorical, n_binary, num_... method estimate_no_of_new_features (line 68) | def estimate_no_of_new_features(self, X: pd.DataFrame, num_classes: in... method _fit (line 76) | def _fit(self, X: pd.DataFrame, y: pd.Series, **kwargs): method _fit_transform (line 269) | def _fit_transform(self, X: pd.DataFrame, y: pd.Series, **kwargs): method _transform (line 281) | def _transform(self, X, **kwargs): method get_default_infer_features_in_args (line 339) | def get_default_infer_features_in_args() -> dict: FILE: features/src/autogluon/features/generators/pipeline.py class PipelineFeatureGenerator (line 20) | class PipelineFeatureGenerator(BulkFeatureGenerator): method __init__ (line 29) | def __init__( method fit_transform (line 69) | def fit_transform(self, X: DataFrame, y=None, feature_metadata_in: Fea... method _fit_transform (line 76) | def _fit_transform(self, X: DataFrame, y=None, **kwargs): method _fit_transform_custom (line 83) | def _fit_transform_custom(self, X_out: DataFrame, type_group_map_speci... method _infer_features_in_full (line 98) | def _infer_features_in_full(self, X: DataFrame, feature_metadata_in: F... method _remove_features_in (line 105) | def _remove_features_in(self, features: list): method _pre_fit_validate (line 110) | def _pre_fit_validate(self, X: DataFrame, **kwargs): method _compute_pre_memory_usage (line 117) | def _compute_pre_memory_usage(self, X: DataFrame): method _compute_post_memory_usage (line 139) | def _compute_post_memory_usage(self, X: DataFrame): method print_feature_metadata_info (line 165) | def print_feature_metadata_info(self, log_level=20): FILE: features/src/autogluon/features/generators/rename.py class RenameFeatureGenerator (line 11) | class RenameFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 30) | def __init__(self, name_prefix=None, name_suffix=None, inplace=False, ... method _fit_transform (line 37) | def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict): method _transform (line 46) | def _transform(self, X: DataFrame) -> DataFrame: method _get_renamed_features (line 53) | def _get_renamed_features(self, X: DataFrame) -> (DataFrame, dict): method get_default_infer_features_in_args (line 68) | def get_default_infer_features_in_args() -> dict: method _more_tags (line 71) | def _more_tags(self): FILE: features/src/autogluon/features/generators/rsfc.py class TimerLog (line 14) | class TimerLog: method __init__ (line 17) | def __init__(self): method block (line 21) | def block(self, name: str): method summary (line 29) | def summary(self, verbose: bool = False) -> dict: class RandomSubsetFeatureCompressionGenerator (line 37) | class RandomSubsetFeatureCompressionGenerator(AbstractFeatureGenerator): method __init__ (line 51) | def __init__( method _select_top_mode_features (line 90) | def _select_top_mode_features(X: pd.DataFrame, k: Optional[int]) -> li... method _select_features_by_dtype_and_cardinality (line 112) | def _select_features_by_dtype_and_cardinality(X: pd.DataFrame, k: Opti... method _sample_unique_subsets (line 130) | def _sample_unique_subsets( method _prepare_X (line 173) | def _prepare_X(self, X: pd.DataFrame) -> pd.DataFrame: method _make_key (line 207) | def _make_key(self, X: pd.DataFrame) -> pd.Series: method collapse_singletons (line 215) | def collapse_singletons(s, threshold=1, label="__single__"): method _fit_transform (line 219) | def _fit_transform(self, X: pd.DataFrame, y: pd.Series, **kwargs): method _transform (line 272) | def _transform(self, X: pd.DataFrame) -> pd.DataFrame: method get_default_infer_features_in_args (line 286) | def get_default_infer_features_in_args() -> dict: FILE: features/src/autogluon/features/generators/selection.py class SpearmanFeatureSelector (line 4) | class SpearmanFeatureSelector(AbstractFeatureGenerator): method __init__ (line 14) | def __init__(self, threshold: float = None, max_features: int = 2000, ... method _fit (line 22) | def _fit(self, X, y): method _transform (line 36) | def _transform(self, X): method _fit_transform (line 39) | def _fit_transform(self, X, y, **kwargs): method get_default_infer_features_in_args (line 44) | def get_default_infer_features_in_args() -> dict: FILE: features/src/autogluon/features/generators/skrub/_sklearn_compat.py function validate_data (line 15) | def validate_data(_estimator, /, **kwargs): FILE: features/src/autogluon/features/generators/skrub/_squashing_scaler.py function _mask_inf (line 17) | def _mask_inf(X): function _set_zeros (line 28) | def _set_zeros(X, zero_cols): function _soft_clip (line 36) | def _soft_clip(X, max_absolute_value, mask_inf): class _MinMaxScaler (line 60) | class _MinMaxScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): method fit (line 79) | def fit(self, X, y=None): method transform (line 86) | def transform(self, X): class SquashingScaler (line 91) | class SquashingScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstima... method __init__ (line 194) | def __init__( method fit (line 202) | def fit(self, X, y=None): method fit_transform (line 222) | def fit_transform(self, X, y=None): method transform (line 303) | def transform(self, X): FILE: features/src/autogluon/features/generators/text_ngram.py class TextNgramFeatureGenerator (line 24) | class TextNgramFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 53) | def __init__( method _fit_transform (line 85) | def _fit_transform(self, X: DataFrame, y: Series = None, problem_type:... method _transform (line 114) | def _transform(self, X: DataFrame) -> DataFrame: method get_default_infer_features_in_args (line 131) | def get_default_infer_features_in_args() -> dict: method _fit_transform_ngrams (line 134) | def _fit_transform_ngrams(self, X): method _generate_ngrams (line 229) | def _generate_ngrams(self, X, downsample_ratio: int = None): method _adjust_vectorizer_memory_usage (line 273) | def _adjust_vectorizer_memory_usage( method _train_vectorizer (line 311) | def _train_vectorizer(text_data: list, vectorizer): method _remove_features_in (line 318) | def _remove_features_in(self, features): FILE: features/src/autogluon/features/generators/text_special.py class TextSpecialFeatureGenerator (line 16) | class TextSpecialFeatureGenerator(AbstractFeatureGenerator): method __init__ (line 42) | def __init__( method _fit_transform (line 61) | def _fit_transform(self, X: DataFrame, **kwargs) -> Tuple[DataFrame, d... method _transform (line 68) | def _transform(self, X: DataFrame) -> DataFrame: method _compute_feature_names_dict (line 71) | def _compute_feature_names_dict(self) -> dict: method get_default_infer_features_in_args (line 94) | def get_default_infer_features_in_args() -> dict: method _filter_symbols (line 97) | def _filter_symbols(self, X: DataFrame, symbols: list) -> dict: method _generate_features_text_special (line 114) | def _generate_features_text_special(self, X: DataFrame) -> DataFrame: method _generate_text_special (line 129) | def _generate_text_special(self, X: Series, feature: str, symbols: lis... method _remove_features_in (line 162) | def _remove_features_in(self, features: list): FILE: features/src/autogluon/features/registry/_feature_generator_registry.py class FeatureGeneratorRegistry (line 10) | class FeatureGeneratorRegistry: method __init__ (line 11) | def __init__(self, cls_list: list[Type[AbstractFeatureGenerator]] | No... method exists (line 20) | def exists(self, fe_cls: Type[AbstractFeatureGenerator]) -> bool: method add (line 23) | def add(self, fe_cls: Type[AbstractFeatureGenerator]): method remove (line 46) | def remove(self, fe_cls: Type[AbstractFeatureGenerator]): method cls_list (line 55) | def cls_list(self) -> list[Type[AbstractFeatureGenerator]]: method keys (line 59) | def keys(self) -> list[str]: method key_to_cls_map (line 62) | def key_to_cls_map(self) -> dict[str, Type[AbstractFeatureGenerator]]: method key_to_cls (line 65) | def key_to_cls(self, key: str) -> Type[AbstractFeatureGenerator]: method key (line 73) | def key(self, fe_cls: Type[AbstractFeatureGenerator]) -> str: method docstring (line 77) | def docstring(self, fe_cls: Type[AbstractFeatureGenerator]) -> str: method to_frame (line 86) | def to_frame(self) -> pd.DataFrame: FILE: features/src/autogluon/features/registry/parse_custom_generator.py class ImportTarget (line 10) | class ImportTarget: function parse_import_target (line 15) | def parse_import_target(s: str) -> ImportTarget: function import_by_target (line 27) | def import_by_target(target: ImportTarget) -> Any: function resolve_fg_class (line 35) | def resolve_fg_class( FILE: features/src/autogluon/features/utils.py function clip_and_astype (line 9) | def clip_and_astype(df: DataFrame, columns: list = None, clip_min=0, cli... function is_useless_feature (line 39) | def is_useless_feature(X: Series) -> bool: function get_smallest_valid_dtype_int (line 44) | def get_smallest_valid_dtype_int(min_val: int, max_val: int): FILE: features/src/autogluon/features/vectorizers.py function vectorizer_auto_ml_default (line 7) | def vectorizer_auto_ml_default(): function get_ngram_freq (line 11) | def get_ngram_freq(vectorizer, transform_matrix): function downscale_vectorizer (line 19) | def downscale_vectorizer(vectorizer, ngram_freq, vocab_size): FILE: features/tests/conftest.py function pytest_addoption (line 4) | def pytest_addoption(parser): function pytest_configure (line 8) | def pytest_configure(config): function pytest_collection_modifyitems (line 12) | def pytest_collection_modifyitems(config, items): FILE: features/tests/features/conftest.py class GeneratorHelper (line 12) | class GeneratorHelper: method fit_transform_assert (line 14) | def fit_transform_assert( class DataHelper (line 115) | class DataHelper: method generate_empty (line 117) | def generate_empty() -> DataFrame: method generate_obj_feature (line 121) | def generate_obj_feature() -> Series: method generate_int_feature (line 125) | def generate_int_feature() -> Series: method generate_cat_feature (line 129) | def generate_cat_feature() -> Series: method generate_float_feature (line 133) | def generate_float_feature() -> Series: method generate_text_feature (line 137) | def generate_text_feature() -> Series: method generate_datetime_as_object_feature (line 153) | def generate_datetime_as_object_feature() -> Series: method generate_datetime_as_object_feature_advanced (line 169) | def generate_datetime_as_object_feature_advanced() -> Series: method generate_datetime_feature (line 183) | def generate_datetime_feature() -> Series: method generate_bool_feature_int (line 187) | def generate_bool_feature_int(name="int_bool") -> DataFrame: method generate_bool_feature_with_nan (line 191) | def generate_bool_feature_with_nan() -> DataFrame: method generate_bool_feature_edgecase (line 195) | def generate_bool_feature_edgecase() -> DataFrame: method generate_bool_feature_extreme_edgecase (line 199) | def generate_bool_feature_extreme_edgecase() -> DataFrame: method generate_multi_feature_standard (line 203) | def generate_multi_feature_standard() -> DataFrame: method generate_useless_category (line 218) | def generate_useless_category() -> DataFrame: method generate_duplicate (line 235) | def generate_duplicate() -> DataFrame: method generate_multi_feature_special (line 262) | def generate_multi_feature_special() -> DataFrame: method generate_multi_feature_full (line 274) | def generate_multi_feature_full() -> DataFrame: function generator_helper (line 287) | def generator_helper(): function data_helper (line 292) | def data_helper(): FILE: features/tests/features/generators/arithmetic/test_operation.py function test_expr_to_canonical_key_commutative_mul (line 7) | def test_expr_to_canonical_key_commutative_mul(): function test_expr_to_canonical_key_commutative_add (line 14) | def test_expr_to_canonical_key_commutative_add(): function test_expr_to_canonical_key_associative_mul (line 21) | def test_expr_to_canonical_key_associative_mul(): function test_expr_to_canonical_key_associative_add (line 28) | def test_expr_to_canonical_key_associative_add(): function test_expr_to_canonical_key_mul_div_interaction (line 35) | def test_expr_to_canonical_key_mul_div_interaction(): function test_expr_to_canonical_key_subtraction_not_commutative (line 44) | def test_expr_to_canonical_key_subtraction_not_commutative(): function test_expr_to_canonical_key_fallback_non_monomial_division (line 51) | def test_expr_to_canonical_key_fallback_non_monomial_division(): FILE: features/tests/features/generators/test_astype.py function test_astype_feature_generator (line 4) | def test_astype_feature_generator(generator_helper, data_helper): function test_astype_feature_generator_bool (line 42) | def test_astype_feature_generator_bool(generator_helper, data_helper): function test_astype_feature_generator_bool_edgecase_with_nan (line 65) | def test_astype_feature_generator_bool_edgecase_with_nan(generator_helpe... function test_astype_feature_generator_bool_edgecase (line 92) | def test_astype_feature_generator_bool_edgecase(generator_helper, data_h... function test_astype_feature_generator_bool_extreme_edgecase (line 114) | def test_astype_feature_generator_bool_extreme_edgecase(generator_helper... FILE: features/tests/features/generators/test_auto_ml_pipeline.py function test_auto_ml_pipeline_feature_generator (line 13) | def test_auto_ml_pipeline_feature_generator(generator_helper, data_helper): function test_auto_ml_pipeline_feature_generator_raw_text (line 118) | def test_auto_ml_pipeline_feature_generator_raw_text(generator_helper, d... function test_auto_ml_pipeline_feature_generator_only_raw_text (line 184) | def test_auto_ml_pipeline_feature_generator_only_raw_text(generator_help... function test_auto_ml_pipeline_feature_generator_duplicates (line 236) | def test_auto_ml_pipeline_feature_generator_duplicates(generator_helper,... function test_auto_ml_pipeline_feature_generator_duplicates_without_dedupe (line 345) | def test_auto_ml_pipeline_feature_generator_duplicates_without_dedupe(ge... function test_add_custom_feature_generators (line 515) | def test_add_custom_feature_generators(): FILE: features/tests/features/generators/test_bulk.py function test_bulk_feature_generator (line 19) | def test_bulk_feature_generator(generator_helper, data_helper): function test_bulk_feature_generator_no_dummy (line 182) | def test_bulk_feature_generator_no_dummy(generator_helper, data_helper): FILE: features/tests/features/generators/test_category.py function test_category_feature_generator (line 6) | def test_category_feature_generator(generator_helper, data_helper): FILE: features/tests/features/generators/test_datetime.py function test_datetime_feature_generator (line 4) | def test_datetime_feature_generator(generator_helper, data_helper): function test_datetime_feature_generator_advanced (line 93) | def test_datetime_feature_generator_advanced(generator_helper, data_help... FILE: features/tests/features/generators/test_drop_duplicates.py function test_drop_duplicates_feature_generator (line 8) | def test_drop_duplicates_feature_generator(generator_helper, data_helper): function test_drop_duplicates_feature_generator_with_dupes (line 57) | def test_drop_duplicates_feature_generator_with_dupes(generator_helper, ... function test_drop_duplicates_numeric_edge_cases (line 130) | def test_drop_duplicates_numeric_edge_cases(generator_helper): function test_drop_duplicates_category_edge_cases (line 186) | def test_drop_duplicates_category_edge_cases(): FILE: features/tests/features/generators/test_dummy.py function test_dummy_feature_generator (line 6) | def test_dummy_feature_generator(generator_helper, data_helper): FILE: features/tests/features/generators/test_fillna.py function test_fillna_feature_generator (line 9) | def test_fillna_feature_generator(generator_helper, data_helper): function test_fillna_object_edgecase_feature_generator (line 52) | def test_fillna_object_edgecase_feature_generator(generator_helper, data... FILE: features/tests/features/generators/test_identity.py function test_identity_feature_generator (line 6) | def test_identity_feature_generator(generator_helper, data_helper): function test_identity_feature_generator_int_float (line 35) | def test_identity_feature_generator_int_float(generator_helper, data_hel... function test_identity_feature_generator_int_float_with_banned_features (line 56) | def test_identity_feature_generator_int_float_with_banned_features(gener... FILE: features/tests/features/generators/test_isnan.py function test_isnan_feature_generator (line 7) | def test_isnan_feature_generator(generator_helper, data_helper): FILE: features/tests/features/generators/test_label_encoder.py function test_label_encoder_feature_generator (line 6) | def test_label_encoder_feature_generator(generator_helper, data_helper): FILE: features/tests/features/generators/test_one_hot_encoder.py function test_one_hot_encoder_feature_generator (line 6) | def test_one_hot_encoder_feature_generator(generator_helper, data_helper): function test_one_hot_encoder_feature_generator_advanced (line 48) | def test_one_hot_encoder_feature_generator_advanced(generator_helper, da... FILE: features/tests/features/generators/test_oof_target_encoder.py function _assert_all_between (line 10) | def _assert_all_between(series: pd.Series, low: float, high: float): function test_oof_target_encoding_regression (line 14) | def test_oof_target_encoding_regression(generator_helper, data_helper): function test_oof_target_encoding_binary (line 69) | def test_oof_target_encoding_binary(generator_helper, data_helper): function test_oof_target_encoding_binary_raises_if_more_than_two_classes (line 109) | def test_oof_target_encoding_binary_raises_if_more_than_two_classes(data... function test_oof_target_encoding_multiclass (line 128) | def test_oof_target_encoding_multiclass(generator_helper, data_helper): function test_oof_target_encoding_keep_original_true (line 178) | def test_oof_target_encoding_keep_original_true(generator_helper, data_h... function test_oof_target_encoding_no_categorical_columns (line 221) | def test_oof_target_encoding_no_categorical_columns(generator_helper): function test_oof_target_encoding_unseen_and_nan_categories (line 250) | def test_oof_target_encoding_unseen_and_nan_categories(): function test_oof_target_encoding_estimate_no_of_new_features (line 286) | def test_oof_target_encoding_estimate_no_of_new_features(data_helper): FILE: features/tests/features/generators/test_pipeline.py function test_pipeline_feature_generator (line 16) | def test_pipeline_feature_generator(generator_helper, data_helper): function test_pipeline_feature_generator_dummy (line 126) | def test_pipeline_feature_generator_dummy(generator_helper, data_helper): function test_pipeline_feature_generator_removal_advanced (line 156) | def test_pipeline_feature_generator_removal_advanced(generator_helper, d... FILE: features/tests/features/generators/test_rename.py function test_rename (line 4) | def test_rename(generator_helper, data_helper): FILE: features/tests/features/generators/test_text_ngram.py function test_text_ngram_feature_generator (line 31) | def test_text_ngram_feature_generator(generator_helper, data_helper): function test_text_ngram_feature_generator_categorical_nan (line 51) | def test_text_ngram_feature_generator_categorical_nan(generator_helper, ... FILE: features/tests/features/generators/test_text_special.py function test_text_special_feature_generator (line 31) | def test_text_special_feature_generator(generator_helper, data_helper): function test_text_special_feature_generator_categorical_nan (line 62) | def test_text_special_feature_generator_categorical_nan(generator_helper... FILE: features/tests/features/test_feature_metadata.py function test_feature_metadata (line 9) | def test_feature_metadata(data_helper): function test_feature_metadata_get_features (line 131) | def test_feature_metadata_get_features(): function test_feature_metadata_equals (line 229) | def test_feature_metadata_equals(): FILE: features/tests/features/test_utils.py function test_get_smallest_valid_dtype_int_unsigned (line 21) | def test_get_smallest_valid_dtype_int_unsigned(min_val, max_val, expecte... function test_get_smallest_valid_dtype_int_signed (line 42) | def test_get_smallest_valid_dtype_int_signed(min_val, max_val, expected_... function test_min_zero_prefers_unsigned (line 47) | def test_min_zero_prefers_unsigned(): function test_get_smallest_valid_dtype_int_raises_for_signed_overflow (line 62) | def test_get_smallest_valid_dtype_int_raises_for_signed_overflow(min_val... function test_get_smallest_valid_dtype_int_raises_for_unsigned_overflow (line 75) | def test_get_smallest_valid_dtype_int_raises_for_unsigned_overflow(min_v... FILE: features/tests/test_check_style.py function test_check_style (line 8) | def test_check_style(): FILE: multimodal/src/autogluon/multimodal/cli/prepare_detection_dataset.py function get_root_dir (line 8) | def get_root_dir(output_dir=None, new_folder_name=None): function get_fname_from_path_or_url (line 23) | def get_fname_from_path_or_url(path_or_url): function download_one_url (line 29) | def download_one_url(url, root_dir, fname=None): function download_urls (line 42) | def download_urls(urls, root_dir, fnames=[]): function unpack (line 52) | def unpack(archived_file_paths, root_dir): function remove_archived_file_paths (line 59) | def remove_archived_file_paths(archived_file_paths): function prepare_dataset (line 66) | def prepare_dataset(output_dir, new_folder_name, urls, fnames=[]): function prepare_coco17 (line 73) | def prepare_coco17(output_dir): function prepare_voc07 (line 88) | def prepare_voc07(output_dir): function prepare_voc12 (line 97) | def prepare_voc12(output_dir): function prepare_voc0712 (line 105) | def prepare_voc0712(output_dir): function prepare_watercolor (line 110) | def prepare_watercolor(output_dir): function prepare_pothole (line 118) | def prepare_pothole(output_dir): function main (line 125) | def main(dataset_name, output_dir): FILE: multimodal/src/autogluon/multimodal/cli/voc2coco.py function get_label2id (line 35) | def get_label2id(labels_path: str) -> Dict[str, int]: function get_annpaths (line 43) | def get_annpaths( function get_image_info (line 97) | def get_image_info(annotation_root, extract_num_from_imgid=True): function get_coco_annotation_from_obj (line 118) | def get_coco_annotation_from_obj(obj, label2id): function convert_xmls_to_cocojson (line 145) | def convert_xmls_to_cocojson( function main (line 188) | def main(): FILE: multimodal/src/autogluon/multimodal/data/collator.py function _pad_arrs_to_max_length (line 8) | def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, round_to=None, max_... function _stack_arrs (line 56) | def _stack_arrs(arrs): class StackCollator (line 63) | class StackCollator: method __call__ (line 69) | def __call__(self, data): class PadCollator (line 84) | class PadCollator: method __init__ (line 108) | def __init__(self, axis=0, pad_val=0, round_to=None, max_length=None, ... method __call__ (line 129) | def __call__(self, data): class TupleCollator (line 166) | class TupleCollator: method __init__ (line 183) | def __init__(self, fn, *args): method __call__ (line 199) | def __call__(self, data): class ListCollator (line 220) | class ListCollator: method __call__ (line 229) | def __call__(self, data): class DictCollator (line 242) | class DictCollator: method __init__ (line 260) | def __init__(self, fn_dict): method __call__ (line 269) | def __call__(self, data): FILE: multimodal/src/autogluon/multimodal/data/datamodule.py class BaseDataModule (line 13) | class BaseDataModule(LightningDataModule): method __init__ (line 22) | def __init__( method set_dataset (line 90) | def set_dataset(self, split): method setup (line 110) | def setup(self, stage): method train_dataloader (line 136) | def train_dataloader(self): method val_dataloader (line 161) | def val_dataloader(self): method test_dataloader (line 185) | def test_dataloader(self): method predict_dataloader (line 209) | def predict_dataloader(self): FILE: multimodal/src/autogluon/multimodal/data/dataset.py class BaseDataset (line 14) | class BaseDataset(torch.utils.data.Dataset): method __init__ (line 22) | def __init__( method __len__ (line 67) | def __len__(self): method __getitem__ (line 77) | def __getitem__(self, idx): FILE: multimodal/src/autogluon/multimodal/data/dataset_mmlab/multi_image_mix_dataset.py class MultiImageMixDataset (line 26) | class MultiImageMixDataset(torch.utils.data.Dataset): method __init__ (line 34) | def __init__( method __len__ (line 100) | def __len__(self): method _load_item (line 110) | def _load_item(self, idx): method __getitem__ (line 153) | def __getitem__(self, idx): class Mosaic (line 220) | class Mosaic(BaseTransform): method __init__ (line 284) | def __init__( method get_indexes (line 303) | def get_indexes(self, dataset: BaseDataset) -> int: method transform (line 317) | def transform(self, results: dict) -> dict: method _mosaic_combine (line 402) | def _mosaic_combine( method __repr__ (line 465) | def __repr__(self): class MixUp (line 474) | class MixUp(BaseTransform): method __init__ (line 535) | def __init__( method get_indexes (line 554) | def get_indexes(self, dataset: BaseDataset) -> int: method transform (line 569) | def transform(self, results: dict) -> dict: method __repr__ (line 671) | def __repr__(self): class RandomAffine (line 682) | class RandomAffine(BaseTransform): method __init__ (line 723) | def __init__( method _get_random_homography_matrix (line 745) | def _get_random_homography_matrix(self, height, width): method transform (line 768) | def transform(self, results: dict) -> dict: method __repr__ (line 797) | def __repr__(self): method _get_rotation_matrix (line 809) | def _get_rotation_matrix(rotate_degrees: float) -> np.ndarray: method _get_scaling_matrix (line 818) | def _get_scaling_matrix(scale_ratio: float) -> np.ndarray: method _get_shear_matrix (line 825) | def _get_shear_matrix(x_shear_degrees: float, y_shear_degrees: float) ... method _get_translation_matrix (line 834) | def _get_translation_matrix(x: float, y: float) -> np.ndarray: FILE: multimodal/src/autogluon/multimodal/data/infer_types.py function is_categorical_column (line 43) | def is_categorical_column( function is_rois_input (line 115) | def is_rois_input(sample): function is_rois_column (line 131) | def is_rois_column(data: pd.Series) -> bool: function is_numerical_column (line 156) | def is_numerical_column( function is_image_column (line 184) | def is_image_column( function is_document_pdf_column (line 262) | def is_document_pdf_column( function is_document_image_column (line 302) | def is_document_image_column( function is_text_column (line 421) | def is_text_column(data: pd.Series) -> bool: function is_identifier_column (line 455) | def is_identifier_column(data: pd.Series, col_name: str, id_mappings: Di... function infer_id_mappings_types (line 498) | def infer_id_mappings_types(id_mappings: Union[Dict[str, Dict], Dict[str... function infer_column_types (line 539) | def infer_column_types( function check_missing_values (line 669) | def check_missing_values( function infer_label_column_type_by_problem_type (line 683) | def infer_label_column_type_by_problem_type( function infer_rois_column_type (line 757) | def infer_rois_column_type( function infer_problem_type (line 767) | def infer_problem_type( function infer_output_shape (line 797) | def infer_output_shape( function set_fallback_column_type (line 858) | def set_fallback_column_type(column_types: Dict, allowable_column_types:... function infer_ner_column_type (line 883) | def infer_ner_column_type(column_types: Dict): FILE: multimodal/src/autogluon/multimodal/data/label_encoder.py class NerLabelEncoder (line 17) | class NerLabelEncoder: method __init__ (line 22) | def __init__(self, config: DictConfig, entity_map: Optional[dict] = No... method fit (line 31) | def fit(self, y: pd.Series, x: pd.Series): method extract_ner_annotations (line 42) | def extract_ner_annotations(self, y: pd.Series): method transform (line 103) | def transform(self, y: pd.Series): method transform_label_for_metric (line 120) | def transform_label_for_metric(self, y: pd.Series, x: pd.Series, token... method inverse_transform (line 155) | def inverse_transform(self, y: List): class CustomLabelEncoder (line 200) | class CustomLabelEncoder: method __init__ (line 205) | def __init__(self, positive_class=None): method _set_attributes (line 211) | def _set_attributes(self): method fit (line 234) | def fit(self, y): method fit_transform (line 253) | def fit_transform(self, y): method transform (line 275) | def transform(self, y): method inverse_transform (line 296) | def inverse_transform(self, y): FILE: multimodal/src/autogluon/multimodal/data/mixup.py class MixupModule (line 6) | class MixupModule(Mixup): method __init__ (line 16) | def __init__( method _mix_elem (line 65) | def _mix_elem(self, x, lam_batch): method _mix_pair (line 86) | def _mix_pair(self, x, lam_batch): method _mix_batch (line 110) | def _mix_batch(self, x, lam): method __call__ (line 127) | def __call__(self, x, target, lam=None): function mixup_others (line 138) | def mixup_others(x, lam): function multimodel_mixup (line 166) | def multimodel_mixup(batch, model, mixup_fn): FILE: multimodal/src/autogluon/multimodal/data/nlpaug.py class InsertPunctuation (line 11) | class InsertPunctuation(Augmenter): method __init__ (line 20) | def __init__( method insert (line 52) | def insert(self, data): method clean (line 82) | def clean(data): method is_duplicate (line 88) | def is_duplicate(dataset, data): FILE: multimodal/src/autogluon/multimodal/data/preprocess_dataframe.py class MultiModalFeaturePreprocessor (line 40) | class MultiModalFeaturePreprocessor(TransformerMixin, BaseEstimator): method __init__ (line 47) | def __init__( method label_column (line 142) | def label_column(self): method column_types (line 146) | def column_types(self): method image_path_names (line 150) | def image_path_names(self): method rois_feature_names (line 154) | def rois_feature_names(self): method image_bytearray_names (line 158) | def image_bytearray_names(self): method image_base64_str_names (line 162) | def image_base64_str_names(self): method image_feature_names (line 166) | def image_feature_names(self): method text_feature_names (line 170) | def text_feature_names(self): method categorical_feature_names (line 174) | def categorical_feature_names(self): method numerical_feature_names (line 178) | def numerical_feature_names(self): method numerical_fill_values (line 182) | def numerical_fill_values(self): method document_feature_names (line 191) | def document_feature_names(self): method ner_feature_names (line 199) | def ner_feature_names(self): method semantic_segmentation_feature_names (line 210) | def semantic_segmentation_feature_names(self): method required_feature_names (line 218) | def required_feature_names(self): method all_column_names (line 228) | def all_column_names(self): method categorical_num_categories (line 232) | def categorical_num_categories(self): method config (line 237) | def config(self): method label_type (line 241) | def label_type(self): method label_scaler (line 245) | def label_scaler(self): method label_generator (line 249) | def label_generator(self): method fit_called (line 253) | def fit_called(self): method fit_x_called (line 257) | def fit_x_called(self): method fit_y_called (line 261) | def fit_y_called(self): method get_column_names (line 264) | def get_column_names(self, modality: str): method _fit_x (line 286) | def _fit_x(self, X: pd.DataFrame): method _fit_y (line 364) | def _fit_y(self, y: pd.Series, X: Optional[pd.DataFrame] = None): method fit (line 404) | def fit(self, X: Optional[pd.DataFrame] = None, y: Optional[pd.Series]... method convert_categorical_to_text (line 421) | def convert_categorical_to_text(col_value: pd.Series, template: str, c... method transform_text (line 438) | def transform_text( method transform_rois (line 488) | def transform_rois( method transform_semantic_segmentation_img (line 532) | def transform_semantic_segmentation_img( method transform_image (line 580) | def transform_image( method transform_document (line 632) | def transform_document( method transform_numerical (line 669) | def transform_numerical( method transform_categorical (line 702) | def transform_categorical( method transform_label (line 739) | def transform_label( method transform_text_ner (line 783) | def transform_text_ner( method transform_label_for_metric (line 815) | def transform_label_for_metric( method transform_prediction (line 855) | def transform_prediction( FILE: multimodal/src/autogluon/multimodal/data/process_categorical.py class CategoricalProcessor (line 13) | class CategoricalProcessor: method __init__ (line 20) | def __init__( method categorical_key (line 46) | def categorical_key(self): method categorical_column_prefix (line 50) | def categorical_column_prefix(self): method collate_fn (line 53) | def collate_fn(self, categorical_column_names: Optional[List] = None) ... method process_one_sample (line 72) | def process_one_sample( method __call__ (line 113) | def __call__( FILE: multimodal/src/autogluon/multimodal/data/process_document.py class DocumentProcessor (line 21) | class DocumentProcessor(ImageProcessor): method __init__ (line 28) | def __init__( method collate_fn (line 107) | def collate_fn(self, text_column_names: Optional[List] = None) -> Dict: method normalize_box (line 136) | def normalize_box(box, width, height): method apply_ocr (line 160) | def apply_ocr(self, doc_image): method get_ocr_features (line 207) | def get_ocr_features(self, doc_path, doc_image): method process_one_sample (line 236) | def process_one_sample( method save_tokenizer (line 347) | def save_tokenizer( method load_tokenizer (line 364) | def load_tokenizer( method __call__ (line 388) | def __call__( FILE: multimodal/src/autogluon/multimodal/data/process_image.py class ImageProcessor (line 36) | class ImageProcessor: method __init__ (line 42) | def __init__( method image_key (line 104) | def image_key(self): method image_valid_num_key (line 108) | def image_valid_num_key(self): method image_column_prefix (line 112) | def image_column_prefix(self): method collate_fn (line 115) | def collate_fn(self, image_column_names: Optional[List] = None, per_gp... method process_one_sample (line 141) | def process_one_sample( method get_image_transform_funcs (line 229) | def get_image_transform_funcs(transform_types: Union[List[str], ListCo... method construct_image_processor (line 319) | def construct_image_processor( method __call__ (line 351) | def __call__( method __getstate__ (line 377) | def __getstate__(self): method __setstate__ (line 382) | def __setstate__(self, state): FILE: multimodal/src/autogluon/multimodal/data/process_label.py class LabelProcessor (line 12) | class LabelProcessor: method __init__ (line 19) | def __init__( method label_key (line 33) | def label_key(self): method collate_fn (line 36) | def collate_fn(self, label_column_names: Optional[List] = None, per_gp... method process_one_sample (line 51) | def process_one_sample( method __call__ (line 72) | def __call__( FILE: multimodal/src/autogluon/multimodal/data/process_mmlab/process_mmdet.py class MMDetProcessor (line 21) | class MMDetProcessor(MMLabProcessor): method __init__ (line 26) | def __init__( method prepare_one_sample (line 65) | def prepare_one_sample( method load_one_sample (line 96) | def load_one_sample( method process_one_loaded_sample (line 121) | def process_one_loaded_sample( method process_one_sample (line 148) | def process_one_sample( method __call__ (line 171) | def __call__( FILE: multimodal/src/autogluon/multimodal/data/process_mmlab/process_mmlab_base.py class MMLabProcessor (line 38) | class MMLabProcessor: method __init__ (line 44) | def __init__( method image_key (line 95) | def image_key(self): method image_valid_num_key (line 99) | def image_valid_num_key(self): method image_column_prefix (line 103) | def image_column_prefix(self): method collate_fn (line 106) | def collate_fn(self, image_column_names: Optional[List] = None, per_gp... method process_one_sample (line 132) | def process_one_sample( method __call__ (line 171) | def __call__( method __getstate__ (line 197) | def __getstate__(self): method __setstate__ (line 201) | def __setstate__(self, state): FILE: multimodal/src/autogluon/multimodal/data/process_mmlab/process_mmocr.py class MMOcrProcessor (line 16) | class MMOcrProcessor(MMLabProcessor): method __init__ (line 21) | def __init__( method process_one_sample (line 54) | def process_one_sample( FILE: multimodal/src/autogluon/multimodal/data/process_ner.py class NerProcessor (line 20) | class NerProcessor: method __init__ (line 25) | def __init__( method collate_fn (line 71) | def collate_fn(self, text_column_names: Optional[List] = None) -> Dict: method process_ner (line 94) | def process_ner( method process_ner_annotations (line 150) | def process_ner_annotations(cls, ner_annotations, ner_text, entity_map... method tokenize_ner_text (line 214) | def tokenize_ner_text(cls, text, tokenizer): method is_space_counted (line 265) | def is_space_counted(words_with_offsets): method save_tokenizer (line 294) | def save_tokenizer( method load_tokenizer (line 311) | def load_tokenizer( method __call__ (line 335) | def __call__( FILE: multimodal/src/autogluon/multimodal/data/process_numerical.py class NumericalProcessor (line 14) | class NumericalProcessor: method __init__ (line 21) | def __init__( method numerical_key (line 53) | def numerical_key(self): method numerical_column_prefix (line 57) | def numerical_column_prefix(self): method collate_fn (line 60) | def collate_fn(self, numerical_column_names: List) -> Dict: method process_one_sample (line 79) | def process_one_sample( method __call__ (line 121) | def __call__( FILE: multimodal/src/autogluon/multimodal/data/process_semantic_seg_img.py class SemanticSegImageProcessor (line 29) | class SemanticSegImageProcessor(ImageProcessor): method __init__ (line 35) | def __init__( method image_key (line 101) | def image_key(self): method label_key (line 105) | def label_key(self): method image_valid_num_key (line 109) | def image_valid_num_key(self): method image_column_prefix (line 113) | def image_column_prefix(self): method mask_label_key (line 117) | def mask_label_key(self): method class_label_key (line 121) | def class_label_key(self): method collate_fn (line 124) | def collate_fn(self, image_column_names: Optional[List] = None, per_gp... method process_one_sample (line 157) | def process_one_sample( method prepare_per_category_binary_masks (line 259) | def prepare_per_category_binary_masks(self, gt): method __call__ (line 276) | def __call__( method get_train_transforms (line 301) | def get_train_transforms(self, train_transforms): method __getstate__ (line 308) | def __getstate__(self): method __setstate__ (line 315) | def __setstate__(self, state): FILE: multimodal/src/autogluon/multimodal/data/process_text.py class TextProcessor (line 28) | class TextProcessor: method __init__ (line 34) | def __init__( method text_token_ids_key (line 110) | def text_token_ids_key(self): method text_segment_ids_key (line 114) | def text_segment_ids_key(self): method choices_ids_key (line 118) | def choices_ids_key(self): method text_valid_length_key (line 122) | def text_valid_length_key(self): method text_column_prefix (line 126) | def text_column_prefix(self): method collate_fn (line 129) | def collate_fn(self, text_column_names: Optional[List] = None) -> Dict: method build_one_token_sequence (line 155) | def build_one_token_sequence( method build_one_token_sequence_from_text (line 234) | def build_one_token_sequence_from_text( method get_special_tokens (line 297) | def get_special_tokens(tokenizer): method get_trimmed_lengths (line 327) | def get_trimmed_lengths( method construct_text_augmenter (line 380) | def construct_text_augmenter( method __call__ (line 417) | def __call__( method __deepcopy__ (line 444) | def __deepcopy__(self, memo): method __getstate__ (line 458) | def __getstate__(self): method __setstate__ (line 463) | def __setstate__(self, state): method save_tokenizer (line 469) | def save_tokenizer( method load_tokenizer (line 486) | def load_tokenizer( method normalize_txt (line 511) | def normalize_txt(text: str) -> str: method register_encoding_decoding_error_handlers (line 524) | def register_encoding_decoding_error_handlers() -> None: FILE: multimodal/src/autogluon/multimodal/data/randaug.py function ShearX (line 14) | def ShearX(img, v): # [-0.3, 0.3] function ShearY (line 21) | def ShearY(img, v): # [-0.3, 0.3] function TranslateX (line 28) | def TranslateX(img, v): # [-150, 150] => percentage: [-0.45, 0.45] function TranslateXabs (line 36) | def TranslateXabs(img, v): # [-150, 150] => percentage: [-0.45, 0.45] function TranslateY (line 43) | def TranslateY(img, v): # [-150, 150] => percentage: [-0.45, 0.45] function TranslateYabs (line 51) | def TranslateYabs(img, v): # [-150, 150] => percentage: [-0.45, 0.45] function Rotate (line 58) | def Rotate(img, v): # [-30, 30] function AutoContrast (line 65) | def AutoContrast(img, _): function Invert (line 69) | def Invert(img, _): function Equalize (line 73) | def Equalize(img, _): function Flip (line 77) | def Flip(img, _): # not from the paper function Solarize (line 81) | def Solarize(img, v): # [0, 256] function SolarizeAdd (line 86) | def SolarizeAdd(img, addition=0, threshold=128): function Posterize (line 95) | def Posterize(img, v): # [4, 8] function Contrast (line 101) | def Contrast(img, v): # [0.1,1.9] function Color (line 106) | def Color(img, v): # [0.1,1.9] function Brightness (line 111) | def Brightness(img, v): # [0.1,1.9] function Sharpness (line 116) | def Sharpness(img, v): # [0.1,1.9] function Cutout (line 121) | def Cutout(img, v): # [0, 60] => percentage: [0, 0.2] function CutoutAbs (line 130) | def CutoutAbs(img, v): # [0, 60] => percentage: [0, 0.2] function SamplePairing (line 151) | def SamplePairing(imgs): # [0, 0.4] function Identity (line 160) | def Identity(img, v): function augment_list (line 164) | def augment_list(): # 16 operations and their ranges class Lighting (line 209) | class Lighting(object): method __init__ (line 212) | def __init__(self, alphastd, eigval, eigvec): method __call__ (line 217) | def __call__(self, img): class CutoutDefault (line 234) | class CutoutDefault(object): method __init__ (line 239) | def __init__(self, length): method __call__ (line 242) | def __call__(self, img): class RandAugment (line 260) | class RandAugment: method __init__ (line 261) | def __init__(self, n, m): method __call__ (line 266) | def __call__(self, img): FILE: multimodal/src/autogluon/multimodal/data/template_engine.py class TemplateEngine (line 11) | class TemplateEngine: method __init__ (line 16) | def __init__(self, template_config: DictConfig): method has_templates (line 52) | def has_templates(self): method get_templates (line 55) | def get_templates(self): method get_max_choice_length (line 58) | def get_max_choice_length(self, tokenizer): method sample_and_apply_template (line 73) | def sample_and_apply_template(self, example: dict): FILE: multimodal/src/autogluon/multimodal/data/templates.py function download_sourceprompt_templates (line 241) | def download_sourceprompt_templates(): function fetching_templates_if_not_exist (line 255) | def fetching_templates_if_not_exist(): function highlight (line 262) | def highlight(input): function choice (line 266) | def choice(choices): function most_frequent (line 270) | def most_frequent(items): class Template (line 285) | class Template(yaml.YAMLObject): method __init__ (line 293) | def __init__(self, name, jinja, reference, metadata=None, answer_choic... method get_id (line 322) | def get_id(self): method get_template_fields (line 330) | def get_template_fields(self): method get_name (line 336) | def get_name(self): method get_reference (line 344) | def get_reference(self): method get_answer_choices_expr (line 352) | def get_answer_choices_expr(self): method get_answer_choices_list (line 360) | def get_answer_choices_list(self, example): method get_fixed_answer_choices_list (line 375) | def get_fixed_answer_choices_list(self): method apply (line 393) | def apply(self, example, truncate=True, truncation_length=2048, highli... method _escape_pipe (line 434) | def _escape_pipe(cls, example): method _unescape_pipe (line 444) | def _unescape_pipe(cls, string): class Metadata (line 448) | class Metadata(yaml.YAMLObject): method __init__ (line 456) | def __init__( class TemplateCollection (line 483) | class TemplateCollection: method __init__ (line 491) | def __init__(self): method keys (line 497) | def keys(self): method __len__ (line 500) | def __len__(self) -> int: method remove (line 503) | def remove(self, dataset_name: str, subset_name: Optional[str] = None)... method _collect_datasets (line 506) | def _collect_datasets(self) -> Dict[Tuple[str, str], "DatasetTemplates"]: method _collect_dataset (line 524) | def _collect_dataset(self, dataset): method get_dataset (line 535) | def get_dataset(self, dataset_name: str, subset_name: Optional[str] = ... method get_templates_count (line 548) | def get_templates_count(self) -> Dict: class DatasetTemplates (line 564) | class DatasetTemplates: method __init__ (line 575) | def __init__(self, dataset_name: str, subset_name: str = None): method sync_mapping (line 585) | def sync_mapping(self) -> None: method all_template_names (line 592) | def all_template_names(self) -> List[str]: method get_template_fields (line 598) | def get_template_fields(self): method folder_path (line 609) | def folder_path(self) -> str: method yaml_path (line 616) | def yaml_path(self) -> str: method format_for_dump (line 619) | def format_for_dump(self) -> Dict: method read_from_file (line 628) | def read_from_file(self) -> Dict: method write_to_file (line 643) | def write_to_file(self) -> None: method add_template (line 655) | def add_template(self, template: "Template") -> None: method remove_template (line 665) | def remove_template(self, template_name: str) -> None: method update_template (line 685) | def update_template( method delete_folder (line 713) | def delete_folder(self) -> None: method __getitem__ (line 728) | def __getitem__(self, template_key: str) -> "Template": method __len__ (line 731) | def __len__(self) -> int: function get_templates_data_frame (line 735) | def get_templates_data_frame(): FILE: multimodal/src/autogluon/multimodal/data/trivial_augmenter.py function scale_parameter (line 21) | def scale_parameter(level, maxval, type): class TransformT (line 41) | class TransformT(object): method __init__ (line 46) | def __init__(self, name, xform_fn): method __repr__ (line 56) | def __repr__(self): method augment (line 59) | def augment(self, level, data): function _rotate_impl (line 72) | def _rotate_impl(pil_img, level): function _solarize_impl (line 86) | def _solarize_impl(pil_img, level): function _posterize_impl (line 98) | def _posterize_impl(pil_img, level): function _enhancer_impl (line 111) | def _enhancer_impl(enhancer): function _shear_x_impl (line 134) | def _shear_x_impl(pil_img, level): function _shear_y_impl (line 148) | def _shear_y_impl(pil_img, level): function _translate_x_impl (line 162) | def _translate_x_impl(pil_img, level): function _translate_y_impl (line 176) | def _translate_y_impl(pil_img, level): function set_image_augmentation_space (line 190) | def set_image_augmentation_space(): function download_nltk (line 210) | def download_nltk(): function set_text_augmentation_space (line 239) | def set_text_augmentation_space(space): class TrivialAugment (line 255) | class TrivialAugment: method __init__ (line 262) | def __init__(self, datatype, max_strength, space=None) -> None: method __call__ (line 284) | def __call__(self, data): method augment_image (line 290) | def augment_image(self, data): method augment_text (line 295) | def augment_text(self, data): FILE: multimodal/src/autogluon/multimodal/data/utils.py function get_collate_fn (line 53) | def get_collate_fn( function apply_df_preprocessor (line 98) | def apply_df_preprocessor( function apply_data_processor (line 139) | def apply_data_processor( function get_per_sample_features (line 188) | def get_per_sample_features( function default_holdout_frac (line 229) | def default_holdout_frac(num_train_rows, hyperparameter_tune=False): function init_df_preprocessor (line 246) | def init_df_preprocessor( function get_image_transforms (line 295) | def get_image_transforms(model_config: DictConfig, model_name: str, adva... function create_data_processor (line 330) | def create_data_processor( function create_fusion_data_processors (line 435) | def create_fusion_data_processors( function turn_on_off_feature_column_info (line 553) | def turn_on_off_feature_column_info( function get_mixup (line 576) | def get_mixup( function data_to_df (line 631) | def data_to_df( function infer_scarcity_mode_by_data_size (line 709) | def infer_scarcity_mode_by_data_size(df_train: pd.DataFrame, scarcity_th... function infer_dtypes_by_model_names (line 731) | def infer_dtypes_by_model_names(model_config: DictConfig): function split_train_tuning_data (line 760) | def split_train_tuning_data( function get_detected_data_types (line 811) | def get_detected_data_types(column_types: Dict): FILE: multimodal/src/autogluon/multimodal/learners/base.py class BaseLearner (line 151) | class BaseLearner(ExportMixin, DistillationMixin, RealtimeMixin): method __init__ (line 154) | def __init__( method path (line 301) | def path(self): method label (line 305) | def label(self): method problem_type (line 309) | def problem_type(self): method problem_property (line 313) | def problem_property(self): method column_types (line 320) | def column_types(self): method eval_metric (line 324) | def eval_metric(self): method validation_metric (line 328) | def validation_metric(self): method total_parameters (line 332) | def total_parameters(self) -> int: method trainable_parameters (line 336) | def trainable_parameters(self) -> int: method model_size (line 342) | def model_size(self) -> float: method set_eval_metric_func (line 351) | def set_eval_metric_func(self): method ensure_fit_ready (line 363) | def ensure_fit_ready(self): method infer_problem_type (line 370) | def infer_problem_type(self, train_data: pd.DataFrame): method setup_save_path (line 381) | def setup_save_path(self, save_path: str): method infer_column_types (line 391) | def infer_column_types( method infer_output_shape (line 418) | def infer_output_shape(self): method prepare_train_tuning_data (line 427) | def prepare_train_tuning_data( method detect_data_scarcity_mode (line 451) | def detect_data_scarcity_mode(self): method update_attributes (line 463) | def update_attributes( method infer_validation_metric (line 486) | def infer_validation_metric(self, is_matching: Optional[bool] = False): method update_hyperparameters (line 500) | def update_hyperparameters(self, hyperparameters: Dict, hyperparameter... method fit_sanity_check (line 530) | def fit_sanity_check(self): method prepare_fit_args (line 537) | def prepare_fit_args( method execute_fit (line 567) | def execute_fit(self): method on_fit_start (line 581) | def on_fit_start( method on_fit_end (line 599) | def on_fit_end( method fit (line 625) | def fit( method init_pretrained (line 676) | def init_pretrained(self): method get_config_per_run (line 702) | def get_config_per_run(self, config, hyperparameters): method get_df_preprocessor_per_run (line 717) | def get_df_preprocessor_per_run( method update_config_by_data_per_run (line 746) | def update_config_by_data_per_run(config, df_preprocessor): method get_model_per_run (line 757) | def get_model_per_run(self, model, config, df_preprocessor): method compile_model_per_run (line 769) | def compile_model_per_run(config, model): method get_peft_param_names_per_run (line 785) | def get_peft_param_names_per_run(model, config): method get_data_processors_per_run (line 796) | def get_data_processors_per_run( method get_validation_metric_per_run (line 823) | def get_validation_metric_per_run(self): method get_mixup_func_per_run (line 831) | def get_mixup_func_per_run(self, config): method get_loss_func_per_run (line 844) | def get_loss_func_per_run(self, config, mixup_active=None): method get_model_postprocess_fn_per_run (line 857) | def get_model_postprocess_fn_per_run(self, loss_func): method get_datamodule_per_run (line 864) | def get_datamodule_per_run( method get_optim_kwargs_per_run (line 890) | def get_optim_kwargs_per_run( method get_litmodule_per_run (line 932) | def get_litmodule_per_run( method get_callbacks_per_run (line 963) | def get_callbacks_per_run(self, save_path=None, config=None, litmodule... method get_plugins_per_run (line 1011) | def get_plugins_per_run(model, peft_param_names=None): method get_tb_logger (line 1019) | def get_tb_logger(save_path): method log_gpu_info (line 1027) | def log_gpu_info(num_gpus, config): method get_grad_steps (line 1037) | def get_grad_steps(num_gpus, config): method get_strategy_per_run (line 1051) | def get_strategy_per_run(num_gpus, config): method update_strategy_and_num_gpus_for_hpo (line 1073) | def update_strategy_and_num_gpus_for_hpo(self, strategy, num_gpus): method get_precision_per_run (line 1080) | def get_precision_per_run(num_gpus: int, precision: Union[str, int], c... method get_num_gpus_and_strategy_per_run (line 1083) | def get_num_gpus_and_strategy_per_run( method post_update_config_per_run (line 1110) | def post_update_config_per_run(config, num_gpus, precision, strategy): method init_trainer_per_run (line 1118) | def init_trainer_per_run( method run_trainer (line 1196) | def run_trainer( method on_fit_per_run_start (line 1229) | def on_fit_per_run_start(self, seed, save_path): method on_fit_per_run_end (line 1234) | def on_fit_per_run_end( method fit_per_run (line 1256) | def fit_per_run( method top_k_average (line 1391) | def top_k_average( method prepare_deepspeed_offloading (line 1528) | def prepare_deepspeed_offloading(self, strategy): method get_pred_writer (line 1558) | def get_pred_writer(self, strategy): method collect_predictions (line 1565) | def collect_predictions(outputs, trainer, pred_writer, num_gpus): method clean_trainer_processes (line 1573) | def clean_trainer_processes(trainer, is_train=True): method update_image_column_types (line 1581) | def update_image_column_types(self, data): method data_to_df (line 1602) | def data_to_df(self, data): method update_realtime_for_interactive_env (line 1618) | def update_realtime_for_interactive_env(realtime: bool, num_gpus: int,... method update_num_gpus_by_data_size (line 1629) | def update_num_gpus_by_data_size( method realtime_predict (line 1638) | def realtime_predict( method on_predict_per_run_start (line 1680) | def on_predict_per_run_start(self, data: Union[str, pd.DataFrame]): method get_predict_batch_size_per_run (line 1684) | def get_predict_batch_size_per_run(self, num_gpus: int, strategy: str): method on_predict_per_run_end (line 1692) | def on_predict_per_run_end(self, trainer): method predict_per_run (line 1695) | def predict_per_run( method ensure_predict_ready (line 1810) | def ensure_predict_ready(self): method on_predict_start (line 1821) | def on_predict_start(self): method evaluate (line 1824) | def evaluate( method _match_queries_and_candidates (line 1904) | def _match_queries_and_candidates( method predict (line 1931) | def predict( method predict_proba (line 1991) | def predict_proba( method extract_embedding (line 2056) | def extract_embedding( method _as_pandas (line 2118) | def _as_pandas( method _load_state_dict (line 2134) | def _load_state_dict( method _replace_model_name_prefix (line 2164) | def _replace_model_name_prefix( method save (line 2175) | def save( method _load_metadata (line 2261) | def _load_metadata( method load (line 2321) | def load( method class_labels (line 2395) | def class_labels(self): method positive_class (line 2414) | def positive_class(self): method fit_summary (line 2440) | def fit_summary(self, verbosity=0, show_plot=False): method list_supported_models (line 2474) | def list_supported_models(self, pretrained=True): method update_strategy_by_env (line 2496) | def update_strategy_by_env(config): method set_num_gpus (line 2508) | def set_num_gpus(self, num_gpus): method get_num_gpus (line 2512) | def get_num_gpus(self): FILE: multimodal/src/autogluon/multimodal/learners/ensemble.py class EnsembleLearner (line 33) | class EnsembleLearner(BaseLearner): method __init__ (line 34) | def __init__( method get_learner_path (line 128) | def get_learner_path(self, learner_path: str): method get_learner_name (line 133) | def get_learner_name(self, learner): method predict_all_for_ensembling (line 144) | def predict_all_for_ensembling( method verify_predictions_labels (line 218) | def verify_predictions_labels(predictions, learners, labels=None): method fit_per_ensemble (line 226) | def fit_per_ensemble( method select_next_best (line 240) | def select_next_best(self, left_learner_indices, selected_learner_indi... method sequential_ensemble (line 258) | def sequential_ensemble( method update_hyperparameters (line 292) | def update_hyperparameters(self, hyperparameters: Dict): method fit_all (line 326) | def fit_all( method on_fit_end (line 380) | def on_fit_end( method update_attributes_by_first_learner (line 390) | def update_attributes_by_first_learner(self, learners: List): method fit_ensemble (line 406) | def fit_ensemble( method fit (line 473) | def fit( method predict (line 531) | def predict( method predict_proba (line 566) | def predict_proba( method evaluate (line 603) | def evaluate( method extract_embedding (line 665) | def extract_embedding( method save (line 676) | def save( method load (line 721) | def load( FILE: multimodal/src/autogluon/multimodal/learners/few_shot_svm.py class FewShotSVMLearner (line 30) | class FewShotSVMLearner(BaseLearner): method __init__ (line 31) | def __init__( method update_attributes (line 86) | def update_attributes( method prepare_train_tuning_data (line 104) | def prepare_train_tuning_data( method infer_problem_type (line 119) | def infer_problem_type(self, train_data: pd.DataFrame): method infer_output_shape (line 122) | def infer_output_shape(self): method prepare_fit_args (line 125) | def prepare_fit_args( method fit_sanity_check (line 144) | def fit_sanity_check(self): method get_svm_per_run (line 152) | def get_svm_per_run(svm: Pipeline): method on_fit_per_run_end (line 157) | def on_fit_per_run_end( method get_datamodule_per_run (line 181) | def get_datamodule_per_run( method update_config_by_data_per_run (line 205) | def update_config_by_data_per_run(config, df_preprocessor): method init_trainer_per_run (line 215) | def init_trainer_per_run( method fit_per_run (line 260) | def fit_per_run( method aggregate_column_features (line 370) | def aggregate_column_features( method predict (line 394) | def predict( method predict_proba (line 427) | def predict_proba( method extract_embedding (line 468) | def extract_embedding( method evaluate (line 513) | def evaluate( method load (line 575) | def load( method save (line 590) | def save( method fit_summary (line 616) | def fit_summary(self, verbosity=0, show_plot=False): FILE: multimodal/src/autogluon/multimodal/learners/matching.py class MatchingLearner (line 93) | class MatchingLearner(BaseLearner): method __init__ (line 100) | def __init__( method query (line 232) | def query(self): method response (line 236) | def response(self): method match_label (line 240) | def match_label(self): method path (line 244) | def path(self): method label (line 248) | def label(self): method problem_type (line 252) | def problem_type(self): method problem_property (line 261) | def problem_property(self): method column_types (line 265) | def column_types(self): method eval_metric (line 269) | def eval_metric(self): method validation_metric (line 273) | def validation_metric(self): method total_parameters (line 277) | def total_parameters(self) -> int: method trainable_parameters (line 281) | def trainable_parameters(self) -> int: method model_size (line 287) | def model_size(self) -> float: method set_verbosity (line 297) | def set_verbosity(self, verbosity: int): method _init_pretrained (line 310) | def _init_pretrained(self): method _ensure_inference_ready (line 365) | def _ensure_inference_ready(self): method prepare_fit_args (line 369) | def prepare_fit_args( method update_attributes (line 398) | def update_attributes( method execute_fit (line 427) | def execute_fit(self): method on_fit_end (line 442) | def on_fit_end( method fit (line 463) | def fit( method _get_matcher_df_preprocessor (line 577) | def _get_matcher_df_preprocessor( method _get_matcher_data_processors (line 626) | def _get_matcher_data_processors( method get_config_per_run (line 678) | def get_config_per_run(self, config, hyperparameters): method on_fit_per_run_end (line 693) | def on_fit_per_run_end( method fit_per_run (line 729) | def fit_per_run( method top_k_average (line 962) | def top_k_average( method _on_predict_start (line 1076) | def _on_predict_start( method _default_predict (line 1189) | def _default_predict( method _realtime_predict (line 1244) | def _realtime_predict( method predict_per_run (line 1307) | def predict_per_run( method _evaluate_symmetric_ranking (line 1399) | def _evaluate_symmetric_ranking(self, data): method _evaluate_ranking (line 1429) | def _evaluate_ranking( method _evaluate_matching (line 1491) | def _evaluate_matching( method evaluate (line 1544) | def evaluate( method predict (line 1645) | def predict( method predict_proba (line 1699) | def predict_proba( method extract_embedding (line 1754) | def extract_embedding( method _as_pandas (line 1824) | def _as_pandas( method _load_state_dict (line 1838) | def _load_state_dict( method _replace_model_name_prefix (line 1858) | def _replace_model_name_prefix( method save (line 1869) | def save( method _load_metadata (line 1990) | def _load_metadata( method load (line 2080) | def load( method class_labels (line 2128) | def class_labels(self): FILE: multimodal/src/autogluon/multimodal/learners/ner.py class NERLearner (line 25) | class NERLearner(BaseLearner): method __init__ (line 26) | def __init__( method infer_problem_type (line 61) | def infer_problem_type(self, train_data: pd.DataFrame): method infer_output_shape (line 64) | def infer_output_shape(self): method update_attributes (line 67) | def update_attributes( method get_validation_metric_per_run (line 89) | def get_validation_metric_per_run(self, output_shape: int): method get_model_per_run (line 97) | def get_model_per_run( method get_optim_kwargs_per_run (line 113) | def get_optim_kwargs_per_run(self, config, validation_metric, custom_m... method get_litmodule_per_run (line 133) | def get_litmodule_per_run( method get_output_shape_per_run (line 150) | def get_output_shape_per_run(df_preprocessor): method on_fit_per_run_end (line 154) | def on_fit_per_run_end( method fit_per_run (line 178) | def fit_per_run( method evaluate (line 299) | def evaluate( method predict (line 387) | def predict( method predict_proba (line 433) | def predict_proba( method save (line 478) | def save( FILE: multimodal/src/autogluon/multimodal/learners/object_detection.py class ObjectDetectionLearner (line 38) | class ObjectDetectionLearner(BaseLearner): method __init__ (line 39) | def __init__( method classes (line 106) | def classes(self): method category_ids (line 115) | def category_ids(self): method setup_detection_train_tuning_data (line 121) | def setup_detection_train_tuning_data(self, max_num_tuning_data, seed,... method prepare_train_tuning_data (line 163) | def prepare_train_tuning_data( method infer_output_shape (line 191) | def infer_output_shape(self, **kwargs): method init_pretrained (line 195) | def init_pretrained(self): method fit (line 204) | def fit( method get_datamodule_per_run (line 254) | def get_datamodule_per_run( method get_strategy_per_run (line 297) | def get_strategy_per_run(self, num_gpus, config): method update_num_gpus_by_strategy (line 305) | def update_num_gpus_by_strategy(self, strategy, num_gpus): method get_optim_kwargs_per_run (line 311) | def get_optim_kwargs_per_run(self, config, validation_metric, custom_m... method get_litmodule_per_run (line 328) | def get_litmodule_per_run( method get_model_per_run (line 347) | def get_model_per_run(self, model, config): method fit_per_run (line 356) | def fit_per_run( method predict_per_run (line 464) | def predict_per_run( method evaluate_coco (line 568) | def evaluate_coco( method evaluate (line 628) | def evaluate( method predict (line 685) | def predict( method predict_proba (line 791) | def predict_proba( method extract_embedding (line 801) | def extract_embedding( method _load_metadata (line 812) | def _load_metadata( method save (line 822) | def save( FILE: multimodal/src/autogluon/multimodal/learners/semantic_segmentation.py class SemanticSegmentationLearner (line 29) | class SemanticSegmentationLearner(BaseLearner): method __init__ (line 30) | def __init__( method get_semantic_segmentation_class_num (line 72) | def get_semantic_segmentation_class_num(self, sample_data_path): method infer_output_shape (line 116) | def infer_output_shape(self): method get_peft_param_names_per_run (line 121) | def get_peft_param_names_per_run(model, config): method get_loss_func_per_run (line 133) | def get_loss_func_per_run(self, config, mixup_active=None): method evaluate_semantic_segmentation (line 142) | def evaluate_semantic_segmentation( method get_litmodule_per_run (line 236) | def get_litmodule_per_run( method on_predict_start (line 259) | def on_predict_start(self, data: pd.DataFrame): method evaluate (line 266) | def evaluate( method predict (line 300) | def predict( method predict_proba (line 367) | def predict_proba( method extract_embedding (line 427) | def extract_embedding( method save_segmentation_result (line 437) | def save_segmentation_result(self, pred: Iterable, data: Union[pd.Data... method post_process_prediction (line 498) | def post_process_prediction(self, data, outputs, ret_type): method get_image_column_name (line 524) | def get_image_column_name(self, data: pd.DataFrame): FILE: multimodal/src/autogluon/multimodal/models/adaptation_layers.py function identity (line 18) | def identity(x): class LoRALayer (line 22) | class LoRALayer: method __init__ (line 43) | def __init__( class IA3LoRALinear (line 62) | class IA3LoRALinear(nn.Linear, LoRALayer): method __init__ (line 84) | def __init__( method reset_parameters (line 107) | def reset_parameters(self): method T (line 114) | def T(self, w): method forward (line 117) | def forward(self, x: torch.Tensor): method train (line 125) | def train(self, mode: bool = True): method eval (line 134) | def eval(self): method extra_repr (line 146) | def extra_repr(self): class IA3Linear (line 152) | class IA3Linear(nn.Linear, LoRALayer): method __init__ (line 173) | def __init__( method forward (line 188) | def forward(self, x: torch.Tensor): method train (line 193) | def train(self, mode: bool = True): method eval (line 201) | def eval(self): method extra_repr (line 212) | def extra_repr(self): class LoRALinear (line 218) | class LoRALinear(nn.Linear, LoRALayer): method __init__ (line 247) | def __init__( method reset_parameters (line 273) | def reset_parameters(self): method T (line 280) | def T(self, w): method train (line 283) | def train(self, mode: bool = True): method eval (line 291) | def eval(self): method forward (line 301) | def forward(self, x: torch.Tensor): class LoRAEmbedding (line 311) | class LoRAEmbedding(nn.Embedding, LoRALayer): method __init__ (line 336) | def __init__( method reset_parameters (line 356) | def reset_parameters(self): method train (line 363) | def train(self, mode: bool = True): method eval (line 371) | def eval(self): method forward (line 379) | def forward(self, x: torch.Tensor): class LoRAMergedLinear (line 398) | class LoRAMergedLinear(nn.Linear, LoRALayer): method __init__ (line 427) | def __init__( method reset_parameters (line 461) | def reset_parameters(self): method zero_pad (line 468) | def zero_pad(self, x): method train (line 474) | def train(self, mode: bool = True): method eval (line 488) | def eval(self): method forward (line 502) | def forward(self, x: torch.Tensor): class LoRAConv2d (line 519) | class LoRAConv2d(nn.Conv2d, LoRALayer): method __init__ (line 548) | def __init__( method reset_parameters (line 571) | def reset_parameters(self): method train (line 578) | def train(self, mode: bool = True): method eval (line 585) | def eval(self): method forward (line 592) | def forward(self, x: torch.Tensor): class ConvLoRALinear (line 606) | class ConvLoRALinear(nn.Linear, LoRALayer): method __init__ (line 636) | def __init__( method reset_parameters (line 676) | def reset_parameters(self): method T (line 683) | def T(self, w): method forward (line 686) | def forward(self, x: torch.Tensor): class MoEGate (line 730) | class MoEGate(nn.Module): method __init__ (line 731) | def __init__(self, d, M=4, K=1, noisy_gating=True): method forward (line 754) | def forward(self, feats, loss_coef=1e-2, noise_epsilon=1e-2): method _gates_to_load (line 786) | def _gates_to_load(self, gates): method cv_squared (line 796) | def cv_squared(self, x): method _prob_in_top_k (line 812) | def _prob_in_top_k(self, clean_values, noisy_values, noise_stddev, noi... class SparseDispatcher (line 846) | class SparseDispatcher(object): method __init__ (line 884) | def __init__(self, num_experts, gates): method dispatch (line 900) | def dispatch(self, inp): method combine (line 917) | def combine(self, expert_out, multiply_by_gates=True): method expert_to_gates (line 952) | def expert_to_gates(self): FILE: multimodal/src/autogluon/multimodal/models/augmenter.py class VAETransformer (line 13) | class VAETransformer(nn.Module): method __init__ (line 14) | def __init__(self, config: DictConfig, in_feautres: int, n_modality: i... method init_parameters (line 41) | def init_parameters(self): method reparameterize (line 45) | def reparameterize(self, mu, logvar): method forward (line 50) | def forward(self, X): class MlpVAE (line 67) | class MlpVAE(nn.Module): method __init__ (line 68) | def __init__(self, input_dim, hidden_dim, z_dim=16) -> None: method init_parameters (line 110) | def init_parameters(self): method reparameterize (line 114) | def reparameterize(self, mu, logvar): method forward (line 119) | def forward(self, x): class Augmenter (line 129) | class Augmenter(nn.Module): method __init__ (line 130) | def __init__( method forward (line 159) | def forward(self, x): method get_layer_ids (line 162) | def get_layer_ids( FILE: multimodal/src/autogluon/multimodal/models/categorical_mlp.py class CategoricalMLP (line 14) | class CategoricalMLP(nn.Module): method __init__ (line 20) | def __init__( method categorical_key (line 105) | def categorical_key(self): method input_keys (line 109) | def input_keys(self): method label_key (line 113) | def label_key(self): method forward (line 116) | def forward( method get_layer_ids (line 146) | def get_layer_ids( FILE: multimodal/src/autogluon/multimodal/models/clip.py class CLIPForImageText (line 35) | class CLIPForImageText(nn.Module): method __init__ (line 41) | def __init__( method text_token_ids_key (line 134) | def text_token_ids_key(self): method text_valid_length_key (line 138) | def text_valid_length_key(self): method image_key (line 142) | def image_key(self): method image_valid_num_key (line 146) | def image_valid_num_key(self): method label_key (line 150) | def label_key(self): method text_column_prefix (line 154) | def text_column_prefix(self): method image_column_prefix (line 158) | def image_column_prefix(self): method text_feature_dim (line 162) | def text_feature_dim(self): method image_feature_dim (line 166) | def image_feature_dim(self): method input_keys (line 170) | def input_keys(self): method forward (line 178) | def forward( method get_layer_ids (line 287) | def get_layer_ids( FILE: multimodal/src/autogluon/multimodal/models/custom_hf_models/modeling_sam_for_conv_lora.py class SamVisionEncoderOutput (line 50) | class SamVisionEncoderOutput(ModelOutput): class SamImageSegmentationOutput (line 81) | class SamImageSegmentationOutput(ModelOutput): class SamPatchEmbeddings (line 117) | class SamPatchEmbeddings(nn.Module): method __init__ (line 124) | def __init__(self, config): method forward (line 138) | def forward(self, pixel_values): class SamMLPBlock (line 152) | class SamMLPBlock(nn.Module): method __init__ (line 153) | def __init__(self, config): method forward (line 159) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: class SamLayerNorm (line 167) | class SamLayerNorm(nn.Module): method __init__ (line 173) | def __init__(self, normalized_shape, eps=1e-6, data_format="channels_l... method forward (line 183) | def forward(self, x: torch.Tensor) -> torch.Tensor: class SamAttention (line 197) | class SamAttention(nn.Module): method __init__ (line 203) | def __init__(self, config, downsample_rate=None): method _separate_heads (line 219) | def _separate_heads(self, hidden_states: Tensor, num_attention_heads: ... method _recombine_heads (line 225) | def _recombine_heads(self, hidden_states: Tensor, point_batch_size: in... method forward (line 230) | def forward(self, query: Tensor, key: Tensor, value: Tensor, attention... class SamTwoWayAttentionBlock (line 260) | class SamTwoWayAttentionBlock(nn.Module): method __init__ (line 261) | def __init__(self, config, attention_downsample_rate: int = 2, skip_fi... method forward (line 294) | def forward( class SamTwoWayTransformer (line 347) | class SamTwoWayTransformer(nn.Module): method __init__ (line 348) | def __init__(self, config: SamMaskDecoderConfig): method forward (line 361) | def forward( class SamFeedForward (line 418) | class SamFeedForward(nn.Module): method __init__ (line 419) | def __init__( method forward (line 430) | def forward(self, hidden_states): class SamMaskDecoder (line 442) | class SamMaskDecoder(nn.Module): method __init__ (line 443) | def __init__(self, config: SamMaskDecoderConfig): method forward (line 471) | def forward( class SamPositionalEmbedding (line 568) | class SamPositionalEmbedding(nn.Module): method __init__ (line 569) | def __init__(self, config): method forward (line 574) | def forward(self, input_coords, input_shape=None): class SamMaskEmbedding (line 591) | class SamMaskEmbedding(nn.Module): method __init__ (line 592) | def __init__(self, config: SamPromptEncoderConfig): method forward (line 606) | def forward(self, masks): class SamPromptEncoder (line 618) | class SamPromptEncoder(nn.Module): method __init__ (line 619) | def __init__(self, config: SamPromptEncoderConfig, shared_patch_embedd... method _embed_points (line 634) | def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pa... method _embed_boxes (line 672) | def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: method forward (line 683) | def forward( class SamVisionAttention (line 730) | class SamVisionAttention(nn.Module): method __init__ (line 733) | def __init__(self, config, window_size): method get_rel_pos (line 758) | def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor)... method add_decomposed_rel_pos (line 790) | def add_decomposed_rel_pos( method forward (line 835) | def forward(self, hidden_states: torch.Tensor, output_attentions=False... class SamVisionLayer (line 871) | class SamVisionLayer(nn.Module): method __init__ (line 872) | def __init__(self, config, window_size): method window_partition (line 880) | def window_partition(self, hidden_states: torch.Tensor, window_size: i... method window_unpartition (line 904) | def window_unpartition( method forward (line 934) | def forward( class SamVisionNeck (line 975) | class SamVisionNeck(nn.Module): method __init__ (line 976) | def __init__(self, config: SamVisionConfig): method forward (line 985) | def forward(self, hidden_states): class SamVisionEncoder (line 995) | class SamVisionEncoder(nn.Module): method __init__ (line 996) | def __init__(self, config: SamVisionConfig): method get_input_embeddings (line 1027) | def get_input_embeddings(self): method forward (line 1030) | def forward( class SamPreTrainedModel (line 1112) | class SamPreTrainedModel(PreTrainedModel): method _init_weights (line 1117) | def _init_weights(self, module): class SamModel (line 1220) | class SamModel(SamPreTrainedModel): method __init__ (line 1223) | def __init__(self, config): method get_input_embeddings (line 1233) | def get_input_embeddings(self): method get_image_wide_positional_embeddings (line 1236) | def get_image_wide_positional_embeddings(self): method get_image_embeddings (line 1250) | def get_image_embeddings( method get_prompt_embeddings (line 1281) | def get_prompt_embeddings( method forward (line 1314) | def forward( FILE: multimodal/src/autogluon/multimodal/models/custom_transformer.py function _is_glu_activation (line 16) | def _is_glu_activation(activation: ModuleType): function _make_nn_module (line 20) | def _make_nn_module(module_type: ModuleType, *args) -> nn.Module: function _all_or_none (line 44) | def _all_or_none(values): function reglu (line 48) | def reglu(x: Tensor) -> Tensor: function geglu (line 60) | def geglu(x: Tensor) -> Tensor: class ReGLU (line 72) | class ReGLU(nn.Module): method forward (line 81) | def forward(self, x: Tensor) -> Tensor: class GEGLU (line 85) | class GEGLU(nn.Module): method forward (line 94) | def forward(self, x: Tensor) -> Tensor: class CLSToken (line 98) | class CLSToken(nn.Module): method __init__ (line 111) | def __init__(self, token_dim: int, initialization: str) -> None: method expand (line 129) | def expand(self, *leading_dimensions: int) -> Tensor: method forward (line 151) | def forward(self, x: Tensor) -> Tensor: class _TokenInitialization (line 156) | class _TokenInitialization(enum.Enum): method from_str (line 161) | def from_str(cls, initialization: str) -> "_TokenInitialization": method apply (line 168) | def apply(self, x: Tensor, d: int) -> None: class MultiheadAttention (line 179) | class MultiheadAttention(nn.Module): method __init__ (line 192) | def __init__( method _reshape (line 248) | def _reshape(self, x: Tensor) -> Tensor: method forward (line 257) | def forward( class AdditiveAttention (line 316) | class AdditiveAttention(nn.Module): method __init__ (line 328) | def __init__( method forward (line 389) | def forward( class Custom_Transformer (line 423) | class Custom_Transformer(nn.Module): class FFN (line 430) | class FFN(nn.Module): method __init__ (line 433) | def __init__( method forward (line 453) | def forward(self, x: Tensor) -> Tensor: class Head (line 460) | class Head(nn.Module): method __init__ (line 463) | def __init__( method forward (line 477) | def forward(self, x: Tensor) -> Tensor: method __init__ (line 484) | def __init__( method _get_kv_compressions (line 665) | def _get_kv_compressions(self, layer): method _start_residual (line 676) | def _start_residual(self, layer, stage, x): method _end_residual (line 685) | def _end_residual(self, layer, stage, x, x_residual): method forward (line 693) | def forward(self, x: Tensor) -> Tensor: FILE: multimodal/src/autogluon/multimodal/models/document_transformer.py class DocumentTransformer (line 27) | class DocumentTransformer(HFAutoModelForTextPrediction): method __init__ (line 32) | def __init__( method is_text_only (line 111) | def is_text_only(self): method text_attention_mask_key (line 132) | def text_attention_mask_key(self): method text_bbox_key (line 136) | def text_bbox_key(self): method document_pixel_value_key (line 140) | def document_pixel_value_key(self): method update_input_data (line 143) | def update_input_data( method forward (line 170) | def forward( FILE: multimodal/src/autogluon/multimodal/models/ft_transformer.py class CategoricalFeatureTokenizer (line 16) | class CategoricalFeatureTokenizer(nn.Module): method __init__ (line 24) | def __init__( method num_tokens (line 66) | def num_tokens(self) -> int: method token_dim (line 71) | def token_dim(self) -> int: method forward (line 75) | def forward(self, x: Tensor) -> Tensor: class Periodic (line 84) | class Periodic(nn.Module): method __init__ (line 85) | def __init__( method cos_sin (line 127) | def cos_sin(self, x: Tensor): method forward (line 130) | def forward(self, x: Tensor): class NLinear (line 135) | class NLinear(nn.Module): method __init__ (line 136) | def __init__(self, n: int, d_in: int, d_out: int, bias: bool = True): method forward (line 147) | def forward(self, x): class NLinearMemoryEfficient (line 156) | class NLinearMemoryEfficient(nn.Module): method __init__ (line 157) | def __init__(self, n: int, d_in: int, d_out: int): method forward (line 161) | def forward(self, x): class NLayerNorm (line 165) | class NLayerNorm(nn.Module): method __init__ (line 166) | def __init__(self, n_features: int, d: int): method forward (line 171) | def forward(self, x: Tensor): class NumericalFeatureTokenizer (line 178) | class NumericalFeatureTokenizer(nn.Module): method __init__ (line 193) | def __init__( method num_tokens (line 229) | def num_tokens(self) -> int: method token_dim (line 234) | def token_dim(self) -> int: method forward (line 238) | def forward( class AutoDis (line 249) | class AutoDis(nn.Module): method __init__ (line 262) | def __init__( method forward (line 285) | def forward(self, x: Tensor): class NumEmbeddings (line 294) | class NumEmbeddings(nn.Module): method __init__ (line 295) | def __init__( method num_tokens (line 412) | def num_tokens(self) -> int: method token_dim (line 418) | def token_dim(self) -> int: method forward (line 423) | def forward(self, x): class FT_Transformer (line 427) | class FT_Transformer(nn.Module): method __init__ (line 434) | def __init__( method categorical_key (line 628) | def categorical_key(self): method numerical_key (line 632) | def numerical_key(self): method input_keys (line 636) | def input_keys(self): method label_key (line 645) | def label_key(self): method forward (line 648) | def forward(self, batch: dict): method get_layer_ids (line 697) | def get_layer_ids( FILE: multimodal/src/autogluon/multimodal/models/fusion/base.py class AbstractMultimodalFusionModel (line 10) | class AbstractMultimodalFusionModel(ABC, nn.Module): method __init__ (line 15) | def __init__( method label_key (line 29) | def label_key(self): method forward (line 33) | def forward(self, *args, **kwargs): method get_layer_ids (line 36) | def get_layer_ids( FILE: multimodal/src/autogluon/multimodal/models/fusion/fusion_mlp.py class MultimodalFusionMLP (line 27) | class MultimodalFusionMLP(AbstractMultimodalFusionModel): method __init__ (line 34) | def __init__( method input_keys (line 138) | def input_keys(self): method label_key (line 146) | def label_key(self): method forward (line 149) | def forward( method get_output_dict (line 214) | def get_output_dict( FILE: multimodal/src/autogluon/multimodal/models/fusion/fusion_ner.py class MultimodalFusionNER (line 16) | class MultimodalFusionNER(AbstractMultimodalFusionModel): method __init__ (line 23) | def __init__( method label_key (line 123) | def label_key(self): method forward (line 126) | def forward( FILE: multimodal/src/autogluon/multimodal/models/fusion/fusion_transformer.py class MultimodalFusionTransformer (line 15) | class MultimodalFusionTransformer(AbstractMultimodalFusionModel): method __init__ (line 22) | def __init__( method label_key (line 183) | def label_key(self): method forward (line 186) | def forward( FILE: multimodal/src/autogluon/multimodal/models/hf_text.py class HFAutoModelForTextPrediction (line 36) | class HFAutoModelForTextPrediction(nn.Module): method __init__ (line 42) | def __init__( method text_token_ids_key (line 155) | def text_token_ids_key(self): method text_segment_ids_key (line 159) | def text_segment_ids_key(self): method text_valid_length_key (line 163) | def text_valid_length_key(self): method input_keys (line 167) | def input_keys(self): method label_key (line 171) | def label_key(self): method text_column_prefix (line 175) | def text_column_prefix(self): method text_feature_dim (line 179) | def text_feature_dim(self): method forward (line 182) | def forward( method get_output_dict (line 275) | def get_output_dict( method get_layer_ids (line 290) | def get_layer_ids(self): method save (line 332) | def save(self, save_path: str = "./"): FILE: multimodal/src/autogluon/multimodal/models/meta_transformer.py class MetaTransformer (line 37) | class MetaTransformer(nn.Module): method __init__ (line 38) | def __init__( method text_token_ids_key (line 176) | def text_token_ids_key(self): method text_valid_length_key (line 180) | def text_valid_length_key(self): method text_segment_ids_key (line 184) | def text_segment_ids_key(self): method image_key (line 188) | def image_key(self): method image_valid_num_key (line 192) | def image_valid_num_key(self): method categorical_key (line 196) | def categorical_key(self): method numerical_key (line 200) | def numerical_key(self): method label_key (line 204) | def label_key(self): method forward (line 207) | def forward( method get_layer_ids (line 296) | def get_layer_ids(self): FILE: multimodal/src/autogluon/multimodal/models/mlp.py class GhostBatchNorm (line 14) | class GhostBatchNorm(nn.Module): method __init__ (line 24) | def __init__( method forward (line 36) | def forward(self, x): class Unit (line 43) | class Unit(nn.Module): method __init__ (line 48) | def __init__( method forward (line 83) | def forward(self, x): class MLP (line 92) | class MLP(nn.Module): method __init__ (line 98) | def __init__( method forward (line 147) | def forward(self, x): FILE: multimodal/src/autogluon/multimodal/models/mmdet_image.py class MMDetAutoModelForObjectDetection (line 30) | class MMDetAutoModelForObjectDetection(nn.Module): method __init__ (line 36) | def __init__( method _reset_classes (line 95) | def _reset_classes(self, classes: list): method _update_classes (line 102) | def _update_classes(self, classes: Optional[list] = None): method _load_checkpoint (line 117) | def _load_checkpoint(self, checkpoint_file): method set_data_preprocessor_device (line 153) | def set_data_preprocessor_device(self): method save (line 159) | def save(self, save_path: str = "./", tokenizers: Optional[dict] = None): method _save_weights (line 168) | def _save_weights(self, save_path=None): method _save_configs (line 174) | def _save_configs(self, save_path=None): method _get_checkpoint_and_config_file (line 180) | def _get_checkpoint_and_config_file(self, checkpoint_name: str = None,... method _load_config (line 273) | def _load_config(self): method image_key (line 285) | def image_key(self): method image_valid_num_key (line 289) | def image_valid_num_key(self): method label_key (line 293) | def label_key(self): method image_column_prefix (line 297) | def image_column_prefix(self): method image_feature_dim (line 301) | def image_feature_dim(self): method forward (line 304) | def forward( method _parse_losses (line 340) | def _parse_losses(self, losses): method _assign_backbone_layers (line 343) | def _assign_backbone_layers(self): method get_layer_ids (line 353) | def get_layer_ids( method get_layer_ids_by_backbone (line 372) | def get_layer_ids_by_backbone( method get_layer_ids_by_head (line 397) | def get_layer_ids_by_head( method get_yolox_layer_ids (line 453) | def get_yolox_layer_ids(self): method convert_megvii_yolox (line 496) | def convert_megvii_yolox(self, source_path): FILE: multimodal/src/autogluon/multimodal/models/mmocr_text_detection.py class MMOCRAutoModelForTextDetection (line 20) | class MMOCRAutoModelForTextDetection(nn.Module): method __init__ (line 26) | def __init__( method image_key (line 58) | def image_key(self): method image_valid_num_key (line 62) | def image_valid_num_key(self): method label_key (line 66) | def label_key(self): method image_column_prefix (line 70) | def image_column_prefix(self): method image_feature_dim (line 74) | def image_feature_dim(self): method forward (line 77) | def forward( method get_layer_ids (line 108) | def get_layer_ids( FILE: multimodal/src/autogluon/multimodal/models/mmocr_text_recognition.py class MMOCRAutoModelForTextRecognition (line 31) | class MMOCRAutoModelForTextRecognition(nn.Module): method __init__ (line 37) | def __init__( method image_key (line 69) | def image_key(self): method image_valid_num_key (line 73) | def image_valid_num_key(self): method label_key (line 77) | def label_key(self): method image_column_prefix (line 81) | def image_column_prefix(self): method image_feature_dim (line 85) | def image_feature_dim(self): method forward (line 88) | def forward( method get_layer_ids (line 126) | def get_layer_ids( FILE: multimodal/src/autogluon/multimodal/models/ner_text.py class HFAutoModelForNER (line 25) | class HFAutoModelForNER(HFAutoModelForTextPrediction): method __init__ (line 30) | def __init__( method input_keys (line 94) | def input_keys(self): method text_token_word_mapping_key (line 104) | def text_token_word_mapping_key(self): method text_word_offsets_key (line 108) | def text_word_offsets_key(self): method forward (line 111) | def forward( method get_output_dict (line 203) | def get_output_dict( method get_layer_ids (line 230) | def get_layer_ids(self): FILE: multimodal/src/autogluon/multimodal/models/numerical_mlp.py class NumericalMLP (line 14) | class NumericalMLP(nn.Module): method __init__ (line 19) | def __init__( method numerical_key (line 98) | def numerical_key(self): method input_keys (line 102) | def input_keys(self): method label_key (line 106) | def label_key(self): method forward (line 109) | def forward( method get_layer_ids (line 137) | def get_layer_ids( FILE: multimodal/src/autogluon/multimodal/models/sam.py function multi_class_mask_decoder_forward (line 17) | def multi_class_mask_decoder_forward( function multi_class_sam_model_forward (line 120) | def multi_class_sam_model_forward( class SAMForSemanticSegmentation (line 257) | class SAMForSemanticSegmentation(nn.Module): method __init__ (line 263) | def __init__( method _load_checkpoint (line 343) | def _load_checkpoint(self, checkpoint_name): method save (line 350) | def save(self, save_path: str = "./"): method image_key (line 355) | def image_key(self): method image_valid_num_key (line 359) | def image_valid_num_key(self): method label_key (line 363) | def label_key(self): method image_column_prefix (line 367) | def image_column_prefix(self): method image_feature_dim (line 371) | def image_feature_dim(self): method mask_label_key (line 375) | def mask_label_key(self): method class_label_key (line 379) | def class_label_key(self): method train (line 382) | def train(self, mode: bool = True): method forward (line 391) | def forward( method get_layer_ids (line 440) | def get_layer_ids(self): FILE: multimodal/src/autogluon/multimodal/models/t_few.py function warn_once (line 40) | def warn_once(logger, msg: str): class TFewModel (line 44) | class TFewModel(nn.Module): method __init__ (line 50) | def __init__( method text_token_ids_key (line 145) | def text_token_ids_key(self): method text_segment_ids_key (line 149) | def text_segment_ids_key(self): method text_valid_length_key (line 153) | def text_valid_length_key(self): method input_keys (line 157) | def input_keys(self): method label_key (line 161) | def label_key(self): method choices_key (line 165) | def choices_key(self): method text_column_prefix (line 169) | def text_column_prefix(self): method text_feature_dim (line 173) | def text_feature_dim(self): method forward (line 176) | def forward( method get_output_dict (line 300) | def get_output_dict( method get_layer_ids (line 325) | def get_layer_ids(self): FILE: multimodal/src/autogluon/multimodal/models/timm_image.py class TimmAutoModelForImagePrediction (line 27) | class TimmAutoModelForImagePrediction(nn.Module): method __init__ (line 33) | def __init__( method image_key (line 133) | def image_key(self): method image_valid_num_key (line 137) | def image_valid_num_key(self): method label_key (line 141) | def label_key(self): method input_keys (line 145) | def input_keys(self): method image_column_prefix (line 149) | def image_column_prefix(self): method image_feature_dim (line 153) | def image_feature_dim(self): method support_variable_input_size (line 156) | def support_variable_input_size(self): method forward (line 166) | def forward( method get_output_dict (line 250) | def get_output_dict( method get_layer_ids (line 270) | def get_layer_ids( method dump_config (line 306) | def dump_config( method save (line 337) | def save(self, save_path: str = "./", tokenizers: Optional[dict] = None): FILE: multimodal/src/autogluon/multimodal/models/utils.py class DummyLayer (line 72) | class DummyLayer(nn.Module): method __init__ (line 78) | def __init__(self): method forward (line 82) | def forward(self, x): function init_weights (line 86) | def init_weights(module: nn.Module): function assign_encoder_layer_ids (line 107) | def assign_encoder_layer_ids( function assign_non_encoder_layer_ids (line 158) | def assign_non_encoder_layer_ids( function split_encoder_non_encoder (line 182) | def split_encoder_non_encoder(names: List[str], post_encoder_patterns: T... function group_param_names (line 219) | def group_param_names( function reverse_layer_ids (line 302) | def reverse_layer_ids( function assign_layer_ids (line 336) | def assign_layer_ids( function get_column_features (line 406) | def get_column_features( function create_adaptation (line 485) | def create_adaptation(peft: str, layer: nn.Module, lora_r: int, lora_alp... function inject_adaptation_to_linear_layer (line 536) | def inject_adaptation_to_linear_layer( function get_model_head (line 596) | def get_model_head(model: nn.Module): function get_hf_config_and_model (line 623) | def get_hf_config_and_model( function apply_sigmoid (line 654) | def apply_sigmoid(output: Dict): function apply_multi_class_semantic_seg_postprocess (line 672) | def apply_multi_class_semantic_seg_postprocess(output: Dict): function get_model_postprocess_fn (line 723) | def get_model_postprocess_fn(problem_type: str, loss_func: _Loss): function get_mmocr_config_and_model (line 751) | def get_mmocr_config_and_model(checkpoint_name: str): function lookup_mmdet_config (line 790) | def lookup_mmdet_config(key, config): function update_mmdet_config (line 807) | def update_mmdet_config(key, value, config): function run_model (line 819) | def run_model(model: nn.Module, batch: dict, trt_model: Optional[nn.Modu... function freeze_model_layers (line 873) | def freeze_model_layers(model, frozen_layers): function get_pretrained_tokenizer (line 898) | def get_pretrained_tokenizer( function extract_value_from_config (line 943) | def extract_value_from_config( function extract_image_hparams_from_config (line 973) | def extract_image_hparams_from_config(model_name: str, config): function image_mean_std (line 1018) | def image_mean_std(norm_type: str): function get_image_size_mean_std (line 1041) | def get_image_size_mean_std( function get_text_segment_num (line 1091) | def get_text_segment_num(config, provided_segment_num: int, checkpoint_n... function get_text_token_max_len (line 1115) | def get_text_token_max_len(provided_max_len, config, tokenizer, checkpoi... function replace_missing_images_with_learnable (line 1159) | def replace_missing_images_with_learnable( function select_model (line 1174) | def select_model( function create_model (line 1268) | def create_model( function create_fusion_model (line 1576) | def create_fusion_model( function apply_peft_adaptation (line 1674) | def apply_peft_adaptation(model: nn.Module, config: DictConfig) -> nn.Mo... function modify_duplicate_model_names (line 1701) | def modify_duplicate_model_names( function list_timm_models (line 1755) | def list_timm_models(pretrained=True): function is_lazy_weight_tensor (line 1759) | def is_lazy_weight_tensor(p: torch.Tensor) -> bool: FILE: multimodal/src/autogluon/multimodal/optim/deepspeed.py class CustomDeepSpeedStrategy (line 34) | class CustomDeepSpeedStrategy(DeepSpeedStrategy): method __init__ (line 47) | def __init__( method init_deepspeed (line 296) | def init_deepspeed(self) -> None: FILE: multimodal/src/autogluon/multimodal/optim/lit_distiller.py class DistillerLitModule (line 22) | class DistillerLitModule(pl.LightningModule): method __init__ (line 29) | def __init__( method _compute_hard_label_loss (line 199) | def _compute_hard_label_loss( method _compute_soft_label_loss (line 217) | def _compute_soft_label_loss( method _compute_output_feature_loss (line 236) | def _compute_output_feature_loss( method _compute_rkd_loss (line 252) | def _compute_rkd_loss( method _compute_softmax_regression_loss (line 269) | def _compute_softmax_regression_loss( method _compute_loss (line 292) | def _compute_loss( method _compute_metric_score (line 335) | def _compute_metric_score( method _shared_step (line 357) | def _shared_step( method training_step (line 373) | def training_step(self, batch, batch_idx): method validation_step (line 396) | def validation_step(self, batch, batch_idx): method configure_optimizers (line 428) | def configure_optimizers(self): method on_before_optimizer_step (line 532) | def on_before_optimizer_step(self, optimizer): FILE: multimodal/src/autogluon/multimodal/optim/lit_matcher.py class MatcherLitModule (line 24) | class MatcherLitModule(pl.LightningModule): method __init__ (line 31) | def __init__( method _compute_loss (line 158) | def _compute_loss( method _compute_metric_score (line 196) | def _compute_metric_score( method _get_label (line 223) | def _get_label(self, batch: Dict): method _shared_step (line 229) | def _shared_step( method training_step (line 252) | def training_step(self, batch, batch_idx): method validation_step (line 275) | def validation_step(self, batch, batch_idx): method predict_step (line 312) | def predict_step(self, batch, batch_idx, dataloader_idx=0): method configure_optimizers (line 353) | def configure_optimizers(self): method on_before_optimizer_step (line 433) | def on_before_optimizer_step(self, optimizer): FILE: multimodal/src/autogluon/multimodal/optim/lit_mmdet.py class MMDetLitModule (line 23) | class MMDetLitModule(pl.LightningModule): method __init__ (line 24) | def __init__( method _base_step (line 59) | def _base_step(self, batch, mode): method _predict_step (line 64) | def _predict_step(self, batch): method _loss_step (line 67) | def _loss_step(self, batch): method _get_map_input (line 70) | def _get_map_input(self, pred_results): method evaluate (line 100) | def evaluate(self, sample, stage=None): method sum_and_log_step_results (line 114) | def sum_and_log_step_results(self, losses, logging=True): method training_step (line 134) | def training_step(self, batch, batch_idx): method validation_step (line 140) | def validation_step(self, batch, batch_idx, dataloader_idx=0): method on_validation_epoch_end (line 148) | def on_validation_epoch_end(self): method test_step (line 160) | def test_step(self, batch, batch_idx, dataloader_idx=0): method predict_step (line 163) | def predict_step(self, batch, batch_idx, dataloader_idx=0): method configure_optimizers (line 168) | def configure_optimizers(self): method on_before_optimizer_step (line 254) | def on_before_optimizer_step(self, optimizer): FILE: multimodal/src/autogluon/multimodal/optim/lit_module.py class LitModule (line 37) | class LitModule(pl.LightningModule): method __init__ (line 44) | def __init__( method _compute_template_loss (line 181) | def _compute_template_loss( method _compute_cross_modal_align_loss (line 214) | def _compute_cross_modal_align_loss(self, multimodal_features): method _compute_loss (line 234) | def _compute_loss( method _compute_metric_score (line 274) | def _compute_metric_score( method _shared_step (line 297) | def _shared_step( method training_step (line 309) | def training_step(self, batch, batch_idx): method on_validation_start (line 353) | def on_validation_start(self) -> None: method validation_step (line 365) | def validation_step(self, batch, batch_idx): method predict_step (line 399) | def predict_step(self, batch, batch_idx, dataloader_idx=0): method configure_optimizers (line 425) | def configure_optimizers(self): method on_before_optimizer_step (line 537) | def on_before_optimizer_step(self, optimizer): FILE: multimodal/src/autogluon/multimodal/optim/lit_ner.py class NerLitModule (line 16) | class NerLitModule(LitModule): method __init__ (line 23) | def __init__( method _compute_loss (line 139) | def _compute_loss( method validation_step (line 160) | def validation_step(self, batch, batch_idx): FILE: multimodal/src/autogluon/multimodal/optim/lit_semantic_seg.py class SemanticSegmentationLitModule (line 16) | class SemanticSegmentationLitModule(LitModule): method _compute_loss (line 23) | def _compute_loss(self, output: Dict, label: torch.Tensor, **kwargs): method _compute_metric_score (line 51) | def _compute_metric_score( method _shared_step (line 64) | def _shared_step( method validation_step (line 86) | def validation_step(self, batch, batch_idx, **kwargs): FILE: multimodal/src/autogluon/multimodal/optim/losses/bce_loss.py class BBCEWithLogitLoss (line 5) | class BBCEWithLogitLoss(nn.Module): method __init__ (line 10) | def __init__(self): method forward (line 13) | def forward(self, input: torch.Tensor, target: torch.Tensor): FILE: multimodal/src/autogluon/multimodal/optim/losses/focal_loss.py class FocalLoss (line 8) | class FocalLoss(nn.Module): method __init__ (line 16) | def __init__( method _parse_alpha (line 46) | def _parse_alpha(self, alpha) -> Optional[torch.Tensor]: method forward (line 64) | def forward(self, input: torch.Tensor, target: torch.Tensor): FILE: multimodal/src/autogluon/multimodal/optim/losses/lemda_loss.py class LemdaLoss (line 8) | class LemdaLoss(nn.Module): method __init__ (line 9) | def __init__(self, mse_weight, kld_weight, consist_weight, consist_thr... method consist_loss (line 18) | def consist_loss(self, p_logits, q_logits): method forward (line 29) | def forward(self, pre_aug, post_aug, vae_mean, vae_var, ori_logits, au... FILE: multimodal/src/autogluon/multimodal/optim/losses/rkd_loss.py class RKDLoss (line 8) | class RKDLoss(nn.Module): method __init__ (line 16) | def __init__(self, distance_loss_weight: Optional[float] = 25.0, angle... method forward (line 31) | def forward(self, feature_student: Optional[torch.Tensor], feature_tea... method pdist (line 76) | def pdist(embeddings: Optional[torch.Tensor], squared: Optional[bool] ... FILE: multimodal/src/autogluon/multimodal/optim/losses/softmax_losses.py class SoftTargetCrossEntropy (line 19) | class SoftTargetCrossEntropy(nn.Module): method __init__ (line 27) | def __init__(self): method forward (line 30) | def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.... class MultiNegativesSoftmaxLoss (line 35) | class MultiNegativesSoftmaxLoss(nn.Module): method __init__ (line 45) | def __init__( method forward (line 74) | def forward(self, features_a, features_b, logit_scale, rank=0, world_s... method gather_features (line 107) | def gather_features( FILE: multimodal/src/autogluon/multimodal/optim/losses/structure_loss.py class StructureLoss (line 6) | class StructureLoss(nn.Module): method forward (line 15) | def forward(self, input: torch.Tensor, target: torch.Tensor): FILE: multimodal/src/autogluon/multimodal/optim/losses/utils.py function get_loss_func (line 32) | def get_loss_func( function get_metric_learning_distance_func (line 105) | def get_metric_learning_distance_func( function infer_matcher_loss (line 126) | def infer_matcher_loss(data_format: str, problem_type: str): function get_matcher_loss_func (line 159) | def get_matcher_loss_func( function get_matcher_miner_func (line 212) | def get_matcher_miner_func( function generate_metric_learning_labels (line 247) | def generate_metric_learning_labels( function get_aug_loss_func (line 288) | def get_aug_loss_func(config: Optional[DictConfig] = None, problem_type:... FILE: multimodal/src/autogluon/multimodal/optim/lr/lr_schedulers.py function _cosine_decay_lr_lambda (line 12) | def _cosine_decay_lr_lambda(current_step, num_warmup_steps, num_training... function get_cosine_schedule_with_warmup (line 19) | def get_cosine_schedule_with_warmup( function _poly_decay_lr_lambda (line 50) | def _poly_decay_lr_lambda( function get_polynomial_decay_schedule_with_warmup (line 65) | def get_polynomial_decay_schedule_with_warmup( function _linear_warmup_lr_lambda (line 112) | def _linear_warmup_lr_lambda(current_step: int, num_warmup_steps: int, n... function get_linear_schedule_with_warmup (line 118) | def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_tra... FILE: multimodal/src/autogluon/multimodal/optim/lr/utils.py function get_lr_scheduler (line 18) | def get_lr_scheduler( function apply_single_lr (line 76) | def apply_single_lr( function apply_two_stages_lr (line 158) | def apply_two_stages_lr( function apply_layerwise_lr_decay (line 246) | def apply_layerwise_lr_decay( FILE: multimodal/src/autogluon/multimodal/optim/metrics/coverage_metrics.py class Coverage (line 6) | class Coverage(Metric): method __init__ (line 7) | def __init__(self, **kwargs): method update (line 15) | def update(self, preds: torch.Tensor, target: torch.Tensor) -> None: method compute (line 21) | def compute(self): FILE: multimodal/src/autogluon/multimodal/optim/metrics/hit_rate_metrics.py class CustomHitRate (line 7) | class CustomHitRate(torchmetrics.Metric): method __init__ (line 13) | def __init__( method update (line 21) | def update( method compute (line 32) | def compute(self): method compute_hit_rate (line 43) | def compute_hit_rate(features_a, features_b, logit_scale, top_ks=[1, 5... FILE: multimodal/src/autogluon/multimodal/optim/metrics/ranking_metrics.py class RankingMetrics (line 13) | class RankingMetrics: method __init__ (line 14) | def __init__( method compute (line 68) | def compute(self, metrics: Union[str, list] = None, k: Optional[int] =... method _compute_one (line 103) | def _compute_one(self, idx, k): function compute_ranking_score (line 178) | def compute_ranking_score( FILE: multimodal/src/autogluon/multimodal/optim/metrics/semantic_seg_metrics.py function _prepare_data (line 13) | def _prepare_data(pred: np.ndarray, gt: np.ndarray) -> tuple: function _get_adaptive_threshold (line 30) | def _get_adaptive_threshold(matrix: np.ndarray, max_value: float = 1) ->... class Fmeasure (line 40) | class Fmeasure(object): method __init__ (line 41) | def __init__(self, beta: float = 1.0): method step (line 61) | def step(self, pred: np.ndarray, gt: np.ndarray): method cal_adaptive_fm (line 72) | def cal_adaptive_fm(self, pred: np.ndarray, gt: np.ndarray) -> float: method cal_pr (line 89) | def cal_pr(self, pred: np.ndarray, gt: np.ndarray) -> tuple: method get_results (line 120) | def get_results(self) -> dict: class MAE_SOD (line 132) | class MAE_SOD(object): method __init__ (line 133) | def __init__(self): method step (line 147) | def step(self, pred: np.ndarray, gt: np.ndarray): method cal_mae (line 154) | def cal_mae(self, pred: np.ndarray, gt: np.ndarray) -> np.ndarray: method get_results (line 162) | def get_results(self) -> dict: class Smeasure (line 171) | class Smeasure(object): method __init__ (line 172) | def __init__(self, alpha: float = 0.5): method step (line 188) | def step(self, pred: np.ndarray, gt: np.ndarray): method cal_sm (line 194) | def cal_sm(self, pred: np.ndarray, gt: np.ndarray) -> float: method object (line 209) | def object(self, pred: np.ndarray, gt: np.ndarray) -> float: method s_object (line 219) | def s_object(self, pred: np.ndarray, gt: np.ndarray) -> float: method region (line 225) | def region(self, pred: np.ndarray, gt: np.ndarray) -> float: method centroid (line 243) | def centroid(self, matrix: np.ndarray) -> tuple: method divide_with_xy (line 261) | def divide_with_xy(self, pred: np.ndarray, gt: np.ndarray, x: int, y: ... method ssim (line 289) | def ssim(self, pred: np.ndarray, gt: np.ndarray) -> float: method get_results (line 314) | def get_results(self) -> dict: class Emeasure (line 323) | class Emeasure(object): method __init__ (line 324) | def __init__(self): method step (line 340) | def step(self, pred: np.ndarray, gt: np.ndarray): method cal_adaptive_em (line 351) | def cal_adaptive_em(self, pred: np.ndarray, gt: np.ndarray) -> float: method cal_changeable_em (line 360) | def cal_changeable_em(self, pred: np.ndarray, gt: np.ndarray) -> np.nd... method cal_em_with_threshold (line 369) | def cal_em_with_threshold(self, pred: np.ndarray, gt: np.ndarray, thre... method cal_em_with_cumsumhistogram (line 407) | def cal_em_with_cumsumhistogram(self, pred: np.ndarray, gt: np.ndarray... method generate_parts_numel_combinations (line 448) | def generate_parts_numel_combinations(self, fg_fg_numel, fg_bg_numel, ... method get_results (line 470) | def get_results(self) -> dict: class WeightedFmeasure (line 480) | class WeightedFmeasure(object): method __init__ (line 481) | def __init__(self, beta: float = 0.3): method step (line 497) | def step(self, pred: np.ndarray, gt: np.ndarray): method cal_wfm (line 506) | def cal_wfm(self, pred: np.ndarray, gt: np.ndarray) -> float: method matlab_style_gauss2D (line 552) | def matlab_style_gauss2D(self, shape: tuple = (7, 7), sigma: int = 5) ... method get_results (line 566) | def get_results(self) -> dict: class Multiclass_IoU (line 575) | class Multiclass_IoU(torchmetrics.Metric): method __init__ (line 581) | def __init__(self, num_classes): method update (line 587) | def update(self, logits, labels): method compute (line 592) | def compute(self): method batch_intersection_union (line 596) | def batch_intersection_union(self, output, target): class Binary_IoU (line 614) | class Binary_IoU(torchmetrics.Metric): method __init__ (line 620) | def __init__( method update (line 627) | def update(self, logits, labels): method compute (line 631) | def compute(self): class Balanced_Error_Rate (line 642) | class Balanced_Error_Rate(torchmetrics.Metric): method __init__ (line 647) | def __init__( method update (line 654) | def update(self, logits, labels): method compute (line 658) | def compute(self): class COD (line 670) | class COD(torchmetrics.Metric): method __init__ (line 671) | def __init__(self): method update (line 676) | def update(self, logits, labels): method compute (line 680) | def compute(self): class SM (line 684) | class SM(COD): method compute (line 685) | def compute(self): class FM (line 700) | class FM(COD): method compute (line 701) | def compute(self): class EM (line 716) | class EM(COD): method compute (line 717) | def compute(self): class MAE (line 733) | class MAE(COD): method compute (line 734) | def compute(self): class Multiclass_IoU_Pred (line 753) | class Multiclass_IoU_Pred: method __init__ (line 759) | def __init__(self, num_classes): method update (line 765) | def update(self, logits, labels): method compute (line 770) | def compute(self): method batch_intersection_union (line 774) | def batch_intersection_union(self, output, target): class Binary_IoU_Pred (line 792) | class Binary_IoU_Pred: method __init__ (line 798) | def __init__( method update (line 805) | def update(self, logits, labels): method compute (line 809) | def compute(self): class Balanced_Error_Rate_Pred (line 820) | class Balanced_Error_Rate_Pred: method __init__ (line 825) | def __init__( method update (line 832) | def update(self, logits, labels): method compute (line 836) | def compute(self): class COD_Pred (line 848) | class COD_Pred: method __init__ (line 849) | def __init__(self): method update (line 854) | def update(self, logits, labels): method compute (line 858) | def compute(self): method reset (line 861) | def reset(self): class SM_Pred (line 866) | class SM_Pred(COD_Pred): method compute (line 867) | def compute(self): class FM_Pred (line 883) | class FM_Pred(COD_Pred): method compute (line 884) | def compute(self): class EM_Pred (line 899) | class EM_Pred(COD_Pred): method compute (line 900) | def compute(self): class MAE_Pred (line 916) | class MAE_Pred(COD_Pred): method compute (line 917) | def compute(self): FILE: multimodal/src/autogluon/multimodal/optim/metrics/utils.py function compute_score (line 63) | def compute_score( function infer_metrics (line 119) | def infer_metrics( function get_minmax_mode (line 218) | def get_minmax_mode( function get_stopping_threshold (line 247) | def get_stopping_threshold(metric_name: str): function get_torchmetric (line 269) | def get_torchmetric( FILE: multimodal/src/autogluon/multimodal/optim/utils.py function get_optimizer (line 30) | def get_optimizer( function get_weight_decay_param_names (line 99) | def get_weight_decay_param_names(model: nn.Module): function get_norm_layer_param_names (line 130) | def get_norm_layer_param_names(model: nn.Module): function get_peft_param_names (line 153) | def get_peft_param_names(norm_param_names: List[str], peft: Optional[str... function remove_parameters_without_grad (line 199) | def remove_parameters_without_grad( function gather_column_features (line 224) | def gather_column_features( FILE: multimodal/src/autogluon/multimodal/predictor.py class MultiModalPredictor (line 36) | class MultiModalPredictor: method __init__ (line 50) | def __init__( method path (line 247) | def path(self): method label (line 254) | def label(self): method query (line 261) | def query(self): method response (line 268) | def response(self): method match_label (line 275) | def match_label(self): method problem_type (line 282) | def problem_type(self): method problem_property (line 289) | def problem_property(self): method column_types (line 296) | def column_types(self): method eval_metric (line 303) | def eval_metric(self): method validation_metric (line 310) | def validation_metric(self): method verbosity (line 318) | def verbosity(self): method total_parameters (line 326) | def total_parameters(self) -> int: method trainable_parameters (line 333) | def trainable_parameters(self) -> int: method model_size (line 340) | def model_size(self) -> float: method classes (line 347) | def classes(self): method class_labels (line 354) | def class_labels(self): method positive_class (line 369) | def positive_class(self): method set_verbosity (line 387) | def set_verbosity(self, verbosity: int): method set_num_gpus (line 406) | def set_num_gpus(self, num_gpus): method get_num_gpus (line 412) | def get_num_gpus(self): method fit (line 418) | def fit( method evaluate (line 563) | def evaluate( method predict (line 638) | def predict( method predict_proba (line 688) | def predict_proba( method extract_embedding (line 737) | def extract_embedding( method save (line 788) | def save(self, path: str, standalone: Optional[bool] = True): method load (line 805) | def load( method dump_model (line 865) | def dump_model(self, save_path: Optional[str] = None): method export_onnx (line 880) | def export_onnx( method optimize_for_inference (line 931) | def optimize_for_inference( method fit_summary (line 958) | def fit_summary(self, verbosity=0, show_plot=False): method list_supported_models (line 978) | def list_supported_models(self, pretrained=True): FILE: multimodal/src/autogluon/multimodal/utils/cache.py class DDPPredictionWriter (line 29) | class DDPPredictionWriter(BasePredictionWriter): method __init__ (line 30) | def __init__( method get_predictions_cache_dir (line 67) | def get_predictions_cache_dir(self, global_rank: int): method get_batch_indices_cache_dir (line 76) | def get_batch_indices_cache_dir(self, global_rank: int): method write_on_epoch_end (line 85) | def write_on_epoch_end( method read_single_gpu_results (line 111) | def read_single_gpu_results(self, global_rank: Optional[int]): method flatten (line 128) | def flatten(self, x): method collate (line 142) | def collate(self, x: List[Dict]): method sort (line 184) | def sort(self, x: Dict, indices: List): method collect_all_gpu_results (line 212) | def collect_all_gpu_results(self, num_gpus): FILE: multimodal/src/autogluon/multimodal/utils/checkpoint.py function average_checkpoints (line 32) | def average_checkpoints( function pl_load (line 84) | def pl_load( function pl_save (line 107) | def pl_save(checkpoint: Dict[str, Any], filepath: Union[str, Path]) -> N... class AutoMMModelCheckpointIO (line 123) | class AutoMMModelCheckpointIO(pl.plugins.CheckpointIO): method __init__ (line 130) | def __init__(self, trainable_param_names, model_name_to_id): method save_checkpoint (line 143) | def save_checkpoint(self, checkpoint: Dict[str, Any], path, storage_op... method load_checkpoint (line 191) | def load_checkpoint(self, path, map_location: Optional[Any] = None) ->... method remove_checkpoint (line 209) | def remove_checkpoint(self, path) -> None: class AutoMMModelCheckpoint (line 224) | class AutoMMModelCheckpoint(pl.callbacks.ModelCheckpoint): method _save_checkpoint (line 236) | def _save_checkpoint(self, trainer, filepath): method _update_best_and_save (line 246) | def _update_best_and_save( FILE: multimodal/src/autogluon/multimodal/utils/colormap.py function colormap (line 99) | def colormap(rgb=False, maximum=255): function random_color (line 117) | def random_color(rgb=False, maximum=255): function random_colors (line 135) | def random_colors(N, rgb=False, maximum=255): FILE: multimodal/src/autogluon/multimodal/utils/config.py function filter_search_space (line 17) | def filter_search_space(hyperparameters: Dict, keys_to_filter: Union[str... function get_default_config (line 53) | def get_default_config(config: Optional[Union[Dict, DictConfig]] = None,... function get_config (line 103) | def get_config( function verify_model_names (line 203) | def verify_model_names(config: DictConfig): function get_name_prefix (line 233) | def get_name_prefix( function customize_model_names (line 263) | def customize_model_names( function save_pretrained_model_configs (line 334) | def save_pretrained_model_configs( function get_local_pretrained_config_paths (line 372) | def get_local_pretrained_config_paths(config: DictConfig, path: str) -> ... function parse_dotlist_conf (line 396) | def parse_dotlist_conf(conf): function apply_omegaconf_overrides (line 441) | def apply_omegaconf_overrides( function replace_none_str (line 490) | def replace_none_str(config: Union[DictConfig, ListConfig, dict, list]): function update_config_by_rules (line 513) | def update_config_by_rules( function update_tabular_config_by_resources (line 541) | def update_tabular_config_by_resources( function get_pretrain_configs_dir (line 601) | def get_pretrain_configs_dir(subfolder: Optional[str] = None): function filter_timm_pretrained_cfg (line 610) | def filter_timm_pretrained_cfg(cfg, remove_source=False, remove_null=True): function update_hyperparameters (line 622) | def update_hyperparameters( function filter_hyperparameters (line 669) | def filter_hyperparameters( function split_hyperparameters (line 757) | def split_hyperparameters(hyperparameters: Dict): function update_ensemble_hyperparameters (line 790) | def update_ensemble_hyperparameters( function make_overrides_backward_compatible (line 818) | def make_overrides_backward_compatible(overrides: Dict): FILE: multimodal/src/autogluon/multimodal/utils/device.py function compute_num_gpus (line 17) | def compute_num_gpus(config_num_gpus: Union[int, float, List], accelerat... function move_to_device (line 54) | def move_to_device(obj: Union[torch.Tensor, nn.Module, Dict, List, Tuple... function get_available_devices (line 94) | def get_available_devices(num_gpus: int, auto_select_gpus: bool): FILE: multimodal/src/autogluon/multimodal/utils/distillation.py class DistillationMixin (line 15) | class DistillationMixin: method setup_distillation (line 16) | def setup_distillation( FILE: multimodal/src/autogluon/multimodal/utils/download.py function is_url (line 31) | def is_url(url_like: str): function download (line 49) | def download( FILE: multimodal/src/autogluon/multimodal/utils/env.py function is_interactive_env (line 12) | def is_interactive_env(): function get_filesystem (line 20) | def get_filesystem(path: Union[str, Path], **kwargs: Any) -> AbstractFil... FILE: multimodal/src/autogluon/multimodal/utils/export.py class ExportMixin (line 22) | class ExportMixin: method dump_model (line 23) | def dump_model(self, save_path: Optional[str] = None): method export_onnx (line 71) | def export_onnx( method optimize_for_inference (line 159) | def optimize_for_inference( method get_processed_batch_for_deployment (line 217) | def get_processed_batch_for_deployment( FILE: multimodal/src/autogluon/multimodal/utils/hpo.py function get_ray_tune_ckpt_callback (line 17) | def get_ray_tune_ckpt_callback(): function hpo_trial (line 32) | def hpo_trial(sampled_hyperparameters, learner, checkpoint_dir=None, **_... function build_final_learner (line 65) | def build_final_learner( function hyperparameter_tune (line 143) | def hyperparameter_tune(hyperparameter_tune_kwargs, resources, is_matchi... FILE: multimodal/src/autogluon/multimodal/utils/inference.py function compute_inference_batch_size (line 38) | def compute_inference_batch_size( function extract_from_output (line 72) | def extract_from_output(outputs: List[Dict], ret_type: str, as_ndarray: ... class RealtimeMixin (line 156) | class RealtimeMixin: method predict_batch (line 157) | def predict_batch( method predict_matcher_batch (line 202) | def predict_matcher_batch( method use_realtime (line 270) | def use_realtime( method process_batch (line 312) | def process_batch( FILE: multimodal/src/autogluon/multimodal/utils/install.py function check_if_packages_installed (line 10) | def check_if_packages_installed(problem_type: str = None, package_names:... function _get_mmlab_installation_guide (line 79) | def _get_mmlab_installation_guide(package_name): FILE: multimodal/src/autogluon/multimodal/utils/label_studio.py class TaskType (line 8) | class TaskType(Enum): function read_from_labelstudio_csv (line 56) | def read_from_labelstudio_csv(path, data_columns, label_columns): function read_from_labelstudio_json (line 84) | def read_from_labelstudio_json(path, data_columns, label_columns): function get_dataframes_by_path (line 151) | def get_dataframes_by_path(path, data_columns, label_columns): class LabelStudioReader (line 174) | class LabelStudioReader: method __init__ (line 180) | def __init__(self, host=None): method set_labelstudio_host (line 188) | def set_labelstudio_host(self, host): method get_columns_by_type (line 193) | def get_columns_by_type(self, type, format): method process_data_str (line 209) | def process_data_str(self, s: str, ls_online): method from_image_classification (line 242) | def from_image_classification(self, path, ls_host_on, data_columns=Non... method from_named_entity_recognition (line 284) | def from_named_entity_recognition(self, path, data_columns=None, label... method from_customize (line 317) | def from_customize(self, path, ls_host_on, data_columns, label_columns): FILE: multimodal/src/autogluon/multimodal/utils/load.py function get_dir_ckpt_paths (line 11) | def get_dir_ckpt_paths(path: str): function get_load_ckpt_paths (line 35) | def get_load_ckpt_paths(ckpt_path: str, dir_path: str, resume: bool): class CustomUnpickler (line 97) | class CustomUnpickler(pickle.Unpickler): method find_class (line 104) | def find_class(self, module, name): function protected_zip_extraction (line 112) | def protected_zip_extraction(zipfile_path, sha1_hash, folder): FILE: multimodal/src/autogluon/multimodal/utils/log.py class LogFilter (line 14) | class LogFilter(logging.Filter): method __init__ (line 19) | def __init__(self, blacklist: Union[str, List[str]]): method filter (line 31) | def filter(self, record): function add_log_filter (line 49) | def add_log_filter(target_logger, log_filter): function remove_log_filter (line 64) | def remove_log_filter(target_logger, log_filter): function apply_log_filter (line 80) | def apply_log_filter(log_filter): function on_fit_start_message (line 103) | def on_fit_start_message(path: Optional[str] = None): function on_fit_per_run_start_message (line 112) | def on_fit_per_run_start_message(save_path, validation_metric_name): function on_fit_end_message (line 125) | def on_fit_end_message(save_path): function get_gpu_message (line 142) | def get_gpu_message(detected_num_gpus: int, used_num_gpus: int, strategy... FILE: multimodal/src/autogluon/multimodal/utils/matcher.py function get_fusion_model_dict (line 20) | def get_fusion_model_dict( function create_fusion_model_dict (line 65) | def create_fusion_model_dict( function make_siamese (line 116) | def make_siamese( function is_share_fusion (line 207) | def is_share_fusion( function create_siamese_model (line 230) | def create_siamese_model( function compute_semantic_similarity (line 294) | def compute_semantic_similarity(a: torch.Tensor, b: torch.Tensor, simila... function semantic_search (line 336) | def semantic_search( function convert_data_for_ranking (line 467) | def convert_data_for_ranking( function compute_matching_probability (line 508) | def compute_matching_probability( FILE: multimodal/src/autogluon/multimodal/utils/misc.py function logits_to_prob (line 15) | def logits_to_prob(logits: np.ndarray): function tensor_to_ndarray (line 36) | def tensor_to_ndarray(tensor: torch.Tensor): function path_expander (line 52) | def path_expander(path, base_folder): function _read_byte (line 57) | def _read_byte(file): function path_to_bytearray_expander (line 64) | def path_to_bytearray_expander(path, base_folder): function _read_base64str (line 69) | def _read_base64str(file): function path_to_base64str_expander (line 77) | def path_to_base64str_expander(path, base_folder): function shopee_dataset (line 82) | def shopee_dataset( function merge_spans (line 117) | def merge_spans(sent, pred, for_visualizer=False): function merge_bio_format (line 160) | def merge_bio_format(data, preds): FILE: multimodal/src/autogluon/multimodal/utils/mmcv.py function assert_tensor_type (line 12) | def assert_tensor_type(func: Callable) -> Callable: class CollateMMDet (line 24) | class CollateMMDet: method __init__ (line 25) | def __init__(self, samples_per_gpu): method __call__ (line 28) | def __call__(self, x): class CollateMMOcr (line 32) | class CollateMMOcr: method __init__ (line 33) | def __init__(self, samples_per_gpu): method __call__ (line 36) | def __call__(self, x): FILE: multimodal/src/autogluon/multimodal/utils/object_detection.py function _get_image_info (line 37) | def _get_image_info(image_path: str): function get_df_unique_classes (line 60) | def get_df_unique_classes(data: pd.DataFrame): function object_detection_df_to_coco (line 101) | def object_detection_df_to_coco(data: pd.DataFrame, save_path: Optional[... function object_detection_data_to_df (line 168) | def object_detection_data_to_df( function sanity_check_dataframe (line 201) | def sanity_check_dataframe(data: pd.DataFrame): function from_str (line 230) | def from_str(data: str) -> pd.DataFrame: function from_list (line 250) | def from_list(data: List[str]) -> pd.DataFrame: function from_dict (line 271) | def from_dict(data: dict) -> pd.DataFrame: function from_voc (line 294) | def from_voc( function import_try_install (line 395) | def import_try_install(package: str, extern_url: Optional[str] = None): function try_import_pycocotools (line 453) | def try_import_pycocotools(): function bbox_xywh_to_xyxy (line 476) | def bbox_xywh_to_xyxy(xywh: Optional[Union[list, tuple, np.ndarray]]): function bbox_ratio_xywh_to_index_xyxy (line 507) | def bbox_ratio_xywh_to_index_xyxy( function bbox_xyxy_to_xywh (line 584) | def bbox_xyxy_to_xywh(xyxy: Optional[Union[list, tuple, np.ndarray]]): function bbox_clip_xyxy (line 615) | def bbox_clip_xyxy( function _check_load_coco_bbox (line 661) | def _check_load_coco_bbox( function from_coco (line 723) | def from_coco( function get_image_filename (line 818) | def get_image_filename(path: str): class COCODataset (line 834) | class COCODataset: method __init__ (line 837) | def __init__(self, anno_file: str, category_ids: Optional[List] = None): method get_image_id_from_path (line 864) | def get_image_id_from_path(self, image_path: str): method save_result (line 879) | def save_result(self, ret: List, data: pd.DataFrame, save_path: str): function cocoeval_torchmetrics (line 914) | def cocoeval_torchmetrics(outputs: List): function cocoeval_pycocotools (line 953) | def cocoeval_pycocotools( function parse_detection_result (line 1007) | def parse_detection_result( function cocoeval (line 1031) | def cocoeval( function dump_voc_classes (line 1079) | def dump_voc_classes(voc_annotation_path: str, voc_class_names_output_pa... function dump_voc_xml_files (line 1112) | def dump_voc_xml_files(voc_annotation_path: str, voc_annotation_xml_outp... function process_voc_annotations (line 1142) | def process_voc_annotations( function from_coco_or_voc (line 1183) | def from_coco_or_voc(file_path: str, splits: Optional[Union[str]] = None... function get_coco_format_classes (line 1207) | def get_coco_format_classes(sample_data_path: str): function get_voc_format_classes (line 1238) | def get_voc_format_classes(root: str): function get_detection_classes (line 1282) | def get_detection_classes(sample_data_path): function visualize_detection (line 1318) | def visualize_detection( function plot_detections (line 1373) | def plot_detections( function add_bbox_with_alpha (line 1462) | def add_bbox_with_alpha(im: np.ndarray, tl: tuple, br: tuple, line_color... function add_text_with_bg_color (line 1494) | def add_text_with_bg_color( function get_color (line 1555) | def get_color(idx): function convert_result_df (line 1561) | def convert_result_df( function save_result_coco_format (line 1608) | def save_result_coco_format(data_path, pred, category_ids, result_path, ... function save_result_voc_format (line 1616) | def save_result_voc_format(pred, result_path): function convert_pred_to_xywh (line 1623) | def convert_pred_to_xywh(pred: Optional[List]) -> Optional[List]: FILE: multimodal/src/autogluon/multimodal/utils/onnx.py function onnx_get_dynamic_axes (line 19) | def onnx_get_dynamic_axes(input_keys: List[str]): function get_provider_name (line 30) | def get_provider_name(provider_config: Union[str, tuple]) -> str: class OnnxModule (line 39) | class OnnxModule(object): method __init__ (line 47) | def __init__(self, onnx_path: Union[str, bytes], providers: Optional[U... method __call__ (line 117) | def __call__(self, *args): method to (line 139) | def to(self, *args): FILE: multimodal/src/autogluon/multimodal/utils/precision.py function convert_to_torch_precision (line 11) | def convert_to_torch_precision(precision: Union[int, str]): function infer_precision (line 48) | def infer_precision( function double_precision_context (line 95) | def double_precision_context(): function get_precision_context (line 105) | def get_precision_context(precision: Union[int, str], device_type: Optio... FILE: multimodal/src/autogluon/multimodal/utils/presets.py function get_default_hpo_setup (line 24) | def get_default_hpo_setup(): function parse_presets_str (line 44) | def parse_presets_str(presets: str): function default (line 54) | def default(presets: str = DEFAULT): function few_shot_classification (line 201) | def few_shot_classification(presets: str = DEFAULT): function zero_shot_image_classification (line 234) | def zero_shot_image_classification(presets: str = DEFAULT): function object_detection (line 284) | def object_detection(presets: str = DEFAULT): function semantic_segmentation (line 368) | def semantic_segmentation(presets: str = DEFAULT): function ocr_text_detection (line 428) | def ocr_text_detection(presets: str = DEFAULT): function ocr_text_recognition (line 463) | def ocr_text_recognition(presets: str = DEFAULT): function feature_extraction (line 497) | def feature_extraction(presets: str = DEFAULT): # TODO: rename the prob... function image_similarity (line 526) | def image_similarity(presets: str = DEFAULT): function text_similarity (line 579) | def text_similarity(presets: str = DEFAULT): function image_text_similarity (line 635) | def image_text_similarity(presets: str = DEFAULT): function ner (line 693) | def ner(presets: str = DEFAULT): function ensemble (line 751) | def ensemble(presets: str = DEFAULT): function list_presets (line 936) | def list_presets(verbose: bool = False): function get_basic_config (line 955) | def get_basic_config(extra: Optional[List[str]] = None): function get_presets (line 981) | def get_presets(problem_type: str, presets: str): function get_ensemble_presets (line 1023) | def get_ensemble_presets(presets): FILE: multimodal/src/autogluon/multimodal/utils/problem_types.py class ProblemTypeProperty (line 48) | class ProblemTypeProperty: method fallback_label_type (line 87) | def fallback_label_type(self): method supported_evaluation_metrics (line 94) | def supported_evaluation_metrics(self): method fallback_evaluation_metric (line 101) | def fallback_evaluation_metric(self): method supported_validation_metrics (line 110) | def supported_validation_metrics(self): method fallback_validation_metric (line 117) | def fallback_validation_metric(self): function infer_problem_type_by_eval_metric (line 285) | def infer_problem_type_by_eval_metric(eval_metric_name: str, problem_typ... FILE: multimodal/src/autogluon/multimodal/utils/registry.py class Registry (line 8) | class Registry: method __init__ (line 14) | def __init__(self, name: str) -> None: method __contains__ (line 24) | def __contains__(self, item): method _do_register (line 27) | def _do_register(self, name: str, obj: object) -> None: method register (line 33) | def register(self, *args): method get (line 65) | def get(self, name: str) -> object: method list_keys (line 71) | def list_keys(self) -> List: method __repr__ (line 74) | def __repr__(self) -> str: method create (line 78) | def create(self, name: str, *args, **kwargs) -> object: method create_with_json (line 94) | def create_with_json(self, name: str, json_str: str): FILE: multimodal/src/autogluon/multimodal/utils/save.py function process_save_path (line 15) | def process_save_path(path, resume: Optional[bool] = False, raise_if_exi... function setup_save_path (line 58) | def setup_save_path( function make_exp_dir (line 92) | def make_exp_dir( FILE: multimodal/src/autogluon/multimodal/utils/strategy.py function is_interactive_strategy (line 8) | def is_interactive_strategy(strategy: str): function run_ddp_only_once (line 15) | def run_ddp_only_once(num_gpus: int, strategy: str): FILE: multimodal/src/autogluon/multimodal/utils/visualizer.py class ColorMode (line 37) | class ColorMode(Enum): function _create_text_labels (line 59) | def _create_text_labels(classes: List[str], scores: List[float]): class VisImage (line 83) | class VisImage: method __init__ (line 84) | def __init__(self, img, scale=1.0): method _setup_figure (line 96) | def _setup_figure(self, img): method reset_image (line 123) | def reset_image(self, img): method save (line 132) | def save(self, filepath): method get_image (line 141) | def get_image(self): class ObjectDetectionVisualizer (line 163) | class ObjectDetectionVisualizer: method __init__ (line 184) | def __init__(self, img_path, scale=1.0, instance_mode=ColorMode.IMAGE): method process_predictions (line 212) | def process_predictions(predictions: pd.DataFrame, conf_threshold: flo... method draw_instance_predictions (line 252) | def draw_instance_predictions(self, predictions: pd.DataFrame, conf_th... method overlay_instances (line 286) | def overlay_instances( method draw_text (line 370) | def draw_text( method draw_box (line 419) | def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"): method _create_grayscale_image (line 459) | def _create_grayscale_image(self, mask=None): method _change_color_brightness (line 470) | def _change_color_brightness(self, color, brightness_factor): class SemanticSegmentationVisualizer (line 498) | class SemanticSegmentationVisualizer: method plot_image (line 503) | def plot_image(self, img_path: str): method plot_mask (line 513) | def plot_mask(self, pred: np.array, output_path: str = None): class NERVisualizer (line 540) | class NERVisualizer: method __init__ (line 545) | def __init__(self, pred, sent, seed): method escape_html (line 554) | def escape_html(text: str) -> str: method html_template (line 566) | def html_template(self, text, label, color): method _repr_html_ (line 584) | def _repr_html_(self): function visualize_ner (line 604) | def visualize_ner(sentence, prediction, seed=0): FILE: multimodal/tests/conftest.py function pytest_addoption (line 4) | def pytest_addoption(parser): function pytest_configure (line 10) | def pytest_configure(config): function pytest_collection_modifyitems (line 16) | def pytest_collection_modifyitems(config, items): FILE: multimodal/tests/test_check_style.py function test_check_style (line 6) | def test_check_style(): FILE: multimodal/tests/unittests/others/test_backbones.py function test_hf_text_init (line 31) | def test_hf_text_init(checkpoint_name): function test_timm_image_init (line 45) | def test_timm_image_init(checkpoint_name): function test_backbone_bart (line 51) | def test_backbone_bart(checkpoint_name, peft): function test_meta_transformer (line 73) | def test_meta_transformer(): FILE: multimodal/tests/unittests/others/test_data_collators.py function test_stack (line 6) | def test_stack(): function test_pad (line 14) | def test_pad(): function test_tuple (line 47) | def test_tuple(): function test_list (line 57) | def test_list(): function test_dict (line 65) | def test_dict(): FILE: multimodal/tests/unittests/others/test_deployment_onnx.py function evaluate (line 29) | def evaluate(predictor, df, onnx_session=None): function test_onnx_export_hf_text (line 51) | def test_onnx_export_hf_text(checkpoint_name): function test_onnx_export_timm_image (line 104) | def test_onnx_export_timm_image(checkpoint_name, num_gpus): function test_onnx_optimize_for_inference (line 204) | def test_onnx_optimize_for_inference(dataset_name, model_names, text_bac... FILE: multimodal/tests/unittests/others/test_dump_model.py function test_dump_timm_image (line 14) | def test_dump_timm_image(): function test_dump_hf_text (line 53) | def test_dump_hf_text(): function test_dump_fusion_model (line 91) | def test_dump_fusion_model(): function test_mmdet_object_detection_save_and_load (line 118) | def test_mmdet_object_detection_save_and_load(): FILE: multimodal/tests/unittests/others/test_ensemble.py function test_ensemble_from_scratch (line 9) | def test_ensemble_from_scratch(): function test_ensemble_with_pretrained_predictors (line 53) | def test_ensemble_with_pretrained_predictors(): FILE: multimodal/tests/unittests/others/test_extract_features.py function test_sentence_transformer_embedding (line 11) | def test_sentence_transformer_embedding(model_name): FILE: multimodal/tests/unittests/others/test_load.py function test_load_intermediate_ckpt (line 12) | def test_load_intermediate_ckpt(): function test_load_fttransformer_pretrained_ckpt (line 30) | def test_load_fttransformer_pretrained_ckpt(): FILE: multimodal/tests/unittests/others/test_losses.py function test_focal_loss_multiclass (line 16) | def test_focal_loss_multiclass(checkpoint_name): FILE: multimodal/tests/unittests/others/test_matcher.py function test_matcher_basic (line 51) | def test_matcher_basic( function test_matcher_realtime_inference (line 185) | def test_matcher_realtime_inference( function test_text_semantic_search (line 241) | def test_text_semantic_search(): function test_image_text_semantic_search (line 310) | def test_image_text_semantic_search(): function test_matcher_hyperparameters_consistency (line 374) | def test_matcher_hyperparameters_consistency(hyperparameters): FILE: multimodal/tests/unittests/others/test_metrics.py function test_metric_log_loss (line 29) | def test_metric_log_loss(metric_name, class_num): function test_metric_bce_with_logits_loss (line 62) | def test_metric_bce_with_logits_loss(problem_type, loss_func_name): function test_f1_metrics_for_multiclass (line 95) | def test_f1_metrics_for_multiclass(eval_metric): function test_infer_metrics (line 153) | def test_infer_metrics( function test_infer_metrics_custom (line 177) | def test_infer_metrics_custom( function test_metric_symmetric_hit_rate (line 189) | def test_metric_symmetric_hit_rate(): function test_custom_metric (line 201) | def test_custom_metric(): function test_metric_spearman_and_pearson (line 250) | def test_metric_spearman_and_pearson(eval_metric): function test_metrics_multiclass (line 264) | def test_metrics_multiclass(checkpoint_name, eval_metric): FILE: multimodal/tests/unittests/others/test_ner.py function get_data (line 29) | def get_data(): function test_ner (line 56) | def test_ner(checkpoint_name, searcher, scheduler): function test_multi_column_ner (line 86) | def test_multi_column_ner(checkpoint_name): function test_ner_standalone (line 105) | def test_ner_standalone(): function test_merge_bio (line 151) | def test_merge_bio(): function test_misc_visualize_ner (line 177) | def test_misc_visualize_ner(): function test_process_ner_annotations (line 189) | def test_process_ner_annotations(): function test_infer_ner_column_type (line 259) | def test_infer_ner_column_type(column_types, gt_column_types): FILE: multimodal/tests/unittests/others/test_object_detection.py function download_sample_images (line 14) | def download_sample_images(): function download_sample_dataset (line 23) | def download_sample_dataset(): function test_mmdet_object_detection_fit_basics (line 37) | def test_mmdet_object_detection_fit_basics(checkpoint_name): function test_mmdet_object_detection_inference_basics (line 82) | def test_mmdet_object_detection_inference_basics(checkpoint_name): function test_mmdet_object_detection_inference_xywh_output (line 136) | def test_mmdet_object_detection_inference_xywh_output(checkpoint_name): function test_mmdet_object_detection_save_and_load (line 176) | def test_mmdet_object_detection_save_and_load(checkpoint_name): function test_mmdet_object_detection_fit_eval_predict_df (line 211) | def test_mmdet_object_detection_fit_eval_predict_df(checkpoint_name): function test_mmdet_object_detection_fit_with_freeze_backbone (line 235) | def test_mmdet_object_detection_fit_with_freeze_backbone(checkpoint_name): function test_detector_hyperparameters_consistency (line 258) | def test_detector_hyperparameters_consistency(): function test_detector_coco_root_setup (line 283) | def test_detector_coco_root_setup(): FILE: multimodal/tests/unittests/others/test_preprocess_data.py function test_label_encoder (line 30) | def test_label_encoder(labels, positive_class): function test_data_to_df (line 76) | def test_data_to_df(data, required_columns, all_columns, is_valid_input): function test_is_url (line 97) | def test_is_url(path, is_valid_url): function test_convert_to_text (line 110) | def test_convert_to_text(convert_categorical_to_text, convert_numerical_... FILE: multimodal/tests/unittests/others/test_presets.py function test_get_presets (line 19) | def test_get_presets(): function test_preset_to_config (line 33) | def test_preset_to_config(): function test_presets_in_init (line 62) | def test_presets_in_init(problem_type, presets): FILE: multimodal/tests/unittests/others/test_process_images.py function test_train_transforms (line 82) | def test_train_transforms(augmentations): function test_variable_input_size_backbone (line 112) | def test_variable_input_size_backbone(checkpoint_name, provided_size, ex... function test_customize_predictor_image_aug (line 154) | def test_customize_predictor_image_aug(train_transforms, val_transforms): function test_customize_matcher_image_aug (line 186) | def test_customize_matcher_image_aug(train_transforms, val_transforms): FILE: multimodal/tests/unittests/others/test_process_multimodal.py function test_mixup (line 11) | def test_mixup(): function test_trivialaugment (line 46) | def test_trivialaugment(): FILE: multimodal/tests/unittests/others/test_process_text.py function test_text_processor_deepcopy_and_dump (line 15) | def test_text_processor_deepcopy_and_dump(): function test_trim_token_sequence (line 85) | def test_trim_token_sequence(lengths, max_length, do_merge, gt_trimmed_l... FILE: multimodal/tests/unittests/others/test_registry.py function test_registry (line 4) | def test_registry(): FILE: multimodal/tests/unittests/others/test_save.py function test_existing_save_path_but_empty_folder (line 19) | def test_existing_save_path_but_empty_folder(save_path): function test_existing_save_path_with_content_inside (line 71) | def test_existing_save_path_with_content_inside(save_path): function test_continuous_training_save_path (line 104) | def test_continuous_training_save_path(): FILE: multimodal/tests/unittests/others/test_semantic_segmentation.py function file2id (line 16) | def file2id(folder_path, file_path, split_str="_"): function get_file_paths (line 25) | def get_file_paths(directory, split_str="_"): function download_binary_semantic_seg_sample_dataset (line 30) | def download_binary_semantic_seg_sample_dataset(): function download_multi_semantic_seg_sample_dataset (line 40) | def download_multi_semantic_seg_sample_dataset(): function get_file_df_binary_semantic_seg (line 50) | def get_file_df_binary_semantic_seg(need_test_gt=False): function get_file_df_multi_semantic_seg (line 68) | def get_file_df_multi_semantic_seg(need_test_gt=False): function test_sam_semantic_segmentation_isic_fit_eval_predict_save_load (line 91) | def test_sam_semantic_segmentation_isic_fit_eval_predict_save_load(check... function test_sam_semantic_segmentation_zero_shot_evaluate_predict (line 126) | def test_sam_semantic_segmentation_zero_shot_evaluate_predict(checkpoint... function test_sam_semantic_segmentation_trans10k_fit_eval_predict_save_load (line 157) | def test_sam_semantic_segmentation_trans10k_fit_eval_predict_save_load(c... function verify_predictor_save_load_for_semantic_seg (line 187) | def verify_predictor_save_load_for_semantic_seg(predictor, df, as_multic... function test_sam_semantic_segmentation_get_class_num_func (line 219) | def test_sam_semantic_segmentation_get_class_num_func(checkpoint_name): function test_sam_semantic_segmentation_lora_insert (line 256) | def test_sam_semantic_segmentation_lora_insert(frozen_layers): function test_sam_semantic_segmentation_non_additive_peft_methods (line 286) | def test_sam_semantic_segmentation_non_additive_peft_methods(peft_method): FILE: multimodal/tests/unittests/others_2/test_classify_doc.py function get_rvl_cdip_sample_data (line 20) | def get_rvl_cdip_sample_data( function test_doc_classification (line 44) | def test_doc_classification(checkpoint_name): function test_pdf_doc_classification (line 71) | def test_pdf_doc_classification(checkpoint_name): function test_doc_classifier_standalone (line 100) | def test_doc_classifier_standalone(): FILE: multimodal/tests/unittests/others_2/test_config.py function test_basic_config (line 10) | def test_basic_config(): function test_get_config (line 18) | def test_get_config(): function test_model_config_selection (line 95) | def test_model_config_selection(model_names): function test_invalid_model_config_selection (line 113) | def test_invalid_model_config_selection(model_names): function test_parse_dotlist_conf (line 129) | def test_parse_dotlist_conf(data, expected): function test_apply_omegaconf_overrides (line 133) | def test_apply_omegaconf_overrides(): function test_none_str_config (line 158) | def test_none_str_config(overrides): FILE: multimodal/tests/unittests/others_2/test_custom_hparams.py function test_hyperparameters_in_terminal_format (line 34) | def test_hyperparameters_in_terminal_format(): function test_hyperparameters_consistency (line 73) | def test_hyperparameters_consistency(hyperparameters): function test_customize_model_names (line 122) | def test_customize_model_names( function test_filter_hyperparameters (line 333) | def test_filter_hyperparameters(hyperparameters, column_types, model_in_... function test_split_hyperparameters (line 378) | def test_split_hyperparameters(train_transforms, val_transforms, empty_a... function test_ensemble_hyperparameters (line 406) | def test_ensemble_hyperparameters(provided_hyperparameters): function test_hyperparameter_backward_compatibility (line 494) | def test_hyperparameter_backward_compatibility(hyperparameters, key_mapp... FILE: multimodal/tests/unittests/others_2/test_custom_training.py function test_gradient_checkpointing (line 26) | def test_gradient_checkpointing(backbone, peft, pooling_mode, precision,... function test_skip_final_val (line 66) | def test_skip_final_val(): function test_fit_with_data_path (line 92) | def test_fit_with_data_path(): function test_train_with_cpu_only (line 102) | def test_train_with_cpu_only(): FILE: multimodal/tests/unittests/others_2/test_distiller.py function test_distillation (line 9) | def test_distillation(): FILE: multimodal/tests/unittests/others_2/test_few_shot.py function test_few_shot_svm_fit_predict (line 27) | def test_few_shot_svm_fit_predict(): function test_few_shot_svm_save_load (line 50) | def test_few_shot_svm_save_load(): function test_few_shot_customize_models (line 113) | def test_few_shot_customize_models(hyperparameters, gt_ckpt_name, gt_mod... function test_one_shot_two_classes (line 126) | def test_one_shot_two_classes(): function test_few_shot_multi_columns (line 152) | def test_few_shot_multi_columns(column_features_pooling_mode): function test_few_shot_standalone (line 184) | def test_few_shot_standalone(): # test standalone feature in MultiModal... FILE: multimodal/tests/unittests/others_2/test_hpo.py function predictor_hpo (line 23) | def predictor_hpo(searcher, scheduler, presets=None): function matcher_hpo (line 72) | def matcher_hpo(searcher, scheduler, presets=None): function test_filter_search_space (line 140) | def test_filter_search_space(hyperparameters, keys_to_filter, expected): function test_invalid_filter_search_space (line 146) | def test_invalid_filter_search_space(hyperparameters, keys_to_filter): function test_predictor_hpo_searchers_schedulers (line 153) | def test_predictor_hpo_searchers_schedulers(searcher, scheduler): function test_predictor_hpo_presets (line 158) | def test_predictor_hpo_presets(presets): function test_matcher_hpo_searchers_schedulers (line 164) | def test_matcher_hpo_searchers_schedulers(searcher, scheduler): function test_matcher_hpo_presets (line 169) | def test_matcher_hpo_presets(presets): function test_hpo_distillation (line 174) | def test_hpo_distillation(): function test_modifying_duplicate_model_names (line 243) | def test_modifying_duplicate_model_names(): FILE: multimodal/tests/unittests/others_2/test_image_formats.py function test_image_bytearray_or_base64_str (line 16) | def test_image_bytearray_or_base64_str(image_type): function test_predict_with_image_str_or_list (line 70) | def test_predict_with_image_str_or_list(): function test_fit_with_invalid_images (line 91) | def test_fit_with_invalid_images(invalid_value): FILE: multimodal/tests/unittests/others_2/test_ner_chinese.py function download_ecommerce (line 12) | def download_ecommerce(): function read_bio (line 23) | def read_bio(sample): function bio_samples_to_df (line 66) | def bio_samples_to_df(samples): function get_data (line 77) | def get_data(): function test_ner_chinese (line 87) | def test_ner_chinese(): FILE: multimodal/tests/unittests/others_2/test_problem_types.py function test_get_problem_type (line 29) | def test_get_problem_type(name): function test_problem_type_in_init (line 35) | def test_problem_type_in_init(name): function test_infer_problem_type (line 53) | def test_infer_problem_type(y_data, provided_problem_type, gt_problem_ty... FILE: multimodal/tests/unittests/others_2/test_text_detection.py function download_sample_images (line 17) | def download_sample_images(): function test_mmocr_text_detection_inference (line 33) | def test_mmocr_text_detection_inference(checkpoint_name): FILE: multimodal/tests/unittests/others_2/test_text_recognition.py function download_sample_images (line 19) | def download_sample_images(): function test_mmocr_text_recognition_inference (line 41) | def test_mmocr_text_recognition_inference(checkpoint_name): FILE: multimodal/tests/unittests/others_2/test_tokenizers.py function test_tokenizer_use_fast (line 34) | def test_tokenizer_use_fast(checkpoint_name, use_fast, tokenizer_type): FILE: multimodal/tests/unittests/others_2/test_zero_shot.py function download_sample_images (line 9) | def download_sample_images(): function test_clip_zero_shot (line 23) | def test_clip_zero_shot(): function test_timm_zero_shot (line 113) | def test_timm_zero_shot(checkpoint_name, num_gpus): function test_matcher_text_similarity (line 147) | def test_matcher_text_similarity(): FILE: multimodal/tests/unittests/predictor/test_predictor.py function test_predictor_basic (line 162) | def test_predictor_basic( function test_predictor_realtime_inference (line 294) | def test_predictor_realtime_inference( function test_predictor_standalone (line 359) | def test_predictor_standalone(): # test standalone feature in MultiModa... FILE: multimodal/tests/unittests/utils/unittest_datasets.py class PetFinderDataset (line 13) | class PetFinderDataset: method __init__ (line 14) | def __init__( method path (line 64) | def path(self): method feature_columns (line 68) | def feature_columns(self): method label_columns (line 95) | def label_columns(self): method train_df (line 99) | def train_df(self): method test_df (line 103) | def test_df(self): method image_columns (line 107) | def image_columns(self): method metric (line 111) | def metric(self): method problem_type (line 115) | def problem_type(self): class HatefulMeMesDataset (line 119) | class HatefulMeMesDataset: method __init__ (line 120) | def __init__( method path (line 157) | def path(self): method feature_columns (line 161) | def feature_columns(self): method label_columns (line 165) | def label_columns(self): method train_df (line 169) | def train_df(self): method test_df (line 173) | def test_df(self): method image_columns (line 177) | def image_columns(self): method metric (line 181) | def metric(self): method problem_type (line 185) | def problem_type(self): class AEDataset (line 189) | class AEDataset: method __init__ (line 190) | def __init__( method path (line 218) | def path(self): method feature_columns (line 222) | def feature_columns(self): method label_columns (line 238) | def label_columns(self): method train_df (line 242) | def train_df(self): method test_df (line 246) | def test_df(self): method metric (line 250) | def metric(self): method problem_type (line 254) | def problem_type(self): class AmazonReviewSentimentCrossLingualDataset (line 258) | class AmazonReviewSentimentCrossLingualDataset: method __init__ (line 259) | def __init__( method path (line 299) | def path(self): method label_columns (line 303) | def label_columns(self): method train_df (line 307) | def train_df(self): method test_df (line 311) | def test_df(self): class IDChangeDetectionDataset (line 315) | class IDChangeDetectionDataset: method __init__ (line 316) | def __init__(self): method feature_columns (line 347) | def feature_columns(self): method label_columns (line 351) | def label_columns(self): method train_df (line 355) | def train_df(self): method test_df (line 359) | def test_df(self): method image_columns (line 363) | def image_columns(self): method metric (line 367) | def metric(self): method problem_type (line 371) | def problem_type(self): method match_label (line 375) | def match_label(self): class Flickr30kDataset (line 379) | class Flickr30kDataset: method __init__ (line 380) | def __init__(self): method feature_columns (line 416) | def feature_columns(self): method label_columns (line 420) | def label_columns(self): method train_df (line 424) | def train_df(self): method val_df (line 428) | def val_df(self): method test_df (line 432) | def test_df(self): method image_columns (line 436) | def image_columns(self): method metric (line 440) | def metric(self): method problem_type (line 444) | def problem_type(self): method match_label (line 448) | def match_label(self): FILE: multimodal/tests/unittests/utils/utils.py function get_home_dir (line 15) | def get_home_dir(): function get_data_home_dir (line 23) | def get_data_home_dir(): function get_repo_url (line 29) | def get_repo_url(): function verify_predictor_save_load (line 37) | def verify_predictor_save_load(predictor, df, verify_embedding=True, cls... function verify_predictor_realtime_inference (line 65) | def verify_predictor_realtime_inference(predictor, df, verify_embedding=... function verify_no_redundant_model_configs (line 81) | def verify_no_redundant_model_configs(predictor): function verify_predict_and_predict_proba (line 87) | def verify_predict_and_predict_proba(test_data, predictor): function verify_predict_as_pandas_and_multiclass (line 94) | def verify_predict_as_pandas_and_multiclass(test_data, predictor): function verify_predict_without_label_column (line 104) | def verify_predict_without_label_column(test_data, predictor, label_col=... function verify_matcher_save_load (line 114) | def verify_matcher_save_load(matcher, df, verify_embedding=True, cls=Mul... function verify_matcher_realtime_inference (line 137) | def verify_matcher_realtime_inference(matcher, df, verify_embedding=True): function evaluate_matcher_ranking (line 156) | def evaluate_matcher_ranking(matcher, test_df, query_column, response_co... function ref_symmetric_hit_rate (line 192) | def ref_symmetric_hit_rate(features_a, features_b, logit_scale, top_ks=[... FILE: release_instructions/add_links_to_release_notes.py function linkify_pull_requests (line 5) | def linkify_pull_requests(text: str) -> str: function linkify_user_mentions (line 20) | def linkify_user_mentions(text: str) -> str: function unlinkify_user_mentions (line 35) | def unlinkify_user_mentions(text: str) -> str: function unlinkify_pull_requests (line 44) | def unlinkify_pull_requests(text: str) -> str: function transform_changelog (line 53) | def transform_changelog(file_path: str, strip_links_for_github_release: ... FILE: tabular/src/autogluon/tabular/configs/config_helper.py class FeatureGeneratorBuilder (line 15) | class FeatureGeneratorBuilder: method __init__ (line 16) | def __init__(self, parent=None): method enable_numeric_features (line 20) | def enable_numeric_features(self, value: bool = True) -> FeatureGenera... method enable_categorical_features (line 29) | def enable_categorical_features(self, value: bool = True) -> FeatureGe... method enable_datetime_features (line 38) | def enable_datetime_features(self, value: bool = True) -> FeatureGener... method enable_text_special_features (line 47) | def enable_text_special_features(self, value: bool = True) -> FeatureG... method enable_text_ngram_features (line 55) | def enable_text_ngram_features(self, value: bool = True) -> FeatureGen... method enable_raw_text_features (line 63) | def enable_raw_text_features(self, value: bool = True) -> FeatureGener... method enable_vision_features (line 71) | def enable_vision_features(self, value: bool = True) -> FeatureGenerat... method vectorizer (line 82) | def vectorizer(self, value: BaseEstimator) -> FeatureGeneratorBuilder: method text_ngram_params (line 90) | def text_ngram_params(self, value: bool = True) -> FeatureGeneratorBui... method build (line 97) | def build(self) -> Union[ConfigBuilder, AutoMLPipelineFeatureGenerator]: class ConfigBuilder (line 106) | class ConfigBuilder: method __init__ (line 107) | def __init__(self): method _valid_keys (line 110) | def _valid_keys(self): method presets (line 114) | def presets(self, presets: Union[str, list, dict]) -> ConfigBuilder: method time_limit (line 135) | def time_limit(self, time_limit: int) -> ConfigBuilder: method hyperparameters (line 145) | def hyperparameters(self, hyperparameters: Union[str, dict]) -> Config... method auto_stack (line 164) | def auto_stack(self, auto_stack: bool = True) -> ConfigBuilder: method num_bag_folds (line 175) | def num_bag_folds(self, num_bag_folds: int) -> ConfigBuilder: method num_bag_sets (line 188) | def num_bag_sets(self, num_bag_sets: int) -> ConfigBuilder: method num_stack_levels (line 198) | def num_stack_levels(self, num_stack_levels: int) -> ConfigBuilder: method holdout_frac (line 208) | def holdout_frac(self, holdout_frac: float) -> ConfigBuilder: method use_bag_holdout (line 219) | def use_bag_holdout(self, use_bag_holdout: bool = True) -> ConfigBuilder: method hyperparameter_tune_kwargs (line 230) | def hyperparameter_tune_kwargs(self, hyperparameter_tune_kwargs: Union... method ag_args (line 250) | def ag_args(self, ag_args: dict) -> ConfigBuilder: method ag_args_fit (line 260) | def ag_args_fit(self, ag_args_fit: dict) -> ConfigBuilder: method ag_args_ensemble (line 270) | def ag_args_ensemble(self, ag_args_ensemble: dict) -> ConfigBuilder: method excluded_model_types (line 280) | def excluded_model_types(self, models: Union[str, list]) -> ConfigBuil... method included_model_types (line 295) | def included_model_types(self, models: Union[str, list]) -> ConfigBuil... method refit_full (line 315) | def refit_full(self, refit_full: Union[bool, str] = True) -> ConfigBui... method set_best_to_refit_full (line 329) | def set_best_to_refit_full(self, set_best_to_refit_full=True) -> Confi... method keep_only_best (line 337) | def keep_only_best(self, keep_only_best=True) -> ConfigBuilder: method save_space (line 348) | def save_space(self, save_space=True) -> ConfigBuilder: method feature_generator (line 359) | def feature_generator(self) -> FeatureGeneratorBuilder: method calibrate (line 366) | def calibrate(self, calibrate=True) -> ConfigBuilder: method build (line 376) | def build(self) -> dict: FILE: tabular/src/autogluon/tabular/configs/feature_generator_presets.py function get_default_feature_generator (line 10) | def get_default_feature_generator(feature_generator, feature_metadata=No... FILE: tabular/src/autogluon/tabular/configs/hyperparameter_configs.py function get_hyperparameter_config_options (line 171) | def get_hyperparameter_config_options(): function get_hyperparameter_config (line 175) | def get_hyperparameter_config(config_name): FILE: tabular/src/autogluon/tabular/configs/pipeline_presets.py function _get_validation_preset (line 11) | def _get_validation_preset(num_train_rows: int, hpo_enabled: bool) -> di... function get_validation_and_stacking_method (line 40) | def get_validation_and_stacking_method( FILE: tabular/src/autogluon/tabular/experimental/_scikit_mixin.py class ScikitMixin (line 7) | class ScikitMixin: method _get_init_args (line 8) | def _get_init_args(self, problem_type: str) -> dict: method _get_fit_args (line 25) | def _get_fit_args(self) -> dict: method _validate_input (line 42) | def _validate_input(self, X): method _combine_X_y (line 52) | def _combine_X_y(self, X, y) -> pd.DataFrame: method leaderboard (line 61) | def leaderboard(self, X, y, **kwargs) -> pd.DataFrame: FILE: tabular/src/autogluon/tabular/experimental/_tabular_classifier.py class TabularClassifier (line 14) | class TabularClassifier(BaseEstimator, ClassifierMixin, ScikitMixin): method __init__ (line 15) | def __init__( method fit (line 35) | def fit(self, X, y): method predict (line 62) | def predict(self, X): method predict_proba (line 76) | def predict_proba(self, X): FILE: tabular/src/autogluon/tabular/experimental/_tabular_regressor.py class TabularRegressor (line 13) | class TabularRegressor(BaseEstimator, RegressorMixin, ScikitMixin): method __init__ (line 14) | def __init__( method fit (line 34) | def fit(self, X, y): method predict (line 52) | def predict(self, X): FILE: tabular/src/autogluon/tabular/experimental/plot_leaderboard.py function _cumulative_min_idx (line 10) | def _cumulative_min_idx(series: pd.Series) -> pd.Series: function compute_cumulative_leaderboard_stats (line 38) | def compute_cumulative_leaderboard_stats(leaderboard: pd.DataFrame) -> p... function compute_cumulative_leaderboard_stats_ensemble (line 69) | def compute_cumulative_leaderboard_stats_ensemble( function plot_leaderboard_from_predictor (line 115) | def plot_leaderboard_from_predictor( function plot_leaderboard (line 159) | def plot_leaderboard( FILE: tabular/src/autogluon/tabular/learner/abstract_learner.py class AbstractTabularLearner (line 37) | class AbstractTabularLearner(AbstractLearner): method __init__ (line 38) | def __init__( method original_features (line 111) | def original_features(self) -> List[str]: method features (line 117) | def features(self): method feature_metadata_in (line 121) | def feature_metadata_in(self): method feature_generators (line 125) | def feature_generators(self): method class_labels (line 129) | def class_labels(self): method class_labels_transformed (line 133) | def class_labels_transformed(self): method positive_class (line 137) | def positive_class(self): method fit (line 158) | def fit(self, X: DataFrame, X_val: DataFrame = None, **kwargs): method _fit (line 164) | def _fit( method predict_proba (line 177) | def predict_proba( method predict (line 202) | def predict( method _post_process_predict (line 232) | def _post_process_predict( method _post_process_predict_proba (line 260) | def _post_process_predict_proba( method predict_proba_multi (line 285) | def predict_proba_multi( method predict_multi (line 370) | def predict_multi( method get_pred_from_proba (line 403) | def get_pred_from_proba( method _validate_fit_input (line 422) | def _validate_fit_input(self, X: DataFrame, **kwargs): method validate_label (line 432) | def validate_label(self, X: DataFrame): method _validate_sample_weight (line 441) | def _validate_sample_weight(self, X, X_val): method _validate_groups (line 472) | def _validate_groups(self, X, X_val): method get_inputs_to_stacker (line 492) | def get_inputs_to_stacker(self, dataset=None, model=None, base_models:... method refit_ensemble_full (line 523) | def refit_ensemble_full(self, model: str | List[str] = "all", **kwargs): method fit_transform_features (line 526) | def fit_transform_features(self, X, y=None, **kwargs): method transform_features (line 536) | def transform_features(self, X): method score (line 541) | def score(self, X: DataFrame, y=None, model: str = None, metric: Score... method score_debug (line 574) | def score_debug( method score_with_pred_proba (line 743) | def score_with_pred_proba( method score_with_pred (line 793) | def score_with_pred( method _validate_class_labels (line 827) | def _validate_class_labels(self, y: Series, eval_metric: Scorer = None): method evaluate_predictions (line 849) | def evaluate_predictions( method extract_label (line 999) | def extract_label(self, X, error_if_missing=True): method leaderboard (line 1009) | def leaderboard( method get_feature_importance (line 1080) | def get_feature_importance( method _remove_nan_label_rows (line 1134) | def _remove_nan_label_rows(X, y): method infer_problem_type (line 1140) | def infer_problem_type(self, y: Series, silent=False): method _infer_problem_type (line 1159) | def _infer_problem_type(y: Series, silent=False): method persist_trainer (line 1163) | def persist_trainer(self, low_memory=False, models="all", with_ancesto... method distill (line 1172) | def distill( method transform_labels (line 1235) | def transform_labels(self, y, inverse=False, proba=False): method calibrate_decision_threshold (line 1248) | def calibrate_decision_threshold( method _verify_metric (line 1284) | def _verify_metric(self, eval_metric: Scorer, problem_type: str): method get_info (line 1292) | def get_info(self, **kwargs): FILE: tabular/src/autogluon/tabular/learner/default_learner.py class DefaultLearner (line 30) | class DefaultLearner(AbstractTabularLearner): method __init__ (line 31) | def __init__(self, trainer_type=AutoTrainer, **kwargs): method _fit (line 43) | def _fit( method _update_infer_limit (line 180) | def _update_infer_limit(self, X: DataFrame, *, infer_limit_batch_size:... method general_data_processing (line 219) | def general_data_processing( method bundle_weights (line 345) | def bundle_weights(self, X: DataFrame | None, w: Series | None, name: ... method set_predefined_weights (line 364) | def set_predefined_weights(self, X, y): method _check_for_non_finite_values (line 385) | def _check_for_non_finite_values(self, X: DataFrame, name: str = "", i... method _apply_cleaner_transform (line 400) | def _apply_cleaner_transform( method adjust_threshold_if_necessary (line 453) | def adjust_threshold_if_necessary(self, y, threshold, holdout_frac, nu... method _adjust_threshold_if_necessary (line 473) | def _adjust_threshold_if_necessary(self, y, threshold, holdout_frac, n... method get_info (line 516) | def get_info(self, include_model_info=False, include_model_failures=Fa... FILE: tabular/src/autogluon/tabular/models/_utils/rapids_utils.py class RapidsModelMixin (line 6) | class RapidsModelMixin: method _get_default_ag_args_ensemble (line 11) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _get_default_resources (line 17) | def _get_default_resources(self): method get_minimum_resources (line 24) | def get_minimum_resources(self, is_gpu_available=False) -> Dict[str, i... method _more_tags (line 30) | def _more_tags(self): FILE: tabular/src/autogluon/tabular/models/_utils/torch_utils.py class TorchThreadManager (line 4) | class TorchThreadManager: method __init__ (line 9) | def __init__(self, num_threads): method __enter__ (line 13) | def __enter__(self): method __exit__ (line 17) | def __exit__(self, exc_type, exc_value, exc_tb): FILE: tabular/src/autogluon/tabular/models/abstract/abstract_torch_model.py class AbstractTorchModel (line 11) | class AbstractTorchModel(AbstractModel): method __init__ (line 16) | def __init__(self, **kwargs): method suggest_device_infer (line 21) | def suggest_device_infer(self, verbose: bool = False) -> str: method to_torch_device (line 53) | def to_torch_device(cls, device: str): method get_device (line 58) | def get_device(self) -> str: method set_device (line 67) | def set_device(self, device: str): method _set_device (line 73) | def _set_device(self, device: str): method _post_fit (line 84) | def _post_fit(self, **kwargs): method save (line 91) | def save(self, path: str = None, verbose=True) -> str: method load (line 111) | def load(cls, path: str, reset_paths=True, verbose=True): method _class_tags (line 145) | def _class_tags(cls): FILE: tabular/src/autogluon/tabular/models/automm/automm_model.py class MultiModalPredictorModel (line 30) | class MultiModalPredictorModel(AbstractModel): method __init__ (line 35) | def __init__(self, **kwargs): method _get_default_auxiliary_params (line 76) | def _get_default_auxiliary_params(self) -> dict: method _get_default_ag_args (line 86) | def _get_default_ag_args(cls) -> dict: method supported_problem_types (line 95) | def supported_problem_types(cls) -> list[str] | None: method _get_default_ag_args_ensemble (line 100) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _set_default_params (line 106) | def _set_default_params(self): method preprocess_fit (line 110) | def preprocess_fit(self, X, y, X_val=None, y_val=None, **kwargs): method _fit (line 120) | def _fit( method _predict_proba (line 233) | def _predict_proba(self, X, **kwargs): method save (line 243) | def save(self, path: str = None, verbose=True) -> str: method load (line 259) | def load(cls, path: str, reset_paths=True, verbose=True): method _get_memory_size (line 269) | def _get_memory_size(self) -> int: method _get_default_resources (line 281) | def _get_default_resources(self): method get_minimum_resources (line 288) | def get_minimum_resources(self, is_gpu_available=False) -> Dict[str, i... method _construct_column_types (line 294) | def _construct_column_types(self) -> dict: method _more_tags (line 318) | def _more_tags(self): method _class_tags (line 323) | def _class_tags(cls): FILE: tabular/src/autogluon/tabular/models/automm/ft_transformer.py class FTTransformerModel (line 16) | class FTTransformerModel(MultiModalPredictorModel): method __init__ (line 20) | def __init__(self, **kwargs): method _fit (line 58) | def _fit(self, X, num_gpus="auto", **kwargs): method _get_default_auxiliary_params (line 67) | def _get_default_auxiliary_params(self) -> dict: method _set_default_params (line 76) | def _set_default_params(self): method get_minimum_resources (line 97) | def get_minimum_resources(self, is_gpu_available=False) -> Dict[str, i... method _get_default_ag_args_ensemble (line 104) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _class_tags (line 114) | def _class_tags(cls): FILE: tabular/src/autogluon/tabular/models/catboost/callbacks.py class MemoryCheckCallback (line 11) | class MemoryCheckCallback: method __init__ (line 25) | def __init__(self, period: int = 10, verbose=False): method after_iteration (line 33) | def after_iteration(self, info): method memory_check (line 42) | def memory_check(self, iter: int) -> bool: class TimeCheckCallback (line 107) | class TimeCheckCallback: method __init__ (line 121) | def __init__(self, time_start, time_limit): method after_iteration (line 125) | def after_iteration(self, info): class EarlyStoppingCallback (line 134) | class EarlyStoppingCallback: method __init__ (line 151) | def __init__(self, stopping_rounds, eval_metric, compare_key="validati... method after_iteration (line 175) | def after_iteration(self, info): FILE: tabular/src/autogluon/tabular/models/catboost/catboost_model.py class CatBoostModel (line 30) | class CatBoostModel(AbstractModel): method __init__ (line 43) | def __init__(self, **kwargs): method _set_default_params (line 47) | def _set_default_params(self): method _get_default_searchspace (line 61) | def _get_default_searchspace(self): method _preprocess (line 64) | def _preprocess(self, X, **kwargs): method _estimate_memory_usage (line 82) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 93) | def _estimate_memory_usage_static( method _fit (line 139) | def _fit( method _estimate_iter_in_time_gpu (line 326) | def _estimate_iter_in_time_gpu( method _predict_proba (line 373) | def _predict_proba(self, X, **kwargs): method _get_default_auxiliary_params (line 385) | def _get_default_auxiliary_params(self) -> dict: method _get_early_stopping_rounds (line 393) | def _get_early_stopping_rounds(self, num_rows_train, strategy="auto"): method _ag_params (line 396) | def _ag_params(self) -> set: method _validate_fit_memory_usage (line 399) | def _validate_fit_memory_usage( method get_minimum_resources (line 413) | def get_minimum_resources(self, is_gpu_available=False): method _get_default_resources (line 422) | def _get_default_resources(self): method supported_problem_types (line 429) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 433) | def _class_tags(cls): method _more_tags (line 438) | def _more_tags(self): FILE: tabular/src/autogluon/tabular/models/catboost/catboost_softclass_utils.py class SoftclassCustomMetric (line 12) | class SoftclassCustomMetric(CustomMetric): method __init__ (line 13) | def __init__(self, metric, is_higher_better, needs_pred_proba): # met... method evaluate (line 17) | def evaluate(self, approxes, target, weight): class SoftLogLossMetric (line 20) | class SoftLogLossMetric(MultiRegressionCustomMetric): method get_final_error (line 21) | def get_final_error(self, error, weight): method is_max_optimal (line 24) | def is_max_optimal(self): method evaluate (line 27) | def evaluate(self, approxes, target, weight): class SoftclassObjective (line 60) | class SoftclassObjective(object): method __init__ (line 61) | def __init__(self): class SoftLogLossObjective (line 64) | class SoftLogLossObjective(MultiRegressionCustomObjective): method calc_ders_multi (line 67) | def calc_ders_multi(self, approxes, targets, weight): FILE: tabular/src/autogluon/tabular/models/catboost/catboost_utils.py class CustomMetric (line 20) | class CustomMetric: method __init__ (line 21) | def __init__(self, metric, is_higher_better, needs_pred_proba): method get_final_error (line 27) | def get_final_error(error, weight): method is_max_optimal (line 30) | def is_max_optimal(self): method evaluate (line 33) | def evaluate(self, approxes, target, weight): function get_catboost_metric_from_ag_metric (line 37) | def get_catboost_metric_from_ag_metric(metric, problem_type, quantile_le... FILE: tabular/src/autogluon/tabular/models/catboost/hyperparameters/parameters.py function get_param_baseline (line 6) | def get_param_baseline(problem_type, num_classes=None): function get_param_binary_baseline (line 17) | def get_param_binary_baseline(): function get_param_multiclass_baseline (line 25) | def get_param_multiclass_baseline(num_classes): function get_param_regression_baseline (line 33) | def get_param_regression_baseline(): FILE: tabular/src/autogluon/tabular/models/catboost/hyperparameters/searchspaces.py function get_default_searchspace (line 7) | def get_default_searchspace(problem_type, num_classes=None): function get_searchspace_multiclass_baseline (line 18) | def get_searchspace_multiclass_baseline(num_classes): function get_searchspace_binary_baseline (line 27) | def get_searchspace_binary_baseline(): function get_searchspace_regression_baseline (line 36) | def get_searchspace_regression_baseline(): FILE: tabular/src/autogluon/tabular/models/ebm/ebm_model.py class EbmCallback (line 20) | class EbmCallback: method __init__ (line 23) | def __init__(self, seconds: float): method __call__ (line 27) | def __call__(self, *args, **kwargs): class EBMModel (line 34) | class EBMModel(AbstractModel): method _fit (line 62) | def _fit( method _set_default_params (line 117) | def _set_default_params(self): method _get_default_searchspace (line 122) | def _get_default_searchspace(self): method _get_default_auxiliary_params (line 125) | def _get_default_auxiliary_params(self) -> dict: method supported_problem_types (line 134) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 138) | def _class_tags(cls) -> dict: method _more_tags (line 141) | def _more_tags(self) -> dict: method _estimate_memory_usage (line 145) | def _estimate_memory_usage(self, X: pd.DataFrame, y: pd.Series | None ... method _estimate_memory_usage_static (line 157) | def _estimate_memory_usage_static( method _validate_fit_memory_usage (line 181) | def _validate_fit_memory_usage(self, mem_error_threshold: float = 1, *... function construct_ebm_params (line 186) | def construct_ebm_params( function get_class_from_problem_type (line 231) | def get_class_from_problem_type(problem_type: str): function get_metric_from_ag_metric (line 245) | def get_metric_from_ag_metric(*, metric: Scorer, problem_type: str): FILE: tabular/src/autogluon/tabular/models/ebm/hyperparameters/parameters.py function get_param_baseline (line 4) | def get_param_baseline(problem_type, num_classes=None): function get_base_params (line 17) | def get_base_params(): function get_param_binary_baseline (line 22) | def get_param_binary_baseline(): function get_param_multiclass_baseline (line 29) | def get_param_multiclass_baseline(num_classes): function get_param_regression_baseline (line 36) | def get_param_regression_baseline(): FILE: tabular/src/autogluon/tabular/models/ebm/hyperparameters/searchspaces.py function get_default_searchspace (line 7) | def get_default_searchspace(problem_type, num_classes=None): function get_base_searchspace (line 18) | def get_base_searchspace(): function get_searchspace_multiclass_baseline (line 55) | def get_searchspace_multiclass_baseline(num_classes): function get_searchspace_binary_baseline (line 62) | def get_searchspace_binary_baseline(): function get_searchspace_regression_baseline (line 69) | def get_searchspace_regression_baseline(): FILE: tabular/src/autogluon/tabular/models/fastainn/callbacks.py class BatchTimeTracker (line 12) | class BatchTimeTracker(Callback): method __init__ (line 17) | def __init__(self, batches_to_measure): method after_batch (line 23) | def after_batch(self): method _time_now (line 32) | def _time_now(self): class EarlyStoppingCallbackWithTimeLimit (line 36) | class EarlyStoppingCallbackWithTimeLimit(TrackerCallback): method __init__ (line 37) | def __init__( method before_fit (line 54) | def before_fit(self): method after_epoch (line 58) | def after_epoch(self): class AgSaveModelCallback (line 90) | class AgSaveModelCallback(TrackerCallback): method __init__ (line 95) | def __init__( method _save (line 112) | def _save(self, name): method after_epoch (line 115) | def after_epoch(self): method after_fit (line 131) | def after_fit(self, **kwargs): FILE: tabular/src/autogluon/tabular/models/fastainn/fastai_helpers.py function export (line 10) | def export(model, filename_or_stream="export.pkl", pickle_module=pickle,... function is_pathlike (line 33) | def is_pathlike(x: Any) -> bool: function medae (line 37) | def medae(inp, targ): FILE: tabular/src/autogluon/tabular/models/fastainn/hyperparameters/parameters.py function get_param_baseline (line 5) | def get_param_baseline(problem_type, num_classes=None): function get_param_multiclass_baseline (line 18) | def get_param_multiclass_baseline(): function get_param_binary_baseline (line 40) | def get_param_binary_baseline(): function get_param_regression_baseline (line 44) | def get_param_regression_baseline(): function get_param_quantile_baseline (line 48) | def get_param_quantile_baseline(): FILE: tabular/src/autogluon/tabular/models/fastainn/hyperparameters/searchspaces.py function get_default_searchspace (line 5) | def get_default_searchspace(problem_type, num_classes=None): function get_searchspace_binary (line 18) | def get_searchspace_binary(): function get_searchspace_multiclass (line 45) | def get_searchspace_multiclass(num_classes): function get_searchspace_regression (line 49) | def get_searchspace_regression(): function get_searchspace_quantile (line 53) | def get_searchspace_quantile(): FILE: tabular/src/autogluon/tabular/models/fastainn/quantile_helpers.py function isotonic (line 9) | def isotonic(input_data, quantile_list): class HuberPinballLoss (line 18) | class HuberPinballLoss(nn.Module): method __init__ (line 21) | def __init__(self, quantile_levels, alpha=0.01): method forward (line 29) | def forward(self, predict_data, target_data): FILE: tabular/src/autogluon/tabular/models/fastainn/tabular_nn_fastai.py class NNFastAiTabularModel (line 65) | class NNFastAiTabularModel(AbstractModel): method __init__ (line 113) | def __init__(self, **kwargs): method _preprocess_train (line 126) | def _preprocess_train(self, X, y, X_val, y_val): method _preprocess (line 171) | def _preprocess(self, X: pd.DataFrame, fit=False, **kwargs): method _fill_missing (line 229) | def _fill_missing(self, df: pd.DataFrame) -> pd.DataFrame: method _fit (line 248) | def _fit( method _get_batch_size (line 429) | def _get_batch_size(self, X, default_batch_size_for_small_inputs=32): method _get_epochs_number (line 440) | def _get_epochs_number( method _measure_batch_times (line 468) | def _measure_batch_times(self, min_batches_count: int) -> float: method _generate_datasets (line 487) | def _generate_datasets(self, X, y, X_val, y_val): method __get_objective_func_name (line 499) | def __get_objective_func_name(self, stopping_metric): method __get_objective_func_to_monitor (line 519) | def __get_objective_func_to_monitor(self, objective_func_name): method _predict_proba (line 533) | def _predict_proba(self, X, **kwargs): method save (line 567) | def save(self, path: str = None, verbose=True) -> str: method load (line 584) | def load(cls, path: str, reset_paths=True, verbose=True): method _model_internals_path (line 607) | def _model_internals_path(self) -> str: method _set_default_params (line 611) | def _set_default_params(self): method _get_default_searchspace (line 617) | def _get_default_searchspace(self): method _get_default_auxiliary_params (line 620) | def _get_default_auxiliary_params(self) -> dict: method _get_default_resources (line 629) | def _get_default_resources(self): method __get_metrics_map (line 635) | def __get_metrics_map(self): method _estimate_memory_usage (line 669) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 680) | def _estimate_memory_usage_static( method _get_hpo_backend (line 688) | def _get_hpo_backend(self): method _get_maximum_resources (line 692) | def _get_maximum_resources(self) -> dict[str, Union[int, float]]: method get_minimum_resources (line 696) | def get_minimum_resources(self, is_gpu_available=False): method supported_problem_types (line 705) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 709) | def _class_tags(cls): method _more_tags (line 716) | def _more_tags(self): FILE: tabular/src/autogluon/tabular/models/image_prediction/image_predictor.py class ImagePredictorModel (line 20) | class ImagePredictorModel(MultiModalPredictorModel): method __init__ (line 31) | def __init__(self, **kwargs): method _has_predict_proba (line 37) | def _has_predict_proba(self): method _get_default_auxiliary_params (line 40) | def _get_default_auxiliary_params(self) -> dict: method _get_default_ag_args (line 52) | def _get_default_ag_args(cls) -> dict: method preprocess_fit (line 61) | def preprocess_fit(self, X, y, X_val=None, y_val=None, **kwargs): method _predict_proba (line 93) | def _predict_proba(self, X, **kwargs): method _compute_dummy_pred_proba (line 116) | def _compute_dummy_pred_proba(self, y): method supported_problem_types (line 135) | def supported_problem_types(cls) -> list[str] | None: FILE: tabular/src/autogluon/tabular/models/imodels/imodels_models.py class _IModelsModel (line 13) | class _IModelsModel(AbstractModel): method __init__ (line 14) | def __init__(self, **kwargs): method get_model (line 23) | def get_model(self): method get_info (line 26) | def get_info(self): method _preprocess (line 31) | def _preprocess(self, X: pd.DataFrame, is_train=False, **kwargs) -> pd... method _fit (line 54) | def _fit(self, X: pd.DataFrame, y: pd.Series, **kwargs): # training d... method _set_default_params (line 61) | def _set_default_params(self): method supported_problem_types (line 69) | def supported_problem_types(cls) -> list[str] | None: method _get_default_auxiliary_params (line 72) | def _get_default_auxiliary_params(self) -> dict: class RuleFitModel (line 83) | class RuleFitModel(_IModelsModel): method get_model (line 87) | def get_model(self): class GreedyTreeModel (line 97) | class GreedyTreeModel(_IModelsModel): method get_model (line 101) | def get_model(self): class BoostedRulesModel (line 112) | class BoostedRulesModel(_IModelsModel): method get_model (line 116) | def get_model(self): class HSTreeModel (line 126) | class HSTreeModel(_IModelsModel): method get_model (line 130) | def get_model(self): class FigsModel (line 140) | class FigsModel(_IModelsModel): method get_model (line 144) | def get_model(self): FILE: tabular/src/autogluon/tabular/models/knn/_knn_loo_variants.py class KNeighborsClassifierLOOMixin (line 19) | class KNeighborsClassifierLOOMixin: method predict_loo (line 21) | def predict_loo(self): method predict_proba_loo (line 57) | def predict_proba_loo(self): class KNeighborsRegressorLOOMixin (line 105) | class KNeighborsRegressorLOOMixin: method predict_loo (line 107) | def predict_loo(self): FILE: tabular/src/autogluon/tabular/models/knn/knn_model.py class KNNModel (line 23) | class KNNModel(AbstractModel): method __init__ (line 32) | def __init__(self, **kwargs): method _get_model_type (line 36) | def _get_model_type(self): method _preprocess (line 54) | def _preprocess(self, X, **kwargs): method _set_default_params (line 59) | def _set_default_params(self): method _get_default_auxiliary_params (line 66) | def _get_default_auxiliary_params(self) -> dict: method _get_default_ag_args (line 76) | def _get_default_ag_args(cls) -> dict: method _get_default_ag_args_ensemble (line 85) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _get_default_searchspace (line 92) | def _get_default_searchspace(self): method _fit (line 96) | def _fit(self, X, y, num_cpus=-1, time_limit=None, sample_weight=None,... method _estimate_memory_usage (line 114) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 125) | def _estimate_memory_usage_static( method _validate_fit_memory_usage (line 137) | def _validate_fit_memory_usage( method predict_proba_oof (line 153) | def predict_proba_oof(self, X, normalize=None, **kwargs): method _predict_proba_oof (line 163) | def _predict_proba_oof(self, X, **kwargs): method _fit_with_samples (line 190) | def _fit_with_samples( method _get_maximum_resources (line 291) | def _get_maximum_resources(self) -> dict[str, int | float]: method _get_default_resources (line 299) | def _get_default_resources(self): method supported_problem_types (line 306) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 310) | def _class_tags(cls): method _more_tags (line 315) | def _more_tags(self): class FAISSModel (line 322) | class FAISSModel(KNNModel): method _get_model_type (line 323) | def _get_model_type(self): method _set_default_params (line 331) | def _set_default_params(self): method _get_default_ag_args_ensemble (line 340) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _more_tags (line 346) | def _more_tags(self): FILE: tabular/src/autogluon/tabular/models/knn/knn_rapids_model.py class KNNRapidsModel (line 17) | class KNNRapidsModel(RapidsModelMixin, KNNModel): method _get_model_type (line 29) | def _get_model_type(self): FILE: tabular/src/autogluon/tabular/models/knn/knn_utils.py function _check_weights (line 15) | def _check_weights(weights): function _get_weights (line 25) | def _get_weights(dist, weights): class FAISSNeighborsRegressor (line 45) | class FAISSNeighborsRegressor: method __init__ (line 46) | def __init__(self, n_neighbors=5, weights="uniform", n_jobs=-1, index_... method fit (line 68) | def fit(self, X, y): method predict (line 82) | def predict(self, X): method __getstate__ (line 105) | def __getstate__(self): method __setstate__ (line 114) | def __setstate__(self, state): class FAISSNeighborsClassifier (line 123) | class FAISSNeighborsClassifier: method __init__ (line 124) | def __init__(self, n_neighbors=5, weights="uniform", n_jobs=-1, index_... method fit (line 147) | def fit(self, X, y): method predict (line 162) | def predict(self, X): method predict_proba (line 176) | def predict_proba(self, X): method __getstate__ (line 197) | def __getstate__(self): method __setstate__ (line 206) | def __setstate__(self, state): FILE: tabular/src/autogluon/tabular/models/lgb/callbacks.py function early_stopping_custom (line 18) | def early_stopping_custom( FILE: tabular/src/autogluon/tabular/models/lgb/hyperparameters/parameters.py function get_lgb_objective (line 8) | def get_lgb_objective(problem_type): function get_param_baseline (line 18) | def get_param_baseline(problem_type): function get_param_binary_baseline (line 31) | def get_param_binary_baseline(): function get_param_multiclass_baseline (line 38) | def get_param_multiclass_baseline(): function get_param_regression_baseline (line 45) | def get_param_regression_baseline(): function get_param_softclass_baseline (line 52) | def get_param_softclass_baseline(): FILE: tabular/src/autogluon/tabular/models/lgb/hyperparameters/searchspaces.py function get_default_searchspace (line 7) | def get_default_searchspace(problem_type): function get_searchspace_multiclass_baseline (line 18) | def get_searchspace_multiclass_baseline(): function get_searchspace_binary_baseline (line 33) | def get_searchspace_binary_baseline(): function get_searchspace_regression_baseline (line 43) | def get_searchspace_regression_baseline(): FILE: tabular/src/autogluon/tabular/models/lgb/lgb_model.py class LGBModel (line 36) | class LGBModel(AbstractModel): method __init__ (line 53) | def __init__(self, **kwargs): method _set_default_params (line 60) | def _set_default_params(self): method _get_default_searchspace (line 65) | def _get_default_searchspace(self): method _get_stopping_metric_internal (line 69) | def _get_stopping_metric_internal(self): method _estimate_memory_usage (line 85) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 97) | def _estimate_memory_usage_static( method _estimate_memory_usage_static_lite (line 127) | def _estimate_memory_usage_static_lite( method _estimate_memory_usage_common (line 145) | def _estimate_memory_usage_common( method _fit (line 187) | def _fit( method _predict_proba (line 456) | def _predict_proba(self, X, num_cpus=0, **kwargs) -> np.ndarray: method _post_process_predictions (line 462) | def _post_process_predictions(self, y_pred_proba) -> np.ndarray: method _clean_column_name_for_lgb (line 488) | def _clean_column_name_for_lgb(column_name): method _rename_columns (line 497) | def _rename_columns(cls, features: list) -> dict: method _preprocess_nonadaptive (line 553) | def _preprocess_nonadaptive(self, X: pd.DataFrame, is_train: bool = Fa... method generate_datasets (line 581) | def generate_datasets( method _get_train_loss_name (line 671) | def _get_train_loss_name(self): method _get_early_stopping_rounds (line 682) | def _get_early_stopping_rounds(self, num_rows_train, strategy="auto"): method _get_default_auxiliary_params (line 685) | def _get_default_auxiliary_params(self) -> dict: method _is_gpu_lgbm_installed (line 694) | def _is_gpu_lgbm_installed(): method _is_cuda_lgbm_installed (line 715) | def _is_cuda_lgbm_installed(): method get_minimum_resources (line 735) | def get_minimum_resources(self, is_gpu_available=False): method _get_default_resources (line 743) | def _get_default_resources(self): method supported_problem_types (line 750) | def supported_problem_types(cls) -> list[str] | None: method _ag_params (line 753) | def _ag_params(self) -> set: method _class_tags (line 757) | def _class_tags(cls): method _more_tags (line 764) | def _more_tags(self): FILE: tabular/src/autogluon/tabular/models/lgb/lgb_utils.py function convert_ag_metric_to_lgbm (line 35) | def convert_ag_metric_to_lgbm(ag_metric_name, problem_type): function func_generator (line 39) | def func_generator(metric, is_higher_better, needs_pred_proba, problem_t... function softclass_lgbobj (line 93) | def softclass_lgbobj(preds, train_data): function construct_dataset (line 107) | def construct_dataset( function train_lgb_model (line 130) | def train_lgb_model(early_stopping_callback_kwargs=None, **train_params): class QuantileBooster (line 143) | class QuantileBooster: method __init__ (line 146) | def __init__(self, quantile_levels: list[float], early_stopping_callba... method fit (line 164) | def fit(self, **train_params_base): method predict (line 191) | def predict(self, X, num_threads=0): method best_iteration (line 198) | def best_iteration(self): method current_iteration (line 201) | def current_iteration(self): FILE: tabular/src/autogluon/tabular/models/lr/hyperparameters/parameters.py function get_param_baseline (line 21) | def get_param_baseline(): function _get_solver (line 34) | def _get_solver(problem_type): FILE: tabular/src/autogluon/tabular/models/lr/hyperparameters/searchspaces.py function get_default_searchspace (line 4) | def get_default_searchspace(problem_type, num_classes=None): FILE: tabular/src/autogluon/tabular/models/lr/lr_model.py class LinearModel (line 33) | class LinearModel(AbstractModel): method __init__ (line 49) | def __init__(self, **kwargs): method _get_model_type (line 54) | def _get_model_type(self): method _tokenize (line 84) | def _tokenize(self, s): method _get_types_of_features (line 87) | def _get_types_of_features(self, df): method _select_features (line 105) | def _select_features(self, df, **kwargs): method _preprocess (line 114) | def _preprocess(self, X, is_train=False, **kwargs): method _preprocess_train (line 122) | def _preprocess_train(self, X, feature_types, vect_max_features): method _set_default_params (line 172) | def _set_default_params(self): method _get_default_searchspace (line 180) | def _get_default_searchspace(self): method _fit (line 183) | def _fit(self, X, y, time_limit=None, num_cpus=-1, sample_weight=None,... method _select_features_handle_text_include (line 278) | def _select_features_handle_text_include( method _select_features_handle_text_only (line 288) | def _select_features_handle_text_only( method _select_features_handle_text_ignore (line 295) | def _select_features_handle_text_ignore( method _select_categorical (line 304) | def _select_categorical(self, df, features): method _select_continuous (line 307) | def _select_continuous(self, df, features): method _select_text (line 319) | def _select_text(self, df, features): method _select_bool (line 322) | def _select_bool(self, df, features): method _get_default_auxiliary_params (line 325) | def _get_default_auxiliary_params(self) -> dict: method _estimate_memory_usage (line 334) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 345) | def _estimate_memory_usage_static( method _get_maximum_resources (line 353) | def _get_maximum_resources(self) -> dict[str, int | float]: method supported_problem_types (line 358) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 362) | def _class_tags(cls): method _more_tags (line 365) | def _more_tags(self): FILE: tabular/src/autogluon/tabular/models/lr/lr_preprocessing_utils.py class OheFeaturesGenerator (line 6) | class OheFeaturesGenerator(BaseEstimator, TransformerMixin): method __init__ (line 7) | def __init__(self): method fit (line 10) | def fit(self, X, y=None): method transform (line 16) | def transform(self, X, y=None): method get_feature_names (line 19) | def get_feature_names(self): class NlpDataPreprocessor (line 23) | class NlpDataPreprocessor(BaseEstimator, TransformerMixin): method __init__ (line 24) | def __init__(self, nlp_cols): method fit (line 27) | def fit(self, X, y=None): method transform (line 30) | def transform(self, X, y=None): FILE: tabular/src/autogluon/tabular/models/lr/lr_rapids_model.py class LinearRapidsModel (line 14) | class LinearRapidsModel(RapidsModelMixin, LinearModel): method _get_model_type (line 26) | def _get_model_type(self): method _set_default_params (line 42) | def _set_default_params(self): method _preprocess (line 50) | def _preprocess(self, X, **kwargs): method _fit (line 56) | def _fit(self, X, y, **kwargs): FILE: tabular/src/autogluon/tabular/models/mitra/_internal/config/config_pretrain.py class ConfigData (line 16) | class ConfigData: method __post_init__ (line 29) | def __post_init__(self): class ConfigModel (line 35) | class ConfigModel: class ConfigPreprocessing (line 41) | class ConfigPreprocessing: class ConfigGradScaler (line 47) | class ConfigGradScaler: method __post_init__ (line 53) | def __post_init__(self): class ConfigOptim (line 61) | class ConfigOptim: method from_hydra (line 84) | def from_hydra(cls, cfg_hydra: DictConfig) -> Self: method __post_init__ (line 94) | def __post_init__(self): class ConfigSaveLoadMixin (line 98) | class ConfigSaveLoadMixin(yaml.YAMLObject): method save (line 99) | def save(self, path: Path) -> None: method load (line 106) | def load(cls, path: Path) -> Self: class ConfigPretrain (line 115) | class ConfigPretrain(ConfigSaveLoadMixin): method from_hydra (line 138) | def from_hydra(cls, cfg_hydra: DictConfig): FILE: tabular/src/autogluon/tabular/models/mitra/_internal/config/config_run.py class ConfigRun (line 12) | class ConfigRun(ConfigSaveLoadMixin): method create (line 19) | def create(cls, device: torch.device, seed: int, model_name: ModelName... FILE: tabular/src/autogluon/tabular/models/mitra/_internal/config/enums.py class StrEnum (line 9) | class StrEnum(str, Enum): method __new__ (line 14) | def __new__(cls, value): method __str__ (line 19) | def __str__(self): class Task (line 23) | class Task(StrEnum): class FeatureType (line 28) | class FeatureType(StrEnum): class SearchType (line 34) | class SearchType(StrEnum): class DatasetSize (line 39) | class DatasetSize(IntEnum): class DataSplit (line 45) | class DataSplit(StrEnum): class Phase (line 51) | class Phase(StrEnum): class ModelName (line 57) | class ModelName(StrEnum): class ModelClass (line 91) | class ModelClass(StrEnum): class DownstreamTask (line 98) | class DownstreamTask(StrEnum): class BenchmarkName (line 103) | class BenchmarkName(StrEnum): class BenchmarkOrigin (line 122) | class BenchmarkOrigin(StrEnum): class GeneratorName (line 127) | class GeneratorName(StrEnum): class MetricName (line 149) | class MetricName(StrEnum): class LossName (line 160) | class LossName(StrEnum): FILE: tabular/src/autogluon/tabular/models/mitra/_internal/core/callbacks.py class EarlyStopping (line 5) | class EarlyStopping: method __init__ (line 6) | def __init__(self, patience=10, delta=0.0001, metric="log_loss"): method __call__ (line 14) | def __call__(self, val_loss): method we_should_stop (line 36) | def we_should_stop(self): class Checkpoint (line 40) | class Checkpoint: method __init__ (line 41) | def __init__(self): method reset (line 45) | def reset(self, net: torch.nn.Module): method __call__ (line 51) | def __call__(self, net: torch.nn.Module, loss: float): method set_to_best (line 58) | def set_to_best(self, net): class EpochStatistics (line 62) | class EpochStatistics: method __init__ (line 63) | def __init__(self) -> None: method update (line 68) | def update(self, loss, score, n): method get (line 73) | def get(self): class TrackOutput (line 77) | class TrackOutput: method __init__ (line 78) | def __init__(self) -> None: method update (line 82) | def update(self, y_true: np.ndarray, y_pred: np.ndarray): method get (line 86) | def get(self): FILE: tabular/src/autogluon/tabular/models/mitra/_internal/core/get_loss.py class CrossEntropyLossExtraBatch (line 9) | class CrossEntropyLossExtraBatch(torch.nn.Module): method __init__ (line 10) | def __init__(self, label_smoothing: float): method forward (line 15) | def forward(self, input, target): function get_loss (line 29) | def get_loss(cfg: ConfigRun): function get_loss_pretrain (line 42) | def get_loss_pretrain(cfg: ConfigPretrain): FILE: tabular/src/autogluon/tabular/models/mitra/_internal/core/get_optimizer.py function get_optimizer (line 7) | def get_optimizer(hyperparams: dict, model: torch.nn.Module) -> torch.op... function get_optimizer_pretrain (line 26) | def get_optimizer_pretrain(cfg: ConfigPretrain, model: torch.nn.Module) ... class GradScaler (line 53) | class GradScaler(torch.amp.GradScaler): method __init__ (line 54) | def __init__( method update (line 71) | def update(self): function move_optimizer_to (line 81) | def move_optimizer_to(optim, device): FILE: tabular/src/autogluon/tabular/models/mitra/_internal/core/get_scheduler.py function get_scheduler (line 9) | def get_scheduler( function get_scheduler_pretrain (line 44) | def get_scheduler_pretrain(cfg: ConfigPretrain, optimizer: torch.optim.O... FILE: tabular/src/autogluon/tabular/models/mitra/_internal/core/prediction_metrics.py class PredictionMetrics (line 14) | class PredictionMetrics: method from_prediction (line 21) | def from_prediction(cls, y_pred: np.ndarray, y_true: np.ndarray, task:... function compute_metrics (line 27) | def compute_metrics(y_pred: np.ndarray, y_true: np.ndarray, task: Task) ... function compute_classification_metrics (line 34) | def compute_classification_metrics(y_pred: np.ndarray, y_true: np.ndarra... function roc_auc_score_multiclass (line 61) | def roc_auc_score_multiclass(y_true, y_pred_proba, multi_class="ovo", av... function compute_regression_metrics (line 81) | def compute_regression_metrics(y_pred: np.ndarray, y_true: np.ndarray) -... class PredictionMetricsTracker (line 95) | class PredictionMetricsTracker: method __init__ (line 101) | def __init__(self, task: Task, preprocessor: Preprocessor) -> None: method reset (line 106) | def reset(self) -> None: method update (line 110) | def update(self, y_pred: torch.Tensor, y_true: torch.Tensor, train: bo... method get_metrics (line 123) | def get_metrics(self) -> PredictionMetrics: FILE: tabular/src/autogluon/tabular/models/mitra/_internal/core/trainer_finetune.py class TrainerFinetune (line 20) | class TrainerFinetune(BaseEstimator): method __init__ (line 21) | def __init__( method set_device (line 74) | def set_device(self, device: str): method post_fit_optimize (line 78) | def post_fit_optimize(self): method train (line 89) | def train(self, x_train: np.ndarray, y_train: np.ndarray, x_val: np.nd... method evaluate (line 212) | def evaluate( method predict (line 274) | def predict(self, x_support: np.ndarray, y_support: np.ndarray, x_quer... method load_params (line 335) | def load_params(self, path): method make_loader (line 338) | def make_loader(self, dataset: torch.utils.data.Dataset, training: boo... method log_start_metrics (line 358) | def log_start_metrics(self, metrics_valid: PredictionMetrics): method log_metrics (line 383) | def log_metrics(self, epoch: int, metrics_train: PredictionMetrics, me... FILE: tabular/src/autogluon/tabular/models/mitra/_internal/data/collator.py class CollatorWithPadding (line 4) | class CollatorWithPadding: method __init__ (line 5) | def __init__( method __call__ (line 13) | def __call__(self, batch: list[dict[str, torch.Tensor]]) -> dict[str, ... FILE: tabular/src/autogluon/tabular/models/mitra/_internal/data/dataset_finetune.py class DatasetFinetune (line 11) | class DatasetFinetune(torch.utils.data.Dataset): method __init__ (line 21) | def __init__( method __len__ (line 58) | def __len__(self): method __getitem__ (line 61) | def __getitem__(self, idx): method split_in_chunks (line 79) | def split_in_chunks(self, x: np.ndarray, batch_size: int) -> list[np.n... function DatasetFinetuneGenerator (line 93) | def DatasetFinetuneGenerator( FILE: tabular/src/autogluon/tabular/models/mitra/_internal/data/dataset_split.py function make_dataset_split (line 9) | def make_dataset_split(x: np.ndarray, y: np.ndarray, task: Task, seed: i... function make_stratified_dataset_split (line 24) | def make_stratified_dataset_split(x, y, n_splits=5, seed=0): function make_standard_dataset_split (line 54) | def make_standard_dataset_split(x, y, seed): FILE: tabular/src/autogluon/tabular/models/mitra/_internal/data/preprocessor.py class NoneTransformer (line 16) | class NoneTransformer(BaseEstimator, TransformerMixin): method fit (line 17) | def fit(self, X, y=None): method transform (line 20) | def transform(self, X): class Preprocessor (line 24) | class Preprocessor: method __init__ (line 33) | def __init__( method fit (line 61) | def fit(self, X: np.ndarray, y: np.ndarray) -> "Preprocessor": method transform_X (line 112) | def transform_X(self, X: np.ndarray): method transform_tabpfn (line 143) | def transform_tabpfn(self, X: np.ndarray): method transform_y (line 195) | def transform_y(self, y: np.ndarray): method inverse_transform_y (line 217) | def inverse_transform_y(self, y: np.ndarray): method fit_transform_quantile_transformer (line 235) | def fit_transform_quantile_transformer(self, X: np.ndarray) -> np.ndar... method determine_which_features_are_singular (line 243) | def determine_which_features_are_singular(self, x: np.ndarray) -> None: method determine_which_features_to_select (line 246) | def determine_which_features_to_select(self, x: np.ndarray, y: np.ndar... method compute_pre_nan_mean (line 259) | def compute_pre_nan_mean(self, x: np.ndarray) -> None: method impute_nan_features_with_mean (line 265) | def impute_nan_features_with_mean(self, x: np.ndarray) -> np.ndarray: method select_features (line 270) | def select_features(self, x: np.ndarray) -> np.ndarray: method cutoff_singular_features (line 280) | def cutoff_singular_features(self, x: np.ndarray, singular_features: n... method calc_mean_std (line 286) | def calc_mean_std(self, x: np.ndarray) -> tuple[np.ndarray, np.ndarray]: method normalize_by_mean_std (line 294) | def normalize_by_mean_std(self, x: np.ndarray, mean: np.ndarray, std: ... method normalize_by_feature_count (line 302) | def normalize_by_feature_count(self, x: np.ndarray) -> np.ndarray: method extend_feature_dim_to_dim_embedding (line 313) | def extend_feature_dim_to_dim_embedding(self, x: np.ndarray, dim_embed... method determine_mix_max_scale (line 324) | def determine_mix_max_scale(self, y: np.ndarray) -> None: method normalize_y (line 329) | def normalize_y(self, y: np.ndarray) -> np.ndarray: method undo_normalize_y (line 333) | def undo_normalize_y(self, y: np.ndarray) -> np.ndarray: method determine_regression_mirror (line 337) | def determine_regression_mirror(self) -> None: method apply_random_mirror_regression (line 340) | def apply_random_mirror_regression(self, y: np.ndarray) -> np.ndarray: method determine_mirror (line 345) | def determine_mirror(self, x: np.ndarray) -> None: method apply_random_mirror_x (line 349) | def apply_random_mirror_x(self, x: np.ndarray) -> np.ndarray: method determine_shuffle_class_order (line 353) | def determine_shuffle_class_order(self) -> None: method randomize_class_order (line 359) | def randomize_class_order(self, y: np.ndarray) -> np.ndarray: method undo_randomize_class_order (line 365) | def undo_randomize_class_order(self, y_logits: np.ndarray) -> np.ndarray: method extract_correct_classes (line 376) | def extract_correct_classes(self, y_logits: np.ndarray) -> np.ndarray: method determine_feature_order (line 383) | def determine_feature_order(self, x: np.ndarray) -> None: method randomize_feature_order (line 387) | def randomize_feature_order(self, x: np.ndarray) -> np.ndarray: FILE: tabular/src/autogluon/tabular/models/mitra/_internal/models/base.py class BaseModel (line 7) | class BaseModel(nn.Module, ABC): method __init__ (line 8) | def __init__(self): method init_weights (line 11) | def init_weights(self): method forward (line 16) | def forward(self, x_support: torch.Tensor, y_support: torch.Tensor, x_... FILE: tabular/src/autogluon/tabular/models/mitra/_internal/models/embedding.py class Tab2DEmbeddingX (line 7) | class Tab2DEmbeddingX(torch.nn.Module): method __init__ (line 8) | def __init__(self, dim: int) -> None: method forward (line 14) | def forward(self, x: torch.Tensor) -> torch.Tensor: class Tab2DQuantileEmbeddingX (line 21) | class Tab2DQuantileEmbeddingX(torch.nn.Module): method __init__ (line 22) | def __init__( method forward (line 30) | def forward( class Tab2DEmbeddingY (line 90) | class Tab2DEmbeddingY(torch.nn.Module): method __init__ (line 91) | def __init__(self, dim: int, n_classes: int) -> None: method forward (line 99) | def forward( class Tab2DEmbeddingYClasses (line 117) | class Tab2DEmbeddingYClasses(torch.nn.Module): method __init__ (line 118) | def __init__( method forward (line 134) | def forward( class Tab2DEmbeddingYRegression (line 152) | class Tab2DEmbeddingYRegression(torch.nn.Module): method __init__ (line 153) | def __init__(self, dim: int) -> None: method forward (line 160) | def forward( FILE: tabular/src/autogluon/tabular/models/mitra/_internal/models/tab2d.py class Tab2D (line 37) | class Tab2D(BaseModel): method __init__ (line 38) | def __init__( method forward (line 96) | def forward( method init_weights (line 193) | def init_weights(self) -> None: method save_pretrained (line 201) | def save_pretrained(self, save_directory: str): method from_pretrained (line 217) | def from_pretrained(cls, path_or_repo_id: str, device: str = "cuda") -... class Padder (line 240) | class Padder(torch.nn.Module): method __init__ (line 241) | def __init__(self, x: torch.Tensor, padding_mask: torch.Tensor, featur... method _init_cpu_fallback (line 258) | def _init_cpu_fallback(self, x: torch.Tensor, n_obs: int, n_feat: int): method _init_flash_attn (line 279) | def _init_flash_attn(self, x: torch.Tensor, n_obs: int, n_feat: int): method base_to_obs (line 310) | def base_to_obs(self, x: torch.Tensor) -> torch.Tensor: method base_to_feat (line 323) | def base_to_feat(self, x: torch.Tensor) -> torch.Tensor: method obs_to_base (line 335) | def obs_to_base(self, x: torch.Tensor) -> torch.Tensor: method feat_to_base (line 350) | def feat_to_base(self, x: torch.Tensor) -> torch.Tensor: method obs_to_feat (line 364) | def obs_to_feat(self, x: torch.Tensor) -> torch.Tensor: method feat_to_obs (line 373) | def feat_to_obs(self, x: torch.Tensor) -> torch.Tensor: class Layer (line 383) | class Layer(torch.nn.Module): method __init__ (line 384) | def __init__(self, dim: int, n_heads: int, use_flash_attn: bool) -> None: method forward (line 401) | def forward( class MultiheadAttention (line 618) | class MultiheadAttention(torch.nn.Module): method __init__ (line 619) | def __init__(self, dim: int, n_heads: int, use_flash_attn: bool) -> None: method forward (line 631) | def forward( FILE: tabular/src/autogluon/tabular/models/mitra/_internal/utils/set_seed.py function set_seed (line 7) | def set_seed(seed: int) -> None: function seed_worker (line 15) | def seed_worker(worker_id: int) -> None: FILE: tabular/src/autogluon/tabular/models/mitra/mitra_model.py class MitraModel (line 19) | class MitraModel(AbstractTorchModel): method __init__ (line 40) | def __init__(self, **kwargs): method _get_default_device (line 46) | def _get_default_device(): method get_model_cls (line 54) | def get_model_cls(self): method _preprocess (line 67) | def _preprocess(self, X: pd.DataFrame, is_train: bool = False, **kwarg... method _fit (line 82) | def _fit( method _set_default_params (line 181) | def _set_default_params(self): method _get_default_auxiliary_params (line 188) | def _get_default_auxiliary_params(self) -> dict: method weights_path (line 199) | def weights_path(self, path: str | None = None) -> str: method save (line 204) | def save(self, path: str = None, verbose=True) -> str: method _save_model_artifact (line 219) | def _save_model_artifact(self, path: str | None): method _load_model_artifact (line 236) | def _load_model_artifact(self): method _set_device (line 245) | def _set_device(self, device: str): method get_device (line 249) | def get_device(self) -> str: method load (line 253) | def load(cls, path: str, reset_paths=True, verbose=True) -> Self: method download_weights (line 262) | def download_weights(cls, repo_id: str): method download_default_weights (line 273) | def download_default_weights(cls): method supported_problem_types (line 288) | def supported_problem_types(cls) -> Optional[List[str]]: method _get_default_ag_args_ensemble (line 292) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _get_default_resources (line 301) | def _get_default_resources(self) -> tuple[int, int]: method _estimate_memory_usage (line 309) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 315) | def _estimate_memory_usage_static( method _estimate_memory_usage_static_cpu_icl (line 333) | def _estimate_memory_usage_static_cpu_icl( method _estimate_memory_usage_static_cpu_ft_icl (line 353) | def _estimate_memory_usage_static_cpu_ft_icl( method _estimate_memory_usage_static_gpu_cpu (line 373) | def _estimate_memory_usage_static_gpu_cpu( method _estimate_memory_usage_static_gpu_gpu (line 388) | def _estimate_memory_usage_static_gpu_gpu( method _class_tags (line 406) | def _class_tags(cls): method _more_tags (line 414) | def _more_tags(self) -> dict: FILE: tabular/src/autogluon/tabular/models/mitra/sklearn_interface.py function _get_default_device (line 41) | def _get_default_device(): class MitraBase (line 61) | class MitraBase(BaseEstimator): method __init__ (line 64) | def __init__( method _create_config (line 126) | def _create_config(self, task, dim_output, time_limit=None): method _split_data (line 179) | def _split_data(self, X, y): method _train_ensemble (line 191) | def _train_ensemble(self, X_train, y_train, X_valid, y_valid, task, di... class MitraClassifier (line 258) | class MitraClassifier(MitraBase, ClassifierMixin): method __init__ (line 261) | def __init__( method fit (line 305) | def fit(self, X, y, X_val=None, y_val=None, time_limit=None): method predict (line 350) | def predict(self, X): method predict_proba (line 370) | def predict_proba(self, X): class MitraRegressor (line 398) | class MitraRegressor(MitraBase, RegressorMixin): method __init__ (line 401) | def __init__( method fit (line 445) | def fit(self, X, y, X_val=None, y_val=None, time_limit=None): method predict (line 481) | def predict(self, X): function mitra_deterministic_context (line 508) | def mitra_deterministic_context(): FILE: tabular/src/autogluon/tabular/models/realmlp/realmlp_model.py function set_logger_level (line 26) | def set_logger_level(logger_name: str, level: int): class RealMLPModel (line 37) | class RealMLPModel(AbstractTorchModel): method __init__ (line 57) | def __init__(self, **kwargs): method get_model_cls (line 66) | def get_model_cls(self, default_hyperparameters: Literal["td", "td_s"]... method get_device (line 87) | def get_device(self) -> str: method _set_device (line 90) | def _set_device(self, device: str): method _fit (line 93) | def _fit( method _predict_proba (line 215) | def _predict_proba(self, X, **kwargs) -> np.ndarray: method _preprocess (line 221) | def _preprocess( method _set_default_params (line 264) | def _set_default_params(self): method supported_problem_types (line 287) | def supported_problem_types(cls) -> list[str] | None: method _get_default_stopping_metric (line 290) | def _get_default_stopping_metric(self): method _get_default_resources (line 293) | def _get_default_resources(self) -> tuple[int, int]: method _estimate_memory_usage (line 301) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 312) | def _estimate_memory_usage_static( method _class_tags (line 372) | def _class_tags(cls) -> dict: method _more_tags (line 375) | def _more_tags(self) -> dict: FILE: tabular/src/autogluon/tabular/models/rf/compilers/native.py class AbstractNativeCompiler (line 5) | class AbstractNativeCompiler: method can_compile (line 10) | def can_compile(): method compile (line 14) | def compile(model, path: str, input_types=None): method save (line 33) | def save(model, path: str): method load (line 39) | def load(path: str): FILE: tabular/src/autogluon/tabular/models/rf/compilers/onnx.py class InferenceSessionWrapper (line 6) | class InferenceSessionWrapper: method __init__ (line 12) | def __init__(self, onnx_bytes): method run (line 17) | def run(self, *args): method get_inputs (line 20) | def get_inputs(self, *args): method get_outputs (line 23) | def get_outputs(self, *args): method __getstate__ (line 26) | def __getstate__(self): method __setstate__ (line 30) | def __setstate__(self, values): class RFOnnxPredictor (line 34) | class RFOnnxPredictor: method __init__ (line 35) | def __init__(self, model): method predict (line 39) | def predict(self, X): method predict_proba (line 45) | def predict_proba(self, X): class RFOnnxCompiler (line 54) | class RFOnnxCompiler: method can_compile (line 59) | def can_compile(): method compile (line 70) | def compile(model, path: str, input_types=None): method save (line 110) | def save(model, path: str) -> str: method load (line 119) | def load(path: str) -> RFOnnxPredictor: FILE: tabular/src/autogluon/tabular/models/rf/rf_model.py class RFModel (line 26) | class RFModel(AbstractModel): method __init__ (line 36) | def __init__(self, **kwargs): method _get_model_type (line 43) | def _get_model_type(self): method _preprocess (line 77) | def _preprocess(self, X, **kwargs): method _set_default_params (line 88) | def _set_default_params(self): method _get_default_searchspace (line 114) | def _get_default_searchspace(self): method _get_num_trees_per_estimator (line 122) | def _get_num_trees_per_estimator(self) -> int: method _get_num_trees_per_estimator_static (line 126) | def _get_num_trees_per_estimator_static(cls, problem_type: str, num_cl... method _estimate_memory_usage (line 137) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 148) | def _estimate_memory_usage_static( method _validate_fit_memory_usage (line 171) | def _validate_fit_memory_usage( method _expected_mem_usage (line 185) | def _expected_mem_usage(self, n_estimators_final, bytes_per_estimator): method _fit (line 189) | def _fit(self, X, y, num_cpus=-1, time_limit=None, sample_weight=None,... method _predict_proba (line 289) | def _predict_proba(self, X, **kwargs): method predict_proba_oof (line 302) | def predict_proba_oof(self, X, normalize=None, **kwargs): method _is_sklearn_1 (line 312) | def _is_sklearn_1(self) -> bool: method _model_supports_oob_pred_proba (line 316) | def _model_supports_oob_pred_proba(self) -> bool: method _predict_proba_oof (line 322) | def _predict_proba_oof(self, X, y, **kwargs): method _get_maximum_resources (line 404) | def _get_maximum_resources(self) -> dict[str, int | float]: method _get_default_auxiliary_params (line 408) | def _get_default_auxiliary_params(self) -> dict: method _get_default_ag_args_ensemble (line 417) | def _get_default_ag_args_ensemble(cls, problem_type=None, **kwargs) ->... method supported_problem_types (line 425) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 429) | def _class_tags(cls): method _more_tags (line 432) | def _more_tags(self): method _valid_compilers (line 443) | def _valid_compilers(cls): method _default_compiler (line 447) | def _default_compiler(cls): FILE: tabular/src/autogluon/tabular/models/rf/rf_quantile.py function weighted_percentile (line 49) | def weighted_percentile(a, q, weights=None, sorter=None, is_filtered=Fal... class BaseTreeQuantileRegressor (line 129) | class BaseTreeQuantileRegressor(BaseDecisionTree): method predict (line 130) | def predict(self, X, quantile=None, check_input=False): method fit (line 166) | def fit(self, X, y, sample_weight=None, check_input=True): class DecisionTreeQuantileRegressor (line 212) | class DecisionTreeQuantileRegressor(BaseTreeQuantileRegressor, DecisionT... method __init__ (line 314) | def __init__( class ExtraTreeQuantileRegressor (line 337) | class ExtraTreeQuantileRegressor(BaseTreeQuantileRegressor, ExtraTreeReg... method __init__ (line 338) | def __init__( function generate_sample_indices (line 361) | def generate_sample_indices(random_state, n_samples): function get_weighted_neighbors_dataframe (line 385) | def get_weighted_neighbors_dataframe(X_leaves, y_train_leaves, y_train, ... function get_quantiles (line 437) | def get_quantiles(neighbors_df, quantile_levels): class BaseForestQuantileRegressor (line 466) | class BaseForestQuantileRegressor(ForestRegressor): method fit (line 467) | def fit(self, X, y, sample_weight=None): method predict (line 527) | def predict(self, X, quantile_levels=None): class RandomForestQuantileRegressor (line 564) | class RandomForestQuantileRegressor(BaseForestQuantileRegressor): method __init__ (line 672) | def __init__( class ExtraTreesQuantileRegressor (line 718) | class ExtraTreesQuantileRegressor(BaseForestQuantileRegressor): method __init__ (line 824) | def __init__( FILE: tabular/src/autogluon/tabular/models/rf/rf_rapids_model.py class RFRapidsModel (line 19) | class RFRapidsModel(RapidsModelMixin, RFModel): method _get_model_type (line 31) | def _get_model_type(self): method _set_default_params (line 40) | def _set_default_params(self): method _fit (line 49) | def _fit(self, X, y, **kwargs): FILE: tabular/src/autogluon/tabular/models/tabdpt/tabdpt_model.py class TabDPTModel (line 36) | class TabDPTModel(AbstractTorchModel): method __init__ (line 43) | def __init__(self, **kwargs): method _fit (line 49) | def _fit( method _get_tabdpt_params (line 78) | def _get_tabdpt_params(self, num_gpus: float) -> tuple[dict, dict]: method _use_flash (line 106) | def _use_flash(num_gpus: float) -> bool: method _post_fit (line 124) | def _post_fit(self, **kwargs): method get_device (line 129) | def get_device(self) -> str: method _set_device (line 132) | def _set_device(self, device: str): method _get_default_resources (line 141) | def _get_default_resources(self) -> tuple[int, int]: method get_minimum_resources (line 149) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method _predict_proba (line 155) | def _predict_proba(self, X, **kwargs) -> np.ndarray: method _preprocess (line 165) | def _preprocess(self, X: pd.DataFrame, **kwargs) -> pd.DataFrame: method supported_problem_types (line 177) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 181) | def _class_tags(cls): method _more_tags (line 184) | def _more_tags(self) -> dict: method _get_default_auxiliary_params (line 187) | def _get_default_auxiliary_params(self) -> dict: method _get_default_ag_args_ensemble (line 199) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _estimate_memory_usage_static (line 209) | def _estimate_memory_usage_static( FILE: tabular/src/autogluon/tabular/models/tabicl/tabicl_model.py class TabICLModel (line 20) | class TabICLModel(AbstractTorchModel): method get_model_cls (line 51) | def get_model_cls(self): method _get_batch_size (line 63) | def _get_batch_size(n_cells: int): method get_checkpoint_version (line 71) | def get_checkpoint_version(self, hyperparameter: dict) -> str: method _fit (line 93) | def _fit( method get_device (line 135) | def get_device(self) -> str: method _set_device (line 139) | def _set_device(self, device: str): method _get_default_auxiliary_params (line 148) | def _get_default_auxiliary_params(self) -> dict: method supported_problem_types (line 161) | def supported_problem_types(cls) -> list[str] | None: method _get_default_resources (line 164) | def _get_default_resources(self) -> tuple[int, int]: method _estimate_memory_usage (line 171) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 182) | def _estimate_memory_usage_static( method _get_default_ag_args_ensemble (line 218) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _class_tags (line 233) | def _class_tags(cls) -> dict: method _more_tags (line 236) | def _more_tags(self) -> dict: FILE: tabular/src/autogluon/tabular/models/tabm/_tabm_internal.py function get_tabm_auto_batch_size (line 34) | def get_tabm_auto_batch_size(n_train: int) -> int: class RTDLQuantileTransformer (line 49) | class RTDLQuantileTransformer(BaseEstimator, TransformerMixin): method __init__ (line 51) | def __init__( method fit (line 65) | def fit(self, X, y=None): method transform (line 87) | def transform(self, X, y=None): method _add_noise (line 91) | def _add_noise(self, X): class TabMOrdinalEncoder (line 95) | class TabMOrdinalEncoder(BaseEstimator, TransformerMixin): method __init__ (line 97) | def __init__(self): method fit (line 101) | def fit(self, X, y=None): method transform (line 117) | def transform(self, X): method get_cardinalities (line 130) | def get_cardinalities(self): class TabMImplementation (line 135) | class TabMImplementation: method __init__ (line 136) | def __init__(self, early_stopping_metric: Scorer, **config): method fit (line 148) | def fit( method predict_raw (line 492) | def predict_raw(self, X: pd.DataFrame) -> torch.Tensor: method predict (line 537) | def predict(self, X: pd.DataFrame) -> np.ndarray: method predict_proba (line 543) | def predict_proba(self, X: pd.DataFrame) -> np.ndarray: FILE: tabular/src/autogluon/tabular/models/tabm/rtdl_num_embeddings.py function _check_input_shape (line 38) | def _check_input_shape(x: Tensor, expected_n_features: int) -> None: class LinearEmbeddings (line 47) | class LinearEmbeddings(nn.Module): method __init__ (line 68) | def __init__(self, n_features: int, d_embedding: int) -> None: method reset_parameters (line 84) | def reset_parameters(self) -> None: method get_output_shape (line 89) | def get_output_shape(self) -> torch.Size: method forward (line 93) | def forward(self, x: Tensor) -> Tensor: class LinearReLUEmbeddings (line 99) | class LinearReLUEmbeddings(nn.Module): method __init__ (line 121) | def __init__(self, n_features: int, d_embedding: int = 32) -> None: method get_output_shape (line 131) | def get_output_shape(self) -> torch.Size: method forward (line 135) | def forward(self, x: Tensor) -> Tensor: class _Periodic (line 142) | class _Periodic(nn.Module): method __init__ (line 151) | def __init__(self, n_features: int, k: int, sigma: float) -> None: method reset_parameters (line 160) | def reset_parameters(self): method forward (line 168) | def forward(self, x: Tensor) -> Tensor: class _NLinear (line 178) | class _NLinear(nn.Module): method __init__ (line 185) | def __init__(self, n: int, in_features: int, out_features: int, bias: ... method reset_parameters (line 191) | def reset_parameters(self): method forward (line 198) | def forward(self, x: torch.Tensor) -> torch.Tensor: class PeriodicEmbeddings (line 215) | class PeriodicEmbeddings(nn.Module): method __init__ (line 252) | def __init__( method get_output_shape (line 290) | def get_output_shape(self) -> torch.Size: method forward (line 298) | def forward(self, x: Tensor) -> Tensor: function _check_bins (line 307) | def _check_bins(bins: list[Tensor]) -> None: function compute_bins (line 337) | def compute_bins( class _PiecewiseLinearEncodingImpl (line 497) | class _PiecewiseLinearEncodingImpl(nn.Module): method __init__ (line 559) | def __init__(self, bins: list[Tensor]) -> None: method get_max_n_bins (line 619) | def get_max_n_bins(self) -> int: method forward (line 622) | def forward(self, x: Tensor) -> Tensor: class PiecewiseLinearEncoding (line 650) | class PiecewiseLinearEncoding(nn.Module): method __init__ (line 666) | def __init__(self, bins: list[Tensor]) -> None: method get_output_shape (line 674) | def get_output_shape(self) -> torch.Size: method forward (line 681) | def forward(self, x: Tensor) -> Tensor: class PiecewiseLinearEmbeddings (line 687) | class PiecewiseLinearEmbeddings(nn.Module): method __init__ (line 696) | def __init__( method get_output_shape (line 746) | def get_output_shape(self) -> torch.Size: method forward (line 752) | def forward(self, x: Tensor) -> Tensor: FILE: tabular/src/autogluon/tabular/models/tabm/tabm_model.py class TabMModel (line 20) | class TabMModel(AbstractTorchModel): method __init__ (line 41) | def __init__(self, **kwargs): method _fit (line 50) | def _fit( method _preprocess (line 120) | def _preprocess( method get_device (line 142) | def get_device(self) -> str: method _set_device (line 145) | def _set_device(self, device: str): method supported_problem_types (line 151) | def supported_problem_types(cls) -> list[str] | None: method _get_default_stopping_metric (line 154) | def _get_default_stopping_metric(self): method _get_default_resources (line 157) | def _get_default_resources(self) -> tuple[int, int]: method _estimate_memory_usage (line 164) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 175) | def _estimate_memory_usage_static( method _estimate_tabm_ram (line 216) | def _estimate_tabm_ram( method _get_default_auxiliary_params (line 266) | def _get_default_auxiliary_params(self) -> dict: method get_tabm_auto_batch_size (line 276) | def get_tabm_auto_batch_size(cls, n_samples: int) -> int: method _class_tags (line 291) | def _class_tags(cls): method _more_tags (line 297) | def _more_tags(self) -> dict: FILE: tabular/src/autogluon/tabular/models/tabm/tabm_reference.py function init_rsqrt_uniform_ (line 22) | def init_rsqrt_uniform_(x: Tensor, d: int) -> Tensor: function init_random_signs_ (line 29) | def init_random_signs_(x: Tensor) -> Tensor: class NLinear (line 36) | class NLinear(nn.Module): method __init__ (line 53) | def __init__(self, n: int, in_features: int, out_features: int, bias: ... method reset_parameters (line 59) | def reset_parameters(self): method forward (line 65) | def forward(self, x: torch.Tensor) -> torch.Tensor: class OneHotEncoding0d (line 77) | class OneHotEncoding0d(nn.Module): method __init__ (line 81) | def __init__(self, cardinalities: list[int]) -> None: method forward (line 85) | def forward(self, x: Tensor) -> Tensor: class ScaleEnsemble (line 112) | class ScaleEnsemble(nn.Module): method __init__ (line 113) | def __init__( method reset_parameters (line 125) | def reset_parameters(self) -> None: method forward (line 135) | def forward(self, x: Tensor) -> Tensor: class LinearEfficientEnsemble (line 140) | class LinearEfficientEnsemble(nn.Module): method __init__ (line 166) | def __init__( method reset_parameters (line 210) | def reset_parameters(self): method forward (line 231) | def forward(self, x: Tensor) -> Tensor: class MLP (line 248) | class MLP(nn.Module): method __init__ (line 249) | def __init__( method forward (line 274) | def forward(self, x: Tensor) -> Tensor: function make_efficient_ensemble (line 282) | def make_efficient_ensemble(module: nn.Module, EnsembleLayer, **kwargs) ... function _get_first_ensemble_layer (line 306) | def _get_first_ensemble_layer(backbone: MLP) -> LinearEfficientEnsemble: function _init_first_adapter (line 314) | def _init_first_adapter( function make_module (line 363) | def make_module(type: str, *args, **kwargs) -> nn.Module: function default_zero_weight_decay_condition (line 373) | def default_zero_weight_decay_condition( function make_parameter_groups (line 390) | def make_parameter_groups( class Model (line 418) | class Model(nn.Module): method __init__ (line 421) | def __init__( method forward (line 569) | def forward(self, x_num: Union[None, Tensor] = None, x_cat: Union[None... FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/config/config_run.py class ConfigRun (line 11) | class ConfigRun: FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/callbacks.py class EarlyStopping (line 12) | class EarlyStopping: method __init__ (line 13) | def __init__(self, patience=10, delta=0.0001): method __call__ (line 20) | def __call__(self, val_loss): method we_should_stop (line 33) | def we_should_stop(self): class Checkpoint (line 37) | class Checkpoint: method __init__ (line 38) | def __init__(self, dirname: Path = None, id: str = None, in_memory: bo... method reset (line 56) | def reset(self): method __call__ (line 62) | def __call__(self, net, loss: float, epoch: int): method save (line 70) | def save(self): method load (line 77) | def load(self): class EpochStatistics (line 86) | class EpochStatistics: method __init__ (line 87) | def __init__(self) -> None: method update (line 92) | def update(self, loss, score, n): method get (line 97) | def get(self): class TrackOutput (line 101) | class TrackOutput: method __init__ (line 102) | def __init__(self) -> None: method update (line 106) | def update(self, y_true: np.ndarray, y_pred: np.ndarray): method get (line 110) | def get(self): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/collator.py class CollatorWithPadding (line 6) | class CollatorWithPadding: method __init__ (line 7) | def __init__(self, pad_to_n_support_samples: Optional[int]) -> None: method __call__ (line 10) | def __call__(self, batch: list[dict[str, torch.Tensor]]) -> dict[str, ... FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/dataset_split.py function make_dataset_split (line 8) | def make_dataset_split( function make_stratified_dataset_split (line 20) | def make_stratified_dataset_split(x, y, rng: Generator = None): function make_standard_dataset_split (line 33) | def make_standard_dataset_split(x, y, rng: Generator = None): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/enums.py class Task (line 1) | class Task: FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/get_loss.py function get_loss (line 6) | def get_loss(task: Task): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/get_optimizer.py function get_optimizer (line 5) | def get_optimizer(hyperparams: dict, model: torch.nn.Module) -> torch.op... FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/get_scheduler.py function get_scheduler (line 5) | def get_scheduler(hyperparams: dict, optimizer: torch.optim.Optimizer): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/trainer_finetune.py class TrainerFinetune (line 29) | class TrainerFinetune(BaseEstimator): method __init__ (line 30) | def __init__( method set_random_seed (line 61) | def set_random_seed(self) -> Generator: method reset_optimizer (line 67) | def reset_optimizer(self): method train (line 71) | def train( method minimize_for_inference (line 194) | def minimize_for_inference(self): method train_epoch (line 199) | def train_epoch( method test_epoch (line 265) | def test_epoch(self, dataloader: torch.utils.data.DataLoader, y_test: ... method _get_memory_size (line 275) | def _get_memory_size(self) -> int: method predict (line 283) | def predict(self, x_support: np.ndarray, y_support: np.ndarray, x_quer... method predict_epoch (line 314) | def predict_epoch(self, dataloader: torch.utils.data.DataLoader) -> np... method make_loader (line 345) | def make_loader(self, dataset, training): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/core/y_transformer.py function create_y_transformer (line 9) | def create_y_transformer(y_train: np.ndarray, task: Task) -> Transformer... class QuantileTransformer1D (line 23) | class QuantileTransformer1D(BaseEstimator, TransformerMixin): method __init__ (line 24) | def __init__(self, output_distribution="normal") -> None: method fit (line 27) | def fit(self, x: np.ndarray): method transform (line 31) | def transform(self, x: np.ndarray): method inverse_transform (line 34) | def inverse_transform(self, x: np.ndarray): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/data/dataset_finetune.py class DatasetFinetune (line 11) | class DatasetFinetune(torch.utils.data.Dataset): method __init__ (line 21) | def __init__( method __len__ (line 56) | def __len__(self): method __getitem__ (line 59) | def __getitem__(self, idx): method split_in_chunks (line 77) | def split_in_chunks(self, x: np.ndarray, batch_size: int) -> list[np.n... function DatasetFinetuneGenerator (line 91) | def DatasetFinetuneGenerator( FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/data/preprocessor.py class Preprocessor (line 13) | class Preprocessor(TransformerMixin, BaseEstimator): method __init__ (line 22) | def __init__( method fit (line 34) | def fit(self, X: np.ndarray, y: np.ndarray): method transform (line 57) | def transform(self, X: np.ndarray): method determine_which_features_are_singular (line 76) | def determine_which_features_are_singular(self, x: np.ndarray) -> None: method determine_which_features_to_select (line 79) | def determine_which_features_to_select(self, x: np.ndarray, y: np.ndar... method compute_pre_nan_mean (line 91) | def compute_pre_nan_mean(self, x: np.ndarray) -> None: method impute_nan_features_with_mean (line 97) | def impute_nan_features_with_mean(self, x: np.ndarray) -> np.ndarray: method select_features (line 102) | def select_features(self, x: np.ndarray) -> np.ndarray: method cutoff_singular_features (line 108) | def cutoff_singular_features(self, x: np.ndarray, singular_features: n... method calc_mean_std (line 114) | def calc_mean_std(self, x: np.ndarray) -> tuple[np.ndarray, np.ndarray]: method normalize_by_mean_std (line 122) | def normalize_by_mean_std(self, x: np.ndarray, mean: np.ndarray, std: ... method normalize_by_feature_count (line 130) | def normalize_by_feature_count(self, x: np.ndarray, max_features) -> n... method extend_feature_dim_to_max_features (line 138) | def extend_feature_dim_to_max_features(self, x: np.ndarray, max_featur... FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/models/foundation/embedding.py class FoundationEmbeddingX (line 6) | class FoundationEmbeddingX(torch.nn.Module): method __init__ (line 7) | def __init__( method forward (line 19) | def forward(self, x_support: torch.Tensor, x_query__: torch.Tensor) ->... class FoundationEmbeddingYFloat (line 30) | class FoundationEmbeddingYFloat(torch.nn.Module): method __init__ (line 31) | def __init__( method forward (line 41) | def forward(self, y_support: torch.Tensor, n_obs_query: int) -> tuple[... class FoundationEmbeddingYInteger (line 53) | class FoundationEmbeddingYInteger(torch.nn.Module): method __init__ (line 54) | def __init__( method forward (line 68) | def forward(self, y_support: torch.Tensor, n_obs_query: int) -> tuple[... class FoundationObservationEmbedding (line 87) | class FoundationObservationEmbedding(torch.nn.Module): method __init__ (line 88) | def __init__(self, dim: int) -> None: method forward (line 95) | def forward(self, batch_size: int, n_obs: int) -> torch.Tensor: FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/models/foundation/foundation_transformer.py class FoundationTransformer (line 11) | class FoundationTransformer(nn.Module, PyTorchModelHubMixin): method __init__ (line 12) | def __init__( method init_weights (line 65) | def init_weights(self): method forward (line 71) | def forward(self, x_support: torch.Tensor, y_support: torch.Tensor, x_... class MultiheadAttention (line 130) | class MultiheadAttention(torch.nn.Module): method __init__ (line 131) | def __init__(self, dim: int, n_heads: int) -> None: method init_weights (line 140) | def init_weights(self): method forward (line 145) | def forward( class SwiGLU (line 166) | class SwiGLU(nn.Module): method forward (line 167) | def forward(self, x): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/results/prediction_metrics.py class PredictionMetrics (line 12) | class PredictionMetrics: method from_prediction (line 19) | def from_prediction(cls, y_pred: np.ndarray, y_true: np.ndarray, task:... function compute_metrics (line 25) | def compute_metrics(y_pred: np.ndarray, y_true: np.ndarray, task: Task, ... function compute_classification_metrics (line 32) | def compute_classification_metrics( function compute_regression_metrics (line 49) | def compute_regression_metrics(y_pred: np.ndarray, y_true: np.ndarray, m... FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/tabpfnmix_classifier.py class TabPFNMixClassifier (line 18) | class TabPFNMixClassifier(BaseEstimator, ClassifierMixin): method __init__ (line 19) | def __init__( method fit (line 57) | def fit( method predict (line 80) | def predict(self, X): method predict_proba (line 85) | def predict_proba(self, X): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/_internal/tabpfnmix_regressor.py class TabPFNMixRegressor (line 18) | class TabPFNMixRegressor(BaseEstimator, RegressorMixin): method __init__ (line 19) | def __init__( method fit (line 59) | def fit( method predict (line 82) | def predict(self, X): FILE: tabular/src/autogluon/tabular/models/tabpfnmix/tabpfnmix_model.py class TabPFNMixModel (line 23) | class TabPFNMixModel(AbstractModel): method __init__ (line 50) | def __init__(self, **kwargs): method _get_model_type (line 55) | def _get_model_type(self): method _set_default_params (line 67) | def _set_default_params(self): method _fit (line 112) | def _fit( method _subsample_data (line 269) | def _subsample_data( method _preprocess (line 283) | def _preprocess(self, X: pd.DataFrame, **kwargs) -> np.ndarray: method save (line 300) | def save(self, path: str = None, verbose=True) -> str: method load (line 317) | def load(cls, path: str, reset_paths=False, verbose=True): method weights_path (line 328) | def weights_path(self) -> str: method supported_problem_types (line 332) | def supported_problem_types(cls) -> list[str] | None: method _get_default_auxiliary_params (line 335) | def _get_default_auxiliary_params(self) -> dict: method _get_maximum_resources (line 344) | def _get_maximum_resources(self) -> dict[str, int | float]: method _get_default_resources (line 348) | def _get_default_resources(self) -> tuple[int, float]: method _estimate_memory_usage (line 354) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method get_minimum_ideal_resources (line 364) | def get_minimum_ideal_resources(self) -> dict[str, int | float]: method _estimate_memory_usage_static (line 368) | def _estimate_memory_usage_static( method _class_tags (line 390) | def _class_tags(cls) -> dict: method _ag_params (line 395) | def _ag_params(self) -> set: method _more_tags (line 398) | def _more_tags(self) -> dict: FILE: tabular/src/autogluon/tabular/models/tabpfnv2/tabpfnv2_5_model.py class TabPFNModel (line 24) | class TabPFNModel(AbstractTorchModel): method __init__ (line 46) | def __init__(self, **kwargs): method _preprocess (line 52) | def _preprocess(self, X: pd.DataFrame, is_train=False, **kwargs) -> pd... method _fit (line 75) | def _fit( method _predict_proba (line 174) | def _predict_proba(self, X, **kwargs) -> np.ndarray: method _get_default_resources (line 188) | def _get_default_resources(self) -> tuple[int, int]: method get_minimum_resources (line 196) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method _set_default_params (line 202) | def _set_default_params(self): method get_device (line 209) | def get_device(self) -> str: method _set_device (line 212) | def _set_device(self, device: str): method supported_problem_types (line 216) | def supported_problem_types(cls) -> list[str] | None: method _get_default_auxiliary_params (line 219) | def _get_default_auxiliary_params(self) -> dict: method _get_default_ag_args_ensemble (line 232) | def _get_default_ag_args_ensemble(cls, **kwargs) -> dict: method _estimate_memory_usage (line 245) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method disable_tabpfn_telemetry (line 256) | def disable_tabpfn_telemetry(cls): method _estimate_memory_usage_static (line 260) | def _estimate_memory_usage_static( method _class_tags (line 292) | def _class_tags(cls): method _more_tags (line 295) | def _more_tags(self) -> dict: method extra_checkpoints_for_tuning (line 299) | def extra_checkpoints_for_tuning(problem_type: str) -> list[str]: method _log_license (line 302) | def _log_license(self, device: str): method _log_cpu_warning (line 305) | def _log_cpu_warning(self, device: str): class RealTabPFNv25Model (line 315) | class RealTabPFNv25Model(TabPFNModel): method extra_checkpoints_for_tuning (line 332) | def extra_checkpoints_for_tuning(problem_type: str) -> list[str]: method _log_license (line 355) | def _log_license(self, device: str): class RealTabPFNv2Model (line 368) | class RealTabPFNv2Model(TabPFNModel): method _get_default_auxiliary_params (line 385) | def _get_default_auxiliary_params(self) -> dict: method _log_license (line 397) | def _log_license(self, device: str): method _estimate_memory_usage_static (line 405) | def _estimate_memory_usage_static( FILE: tabular/src/autogluon/tabular/models/tabprep/prep_lgb_model.py class PrepLGBModel (line 7) | class PrepLGBModel(ModelAgnosticPrepMixin, LGBModel): method _estimate_memory_usage_static (line 12) | def _estimate_memory_usage_static(cls, **kwargs) -> int: method _estimate_memory_usage_static_lite (line 18) | def _estimate_memory_usage_static_lite(cls, **kwargs) -> int: FILE: tabular/src/autogluon/tabular/models/tabprep/prep_mixin.py function _recursive_expand_prep_param (line 16) | def _recursive_expand_prep_param(prep_param: tuple | list[list | tuple])... class ModelAgnosticPrepMixin (line 44) | class ModelAgnosticPrepMixin: method _estimate_dtypes_after_preprocessing (line 45) | def _estimate_dtypes_after_preprocessing(self, X: pd.DataFrame, **kwar... method _estimate_memory_usage (line 77) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _init_preprocessor (line 118) | def _init_preprocessor( method _recursive_init_preprocessors (line 137) | def _recursive_init_preprocessors(self, prep_param: tuple | list[list ... method get_preprocessor (line 166) | def get_preprocessor(self) -> AbstractFeatureGenerator | None: method _preprocess (line 193) | def _preprocess(self, X: pd.DataFrame, y=None, is_train: bool = False,... FILE: tabular/src/autogluon/tabular/models/tabular_nn/compilers/native.py class AbstractNativeCompiler (line 5) | class AbstractNativeCompiler: method can_compile (line 10) | def can_compile(): method compile (line 14) | def compile(model, path: str, input_types=None): method save (line 32) | def save(model, path: str): method load (line 38) | def load(path: str): FILE: tabular/src/autogluon/tabular/models/tabular_nn/compilers/onnx.py function quantile_transformer_shape_calculator (line 6) | def quantile_transformer_shape_calculator(operator): function quantile_transformer_converter (line 14) | def quantile_transformer_converter(scope, operator, container): function onehot_handle_unknown_transformer_shape_calculator (line 111) | def onehot_handle_unknown_transformer_shape_calculator(operator): function onehot_handle_unknown_transformer_converter (line 119) | def onehot_handle_unknown_transformer_converter(scope, operator, contain... function ordinal_handle_unknown_transformer_shape_calculator (line 123) | def ordinal_handle_unknown_transformer_shape_calculator(operator): function ordinal_handle_unknown_transformer_converter (line 131) | def ordinal_handle_unknown_transformer_converter(scope, operator, contai... function _encoder_handle_unknown_transformer_converter (line 135) | def _encoder_handle_unknown_transformer_converter(scope, operator, conta... class InferenceSessionWrapper (line 240) | class InferenceSessionWrapper: method __init__ (line 246) | def __init__(self, onnx_bytes): method run (line 251) | def run(self, *args): method get_inputs (line 254) | def get_inputs(self, *args): method get_outputs (line 257) | def get_outputs(self, *args): method __getstate__ (line 260) | def __getstate__(self): method __setstate__ (line 264) | def __setstate__(self, values): class TabularNeuralNetTorchOnnxTransformer (line 268) | class TabularNeuralNetTorchOnnxTransformer: method __init__ (line 269) | def __init__(self, model): method transform (line 275) | def transform(self, X): class TabularNeuralNetTorchOnnxCompiler (line 314) | class TabularNeuralNetTorchOnnxCompiler: method can_compile (line 319) | def can_compile(): method compile (line 330) | def compile(model, path: str, input_types=None): method save (line 389) | def save(model, path: str) -> str: method load (line 397) | def load(path: str) -> TabularNeuralNetTorchOnnxTransformer: FILE: tabular/src/autogluon/tabular/models/tabular_nn/hyperparameters/parameters.py function get_fixed_params (line 8) | def get_fixed_params(framework): function get_hyper_params (line 19) | def get_hyper_params(framework): function get_quantile_hyper_params (line 66) | def get_quantile_hyper_params(framework): function get_default_param (line 80) | def get_default_param(problem_type, framework, num_classes=None): function get_param_binary (line 93) | def get_param_binary(framework): function get_param_multiclass (line 99) | def get_param_multiclass(framework, num_classes): function get_param_regression (line 103) | def get_param_regression(framework): function get_param_quantile (line 107) | def get_param_quantile(framework): function merge_framework_params (line 115) | def merge_framework_params(framework, shared_params, pytorch_params): FILE: tabular/src/autogluon/tabular/models/tabular_nn/hyperparameters/searchspaces.py function get_default_searchspace (line 9) | def get_default_searchspace(problem_type, framework, num_classes=None): function get_searchspace_multiclass (line 39) | def get_searchspace_multiclass(framework, num_classes): function get_searchspace_binary (line 43) | def get_searchspace_binary(framework): function get_searchspace_regression (line 47) | def get_searchspace_regression(framework): function get_searchspace_quantile (line 57) | def get_searchspace_quantile(framework): FILE: tabular/src/autogluon/tabular/models/tabular_nn/torch/tabular_nn_torch.py class TabularNeuralNetTorchModel (line 42) | class TabularNeuralNetTorchModel(AbstractNeuralNetworkModel): method __init__ (line 61) | def __init__(self, **kwargs): method _set_default_params (line 74) | def _set_default_params(self): method _get_default_auxiliary_params (line 80) | def _get_default_auxiliary_params(self) -> dict: method _get_default_searchspace (line 89) | def _get_default_searchspace(self): method _get_num_net_outputs (line 92) | def _get_num_net_outputs(self): method _get_device (line 104) | def _get_device(self, num_gpus): method _set_net_defaults (line 130) | def _set_net_defaults(self, train_dataset, params): method _get_default_loss_function (line 141) | def _get_default_loss_function(self): method _prepare_params (line 152) | def _prepare_params(params): method _fit (line 183) | def _fit( method _get_net (line 284) | def _get_net(self, train_dataset, params): method _train_net (line 301) | def _train_net( method _get_early_stopping_strategy (line 561) | def _get_early_stopping_strategy(self, num_rows_train: int): method _get_early_stop_default (line 578) | def _get_early_stop_default(self): method _get_early_stopping_rounds (line 581) | def _get_early_stopping_rounds(self, num_rows_train, strategy="auto"): method _generate_curves (line 584) | def _generate_curves( method _assert_valid_metric (line 652) | def _assert_valid_metric(self, metric: int | float, best_epoch: int) -... method _predict_proba (line 682) | def _predict_proba(self, X, **kwargs): method _predict_tabular_data (line 699) | def _predict_tabular_data(self, new_data, process=True): method _generate_dataset (line 714) | def _generate_dataset( method _process_test_data (line 766) | def _process_test_data(self, df, labels=None): method _process_train_data (line 796) | def _process_train_data( method _init_optimizer (line 859) | def _init_optimizer(self, optimizer, learning_rate, weight_decay): method reduce_memory_size (line 876) | def reduce_memory_size(self, remove_fit=True, requires_save=True, **kw... method _get_default_stopping_metric (line 881) | def _get_default_stopping_metric(self): method _estimate_memory_usage (line 884) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 895) | def _estimate_memory_usage_static( method _get_maximum_resources (line 903) | def _get_maximum_resources(self) -> Dict[str, Union[int, float]]: method _get_default_resources (line 907) | def _get_default_resources(self): method save (line 913) | def save(self, path: str = None, verbose=True) -> str: method load (line 926) | def load(cls, path: str, reset_paths=True, verbose=True): method _get_hpo_backend (line 983) | def _get_hpo_backend(self): method get_minimum_resources (line 987) | def get_minimum_resources(self, is_gpu_available=False): method _valid_compilers (line 997) | def _valid_compilers(cls): method _default_compiler (line 1001) | def _default_compiler(cls): method _ag_params (line 1004) | def _ag_params(self) -> set: method _get_input_types (line 1007) | def _get_input_types(self, batch_size=None): method compile (line 1013) | def compile(self, compiler_configs=None): method _compile (line 1030) | def _compile(self, **kwargs): method supported_problem_types (line 1051) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 1055) | def _class_tags(cls): method _more_tags (line 1061) | def _more_tags(self): FILE: tabular/src/autogluon/tabular/models/tabular_nn/torch/tabular_torch_dataset.py class TabularTorchDataset (line 13) | class TabularTorchDataset(torch.utils.data.IterableDataset): method __init__ (line 50) | def __init__(self, processed_array, feature_arraycol_map, feature_type... method __iter__ (line 130) | def __iter__(self): method __len__ (line 171) | def __len__(self): method has_vector_features (line 174) | def has_vector_features(self): method num_embed_features (line 178) | def num_embed_features(self): method num_vector_features (line 182) | def num_vector_features(self): method get_labels (line 186) | def get_labels(self): method getNumCategoriesEmbeddings (line 193) | def getNumCategoriesEmbeddings(self): method get_feature_data (line 213) | def get_feature_data(self, feature): method save (line 231) | def save(self, file_prefix=""): method load (line 240) | def load(cls, file_prefix=""): method build_loader (line 247) | def build_loader(self, batch_size, num_workers, is_test=False): FILE: tabular/src/autogluon/tabular/models/tabular_nn/torch/torch_network_modules.py class EmbedNet (line 14) | class EmbedNet(nn.Module): method __init__ (line 19) | def __init__( method _set_params (line 134) | def _set_params( method init_params (line 160) | def init_params(self): method forward (line 165) | def forward(self, data_batch): method huber_pinball_loss (line 197) | def huber_pinball_loss(self, input_data, target_data): method margin_loss (line 217) | def margin_loss(self, input_data, margin_scale=0.0001): method quantile_loss (line 234) | def quantile_loss(self, predict_data, target_data, margin): method compute_loss (line 242) | def compute_loss(self, data_batch, loss_function=None, gamma=None): method predict (line 261) | def predict(self, input_data): FILE: tabular/src/autogluon/tabular/models/tabular_nn/utils/categorical_encoders.py function _encode_numpy (line 23) | def _encode_numpy(values, uniques=None, encode=False, check_unknown=True): function _encode_python (line 43) | def _encode_python(values, uniques=None, encode=False): function _encode (line 59) | def _encode(values, uniques=None, encode=False, check_unknown=True): function _encode_check_unknown (line 101) | def _encode_check_unknown(values, uniques, return_mask=False): class _BaseEncoder (line 147) | class _BaseEncoder(BaseEstimator, TransformerMixin): method _check_X (line 153) | def _check_X(self, X): method _get_feature (line 194) | def _get_feature(self, X, feature_idx): method _fit (line 201) | def _fit(self, X, handle_unknown="error"): method _find_infrequent_category_indices (line 237) | def _find_infrequent_category_indices(self, Xi): method _transform (line 243) | def _transform(self, X, handle_unknown="error"): method _more_tags (line 312) | def _more_tags(self): method __sklearn_tags__ (line 315) | def __sklearn_tags__(self): class OneHotMergeRaresHandleUnknownEncoder (line 349) | class OneHotMergeRaresHandleUnknownEncoder(_BaseEncoder): method __init__ (line 424) | def __init__(self, categories="auto", drop=None, sparse=True, dtype=np... method _validate_keywords (line 434) | def _validate_keywords(self): method _compute_drop_idx (line 448) | def _compute_drop_idx(self): method _convert_cat_to_int (line 479) | def _convert_cat_to_int(self, X): method fit (line 485) | def fit(self, X, y=None): method transform (line 520) | def transform(self, X): method inverse_transform (line 600) | def inverse_transform(self, X): method get_feature_names (line 681) | def get_feature_names(self, input_features=None): class OrdinalMergeRaresHandleUnknownEncoder (line 716) | class OrdinalMergeRaresHandleUnknownEncoder(_BaseEncoder): method __init__ (line 759) | def __init__(self, categories="auto", dtype=np.float64, max_levels=None): method fit (line 765) | def fit(self, X, y=None): method transform (line 790) | def transform(self, X): method inverse_transform (line 820) | def inverse_transform(self, X): FILE: tabular/src/autogluon/tabular/models/tabular_nn/utils/data_preprocessor.py function create_preprocessor (line 17) | def create_preprocessor( function convert_df_dtype_to_str (line 69) | def convert_df_dtype_to_str(df): function get_feature_arraycol_map (line 73) | def get_feature_arraycol_map(processor, max_category_levels): function get_feature_type_map (line 103) | def get_feature_type_map(feature_arraycol_map, types_of_features): FILE: tabular/src/autogluon/tabular/models/tabular_nn/utils/nn_architecture_utils.py function get_embed_sizes (line 8) | def get_embed_sizes(train_dataset, params, num_categs_per_feature): function infer_y_range (line 23) | def infer_y_range(y_vals, y_range_extend): function get_default_layers (line 40) | def get_default_layers(problem_type, num_net_outputs, max_layer_width): function default_numeric_embed_dim (line 58) | def default_numeric_embed_dim(train_dataset, max_layer_width, first_laye... FILE: tabular/src/autogluon/tabular/models/text_prediction/text_prediction_v1_model.py class TextPredictorModel (line 21) | class TextPredictorModel(MultiModalPredictorModel): method _get_default_auxiliary_params (line 27) | def _get_default_auxiliary_params(self) -> dict: method supported_problem_types (line 37) | def supported_problem_types(cls) -> list[str] | None: FILE: tabular/src/autogluon/tabular/models/xgboost/callbacks.py class CustomMetricCallback (line 17) | class CustomMetricCallback(TrainingCallback): method __init__ (line 53) | def __init__(self, scorers, eval_sets, problem_type, use_error=True): method after_iteration (line 61) | def after_iteration(self, model, epoch, evals_log): class EarlyStoppingCustom (line 79) | class EarlyStoppingCustom(EarlyStopping): method __init__ (line 90) | def __init__(self, rounds, time_limit=None, start_time=None, verbose=F... method before_training (line 108) | def before_training(self, model): method after_iteration (line 116) | def after_iteration(self, model, epoch, evals_log): method _time_check (line 130) | def _time_check(self, model, epoch): method _memory_check (line 144) | def _memory_check(self, model): FILE: tabular/src/autogluon/tabular/models/xgboost/hyperparameters/parameters.py function get_param_baseline (line 8) | def get_param_baseline(problem_type, num_classes=None): function get_base_params (line 21) | def get_base_params(): function get_param_binary_baseline (line 31) | def get_param_binary_baseline(): function get_param_multiclass_baseline (line 41) | def get_param_multiclass_baseline(num_classes): function get_param_regression_baseline (line 52) | def get_param_regression_baseline(): FILE: tabular/src/autogluon/tabular/models/xgboost/hyperparameters/searchspaces.py function get_default_searchspace (line 9) | def get_default_searchspace(problem_type, num_classes=None): function get_base_searchspace (line 20) | def get_base_searchspace(): function get_searchspace_multiclass_baseline (line 38) | def get_searchspace_multiclass_baseline(num_classes): function get_searchspace_binary_baseline (line 48) | def get_searchspace_binary_baseline(): function get_searchspace_regression_baseline (line 57) | def get_searchspace_regression_baseline(): FILE: tabular/src/autogluon/tabular/models/xgboost/xgboost_model.py class XGBoostModel (line 26) | class XGBoostModel(AbstractModel): method __init__ (line 38) | def __init__(self, **kwargs): method _set_default_params (line 44) | def _set_default_params(self): method _get_default_searchspace (line 49) | def _get_default_searchspace(self): method _get_default_auxiliary_params (line 52) | def _get_default_auxiliary_params(self) -> dict: method get_eval_metric (line 61) | def get_eval_metric(self): method _preprocess (line 69) | def _preprocess(self, X, is_train=False, max_category_levels=None, **k... method _fit (line 82) | def _fit( method _predict_proba (line 250) | def _predict_proba(self, X, num_cpus=-1, **kwargs): method _get_early_stopping_rounds (line 264) | def _get_early_stopping_rounds(self, num_rows_train, strategy="auto"): method _get_num_classes (line 267) | def _get_num_classes(self, y): method _ag_params (line 279) | def _ag_params(self) -> set: method _estimate_memory_usage (line 282) | def _estimate_memory_usage(self, X: pd.DataFrame, **kwargs) -> int: method _estimate_memory_usage_static (line 293) | def _estimate_memory_usage_static( method _validate_fit_memory_usage (line 346) | def _validate_fit_memory_usage( method get_minimum_resources (line 360) | def get_minimum_resources(self, is_gpu_available=False): method _get_default_resources (line 369) | def _get_default_resources(self): method save (line 375) | def save(self, path: str = None, verbose=True) -> str: method load (line 388) | def load(cls, path: str, reset_paths=True, verbose=True): method supported_problem_types (line 398) | def supported_problem_types(cls) -> list[str] | None: method _class_tags (line 402) | def _class_tags(cls): method _more_tags (line 408) | def _more_tags(self): FILE: tabular/src/autogluon/tabular/models/xgboost/xgboost_utils.py function convert_ag_metric_to_xgbm (line 26) | def convert_ag_metric_to_xgbm(ag_metric_name, problem_type): function func_generator (line 30) | def func_generator(metric: Scorer, problem_type: str): function learning_curve_func_generator (line 79) | def learning_curve_func_generator(metric: Scorer, problem_type: str, use... class OheFeatureGenerator (line 122) | class OheFeatureGenerator(BaseEstimator, TransformerMixin): method __init__ (line 123) | def __init__(self, max_levels=None): method fit (line 131) | def fit(self, X, y=None): method transform (line 152) | def transform(self, X, y=None): method get_feature_names (line 160) | def get_feature_names(self): method get_feature_types (line 163) | def get_feature_types(self): method get_original_feature_names (line 166) | def get_original_feature_names(self): FILE: tabular/src/autogluon/tabular/models/xt/xt_model.py class XTModel (line 8) | class XTModel(RFModel): method _get_model_type (line 17) | def _get_model_type(self): method supported_problem_types (line 32) | def supported_problem_types(cls) -> list[str] | None: FILE: tabular/src/autogluon/tabular/predictor/interpretable_predictor.py class InterpretableTabularPredictor (line 12) | class InterpretableTabularPredictor(TabularPredictor): method fit (line 28) | def fit(self, train_data, tuning_data=None, time_limit=None, *, preset... method _validate_fit_extra_kwargs (line 45) | def _validate_fit_extra_kwargs(self, kwargs, extra_valid_keys=None) ->... method leaderboard_interpretable (line 58) | def leaderboard_interpretable(self, verbose: bool = False, **kwargs) -... method print_interpretable_rules (line 85) | def print_interpretable_rules(self, complexity_threshold: int = 10, mo... method explain_classification_errors (line 109) | def explain_classification_errors(self, data, model=None, print_rules:... FILE: tabular/src/autogluon/tabular/predictor/predictor.py class TabularPredictor (line 92) | class TabularPredictor: method __init__ (line 202) | def __init__( method classes_ (line 262) | def classes_(self) -> list: method class_labels (line 274) | def class_labels(self) -> list: method class_labels_internal (line 279) | def class_labels_internal(self) -> list: method class_labels_internal_map (line 289) | def class_labels_internal_map(self) -> dict: method quantile_levels (line 301) | def quantile_levels(self) -> list[float]: method eval_metric (line 305) | def eval_metric(self) -> Scorer: method original_features (line 310) | def original_features(self) -> list[str]: method problem_type (line 316) | def problem_type(self) -> str: method decision_threshold (line 321) | def decision_threshold(self) -> float | None: method set_decision_threshold (line 337) | def set_decision_threshold(self, decision_threshold: float): method features (line 357) | def features(self, feature_stage: str = "original") -> list: method has_val (line 379) | def has_val(self) -> bool: method feature_metadata (line 387) | def feature_metadata(self) -> FeatureMetadata: method feature_metadata_in (line 400) | def feature_metadata_in(self) -> FeatureMetadata: method label (line 413) | def label(self) -> str | int: method path (line 420) | def path(self) -> str: method fit (line 425) | def fit( method _fit (line 1454) | def _fit(self, ag_fit_kwargs: dict, ag_post_fit_kwargs: dict): method _dynamic_stacking (line 1462) | def _dynamic_stacking( method _sub_fit_memory_save_wrapper (line 1645) | def _sub_fit_memory_save_wrapper( method _post_fit (line 1807) | def _post_fit( method _can_calibrate_decision_threshold (line 1974) | def _can_calibrate_decision_threshold(self) -> bool: method fit_extra (line 1978) | def fit_extra( method _get_all_fit_extra_args (line 2210) | def _get_all_fit_extra_args(self): method _fit_weighted_ensemble_pseudo (line 2216) | def _fit_weighted_ensemble_pseudo(self): method _predict_pseudo (line 2237) | def _predict_pseudo(self, X_test: pd.DataFrame, use_ensemble: bool): method _run_pseudolabeling (line 2256) | def _run_pseudolabeling( method fit_pseudolabel (line 2394) | def fit_pseudolabel( method predict (line 2558) | def predict( method predict_proba (line 2610) | def predict_proba( method predict_from_proba (line 2668) | def predict_from_proba( method can_predict_proba (line 2711) | def can_predict_proba(self) -> bool: method is_fit (line 2720) | def is_fit(self) -> bool: method evaluate (line 2726) | def evaluate( method evaluate_predictions (line 2796) | def evaluate_predictions( method leaderboard (line 2858) | def leaderboard( method learning_curves (line 3041) | def learning_curves(self) -> tuple[dict, dict]: method model_failures (line 3099) | def model_failures(self, verbose: bool = False) -> pd.DataFrame: method predict_proba_multi (line 3145) | def predict_proba_multi( method predict_multi (line 3225) | def predict_multi( method predict_multi (line 3236) | def predict_multi( method predict_multi (line 3247) | def predict_multi( method fit_summary (line 3322) | def fit_summary(self, verbosity: int = 3, show_plot: bool = False) -> ... method transform_features (line 3468) | def transform_features( method transform_labels (line 3546) | def transform_labels( method feature_importance (line 3581) | def feature_importance( method compile (line 3735) | def compile(self, models="best", with_ancestors=True, compiler_configs... method persist (line 3788) | def persist(self, models="best", with_ancestors=True, max_memory=0.4) ... method unpersist (line 3828) | def unpersist(self, models="all") -> list[str]: method refit_full (line 3850) | def refit_full( method model_best (line 3996) | def model_best(self) -> str: method _model_best (line 4008) | def _model_best(self, can_infer=None) -> str: method set_model_best (line 4017) | def set_model_best(self, model: str, save_trainer: bool = False): method model_refit_map (line 4039) | def model_refit_map(self, inverse=False) -> dict[str, str]: method info (line 4057) | def info(self): method model_info (line 4073) | def model_info(self, model: str) -> dict: method model_hyperparameters (line 4093) | def model_hyperparameters( method fit_weighted_ensemble (line 4152) | def fit_weighted_ensemble( method calibrate_decision_threshold (line 4266) | def calibrate_decision_threshold( method predict_oof (line 4352) | def predict_oof( method predict_proba_oof (line 4408) | def predict_proba_oof( method positive_class (line 4547) | def positive_class(self) -> int | str: method load_data_internal (line 4559) | def load_data_internal(self, data="train", return_X=True, return_y=True): method save_space (line 4609) | def save_space(self, remove_data=True, remove_fit_stack=True, requires... method delete_models (line 4650) | def delete_models( method disk_usage (line 4703) | def disk_usage(self) -> int: method disk_usage_per_file (line 4709) | def disk_usage_per_file(self, *, sort_by: str = "size", include_path_i... method model_names (line 4733) | def model_names( method distill (line 4776) | def distill( method plot_ensemble_model (line 4888) | def plot_ensemble_model( method _summarize (line 5008) | def _summarize(key, msg, results): method _get_dataset (line 5013) | def _get_dataset(data, allow_nan: bool = False) -> pd.DataFrame | None: method _validate_hyperparameter_tune_kwargs (line 5031) | def _validate_hyperparameter_tune_kwargs(self, hyperparameter_tune_kwa... method _set_hyperparameter_tune_kwargs_in_ag_args (line 5065) | def _set_hyperparameter_tune_kwargs_in_ag_args(self, hyperparameter_tu... method _set_post_fit_vars (line 5079) | def _set_post_fit_vars(self, learner: AbstractTabularLearner = None): method _load_version_file (line 5088) | def _load_version_file(cls, path: str) -> str: method _load_metadata_file (line 5114) | def _load_metadata_file(cls, path: str, silent: bool = True): method _save_version_file (line 5118) | def _save_version_file(self, silent: bool = False): method _save_metadata_file (line 5123) | def _save_metadata_file(self, silent: bool = False): method save (line 5136) | def save(self, silent: bool = False): method _load (line 5165) | def _load(cls, path: str) -> "TabularPredictor": method load (line 5175) | def load( method load_log (line 5296) | def load_log(cls, predictor_path: str = None, log_file_path: Optional[... method _setup_log_to_file (line 5327) | def _setup_log_to_file(self, log_file_path: str): method _validate_init_kwargs (line 5335) | def _validate_init_kwargs(kwargs): method _validate_fit_kwargs (line 5349) | def _validate_fit_kwargs(self, *, kwargs: dict) -> dict: method _validate_calibrate_decision_threshold (line 5385) | def _validate_calibrate_decision_threshold(self, calibrate_decision_th... method _validate_num_cpus (line 5393) | def _validate_num_cpus(self, num_cpus: int | str): method _validate_num_gpus (line 5405) | def _validate_num_gpus(self, num_gpus: int | float | str): method _validate_and_set_memory_limit (line 5419) | def _validate_and_set_memory_limit(self, memory_limit: float | str): method _validate_fit_strategy (line 5437) | def _validate_fit_strategy(self, fit_strategy: str): method _fit_extra_kwargs_dict (line 5442) | def _fit_extra_kwargs_dict(self) -> dict: method _sanitize_dynamic_stacking_kwargs (line 5484) | def _sanitize_dynamic_stacking_kwargs(kwargs: dict) -> tuple[dict, lis... method _validate_fit_extra_kwargs (line 5553) | def _validate_fit_extra_kwargs(self, kwargs: dict, extra_valid_keys: l... method _prune_data_features (line 5592) | def _prune_data_features(self, train_features: list, other_features: l... method _validate_fit_data (line 5616) | def _validate_fit_data( method _validate_single_fit_dataset (line 5654) | def _validate_single_fit_dataset( method _initialize_learning_curve_params (line 5703) | def _initialize_learning_curve_params( method _validate_unique_indices (line 5771) | def _validate_unique_indices(data: pd.DataFrame, name: str): method _validate_infer_limit (line 5782) | def _validate_infer_limit(infer_limit: float, infer_limit_batch_size: ... method _set_feature_generator (line 5803) | def _set_feature_generator(self, feature_generator="auto", feature_met... method _sanitize_stack_args (line 5813) | def _sanitize_stack_args( method clone (line 5869) | def clone(self, path: str, *, return_clone: bool = False, dirs_exist_o... method clone_for_deployment (line 5901) | def clone_for_deployment( method simulation_artifact (line 5958) | def simulation_artifact(self, test_data: pd.DataFrame = None) -> dict: method _check_if_hyperparameters_handle_text (line 6028) | def _check_if_hyperparameters_handle_text(hyperparameters: dict) -> bool: method _validate_hyperparameters (line 6058) | def _validate_hyperparameters(hyperparameters: dict): method _sanitize_pseudo_data (line 6091) | def _sanitize_pseudo_data( method _assert_is_fit (line 6112) | def _assert_is_fit(self, message_suffix: str = None): function _safe_rmtree (line 6122) | def _safe_rmtree(path: str, retries: int = 5, delay: float = 0.5): function _dystack (line 6142) | def _dystack( FILE: tabular/src/autogluon/tabular/registry/_model_registry.py class ModelRegistry (line 14) | class ModelRegistry: method __init__ (line 39) | def __init__(self, model_cls_list: list[Type[AbstractModel]] | None = ... method exists (line 48) | def exists(self, model_cls: Type[AbstractModel]) -> bool: method add (line 51) | def add(self, model_cls: Type[AbstractModel]): method remove (line 82) | def remove(self, model_cls: Type[AbstractModel]): method model_cls_list (line 91) | def model_cls_list(self) -> list[Type[AbstractModel]]: method keys (line 95) | def keys(self) -> list[str]: method key_to_cls_map (line 98) | def key_to_cls_map(self) -> dict[str, Type[AbstractModel]]: method key_to_cls (line 101) | def key_to_cls(self, key: str) -> Type[AbstractModel]: method priority_map (line 109) | def priority_map(self, problem_type: str | None = None) -> dict[Type[A... method key (line 112) | def key(self, model_cls: Type[AbstractModel]) -> str: method name_map (line 116) | def name_map(self) -> dict[Type[AbstractModel], str]: method name (line 119) | def name(self, model_cls: Type[AbstractModel]) -> str: method priority (line 123) | def priority(self, model_cls: Type[AbstractModel], problem_type: str |... method docstring (line 127) | def docstring(self, model_cls: Type[AbstractModel]) -> str: method to_frame (line 136) | def to_frame(self) -> pd.DataFrame: FILE: tabular/src/autogluon/tabular/testing/fit_helper.py class DatasetLoaderHelper (line 38) | class DatasetLoaderHelper: method load_dataset (line 91) | def load_dataset(name: str, directory_prefix: str = "./datasets/") -> ... method load_data (line 111) | def load_data( class FitHelper (line 155) | class FitHelper: method fit_and_validate_dataset (line 161) | def fit_and_validate_dataset( method load_dataset (line 374) | def load_dataset(name: str, directory_prefix: str = "./datasets/") -> ... method fit_dataset (line 378) | def fit_dataset( method verify_model (line 417) | def verify_model( function stacked_overfitting_assert (line 573) | def stacked_overfitting_assert( function _verify_model_seed (line 591) | def _verify_model_seed(model: AbstractModel): FILE: tabular/src/autogluon/tabular/testing/generate_datasets.py function generate_toy_binary_dataset (line 9) | def generate_toy_binary_dataset(): function generate_toy_multiclass_dataset (line 26) | def generate_toy_multiclass_dataset(): function generate_toy_regression_dataset (line 43) | def generate_toy_regression_dataset(): function generate_toy_quantile_dataset (line 60) | def generate_toy_quantile_dataset(): function generate_toy_quantile_single_level_dataset (line 67) | def generate_toy_quantile_single_level_dataset(): function generate_toy_binary_10_dataset (line 74) | def generate_toy_binary_10_dataset(): function generate_toy_multiclass_10_dataset (line 91) | def generate_toy_multiclass_10_dataset(): function generate_toy_regression_10_dataset (line 108) | def generate_toy_regression_10_dataset(): function generate_toy_quantile_10_dataset (line 125) | def generate_toy_quantile_10_dataset(): function generate_toy_multiclass_30_dataset (line 132) | def generate_toy_multiclass_30_dataset(): function generate_toy_multiclass_n_dataset (line 144) | def generate_toy_multiclass_n_dataset(n_samples, n_features, n_classes) ... FILE: tabular/src/autogluon/tabular/testing/model_fit_helper.py class ModelFitHelper (line 16) | class ModelFitHelper: method fit_and_validate_dataset (line 22) | def fit_and_validate_dataset( method fit_dataset (line 71) | def fit_dataset( FILE: tabular/src/autogluon/tabular/trainer/abstract_trainer.py class AbstractTabularTrainer (line 70) | class AbstractTabularTrainer(AbstractTrainer[AbstractModel]): method __init__ (line 140) | def __init__( method _path_attr (line 247) | def _path_attr(self) -> str: method has_val (line 252) | def has_val(self) -> bool: method num_rows_val_for_calibration (line 257) | def num_rows_val_for_calibration(self) -> int: method time_left (line 268) | def time_left(self) -> float | None: method logger (line 282) | def logger(self) -> logging.Logger: method log (line 285) | def log(self, level: int, msg, *args, **kwargs): method load_X (line 288) | def load_X(self): method load_X_val (line 294) | def load_X_val(self): method load_y (line 300) | def load_y(self): method load_y_val (line 306) | def load_y_val(self): method load_data (line 312) | def load_data(self): method save_X (line 320) | def save_X(self, X, verbose=True): method save_X_val (line 325) | def save_X_val(self, X, verbose=True): method save_X_test (line 330) | def save_X_test(self, X, verbose=True): method save_y (line 335) | def save_y(self, y, verbose=True): method save_y_val (line 340) | def save_y_val(self, y, verbose=True): method save_y_test (line 345) | def save_y_test(self, y, verbose=True): method get_model_names (line 350) | def get_model_names( method get_max_level (line 375) | def get_max_level(self, stack_name: str | None = None, models: list[st... method construct_model_templates (line 383) | def construct_model_templates(self, hyperparameters: dict[str, Any]) -... method construct_model_templates_distillation (line 387) | def construct_model_templates_distillation( method get_model_level (line 393) | def get_model_level(self, model_name: str) -> int: method fit (line 396) | def fit(self, X, y, hyperparameters: dict, X_val=None, y_val=None, **k... method train_multi_levels (line 400) | def train_multi_levels( method _fit_setup (line 547) | def _fit_setup( method _fit_cleanup (line 602) | def _fit_cleanup(self): method _callbacks_setup (line 612) | def _callbacks_setup(self, **kwargs): method _callbacks_conclude (line 616) | def _callbacks_conclude(self): method reset_callbacks (line 620) | def reset_callbacks(self): method _filter_base_models_via_infer_limit (line 626) | def _filter_base_models_via_infer_limit( method stack_new_level (line 732) | def stack_new_level( method stack_new_level_core (line 803) | def stack_new_level_core( method _stack_new_level_aux (line 951) | def _stack_new_level_aux( method stack_new_level_aux (line 980) | def stack_new_level_aux( method predict (line 1061) | def predict(self, X: pd.DataFrame, model: str | None = None) -> np.nda... method predict_proba (line 1066) | def predict_proba(self, X: pd.DataFrame, model: str | None = None) -> ... method _get_best (line 1071) | def _get_best(self) -> str: method get_inputs_to_model (line 1078) | def get_inputs_to_model( method score (line 1116) | def score( method score_with_y_pred_proba (line 1144) | def score_with_y_pred_proba( method score_with_y_pred (line 1170) | def score_with_y_pred( method _construct_model_pred_order (line 1192) | def _construct_model_pred_order(self, models: list[str]) -> list[str]: method _construct_model_pred_order_with_pred_dict (line 1222) | def _construct_model_pred_order_with_pred_dict( method get_models_attribute_dict (line 1272) | def get_models_attribute_dict(self, attribute: str, models: list | Non... method get_model_pred_proba_dict (line 1293) | def get_model_pred_proba_dict( method get_model_oof_dict (line 1377) | def get_model_oof_dict(self, models: list[str]) -> dict: method get_model_pred_dict (line 1383) | def get_model_pred_dict(self, X: pd.DataFrame, models: list[str], reco... method get_model_oof (line 1428) | def get_model_oof(self, model: str, use_refit_parent: bool = False) ->... method get_model_learning_curves (line 1454) | def get_model_learning_curves(self, model: str) -> dict: method _update_pred_proba_dict_with_val_cache (line 1459) | def _update_pred_proba_dict_with_val_cache(self, model_set: set, model... method get_inputs_to_stacker (line 1476) | def get_inputs_to_stacker( method get_feature_metadata (line 1544) | def get_feature_metadata( method _get_stack_column_names (line 1590) | def _get_stack_column_names(self, models: list[str]) -> tuple[list[str... method refit_single_full (line 1614) | def refit_single_full( method refit_ensemble_full (line 1767) | def refit_ensemble_full(self, model: str | list[str] = "all", **kwargs... method get_refit_full_parent (line 1822) | def get_refit_full_parent(self, model: str) -> str: method get_model_best (line 1826) | def get_model_best( method save_model (line 1919) | def save_model(self, model, reduce_memory=True): method save (line 1928) | def save(self) -> None: method compile (line 1936) | def compile(self, model_names="all", with_ancestors=False, compiler_co... method persist (line 2016) | def persist(self, model_names="all", with_ancestors=False, max_memory=... method unpersist (line 2088) | def unpersist(self, model_names="all") -> list: method generate_weighted_ensemble (line 2107) | def generate_weighted_ensemble( method _train_single (line 2206) | def _train_single( method _train_and_save (line 2234) | def _train_and_save( method _get_model_metadata (line 2460) | def _get_model_metadata(self, model: AbstractModel, stack_name: str = ... method _add_model (line 2506) | def _add_model( method _path_attr_model (line 2584) | def _path_attr_model(self, model: str): method _path_to_model_attr (line 2588) | def _path_to_model_attr(self, model: str, attribute: str): method _save_model_y_pred_proba_val (line 2592) | def _save_model_y_pred_proba_val(self, model: str, y_pred_proba_val): method _load_model_y_pred_proba_val (line 2598) | def _load_model_y_pred_proba_val(self, model: str): method _update_model_attr (line 2605) | def _update_model_attr(self, model: str, **attributes): method _log_model_stats (line 2611) | def _log_model_stats(self, model, _is_refit=False, is_distributed_main... method _train_single_full (line 2702) | def _train_single_full( method _check_raise_exception (line 2899) | def _check_raise_exception( method _callbacks_before_fit (line 2947) | def _callbacks_before_fit( method _callbacks_after_fit (line 2975) | def _callbacks_after_fit( method _train_multi_repeats (line 2997) | def _train_multi_repeats( method _train_multi_initial (line 3065) | def _train_multi_initial( method _train_multi_fold (line 3198) | def _train_multi_fold( method _train_multi_fold_parallel (line 3308) | def _train_multi_fold_parallel( method _train_multi (line 3511) | def _train_multi( method _train_multi_and_ensemble (line 3575) | def _train_multi_and_ensemble( method _predict_model (line 3634) | def _predict_model(self, X: pd.DataFrame, model: str, model_pred_proba... method _predict_proba_model (line 3638) | def _predict_proba_model( method _proxy_model_feature_prune (line 3648) | def _proxy_model_feature_prune( method _get_default_proxy_model_class (line 3718) | def _get_default_proxy_model_class(self): method _retain_better_pruned_models (line 3721) | def _retain_better_pruned_models( method get_feature_importance (line 3775) | def get_feature_importance(self, model=None, X=None, y=None, raw=True,... method _get_feature_importance_raw (line 3839) | def _get_feature_importance_raw(self, X, y, model, eval_metric=None, *... method _get_models_load_info (line 3860) | def _get_models_load_info(self, model_names): method get_model_attribute_full (line 3866) | def get_model_attribute_full(self, model: str | list[str], attribute: ... method get_models_attribute_full (line 3903) | def get_models_attribute_full(self, models: list[str], attribute: str,... method get_minimum_models_set (line 3914) | def get_minimum_models_set(self, models: list) -> list: method get_base_model_names (line 3922) | def get_base_model_names(self, model) -> list: method model_refit_map (line 3928) | def model_refit_map(self, inverse=False) -> dict[str, str]: method model_exists (line 3939) | def model_exists(self, model: str) -> bool: method _flatten_model_info (line 3942) | def _flatten_model_info(self, model_info: dict) -> dict: method leaderboard (line 3994) | def leaderboard(self, extra_info=False, refit_full: bool | None = None... method model_failures (line 4174) | def model_failures(self) -> pd.DataFrame: method get_info (line 4257) | def get_info(self, include_model_info=False, include_model_failures=Tr... method reduce_memory_size (line 4322) | def reduce_memory_size( method delete_models (line 4377) | def delete_models( method _delete_model_from_graph (line 4440) | def _delete_model_from_graph(self, model: str): method _process_hyperparameters (line 4448) | def _process_hyperparameters(hyperparameters: dict) -> dict: method distill (line 4451) | def distill( method _get_model_fit_kwargs (line 4667) | def _get_model_fit_kwargs( method _get_bagged_model_fit_kwargs (line 4711) | def _get_bagged_model_fit_kwargs( method _get_feature_prune_proxy_model (line 4728) | def _get_feature_prune_proxy_model(self, proxy_model_class: AbstractMo... method calibrate_model (line 4776) | def calibrate_model( method calibrate_decision_threshold (line 4877) | def calibrate_decision_threshold( method _validate_num_classes (line 4953) | def _validate_num_classes(num_classes: int | None, problem_type: str): method _validate_quantile_levels (line 4972) | def _validate_quantile_levels(quantile_levels: list[float] | np.ndarra... function _detached_train_multi_fold (line 4989) | def _detached_train_multi_fold( function _remote_train_multi_fold (line 5038) | def _remote_train_multi_fold( function _detached_refit_single_full (line 5102) | def _detached_refit_single_full( function _remote_refit_single_full (line 5200) | def _remote_refit_single_full( FILE: tabular/src/autogluon/tabular/trainer/auto_trainer.py class AutoTrainer (line 16) | class AutoTrainer(AbstractTabularTrainer): method construct_model_templates (line 17) | def construct_model_templates(self, hyperparameters, **kwargs): method fit (line 42) | def fit( method construct_model_templates_distillation (line 168) | def construct_model_templates_distillation(self, hyperparameters, **kw... method _get_default_proxy_model_class (line 187) | def _get_default_proxy_model_class(self): method compile (line 190) | def compile(self, model_names="all", with_ancestors=False, compiler_co... method _get_model_types_map (line 205) | def _get_model_types_map(self) -> dict[str, AbstractModel]: FILE: tabular/src/autogluon/tabular/trainer/model_presets/presets.py function get_preset_models (line 70) | def get_preset_models( function clean_model_cfg (line 178) | def clean_model_cfg( function _verify_model_cfg (line 233) | def _verify_model_cfg(model_cfg, model_type) -> dict: function is_model_cfg_valid (line 262) | def is_model_cfg_valid(model_cfg, level=1, problem_type=None): function model_factory (line 284) | def model_factory( function get_preset_models_softclass (line 368) | def get_preset_models_softclass(hyperparameters, invalid_model_names: li... FILE: tabular/src/autogluon/tabular/trainer/model_presets/presets_distill.py function get_preset_models_distillation (line 20) | def get_preset_models_distillation( FILE: tabular/src/autogluon/tabular/tuning/feature_pruner.py class FeaturePruner (line 10) | class FeaturePruner: method __init__ (line 11) | def __init__(self, model_base, threshold_baseline=0.004, is_fit=False): method evaluate (line 29) | def evaluate(self): method tune (line 48) | def tune(self, X, y, X_val, y_val, X_holdout, y_holdout, total_runs=999): method adjust_threshold (line 142) | def adjust_threshold(gain_df, threshold): FILE: tabular/tests/conftest.py function pytest_addoption (line 11) | def pytest_addoption(parser): function pytest_configure (line 18) | def pytest_configure(config): function pytest_collection_modifyitems (line 25) | def pytest_collection_modifyitems(config, items): function mock_system_resourcses (line 69) | def mock_system_resourcses(num_cpus=None, num_gpus=None): function mock_system_resources_ctx_mgr (line 82) | def mock_system_resources_ctx_mgr(): function mock_num_cpus (line 87) | def mock_num_cpus(): function mock_num_gpus (line 92) | def mock_num_gpus(): function k_fold (line 97) | def k_fold(): FILE: tabular/tests/regressiontests/test_tabular_lite.py function selenium_standalone_micropip (line 15) | def selenium_standalone_micropip(selenium_standalone): function test_train_classifier (line 57) | def test_train_classifier(selenium_standalone_micropip): FILE: tabular/tests/regressiontests/test_tabular_regression.py function myfloor (line 44) | def myfloor(x, base=0.01): function inner_test_tabular (line 49) | def inner_test_tabular(testname): function test_tabular_score (line 164) | def test_tabular_score(testname): FILE: tabular/tests/regressiontests/utils.py function make_dataset (line 312) | def make_dataset(request, seed): FILE: tabular/tests/test_check_style.py function test_check_style (line 6) | def test_check_style(): FILE: tabular/tests/unittests/calibrate/test_calibrate.py function test_calibrate_binary (line 4) | def test_calibrate_binary(): function test_calibrate_binary_bag (line 15) | def test_calibrate_binary_bag(): function test_calibrate_multiclass (line 27) | def test_calibrate_multiclass(): function test_calibrate_multiclass_bag (line 38) | def test_calibrate_multiclass_bag(): function test_calibrate_quantile (line 50) | def test_calibrate_quantile(): function test_calibrate_quantile_bag (line 61) | def test_calibrate_quantile_bag(): FILE: tabular/tests/unittests/callbacks/test_callbacks.py function test_early_stopping_count_callback (line 12) | def test_early_stopping_count_callback(): function test_early_stopping_count_callback_as_list (line 29) | def test_early_stopping_count_callback_as_list(): function test_early_stopping_callback (line 46) | def test_early_stopping_callback(): function test_early_stopping_callback_v2 (line 68) | def test_early_stopping_callback_v2(): function test_early_stopping_callback_v3 (line 95) | def test_early_stopping_callback_v3(): function test_early_stopping_ensemble_callback (line 123) | def test_early_stopping_ensemble_callback(): function test_early_stopping_ensemble_callback_v2 (line 146) | def test_early_stopping_ensemble_callback_v2(): function test_early_stopping_ensemble_callback_v3 (line 174) | def test_early_stopping_ensemble_callback_v3(): FILE: tabular/tests/unittests/configs/test_config_helper.py function test_presets (line 11) | def test_presets(): function test_presets_invalid_option (line 28) | def test_presets_invalid_option(): function test_excluded_model_types (line 42) | def test_excluded_model_types(): function test_excluded_model_types_invalid_option (line 59) | def test_excluded_model_types_invalid_option(): function test_included_model_types (line 67) | def test_included_model_types(): function test_included_model_types_invalid_option (line 94) | def test_included_model_types_invalid_option(): function test_time_limit (line 108) | def test_time_limit(): function test_time_limit_invalid_option (line 118) | def test_time_limit_invalid_option(): function test_hyperparameters_str (line 123) | def test_hyperparameters_str(): function test_hyperparameters_dict (line 129) | def test_hyperparameters_dict(): function test_hyperparameters__invalid_option (line 142) | def test_hyperparameters__invalid_option(): function test_auto_stack (line 156) | def test_auto_stack(): function test_use_bag_holdout (line 161) | def test_use_bag_holdout(): function test_num_bag_folds (line 166) | def test_num_bag_folds(): function test_num_bag_sets (line 172) | def test_num_bag_sets(): function test_num_stack_levels (line 178) | def test_num_stack_levels(): function test_holdout_frac (line 184) | def test_holdout_frac(): function test_hyperparameter_tune_kwargs (line 193) | def test_hyperparameter_tune_kwargs(): function test_ag_args (line 205) | def test_ag_args(): function test_ag_args_fit (line 209) | def test_ag_args_fit(): function test_ag_args_ensemble (line 213) | def test_ag_args_ensemble(): function test_set_best_to_refit_full (line 217) | def test_set_best_to_refit_full(): function test_keep_only_best (line 222) | def test_keep_only_best(): function test_save_space (line 227) | def test_save_space(): function test_calibrate (line 232) | def test_calibrate(): function test_use_bag_holdout (line 237) | def test_use_bag_holdout(): function test_refit_full (line 242) | def test_refit_full(): function test_feature_generator (line 248) | def test_feature_generator(): function test_feature_generator_2 (line 299) | def test_feature_generator_2(): function test_feature_generator_builder_standalone (line 338) | def test_feature_generator_builder_standalone(): FILE: tabular/tests/unittests/configs/test_pipline_presets.py function test_default_get_validation_and_stacking_method (line 7) | def test_default_get_validation_and_stacking_method(): function test_auto_stack_get_validation_and_stacking_method (line 77) | def test_auto_stack_get_validation_and_stacking_method(metadata_and_expe... FILE: tabular/tests/unittests/configs/test_presets.py class TestPresets (line 7) | class TestPresets(unittest.TestCase): method test_presets (line 8) | def test_presets(self): FILE: tabular/tests/unittests/data/test_label_cleaner.py function test_label_cleaner_binary (line 15) | def test_label_cleaner_binary(): function test_label_cleaner_multiclass (line 81) | def test_label_cleaner_multiclass(): function test_label_cleaner_multiclass_to_binary (line 136) | def test_label_cleaner_multiclass_to_binary(): function test_label_cleaner_regression (line 218) | def test_label_cleaner_regression(): function test_label_softclass (line 250) | def test_label_softclass(): FILE: tabular/tests/unittests/data/test_learning_curves.py function get_default_model_name (line 16) | def get_default_model_name(model: str) -> str: function get_one_model_problem (line 70) | def get_one_model_problem(): function get_all_models (line 76) | def get_all_models(): function get_all_problems (line 81) | def get_all_problems(): function get_all_model_problems (line 86) | def get_all_model_problems(): function get_all_model_problem_metrics (line 90) | def get_all_model_problem_metrics(): function get_subset_model_problem_metrics (line 96) | def get_subset_model_problem_metrics(): function test_off (line 107) | def test_off(problem_type, model, get_dataset_map): function test_flag_false (line 122) | def test_flag_false(problem_type, model, get_dataset_map): function test_flag_true (line 138) | def test_flag_true(problem_type, model, get_dataset_map): function test_metrics (line 160) | def test_metrics(problem_type, model, get_dataset_map): function custom_metric (line 190) | def custom_metric(y_true, y_pred): function test_custom_metrics (line 195) | def test_custom_metrics(problem_type, model, get_dataset_map): function test_metric_format (line 226) | def test_metric_format(problem_type, model, metric, use_error, get_datas... function test_with_test_data (line 268) | def test_with_test_data(problem_type, model, get_dataset_map, get_defaul... function test_correctness (line 315) | def test_correctness(problem_type, model, metric, get_dataset_map): function test_supported_class_tags (line 373) | def test_supported_class_tags(learning_curve_supported_class): function get_dataset_map (line 378) | def get_dataset_map(): function get_default_metrics (line 387) | def get_default_metrics(): FILE: tabular/tests/unittests/dynamic_stacking/test_dynamic_stacking.py function test_spot_and_avoid_stacked_overfitting (line 22) | def test_spot_and_avoid_stacked_overfitting(): function test_dynamic_stacking_hps (line 50) | def test_dynamic_stacking_hps(): function test_no_dynamic_stacking (line 102) | def test_no_dynamic_stacking(): function test_dynamic_stacking_fit_extra (line 124) | def test_dynamic_stacking_fit_extra(): function test_dynamic_stacking_with_time_limit (line 163) | def test_dynamic_stacking_with_time_limit(): function test_dynamic_stacking_run_twice_parallel_fold_fitting_strategy (line 197) | def test_dynamic_stacking_run_twice_parallel_fold_fitting_strategy(): FILE: tabular/tests/unittests/edgecases/test_edgecases.py function test_no_weighted_ensemble (line 11) | def test_no_weighted_ensemble(): function test_no_full_last_level_weighted_ensemble (line 25) | def test_no_full_last_level_weighted_ensemble(): function test_no_full_last_level_weighted_ensemble_additionally (line 44) | def test_no_full_last_level_weighted_ensemble_additionally(): function test_full_last_level_weighted_ensemble_additionally (line 64) | def test_full_last_level_weighted_ensemble_additionally(): function test_full_last_level_weighted_ensemble (line 89) | def test_full_last_level_weighted_ensemble(): function test_max_sets (line 108) | def test_max_sets(): function test_num_folds (line 131) | def test_num_folds(): function test_num_folds_hpo (line 154) | def test_num_folds_hpo(): function test_use_bag_holdout_calibrate (line 183) | def test_use_bag_holdout_calibrate(): function test_num_folds_parallel (line 207) | def test_num_folds_parallel(capsys): function test_raises_num_cpus_float (line 230) | def test_raises_num_cpus_float(): function test_raises_num_cpus_zero (line 243) | def test_raises_num_cpus_zero(): function test_raises_num_gpus_neg (line 256) | def test_raises_num_gpus_neg(): function test_delay_bag_sets (line 270) | def test_delay_bag_sets(delay_bag_sets): FILE: tabular/tests/unittests/experimental/test_scikit_api.py function test_scikit_api_binary (line 7) | def test_scikit_api_binary(): function test_scikit_api_multiclass (line 20) | def test_scikit_api_multiclass(): function test_scikit_api_regression (line 33) | def test_scikit_api_regression(): FILE: tabular/tests/unittests/models/advanced/test_bagged_deterministic.py function test_bagged_deterministic (line 22) | def test_bagged_deterministic(): FILE: tabular/tests/unittests/models/advanced/test_model_random_seed.py function test_bagged_random_seed (line 68) | def test_bagged_random_seed(hyperparameters, ag_args_ensemble, expected_... FILE: tabular/tests/unittests/models/advanced/test_refit_full.py function test_refit_full_train_data_extra (line 7) | def test_refit_full_train_data_extra(): function test_refit_full_train_data_extra_bag (line 44) | def test_refit_full_train_data_extra_bag(): FILE: tabular/tests/unittests/models/advanced/test_stack_feature_usage.py function test_stack_feature_usage_binary (line 13) | def test_stack_feature_usage_binary(): function test_stack_feature_usage_multiclass (line 26) | def test_stack_feature_usage_multiclass(): function test_stack_feature_usage_regression (line 39) | def test_stack_feature_usage_regression(): function test_stack_feature_usage_binary_all (line 52) | def test_stack_feature_usage_binary_all(): function test_stack_feature_usage_binary_only_max_models (line 65) | def test_stack_feature_usage_binary_only_max_models(): function _fit_predictor_stack_feature_usage (line 78) | def _fit_predictor_stack_feature_usage( function _assert_stack_features (line 116) | def _assert_stack_features(predictor: TabularPredictor, expected_ancesto... FILE: tabular/tests/unittests/models/test_advanced.py function test_bagged_predict_children (line 7) | def test_bagged_predict_children(): function test_resource_constraints (line 24) | def test_resource_constraints(): FILE: tabular/tests/unittests/models/test_automm.py function test_automm_sts (line 9) | def test_automm_sts(): function test_handle_text_automm (line 26) | def test_handle_text_automm(): FILE: tabular/tests/unittests/models/test_catboost.py function test_catboost (line 14) | def test_catboost(): function test_catboost_can_train_with_nondefault_regression_eval_metrics (line 22) | def test_catboost_can_train_with_nondefault_regression_eval_metrics(eval... FILE: tabular/tests/unittests/models/test_dummy.py function test_no_models_will_raise (line 11) | def test_no_models_will_raise(): function test_no_models (line 24) | def test_no_models(): function test_no_models_raise (line 46) | def test_no_models_raise(): function test_raise_on_model_failure (line 79) | def test_raise_on_model_failure(): function test_raise_on_fit_args (line 98) | def test_raise_on_fit_args(): function test_dummy (line 189) | def test_dummy(): function test_dummy_binary_model (line 197) | def test_dummy_binary_model(): function test_dummy_multiclass_model (line 203) | def test_dummy_multiclass_model(): function test_dummy_regression_model (line 209) | def test_dummy_regression_model(): function test_dummy_binary_absolute_path (line 215) | def test_dummy_binary_absolute_path(): function test_dummy_binary_absolute_path_stack (line 229) | def test_dummy_binary_absolute_path_stack(): function test_dummy_binary_model_absolute_path (line 244) | def test_dummy_binary_model_absolute_path(): function test_dummy_ag_ens_hyperparameter (line 254) | def test_dummy_ag_ens_hyperparameter(): FILE: tabular/tests/unittests/models/test_ebm.py function test_ebm (line 7) | def test_ebm(): function test_ebm_binary (line 14) | def test_ebm_binary(): function test_ebm_multiclass (line 22) | def test_ebm_multiclass(): function test_ebm_regression (line 30) | def test_ebm_regression(): FILE: tabular/tests/unittests/models/test_image_predictor.py function test_image_predictor_multiclass (line 9) | def test_image_predictor_multiclass(): function test_image_predictor_regression (line 29) | def test_image_predictor_regression(): FILE: tabular/tests/unittests/models/test_knn.py function test_knn (line 5) | def test_knn(): FILE: tabular/tests/unittests/models/test_lightgbm.py function test_lightgbm (line 9) | def test_lightgbm(): function test_lightgbm_binary_model (line 17) | def test_lightgbm_binary_model(): function test_lightgbm_multiclass_model (line 23) | def test_lightgbm_multiclass_model(): function test_lightgbm_regression_model (line 29) | def test_lightgbm_regression_model(): function test_lightgbm_quantile_model (line 35) | def test_lightgbm_quantile_model(): function test_lightgbm_binary_with_calibrate_decision_threshold (line 48) | def test_lightgbm_binary_with_calibrate_decision_threshold(): function test_lightgbm_binary_with_calibrate_decision_threshold_bagged_refit (line 101) | def test_lightgbm_binary_with_calibrate_decision_threshold_bagged_refit(): function test_clean_column_name_replaces_expected_symbols (line 156) | def test_clean_column_name_replaces_expected_symbols(): function test_rename_columns_outputs_are_unique_when_inputs_unique (line 164) | def test_rename_columns_outputs_are_unique_when_inputs_unique(): function test_rename_columns_resolves_cleaning_collisions_with_suffixes (line 171) | def test_rename_columns_resolves_cleaning_collisions_with_suffixes(): function test_rename_columns_avoids_clashing_with_existing_feature_names (line 181) | def test_rename_columns_avoids_clashing_with_existing_feature_names(): function test_rename_columns_is_order_dependent_but_still_unique (line 193) | def test_rename_columns_is_order_dependent_but_still_unique(): function test_non_string_features_are_returned_as_is_and_can_collide (line 208) | def test_non_string_features_are_returned_as_is_and_can_collide(): function test_duplicate_input_feature_names_raises_value_error (line 217) | def test_duplicate_input_feature_names_raises_value_error(): function test_bool_int_key_collision_raises_value_error (line 226) | def test_bool_int_key_collision_raises_value_error(): FILE: tabular/tests/unittests/models/test_lightgbm_prep.py function test_lightgbm (line 5) | def test_lightgbm(): FILE: tabular/tests/unittests/models/test_linear.py function test_linear (line 5) | def test_linear(): FILE: tabular/tests/unittests/models/test_mitra.py function test_mitra (line 11) | def test_mitra(): FILE: tabular/tests/unittests/models/test_realmlp.py function test_realmlp (line 7) | def test_realmlp(): FILE: tabular/tests/unittests/models/test_rf.py function test_rf (line 9) | def test_rf(): function test_rf_compile_onnx (line 16) | def test_rf_compile_onnx(): function test_rf_binary_compile_onnx_as_ag_arg (line 32) | def test_rf_binary_compile_onnx_as_ag_arg(): function test_rf_binary_compile_onnx_no_config_bagging (line 43) | def test_rf_binary_compile_onnx_no_config_bagging(): FILE: tabular/tests/unittests/models/test_tabdpt.py function test_tabdpt (line 7) | def test_tabdpt(): FILE: tabular/tests/unittests/models/test_tabicl.py function test_tabicl (line 7) | def test_tabicl(): FILE: tabular/tests/unittests/models/test_tabm.py function test_tabm (line 7) | def test_tabm(): FILE: tabular/tests/unittests/models/test_tabpfnv2.py function test_tabpfnv2 (line 7) | def test_tabpfnv2(): FILE: tabular/tests/unittests/models/test_tabular_nn.py function test_tabular_nn (line 10) | def test_tabular_nn(): function test_tabular_nn_binary_compile_onnx (line 16) | def test_tabular_nn_binary_compile_onnx(): function test_tabular_nn_binary_compile_onnx_as_ag_arg (line 32) | def test_tabular_nn_binary_compile_onnx_as_ag_arg(): function test_tabular_nn_multiclass_compile_onnx (line 53) | def test_tabular_nn_multiclass_compile_onnx(): function test_tabular_nn_regression_compile_onnx (line 69) | def test_tabular_nn_regression_compile_onnx(): FILE: tabular/tests/unittests/models/test_tabular_nn_fastai.py function test_tabular_nn_fastai (line 14) | def test_tabular_nn_fastai(): function test_get_epochs_number (line 43) | def test_get_epochs_number(test_input): function test_get_batch_size_with_bs_provided (line 69) | def test_get_batch_size_with_bs_provided(test_input): function test_BatchTimeTracker (line 78) | def test_BatchTimeTracker(): FILE: tabular/tests/unittests/models/test_text_prediction_v1_model.py function test_text_prediction_v1_sts (line 8) | def test_text_prediction_v1_sts(): FILE: tabular/tests/unittests/models/test_xgboost.py function test_xgboost (line 7) | def test_xgboost(): function test_xgboost_binary_enable_categorical (line 14) | def test_xgboost_binary_enable_categorical(): FILE: tabular/tests/unittests/models/test_xt.py function test_xt (line 7) | def test_xt(): function test_xt_binary_compile_onnx (line 14) | def test_xt_binary_compile_onnx(): function test_xt_multiclass_compile_onnx (line 25) | def test_xt_multiclass_compile_onnx(): function test_xt_regression_compile_onnx (line 36) | def test_xt_regression_compile_onnx(): FILE: tabular/tests/unittests/pseudolabel/pseudo_filter.py function get_default_args (line 10) | def get_default_args(func): function get_default_pseudo_test_args (line 15) | def get_default_pseudo_test_args(): function test_regression_pseudofilter (line 25) | def test_regression_pseudofilter(): function test_classification_pseudofilter (line 34) | def test_classification_pseudofilter(): FILE: tabular/tests/unittests/registry/test_model_registry.py function test_model_cls_key (line 173) | def test_model_cls_key(model_cls: Type[AbstractModel]): function verify_registry (line 178) | def verify_registry(model_cls: Type[AbstractModel], model_registry: Mode... function test_model_registry (line 210) | def test_model_registry(model_cls: Type[AbstractModel]): function test_model_registry_new (line 217) | def test_model_registry_new(): function test_no_unknown_model_cls_registered (line 241) | def test_no_unknown_model_cls_registered(): function test_model_cls_name (line 250) | def test_model_cls_name(model_cls: Type[AbstractModel]): function test_model_cls_priority (line 260) | def test_model_cls_priority(model_cls: Type[AbstractModel]): function test_model_cls_priority_by_problem_type (line 270) | def test_model_cls_priority_by_problem_type(model_cls: Type[AbstractMode... function test_model_cls_all_present (line 284) | def test_model_cls_all_present(): function test_model_cls_no_duplicate_keys (line 289) | def test_model_cls_no_duplicate_keys(): FILE: tabular/tests/unittests/resource_allocation/test_bagging_resource_allocation.py class DummyBaseModel (line 15) | class DummyBaseModel(AbstractModel): method __init__ (line 16) | def __init__(self, minimum_resources=None, default_resources=None, **k... method get_minimum_resources (line 21) | def get_minimum_resources(self, **kwargs): method _get_default_resources (line 24) | def _get_default_resources(self): class DummyModel (line 30) | class DummyModel(DummyBaseModel): class DummyBaggedModel (line 34) | class DummyBaggedModel(BaggedEnsembleModel): function _prepare_data (line 38) | def _prepare_data(): function _construct_dummy_fold_strategy (line 48) | def _construct_dummy_fold_strategy( function test_bagging_invalid_resources_per_fold (line 95) | def test_bagging_invalid_resources_per_fold(fold_strategy_cls): function test_parallel_bagging_resources_per_fold (line 118) | def test_parallel_bagging_resources_per_fold(): function test_parallel_bagging_no_resources_per_fold (line 148) | def test_parallel_bagging_no_resources_per_fold(): function test_sequential_bagging_resources_per_fold (line 176) | def test_sequential_bagging_resources_per_fold(): function test_sequential_bagging_no_resources_per_fold (line 202) | def test_sequential_bagging_no_resources_per_fold(): FILE: tabular/tests/unittests/resource_allocation/test_custom_memory_limit.py function get_and_assert_max_memory (line 7) | def get_and_assert_max_memory(): function test_custom_memory_soft_limit_tabular_fit (line 34) | def test_custom_memory_soft_limit_tabular_fit(get_and_assert_max_memory): FILE: tabular/tests/unittests/resource_allocation/test_gpu_assignment.py class DummyBaseModel (line 18) | class DummyBaseModel(AbstractModel): method __init__ (line 19) | def __init__(self, minimum_resources=None, default_resources=None, **k... method get_minimum_resources (line 24) | def get_minimum_resources(self, **kwargs): method _get_default_resources (line 27) | def _get_default_resources(self): class DummyModel (line 33) | class DummyModel(DummyBaseModel): class DummyBaggedModel (line 37) | class DummyBaggedModel(BaggedEnsembleModel): function _prepare_data (line 41) | def _prepare_data(): function _construct_parallel_fold_strategy (line 50) | def _construct_parallel_fold_strategy( function _create_ray_gpu_reporter (line 139) | def _create_ray_gpu_reporter(): class TestRayGpuAssignmentIntegration (line 198) | class TestRayGpuAssignmentIntegration: method _get_system_gpu_count (line 202) | def _get_system_gpu_count(): method _check_ray_init (line 211) | def _check_ray_init(self, num_cpus, num_gpus): method _calculate_assignments (line 218) | def _calculate_assignments(self, strategy, num_tasks, gpus_per_task, t... method _submit_and_collect_tasks (line 227) | def _submit_and_collect_tasks(self, ray_task_report_gpu_info, gpu_assi... method _verify_assignment_structure (line 238) | def _verify_assignment_structure(self, gpu_assignments, num_tasks, exp... method _verify_cuda_visible_devices (line 246) | def _verify_cuda_visible_devices(self, results, expected_cuda_visible): method _verify_torch_gpu_count (line 255) | def _verify_torch_gpu_count(self, results, expected_gpu_counts): method test_ray_gpu_assignment_no_gpu_integration (line 265) | def test_ray_gpu_assignment_no_gpu_integration(self): method test_ray_gpu_assignment_single_gpu_task_execution (line 284) | def test_ray_gpu_assignment_single_gpu_task_execution(self): method test_ray_gpu_assignment_insufficient_gpus_multiple_tasks (line 315) | def test_ray_gpu_assignment_insufficient_gpus_multiple_tasks(self): method test_ray_gpu_assignment_multiple_gpus_consecutive_task_execution (line 348) | def test_ray_gpu_assignment_multiple_gpus_consecutive_task_execution(s... method test_ray_gpu_assignment_multiple_gpus_round_robin_task_execution (line 381) | def test_ray_gpu_assignment_multiple_gpus_round_robin_task_execution(s... method test_ray_gpu_assignment_concurrent_task_execution (line 419) | def test_ray_gpu_assignment_concurrent_task_execution(self): method test_ray_gpu_assignment_edge_case_fractional_round_robin (line 453) | def test_ray_gpu_assignment_edge_case_fractional_round_robin(self): method test_ray_gpu_assignment_edge_case_float_gpu_per_task_raises (line 485) | def test_ray_gpu_assignment_edge_case_float_gpu_per_task_raises(self): method test_ray_gpu_assignment_gpus_per_task_greater_than_total_task (line 510) | def test_ray_gpu_assignment_gpus_per_task_greater_than_total_task(self): FILE: tabular/tests/unittests/resource_allocation/test_hpo_resource_allocation.py class DummyBaseModel (line 9) | class DummyBaseModel(AbstractModel): method __init__ (line 10) | def __init__(self, minimum_resources=None, default_resources=None, **k... method get_minimum_resources (line 15) | def get_minimum_resources(self, **kwargs): method _get_default_resources (line 18) | def _get_default_resources(self): class DummyModel (line 24) | class DummyModel(DummyBaseModel): class DummyBaggedModel (line 28) | class DummyBaggedModel(BaggedEnsembleModel): function _initialize_executor (line 35) | def _initialize_executor(executor_cls, hyperparameter_tune_kwargs): function test_hpo_and_bagging_invalid_resources_per_fold_more_than_total (line 42) | def test_hpo_and_bagging_invalid_resources_per_fold_more_than_total(mock... function test_hpo_and_bagging_invalid_resources_per_fold_less_than_minimum (line 62) | def test_hpo_and_bagging_invalid_resources_per_fold_less_than_minimum(mo... function test_hpo_and_bagging_invalid_resources_per_trial_more_than_total (line 82) | def test_hpo_and_bagging_invalid_resources_per_trial_more_than_total(moc... function test_hpo_and_bagging_invalid_resources_per_trial_less_than_minimum (line 102) | def test_hpo_and_bagging_invalid_resources_per_trial_less_than_minimum(m... function test_hpo_and_bagging_valid_resources_per_fold_and_valid_resources_per_trial (line 122) | def test_hpo_and_bagging_valid_resources_per_fold_and_valid_resources_pe... function test_hpo_and_bagging_valid_resources_per_fold_and_no_resources_per_trial (line 145) | def test_hpo_and_bagging_valid_resources_per_fold_and_no_resources_per_t... function test_hpo_and_bagging_valid_resources_per_trial_and_no_resources_per_fold (line 168) | def test_hpo_and_bagging_valid_resources_per_trial_and_no_resources_per_... function test_hpo_and_bagging_no_resources_per_trial_and_no_resources_per_fold (line 190) | def test_hpo_and_bagging_no_resources_per_trial_and_no_resources_per_fol... function test_hpo_without_bagging_invalid_resources_per_trial_more_than_total_resources (line 208) | def test_hpo_without_bagging_invalid_resources_per_trial_more_than_total... function test_hpo_without_bagging_invalid_resources_per_trial_less_than_minimum_resources (line 226) | def test_hpo_without_bagging_invalid_resources_per_trial_less_than_minim... function test_hpo_without_bagging_valid_resources_per_trial (line 244) | def test_hpo_without_bagging_valid_resources_per_trial(mock_system_resou... function test_hpo_without_bagging_no_resources_per_trial (line 261) | def test_hpo_without_bagging_no_resources_per_trial(mock_system_resource... FILE: tabular/tests/unittests/resource_allocation/test_resource_allocation_combined.py class DummyBaseModel (line 16) | class DummyBaseModel(AbstractModel): method __init__ (line 17) | def __init__(self, minimum_resources=None, default_resources=None, **k... method get_minimum_resources (line 22) | def get_minimum_resources(self, **kwargs): method _get_default_resources (line 25) | def _get_default_resources(self): class DummyModel (line 31) | class DummyModel(DummyBaseModel): class DummyBaggedModel (line 35) | class DummyBaggedModel(BaggedEnsembleModel): function _initialize_executor (line 42) | def _initialize_executor(executor_cls, hyperparameter_tune_kwargs): function _prepare_data (line 48) | def _prepare_data(): function _construct_dummy_fold_strategy (line 58) | def _construct_dummy_fold_strategy( function _test_bagging (line 86) | def _test_bagging( function _test_functionality (line 110) | def _test_functionality(mock_system_resources_ctx_mgr, test_args): function test_resource_allocation_combined_valid (line 1040) | def test_resource_allocation_combined_valid(mock_system_resources_ctx_mg... FILE: tabular/tests/unittests/resource_allocation/test_resources_mocking.py function test_resources_mocking (line 4) | def test_resources_mocking(mock_system_resources_ctx_mgr, mock_num_cpus,... FILE: tabular/tests/unittests/resource_allocation/test_total_resource_allocation.py class DummyBaseModel (line 8) | class DummyBaseModel(AbstractModel): method __init__ (line 9) | def __init__(self, minimum_resources={}, **kwargs): method get_minimum_resources (line 13) | def get_minimum_resources(self, **kwargs): method _get_default_resources (line 16) | def _get_default_resources(self): class DummyModel (line 22) | class DummyModel(DummyBaseModel): class DummyBaggedModel (line 26) | class DummyBaggedModel(BaggedEnsembleModel): function test_bagged_model_with_total_resources (line 30) | def test_bagged_model_with_total_resources(mock_system_resources_ctx_mgr... function test_bagged_model_with_total_resources_and_ensemble_resources (line 54) | def test_bagged_model_with_total_resources_and_ensemble_resources( function test_bagged_model_with_total_resources_but_no_gpu_specified (line 94) | def test_bagged_model_with_total_resources_but_no_gpu_specified( function test_bagged_model_without_total_resources_but_with_ensemble_resources (line 114) | def test_bagged_model_without_total_resources_but_with_ensemble_resources( function test_bagged_model_without_total_resources_and_without_model_resources (line 146) | def test_bagged_model_without_total_resources_and_without_model_resources( function test_nonbagged_model_with_total_resources (line 164) | def test_nonbagged_model_with_total_resources(mock_system_resources_ctx_... function test_nonbagged_model_with_total_resources_but_no_gpu_specified (line 175) | def test_nonbagged_model_with_total_resources_but_no_gpu_specified( function test_nonbagged_model_with_total_resources_and_model_resources (line 190) | def test_nonbagged_model_with_total_resources_and_model_resources( function test_nonbagged_model_without_total_resources (line 213) | def test_nonbagged_model_without_total_resources(mock_system_resources_c... function test_nonbagged_model_without_total_resources_but_with_model_resources (line 222) | def test_nonbagged_model_without_total_resources_but_with_model_resources( function test_nonbagged_model_without_total_resources_and_without_model_resources (line 236) | def test_nonbagged_model_without_total_resources_and_without_model_resou... FILE: tabular/tests/unittests/test_tabular.py function test_tabular (line 40) | def test_tabular(): function _assert_predict_dict_identical_to_predict (line 48) | def _assert_predict_dict_identical_to_predict(predictor: TabularPredicto... function _assert_predict_proba_dict_identical_to_predict_proba (line 72) | def _assert_predict_proba_dict_identical_to_predict_proba(predictor: Tab... function test_advanced_functionality (line 100) | def test_advanced_functionality(): function _assert_predictor_size (line 346) | def _assert_predictor_size(predictor: TabularPredictor): function test_advanced_functionality_bagging (line 354) | def test_advanced_functionality_bagging(): function verify_predictor (line 437) | def verify_predictor( function run_tabular_benchmarks (line 519) | def run_tabular_benchmarks( function test_pseudolabeling (line 557) | def test_pseudolabeling(): function test_tabular_bag_stack_hpo (line 660) | def test_tabular_bag_stack_hpo(): function test_tabular_hpo (line 689) | def test_tabular_hpo(): function test_tabular_feature_prune (line 704) | def test_tabular_feature_prune(): function _construct_tabular_bag_test_config (line 729) | def _construct_tabular_bag_test_config(fold_fitting_strategy) -> dict: function test_tabular_parallel_local_bagging (line 752) | def test_tabular_parallel_local_bagging(): function test_tabular_sequential_local_bagging (line 757) | def test_tabular_sequential_local_bagging(): function test_sample_weight (line 762) | def test_sample_weight(): function test_tabular_bag_stack (line 805) | def test_tabular_bag_stack(): function test_tabular_bag_stack_use_bag_holdout (line 838) | def test_tabular_bag_stack_use_bag_holdout(): function test_tabular_raise_on_nonfinite_float_labels (line 877) | def test_tabular_raise_on_nonfinite_float_labels(): function test_tabular_raise_on_nonfinite_class_labels (line 890) | def test_tabular_raise_on_nonfinite_class_labels(): function test_tabular_log_to_file (line 903) | def test_tabular_log_to_file(): FILE: timeseries/src/autogluon/timeseries/configs/hyperparameter_presets.py function get_hyperparameter_presets (line 4) | def get_hyperparameter_presets() -> dict[str, dict[str, dict[str, Any] |... FILE: timeseries/src/autogluon/timeseries/configs/predictor_presets.py function get_predictor_presets (line 15) | def get_predictor_presets() -> dict[str, Any]: FILE: timeseries/src/autogluon/timeseries/dataset/ts_dataframe.py class TimeSeriesDataFrame (line 23) | class TimeSeriesDataFrame(pd.DataFrame): method __init__ (line 123) | def __init__( method _constructor (line 157) | def _constructor(self) -> Type[TimeSeriesDataFrame]: method _constructor_from_mgr (line 160) | def _constructor_from_mgr(self, mgr, axes): method _construct_tsdf_from_data_frame (line 168) | def _construct_tsdf_from_data_frame( method _construct_tsdf_from_iterable_dataset (line 200) | def _construct_tsdf_from_iterable_dataset(cls, iterable_dataset: Itera... method _validate_multi_index_data_frame (line 218) | def _validate_multi_index_data_frame(cls, data: pd.DataFrame): method _validate_data_frame (line 236) | def _validate_data_frame(cls, df: pd.DataFrame): method _validate_iterable (line 255) | def _validate_iterable(cls, data: Iterable): method from_data_frame (line 272) | def from_data_frame( method from_path (line 316) | def from_path( method from_iterable_dataset (line 363) | def from_iterable_dataset(cls, iterable_dataset: Iterable, num_cpus: i... method item_ids (line 392) | def item_ids(self) -> pd.Index: method _construct_static_features (line 397) | def _construct_static_features( method static_features (line 420) | def static_features(self): method static_features (line 424) | def static_features(self, value: pd.DataFrame | None): method infer_frequency (line 457) | def infer_frequency(self, num_items: int | None = None, raise_if_irreg... method freq (line 529) | def freq(self): method num_items (line 539) | def num_items(self): method num_timesteps_per_item (line 543) | def num_timesteps_per_item(self) -> pd.Series: method copy (line 552) | def copy(self: TimeSeriesDataFrame, deep: bool = True) -> TimeSeriesDa... method __finalize__ (line 572) | def __finalize__( # noqa method split_by_time (line 582) | def split_by_time(self, cutoff_time: pd.Timestamp) -> tuple[TimeSeries... method slice_by_timestep (line 605) | def slice_by_timestep(self, start_index: int | None = None, end_index:... method slice_by_time (line 747) | def slice_by_time(self, start_time: pd.Timestamp, end_time: pd.Timesta... method from_pickle (line 773) | def from_pickle(cls, filepath_or_buffer: Any) -> TimeSeriesDataFrame: method fill_missing_values (line 793) | def fill_missing_values(self, method: str = "auto", value: float = 0.0... method dropna (line 882) | def dropna(self, how: str = "any") -> TimeSeriesDataFrame: # type: ig... method assign (line 899) | def assign(self, **kwargs) -> TimeSeriesDataFrame: method sort_index (line 904) | def sort_index(self, *args, **kwargs) -> TimeSeriesDataFrame: method get_model_inputs_for_scoring (line 907) | def get_model_inputs_for_scoring( method train_test_split (line 936) | def train_test_split( method convert_frequency (line 983) | def convert_frequency( method to_data_frame (line 1110) | def to_data_frame(self) -> pd.DataFrame: method get_indptr (line 1114) | def get_indptr(self) -> np.ndarray: method query (line 1124) | def query( # type: ignore method reindex (line 1128) | def reindex(*args, **kwargs) -> Self: ... # type: ignore method __new__ (line 1131) | def __new__(cls, data: pd.DataFrame, static_features: pd.DataFrame | N... method __new__ (line 1133) | def __new__( method __getitem__ (line 1147) | def __getitem__(self, items: list[str]) -> Self: ... # type: ignore method __getitem__ (line 1149) | def __getitem__(self, item: str) -> pd.Series: ... # type: ignore FILE: timeseries/src/autogluon/timeseries/learner.py class TimeSeriesLearner (line 19) | class TimeSeriesLearner(AbstractLearner): method __init__ (line 24) | def __init__( method load_trainer (line 51) | def load_trainer(self) -> TimeSeriesTrainer: # type: ignore method fit (line 55) | def fit( method _align_covariates_with_forecast_index (line 125) | def _align_covariates_with_forecast_index( method predict (line 165) | def predict( method score (line 186) | def score( method evaluate (line 196) | def evaluate( method get_feature_importance (line 206) | def get_feature_importance( method leaderboard (line 275) | def leaderboard( method get_info (line 288) | def get_info(self, include_model_info: bool = False, **kwargs) -> dict... method persist_trainer (line 305) | def persist_trainer( method unpersist_trainer (line 319) | def unpersist_trainer(self) -> list[str]: method refit_full (line 332) | def refit_full(self, model: str = "all") -> dict[str, str]: method backtest_predictions (line 335) | def backtest_predictions( method backtest_targets (line 353) | def backtest_targets( FILE: timeseries/src/autogluon/timeseries/metrics/__init__.py function check_get_evaluation_metric (line 56) | def check_get_evaluation_metric( FILE: timeseries/src/autogluon/timeseries/metrics/abstract.py class TimeSeriesScorer (line 12) | class TimeSeriesScorer: method __init__ (line 57) | def __init__( method sign (line 70) | def sign(self) -> int: method name (line 74) | def name(self) -> str: method __repr__ (line 77) | def __repr__(self) -> str: method __str__ (line 80) | def __str__(self) -> str: method name_with_sign (line 84) | def name_with_sign(self) -> str: method __call__ (line 91) | def __call__( method compute_metric (line 136) | def compute_metric( method save_past_metrics (line 167) | def save_past_metrics( method clear_past_metrics (line 183) | def clear_past_metrics(self) -> None: method error (line 190) | def error(self, *args, **kwargs): method _safemean (line 195) | def _safemean(array: np.ndarray | pd.Series) -> float: method _get_point_forecast_score_inputs (line 200) | def _get_point_forecast_score_inputs( method _get_quantile_forecast_score_inputs (line 217) | def _get_quantile_forecast_score_inputs( method check_get_horizon_weight (line 239) | def check_get_horizon_weight(horizon_weight: None, prediction_length: ... method check_get_horizon_weight (line 242) | def check_get_horizon_weight( method check_get_horizon_weight (line 247) | def check_get_horizon_weight( FILE: timeseries/src/autogluon/timeseries/metrics/point.py class RMSE (line 16) | class RMSE(TimeSeriesScorer): method compute_metric (line 40) | def compute_metric( class MSE (line 55) | class MSE(TimeSeriesScorer): method compute_metric (line 79) | def compute_metric( class MAE (line 94) | class MAE(TimeSeriesScorer): method compute_metric (line 116) | def compute_metric( class WAPE (line 131) | class WAPE(TimeSeriesScorer): method compute_metric (line 156) | def compute_metric( class SMAPE (line 172) | class SMAPE(TimeSeriesScorer): method compute_metric (line 194) | def compute_metric( class MAPE (line 209) | class MAPE(TimeSeriesScorer): method compute_metric (line 231) | def compute_metric( class MASE (line 246) | class MASE(TimeSeriesScorer): method __init__ (line 279) | def __init__( method save_past_metrics (line 290) | def save_past_metrics( method clear_past_metrics (line 297) | def clear_past_metrics(self) -> None: method compute_metric (line 300) | def compute_metric( class RMSSE (line 319) | class RMSSE(TimeSeriesScorer): method __init__ (line 353) | def __init__( method save_past_metrics (line 364) | def save_past_metrics( method clear_past_metrics (line 371) | def clear_past_metrics(self) -> None: method compute_metric (line 374) | def compute_metric( class RMSLE (line 392) | class RMSLE(TimeSeriesScorer): method compute_metric (line 415) | def compute_metric( method __call__ (line 431) | def __call__( class WCD (line 448) | class WCD(TimeSeriesScorer): method __init__ (line 470) | def __init__( method _fast_cumsum (line 486) | def _fast_cumsum(self, y: np.ndarray) -> np.ndarray: method compute_metric (line 491) | def compute_metric( FILE: timeseries/src/autogluon/timeseries/metrics/quantile.py class WQL (line 12) | class WQL(TimeSeriesScorer): method compute_metric (line 37) | def compute_metric( class SQL (line 59) | class SQL(TimeSeriesScorer): method __init__ (line 92) | def __init__( method save_past_metrics (line 103) | def save_past_metrics( method clear_past_metrics (line 110) | def clear_past_metrics(self) -> None: method compute_metric (line 113) | def compute_metric( FILE: timeseries/src/autogluon/timeseries/metrics/utils.py function _get_seasonal_diffs (line 6) | def _get_seasonal_diffs(*, y_past: pd.Series, seasonal_period: int = 1) ... function in_sample_abs_seasonal_error (line 10) | def in_sample_abs_seasonal_error(*, y_past: pd.Series, seasonal_period: ... function in_sample_squared_seasonal_error (line 16) | def in_sample_squared_seasonal_error(*, y_past: pd.Series, seasonal_peri... FILE: timeseries/src/autogluon/timeseries/models/abstract/abstract_timeseries_model.py class TimeSeriesModelBase (line 33) | class TimeSeriesModelBase(ModelBase, ABC): method __init__ (line 76) | def __init__( method __repr__ (line 136) | def __repr__(self) -> str: method rename (line 139) | def rename(self, name: str) -> None: method set_contexts (line 146) | def set_contexts(self, path_context): method cache_oof_predictions (line 150) | def cache_oof_predictions(self, predictions: TimeSeriesDataFrame | lis... method _check_and_split_hyperparameters (line 156) | def _check_and_split_hyperparameters( method save (line 192) | def save(self, path: str | None = None, verbose: bool = True) -> str: method load (line 213) | def load(cls, path: str, reset_paths: bool = True, load_oof: bool = Fa... method load_oof_predictions (line 223) | def load_oof_predictions(cls, path: str, verbose: bool = True) -> list... method supports_known_covariates (line 228) | def supports_known_covariates(self) -> bool: method supports_past_covariates (line 235) | def supports_past_covariates(self) -> bool: method supports_static_features (line 239) | def supports_static_features(self) -> bool: method get_oof_predictions (line 245) | def get_oof_predictions(self): method _get_default_hyperparameters (line 250) | def _get_default_hyperparameters(self) -> dict: method get_hyperparameters (line 253) | def get_hyperparameters(self) -> dict: method get_hyperparameter (line 257) | def get_hyperparameter(self, key: str) -> Any: method get_info (line 261) | def get_info(self) -> dict: method load_info (line 281) | def load_info(cls, path: str, load_model_if_required: bool = True) -> ... method _is_gpu_available (line 293) | def _is_gpu_available(self) -> bool: method _get_system_resources (line 297) | def _get_system_resources() -> dict[str, Any]: method _get_model_base (line 306) | def _get_model_base(self) -> Self: method persist (line 309) | def persist(self) -> Self: method _more_tags (line 316) | def _more_tags(self) -> dict: method get_params (line 336) | def get_params(self) -> dict: method convert_to_refit_full_via_copy (line 356) | def convert_to_refit_full_via_copy(self) -> Self: method convert_to_refit_full_template (line 371) | def convert_to_refit_full_template(self) -> Self: class AbstractTimeSeriesModel (line 391) | class AbstractTimeSeriesModel(TimeSeriesModelBase, TimeSeriesTunable, me... method __init__ (line 398) | def __init__( method _initialize_transforms_and_regressor (line 426) | def _initialize_transforms_and_regressor(self) -> None: method allowed_hyperparameters (line 442) | def allowed_hyperparameters(self) -> list[str]: method fit (line 446) | def fit( method _fit (line 540) | def _fit( method _check_fit_params (line 559) | def _check_fit_params(self): method _log_unused_hyperparameters (line 567) | def _log_unused_hyperparameters(self, extra_allowed_hyperparameters: l... method predict (line 580) | def predict( method get_forecast_horizon_index (line 654) | def get_forecast_horizon_index(self, data: TimeSeriesDataFrame) -> pd.... method _predict (line 661) | def _predict( method _preprocess_time_limit (line 670) | def _preprocess_time_limit(self, time_limit: float) -> float: method _get_search_space (line 681) | def _get_search_space(self): method _score_with_predictions (line 688) | def _score_with_predictions( method score (line 700) | def score(self, data: TimeSeriesDataFrame) -> float: method score_and_cache_oof (line 723) | def score_and_cache_oof( method preprocess (line 742) | def preprocess( FILE: timeseries/src/autogluon/timeseries/models/abstract/model_trial.py function model_trial (line 13) | def model_trial( function fit_and_save_model (line 66) | def fit_and_save_model(model, fit_kwargs, train_data, val_data, eval_met... function skip_hpo (line 90) | def skip_hpo(model, train_data, val_data, time_limit=None): FILE: timeseries/src/autogluon/timeseries/models/abstract/tunable.py class TimeSeriesTunable (line 30) | class TimeSeriesTunable(Tunable, ABC): method __init__ (line 32) | def __init__(self) -> None: method hyperparameter_tune (line 37) | def hyperparameter_tune( method _get_default_hpo_executor (line 113) | def _get_default_hpo_executor(self) -> HpoExecutor: method _get_hpo_backend (line 128) | def _get_hpo_backend(self) -> str: method _get_hpo_train_fn_kwargs (line 134) | def _get_hpo_train_fn_kwargs(self, **train_fn_kwargs) -> dict: method estimate_memory_usage (line 141) | def estimate_memory_usage(self, *args, **kwargs) -> float | None: method get_minimum_resources (line 147) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method _save_with_data (line 152) | def _save_with_data( method _get_model_base (line 168) | def _get_model_base(self) -> Self: method _is_gpu_available (line 172) | def _is_gpu_available(self) -> bool: method _get_search_space (line 176) | def _get_search_space(self) -> dict[str, Any]: method get_params (line 180) | def get_params(self) -> dict: method _get_system_resources (line 188) | def _get_system_resources() -> dict[str, Any]: FILE: timeseries/src/autogluon/timeseries/models/autogluon_tabular/mlforecast.py class TabularModel (line 33) | class TabularModel(BaseEstimator): method __init__ (line 36) | def __init__(self, model_class: Type[AbstractTabularModel], model_kwar... method fit (line 41) | def fit(self, X: pd.DataFrame, y: pd.Series, X_val: pd.DataFrame, y_va... method predict (line 48) | def predict(self, X: pd.DataFrame, **kwargs): method get_params (line 52) | def get_params(self, deep=True): class AbstractMLForecastModel (line 60) | class AbstractMLForecastModel(AbstractTimeSeriesModel): method __init__ (line 64) | def __init__( method _initialize_transforms_and_regressor (line 96) | def _initialize_transforms_and_regressor(self): method allowed_hyperparameters (line 102) | def allowed_hyperparameters(self) -> list[str]: method preprocess (line 114) | def preprocess( method _get_default_hyperparameters (line 135) | def _get_default_hyperparameters(self) -> dict[str, Any]: method _create_tabular_model (line 143) | def _create_tabular_model(self, model_name: str, model_hyperparameters... method _get_mlforecast_init_args (line 146) | def _get_mlforecast_init_args( method _mask_df (line 205) | def _mask_df(self, df: pd.DataFrame) -> pd.DataFrame: method _shorten_all_series (line 214) | def _shorten_all_series(mlforecast_df: pd.DataFrame, max_length: int) ... method _generate_train_val_dfs (line 218) | def _generate_train_val_dfs( method _to_mlforecast_df (line 270) | def _to_mlforecast_df( method _fit (line 312) | def _fit( method get_tabular_model (line 362) | def get_tabular_model(self) -> TabularModel: method _save_residuals_std (line 369) | def _save_residuals_std(self, val_df: pd.DataFrame) -> None: method _remove_short_ts_and_generate_fallback_forecast (line 389) | def _remove_short_ts_and_generate_fallback_forecast( method _add_gaussian_quantiles (line 434) | def _add_gaussian_quantiles( method _more_tags (line 461) | def _more_tags(self) -> dict[str, Any]: class DirectTabularModel (line 465) | class DirectTabularModel(AbstractMLForecastModel): method is_quantile_model (line 513) | def is_quantile_model(self) -> bool: method get_hyperparameters (line 516) | def get_hyperparameters(self) -> dict[str, Any]: method _mask_df (line 528) | def _mask_df(self, df: pd.DataFrame) -> pd.DataFrame: method _save_residuals_std (line 541) | def _save_residuals_std(self, val_df: pd.DataFrame) -> None: method _predict (line 548) | def _predict( method _postprocess_predictions (line 616) | def _postprocess_predictions( method _create_tabular_model (line 629) | def _create_tabular_model(self, model_name: str, model_hyperparameters... class RecursiveTabularModel (line 650) | class RecursiveTabularModel(AbstractMLForecastModel): method get_hyperparameters (line 697) | def get_hyperparameters(self) -> dict[str, Any]: method _predict (line 706) | def _predict( method _create_tabular_model (line 755) | def _create_tabular_model(self, model_name: str, model_hyperparameters... FILE: timeseries/src/autogluon/timeseries/models/autogluon_tabular/per_step.py class PerStepTabularModel (line 29) | class PerStepTabularModel(AbstractTimeSeriesModel): method __init__ (line 86) | def __init__(self, *args, **kwargs): method allowed_hyperparameters (line 100) | def allowed_hyperparameters(self) -> list[str]: method _ag_to_nixtla (line 116) | def _ag_to_nixtla(self) -> dict: method _get_default_hyperparameters (line 123) | def _get_default_hyperparameters(self): method _fit_single_model (line 134) | def _fit_single_model( method _get_n_jobs (line 215) | def _get_n_jobs( method preprocess (line 237) | def preprocess( method _get_train_df (line 265) | def _get_train_df( method _get_lags_for_step (line 294) | def _get_lags_for_step( method _fit (line 305) | def _fit( method _get_residuals_std_path (line 409) | def _get_residuals_std_path(cls, model_path: str) -> str: method _predict_with_single_model (line 414) | def _predict_with_single_model( method _predict (line 457) | def _predict( method _more_tags (line 512) | def _more_tags(self) -> dict[str, Any]: FILE: timeseries/src/autogluon/timeseries/models/autogluon_tabular/transforms.py class MLForecastScaler (line 17) | class MLForecastScaler(BaseTargetTransform): method __init__ (line 18) | def __init__(self, scaler_type: Literal["standard", "min_max", "mean_a... method _df_to_tsdf (line 23) | def _df_to_tsdf(self, df: pd.DataFrame) -> TimeSeriesDataFrame: method _tsdf_to_df (line 30) | def _tsdf_to_df(self, ts_df: TimeSeriesDataFrame) -> pd.DataFrame: method fit_transform (line 37) | def fit_transform(self, df: pd.DataFrame) -> pd.DataFrame: # type: ig... method inverse_transform (line 42) | def inverse_transform(self, df: pd.DataFrame) -> pd.DataFrame: # type... function apply_inverse_transform (line 48) | def apply_inverse_transform( FILE: timeseries/src/autogluon/timeseries/models/chronos/chronos2.py class Chronos2Model (line 15) | class Chronos2Model(AbstractTimeSeriesModel): method __init__ (line 92) | def __init__( method model_path (line 115) | def model_path(self) -> str: method save (line 127) | def save(self, path: str | None = None, verbose: bool = True) -> str: method _fit (line 135) | def _fit( method get_hyperparameters (line 155) | def get_hyperparameters(self) -> dict: method _get_default_hyperparameters (line 166) | def _get_default_hyperparameters(self) -> dict: method allowed_hyperparameters (line 188) | def allowed_hyperparameters(self) -> list[str]: method _remove_disabled_covariates (line 210) | def _remove_disabled_covariates( method _predict (line 226) | def _predict( method load_model_pipeline (line 275) | def load_model_pipeline(self): method persist (line 289) | def persist(self) -> Self: method _update_transformers_loggers (line 293) | def _update_transformers_loggers(self, log_level: int): method _fine_tune (line 299) | def _fine_tune( method _more_tags (line 384) | def _more_tags(self) -> dict[str, Any]: method _is_gpu_available (line 392) | def _is_gpu_available(self) -> bool: FILE: timeseries/src/autogluon/timeseries/models/chronos/model.py class ChronosModel (line 84) | class ChronosModel(AbstractTimeSeriesModel): method __init__ (line 200) | def __init__( method save (line 234) | def save(self, path: str | None = None, verbose: bool = True) -> str: method load (line 243) | def load(cls, path: str, reset_paths: bool = True, load_oof: bool = Fa... method _is_gpu_available (line 255) | def _is_gpu_available(self) -> bool: method model_pipeline (line 261) | def model_pipeline(self) -> Any: # of type BaseChronosPipeline method ag_default_config (line 268) | def ag_default_config(self) -> dict[str, Any]: method min_num_gpus (line 278) | def min_num_gpus(self) -> int: method default_batch_size (line 285) | def default_batch_size(self) -> int: method default_torch_dtype (line 292) | def default_torch_dtype(self) -> Any: method get_minimum_resources (line 298) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method load_model_pipeline (line 305) | def load_model_pipeline(self, is_training: bool = False): method persist (line 329) | def persist(self) -> "ChronosModel": method _has_tf32 (line 334) | def _has_tf32(self): method get_hyperparameters (line 339) | def get_hyperparameters(self) -> dict: method _get_default_hyperparameters (line 351) | def _get_default_hyperparameters(self) -> dict: method allowed_hyperparameters (line 371) | def allowed_hyperparameters(self) -> list[str]: method _get_fine_tune_trainer_kwargs (line 392) | def _get_fine_tune_trainer_kwargs(self, init_args, eval_during_fine_tu... method _validate_and_assign_attributes (line 424) | def _validate_and_assign_attributes(self, model_params: dict): method _fit (line 443) | def _fit( method _get_inference_data_loader (line 621) | def _get_inference_data_loader( method _get_context_length (line 645) | def _get_context_length(self, data: TimeSeriesDataFrame) -> int: method _predict (line 652) | def _predict( method _more_tags (line 732) | def _more_tags(self) -> dict: FILE: timeseries/src/autogluon/timeseries/models/chronos/utils.py class PseudoShuffledIterableDataset (line 26) | class PseudoShuffledIterableDataset(IterableDataset): method __init__ (line 39) | def __init__(self, base_dataset, shuffle_buffer_size: int = 100) -> None: method __iter__ (line 46) | def __iter__(self): class ChronosFineTuningDataset (line 60) | class ChronosFineTuningDataset(IterableDataset): method __init__ (line 90) | def __init__( method _create_instance_splitter (line 110) | def _create_instance_splitter(self, mode: str): method _create_training_data (line 129) | def _create_training_data(self, data: Iterable[dict]): method _create_validation_data (line 135) | def _create_validation_data(self, data: Iterable[dict]): method to_chronos_format (line 139) | def to_chronos_format(self, entry: dict) -> dict: method to_chronos_bolt_format (line 166) | def to_chronos_bolt_format(self, entry: dict) -> dict: method __iter__ (line 185) | def __iter__(self) -> Iterator: method shuffle (line 197) | def shuffle(self, shuffle_buffer_size: int | None = None): function left_pad_and_stack_1D (line 211) | def left_pad_and_stack_1D(tensors: list[torch.Tensor]) -> torch.Tensor: class ChronosInferenceDataset (line 222) | class ChronosInferenceDataset: method __init__ (line 225) | def __init__( method __len__ (line 238) | def __len__(self): method _get_context (line 241) | def _get_context(self, a: np.ndarray, pad_value=np.nan): method __getitem__ (line 249) | def __getitem__(self, idx) -> np.ndarray: class ChronosInferenceDataLoader (line 256) | class ChronosInferenceDataLoader(torch.utils.data.DataLoader): method __init__ (line 257) | def __init__(self, *args, **kwargs): method __iter__ (line 261) | def __iter__(self): # type: ignore class EvaluateAndSaveFinalStepCallback (line 267) | class EvaluateAndSaveFinalStepCallback(TrainerCallback): method on_step_end (line 270) | def on_step_end(self, args, state, control, **kwargs): class TimeLimitCallback (line 277) | class TimeLimitCallback(TrainerCallback): method __init__ (line 278) | def __init__(self, time_limit: float): method on_train_begin (line 290) | def on_train_begin(self, args, state, control, **kwargs): method on_step_end (line 293) | def on_step_end(self, args, state, control, **kwargs): class LoggerCallback (line 300) | class LoggerCallback(TrainerCallback): method on_log (line 301) | def on_log(self, args, state, control, logs=None, **kwargs): function timeout_callback (line 308) | def timeout_callback(seconds: float | None) -> Callable: function update_output_quantiles (line 319) | def update_output_quantiles(model: ChronosBoltModelForForecasting, new_q... FILE: timeseries/src/autogluon/timeseries/models/ensemble/__init__.py function get_ensemble_class (line 7) | def get_ensemble_class(name: str): FILE: timeseries/src/autogluon/timeseries/models/ensemble/abstract.py class AbstractTimeSeriesEnsembleModel (line 13) | class AbstractTimeSeriesEnsembleModel(TimeSeriesModelBase, ABC): method model_names (line 23) | def model_names(self) -> list[str]: method fit (line 28) | def fit( method _fit (line 70) | def _fit( method predict (line 83) | def predict(self, data: dict[str, TimeSeriesDataFrame], **kwargs) -> T... method _predict (line 98) | def _predict(self, data: dict[str, TimeSeriesDataFrame], **kwargs) -> ... method remap_base_models (line 102) | def remap_base_models(self, model_refit_map: dict[str, str]) -> None: FILE: timeseries/src/autogluon/timeseries/models/ensemble/array_based/abstract.py class ArrayBasedTimeSeriesEnsembleModel (line 14) | class ArrayBasedTimeSeriesEnsembleModel(AbstractTimeSeriesEnsembleModel,... method __init__ (line 34) | def __init__( method _get_default_hyperparameters (line 60) | def _get_default_hyperparameters(self) -> dict[str, Any]: method to_array (line 67) | def to_array(df: TimeSeriesDataFrame) -> np.ndarray: method _get_base_model_predictions (line 93) | def _get_base_model_predictions( method _isotonize (line 130) | def _isotonize(self, prediction_array: np.ndarray) -> np.ndarray: method _fit (line 148) | def _fit( method _get_ensemble_regressor (line 177) | def _get_ensemble_regressor(self) -> EnsembleRegressor: method _predict (line 180) | def _predict(self, data: dict[str, TimeSeriesDataFrame], **kwargs) -> ... method model_names (line 212) | def model_names(self) -> list[str]: method remap_base_models (line 215) | def remap_base_models(self, model_refit_map: dict[str, str]) -> None: method _filter_failed_models (line 219) | def _filter_failed_models( FILE: timeseries/src/autogluon/timeseries/models/ensemble/array_based/models.py class MedianEnsemble (line 16) | class MedianEnsemble(ArrayBasedTimeSeriesEnsembleModel): method _get_ensemble_regressor (line 32) | def _get_ensemble_regressor(self) -> MedianEnsembleRegressor: class BaseTabularEnsemble (line 36) | class BaseTabularEnsemble(ArrayBasedTimeSeriesEnsembleModel, ABC): method _get_default_hyperparameters (line 39) | def _get_default_hyperparameters(self) -> dict[str, Any]: method _get_ensemble_regressor (line 44) | def _get_ensemble_regressor(self): class TabularEnsemble (line 53) | class TabularEnsemble(BaseTabularEnsemble): class PerQuantileTabularEnsemble (line 81) | class PerQuantileTabularEnsemble(BaseTabularEnsemble): class LinearStackerEnsemble (line 109) | class LinearStackerEnsemble(ArrayBasedTimeSeriesEnsembleModel): method _get_default_hyperparameters (line 148) | def _get_default_hyperparameters(self) -> dict[str, Any]: method _get_ensemble_regressor (line 161) | def _get_ensemble_regressor(self) -> LinearStackerEnsembleRegressor: method _fit (line 172) | def _fit( FILE: timeseries/src/autogluon/timeseries/models/ensemble/array_based/regressor/abstract.py class EnsembleRegressor (line 7) | class EnsembleRegressor(ABC): method __init__ (line 8) | def __init__(self, *args, **kwargs): method fit (line 12) | def fit( method predict (line 41) | def predict( class MedianEnsembleRegressor (line 70) | class MedianEnsembleRegressor(EnsembleRegressor): method fit (line 71) | def fit( method predict (line 80) | def predict( FILE: timeseries/src/autogluon/timeseries/models/ensemble/array_based/regressor/linear_stacker.py class LinearStackerEnsembleRegressor (line 11) | class LinearStackerEnsembleRegressor(EnsembleRegressor): method __init__ (line 43) | def __init__( method _compute_weight_shape (line 63) | def _compute_weight_shape(self, base_model_predictions_shape: tuple) -... method make_weighted_average_module (line 78) | def make_weighted_average_module(self, base_model_predictions_shape: t... method fit (line 94) | def fit( method predict (line 168) | def predict( FILE: timeseries/src/autogluon/timeseries/models/ensemble/array_based/regressor/per_quantile_tabular.py class PerQuantileTabularEnsembleRegressor (line 15) | class PerQuantileTabularEnsembleRegressor(EnsembleRegressor): method __init__ (line 18) | def __init__( method fit (line 44) | def fit( method _get_feature_df (line 70) | def _get_feature_df(self, predictions: np.ndarray, index: int) -> pd.D... method predict (line 78) | def predict( FILE: timeseries/src/autogluon/timeseries/models/ensemble/array_based/regressor/tabular.py class TabularEnsembleRegressor (line 14) | class TabularEnsembleRegressor(EnsembleRegressor): method __init__ (line 17) | def __init__( method fit (line 34) | def fit( method predict (line 47) | def predict( method _get_feature_df (line 70) | def _get_feature_df( method _get_feature_names (line 85) | def _get_feature_names(self, num_models: int) -> list[str]: method _get_median_quantile_index (line 95) | def _get_median_quantile_index(self): FILE: timeseries/src/autogluon/timeseries/models/ensemble/ensemble_selection.py class TimeSeriesEnsembleSelection (line 12) | class TimeSeriesEnsembleSelection(EnsembleSelection): method __init__ (line 13) | def __init__( method fit (line 47) | def fit( # type: ignore method _fit (line 59) | def _fit( # type: ignore method _calculate_regret (line 102) | def _calculate_regret( # type: ignore function fit_time_series_ensemble_selection (line 131) | def fit_time_series_ensemble_selection( FILE: timeseries/src/autogluon/timeseries/models/ensemble/per_item_greedy.py class PerItemGreedyEnsemble (line 18) | class PerItemGreedyEnsemble(AbstractTimeSeriesEnsembleModel): method __init__ (line 45) | def __init__(self, name: str | None = None, **kwargs): method model_names (line 53) | def model_names(self) -> list[str]: method _get_default_hyperparameters (line 56) | def _get_default_hyperparameters(self) -> dict[str, Any]: method _fit (line 59) | def _fit( method _split_predictions_per_item (line 107) | def _split_predictions_per_item( method _split_data_per_item (line 125) | def _split_data_per_item(self, data_per_window: list[TimeSeriesDataFra... method _fit_item_ensemble (line 138) | def _fit_item_ensemble( method _predict (line 154) | def _predict(self, data: dict[str, TimeSeriesDataFrame], **kwargs) -> ... method remap_base_models (line 171) | def remap_base_models(self, model_refit_map: dict[str, str]) -> None: FILE: timeseries/src/autogluon/timeseries/models/ensemble/weighted/abstract.py class AbstractWeightedTimeSeriesEnsembleModel (line 11) | class AbstractWeightedTimeSeriesEnsembleModel(AbstractTimeSeriesEnsemble... method __init__ (line 19) | def __init__(self, name: str | None = None, **kwargs): method model_names (line 24) | def model_names(self) -> list[str]: method model_weights (line 28) | def model_weights(self) -> np.ndarray: method _predict (line 31) | def _predict(self, data: dict[str, TimeSeriesDataFrame], **kwargs) -> ... method get_info (line 35) | def get_info(self) -> dict: method remap_base_models (line 40) | def remap_base_models(self, model_refit_map: dict[str, str]) -> None: FILE: timeseries/src/autogluon/timeseries/models/ensemble/weighted/basic.py class SimpleAverageEnsemble (line 10) | class SimpleAverageEnsemble(AbstractWeightedTimeSeriesEnsembleModel): method _fit (line 19) | def _fit( class PerformanceWeightedEnsemble (line 32) | class PerformanceWeightedEnsemble(AbstractWeightedTimeSeriesEnsembleModel): method _get_default_hyperparameters (line 58) | def _get_default_hyperparameters(self) -> dict[str, Any]: method _fit (line 61) | def _fit( FILE: timeseries/src/autogluon/timeseries/models/ensemble/weighted/greedy.py class GreedyEnsemble (line 13) | class GreedyEnsemble(AbstractWeightedTimeSeriesEnsembleModel): method __init__ (line 35) | def __init__(self, name: str | None = None, **kwargs): method _get_default_hyperparameters (line 42) | def _get_default_hyperparameters(self) -> dict[str, Any]: method _fit (line 45) | def _fit( FILE: timeseries/src/autogluon/timeseries/models/gluonts/abstract.py class AbstractGluonTSModel (line 40) | class AbstractGluonTSModel(AbstractTimeSeriesModel): method __init__ (line 73) | def __init__( method save (line 108) | def save(self, path: str | None = None, verbose: bool = True) -> str: method load (line 126) | def load( method supports_cat_covariates (line 139) | def supports_cat_covariates(self) -> bool: method _get_hpo_backend (line 142) | def _get_hpo_backend(self): method _deferred_init_hyperparameters (line 145) | def _deferred_init_hyperparameters(self, dataset: TimeSeriesDataFrame)... method _get_default_hyperparameters (line 202) | def _get_default_hyperparameters(self): method get_hyperparameters (line 220) | def get_hyperparameters(self) -> dict: method _get_estimator_init_args (line 238) | def _get_estimator_init_args(self) -> dict[str, Any]: method _get_estimator_class (line 243) | def _get_estimator_class(self) -> Type[GluonTSEstimator]: method _get_estimator (line 246) | def _get_estimator(self) -> GluonTSEstimator: method _is_gpu_available (line 276) | def _is_gpu_available(self) -> bool: method get_minimum_resources (line 281) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method _to_gluonts_dataset (line 289) | def _to_gluonts_dataset(self, time_series_df: None, known_covariates=N... method _to_gluonts_dataset (line 291) | def _to_gluonts_dataset(self, time_series_df: TimeSeriesDataFrame, kno... method _to_gluonts_dataset (line 292) | def _to_gluonts_dataset( method _fit (line 389) | def _fit( method _get_callbacks (line 440) | def _get_callbacks( method _predict (line 455) | def _predict( method _predict_gluonts_forecasts (line 472) | def _predict_gluonts_forecasts( method _stack_quantile_forecasts (line 487) | def _stack_quantile_forecasts(self, forecasts: list[QuantileForecast],... method _stack_sample_forecasts (line 500) | def _stack_sample_forecasts(self, forecasts: list[SampleForecast], ite... method _stack_distribution_forecasts (line 512) | def _stack_distribution_forecasts( method _gluonts_forecasts_to_data_frame (line 563) | def _gluonts_forecasts_to_data_frame( method _more_tags (line 583) | def _more_tags(self) -> dict: FILE: timeseries/src/autogluon/timeseries/models/gluonts/dataset.py class SimpleGluonTSDataset (line 12) | class SimpleGluonTSDataset(GluonTSDataset): method __init__ (line 15) | def __init__( method _astype (line 51) | def _astype(array: np.ndarray | None, dtype: Type[np.generic]) -> np.n... method _get_freq_for_period (line 58) | def _get_freq_for_period(freq: str) -> str: method __len__ (line 76) | def __len__(self): method __iter__ (line 79) | def __iter__(self) -> Iterator[dict[str, Any]]: FILE: timeseries/src/autogluon/timeseries/models/gluonts/models.py class DeepARModel (line 24) | class DeepARModel(AbstractGluonTSModel): method _get_estimator_class (line 87) | def _get_estimator_class(self) -> Type[GluonTSEstimator]: method _get_estimator_init_args (line 92) | def _get_estimator_init_args(self) -> dict[str, Any]: class SimpleFeedForwardModel (line 103) | class SimpleFeedForwardModel(AbstractGluonTSModel): method _get_estimator_class (line 143) | def _get_estimator_class(self) -> Type[GluonTSEstimator]: class TemporalFusionTransformerModel (line 149) | class TemporalFusionTransformerModel(AbstractGluonTSModel): method _get_estimator_class (line 209) | def _get_estimator_class(self) -> Type[GluonTSEstimator]: method _get_default_hyperparameters (line 214) | def _get_default_hyperparameters(self): method _get_estimator_init_args (line 219) | def _get_estimator_init_args(self) -> dict[str, Any]: class DLinearModel (line 242) | class DLinearModel(AbstractGluonTSModel): method _get_default_hyperparameters (line 289) | def _get_default_hyperparameters(self): method _get_estimator_class (line 294) | def _get_estimator_class(self) -> Type[GluonTSEstimator]: class PatchTSTModel (line 300) | class PatchTSTModel(AbstractGluonTSModel): method _get_estimator_class (line 351) | def _get_estimator_class(self) -> Type[GluonTSEstimator]: method _get_default_hyperparameters (line 356) | def _get_default_hyperparameters(self): method _get_estimator_init_args (line 359) | def _get_estimator_init_args(self) -> dict[str, Any]: class WaveNetModel (line 365) | class WaveNetModel(AbstractGluonTSModel): method _get_estimator_class (line 431) | def _get_estimator_class(self) -> Type[GluonTSEstimator]: method _get_estimator_init_args (line 436) | def _get_estimator_init_args(self) -> dict[str, Any]: class TiDEModel (line 449) | class TiDEModel(AbstractGluonTSModel): method _get_estimator_class (line 522) | def _get_estimator_class(self) -> Type[GluonTSEstimator]: method _get_default_hyperparameters (line 527) | def _get_default_hyperparameters(self): method _get_estimator_init_args (line 543) | def _get_estimator_init_args(self) -> dict[str, Any]: FILE: timeseries/src/autogluon/timeseries/models/local/abstract_local_model.py class AbstractLocalModel (line 22) | class AbstractLocalModel(AbstractTimeSeriesModel): method __init__ (line 44) | def __init__( method allowed_hyperparameters (line 69) | def allowed_hyperparameters(self) -> list[str]: method preprocess (line 76) | def preprocess( method _get_default_hyperparameters (line 87) | def _get_default_hyperparameters(self) -> dict: method _compute_n_jobs (line 95) | def _compute_n_jobs(n_jobs: int | float) -> int: method _fit (line 103) | def _fit(self, train_data: TimeSeriesDataFrame, time_limit: int | None... method _get_dummy_forecast (line 125) | def _get_dummy_forecast(self, train_data: TimeSeriesDataFrame, max_num... method _update_local_model_args (line 134) | def _update_local_model_args(self, local_model_args: dict[str, Any]) -... method _predict (line 137) | def _predict(self, data: TimeSeriesDataFrame, **kwargs) -> TimeSeriesD... method _predict_wrapper (line 180) | def _predict_wrapper( method _predict_with_local_model (line 213) | def _predict_with_local_model( function seasonal_naive_forecast (line 221) | def seasonal_naive_forecast( function get_quantile_function (line 267) | def get_quantile_function(q: float) -> Callable: FILE: timeseries/src/autogluon/timeseries/models/local/naive.py class NaiveModel (line 11) | class NaiveModel(AbstractLocalModel): method _predict_with_local_model (line 30) | def _predict_with_local_model( method _more_tags (line 42) | def _more_tags(self) -> dict: class SeasonalNaiveModel (line 46) | class SeasonalNaiveModel(AbstractLocalModel): method _predict_with_local_model (line 73) | def _predict_with_local_model( method _more_tags (line 85) | def _more_tags(self) -> dict: class AverageModel (line 89) | class AverageModel(AbstractLocalModel): method _predict_with_local_model (line 108) | def _predict_with_local_model( method _more_tags (line 118) | def _more_tags(self) -> dict: class SeasonalAverageModel (line 122) | class SeasonalAverageModel(AbstractLocalModel): method _predict_with_local_model (line 148) | def _predict_with_local_model( method _more_tags (line 171) | def _more_tags(self) -> dict: FILE: timeseries/src/autogluon/timeseries/models/local/npts.py class NPTSModel (line 9) | class NPTSModel(AbstractLocalModel): method _update_local_model_args (line 49) | def _update_local_model_args(self, local_model_args: dict) -> dict: method _predict_with_local_model (line 55) | def _predict_with_local_model( method _more_tags (line 99) | def _more_tags(self) -> dict: FILE: timeseries/src/autogluon/timeseries/models/local/statsforecast.py class AbstractStatsForecastModel (line 12) | class AbstractStatsForecastModel(AbstractLocalModel): method _update_local_model_args (line 17) | def _update_local_model_args(self, local_model_args: dict[str, Any]) -... method _get_model_type (line 22) | def _get_model_type(self, variant: str | None = None) -> Type: method _get_local_model (line 25) | def _get_local_model(self, local_model_args: dict): method _get_point_forecast (line 31) | def _get_point_forecast( method _predict_with_local_model (line 40) | def _predict_with_local_model( class AbstractProbabilisticStatsForecastModel (line 48) | class AbstractProbabilisticStatsForecastModel(AbstractStatsForecastModel): method _predict_with_local_model (line 49) | def _predict_with_local_model( method _get_confidence_levels (line 64) | def _get_confidence_levels(self) -> tuple[list[float], dict[str, str]]: class AutoARIMAModel (line 77) | class AutoARIMAModel(AbstractProbabilisticStatsForecastModel): method _update_local_model_args (line 159) | def _update_local_model_args(self, local_model_args: dict) -> dict: method _get_model_type (line 165) | def _get_model_type(self, variant: str | None = None): class ARIMAModel (line 171) | class ARIMAModel(AbstractProbabilisticStatsForecastModel): method _update_local_model_args (line 230) | def _update_local_model_args(self, local_model_args: dict) -> dict: method _get_model_type (line 235) | def _get_model_type(self, variant: str | None = None): class AutoETSModel (line 241) | class AutoETSModel(AbstractProbabilisticStatsForecastModel): method _get_model_type (line 280) | def _get_model_type(self, variant: str | None = None): method _update_local_model_args (line 285) | def _update_local_model_args(self, local_model_args: dict) -> dict: method _predict_with_local_model (line 291) | def _predict_with_local_model( class ETSModel (line 305) | class ETSModel(AutoETSModel): method _update_local_model_args (line 338) | def _update_local_model_args(self, local_model_args: dict) -> dict: class DynamicOptimizedThetaModel (line 344) | class DynamicOptimizedThetaModel(AbstractProbabilisticStatsForecastModel): method _get_model_type (line 383) | def _get_model_type(self, variant: str | None = None): class ThetaModel (line 389) | class ThetaModel(AbstractProbabilisticStatsForecastModel): method _get_model_type (line 428) | def _get_model_type(self, variant: str | None = None): class AbstractConformalizedStatsForecastModel (line 434) | class AbstractConformalizedStatsForecastModel(AbstractStatsForecastModel): method _get_nonconformity_scores (line 449) | def _get_nonconformity_scores( method _predict_with_local_model (line 478) | def _predict_with_local_model( class AutoCESModel (line 508) | class AutoCESModel(AbstractProbabilisticStatsForecastModel): method _get_model_type (line 549) | def _get_model_type(self, variant: str | None = None): method _update_local_model_args (line 554) | def _update_local_model_args(self, local_model_args: dict) -> dict: method _get_point_forecast (line 559) | def _get_point_forecast(self, time_series: pd.Series, local_model_args... class AbstractStatsForecastIntermittentDemandModel (line 570) | class AbstractStatsForecastIntermittentDemandModel(AbstractConformalized... method _update_local_model_args (line 571) | def _update_local_model_args(self, local_model_args: dict[str, Any]) -... method _predict_with_local_model (line 575) | def _predict_with_local_model( class ADIDAModel (line 585) | class ADIDAModel(AbstractStatsForecastIntermittentDemandModel): method _get_model_type (line 613) | def _get_model_type(self, variant: str | None = None): class CrostonModel (line 619) | class CrostonModel(AbstractStatsForecastIntermittentDemandModel): method _get_model_type (line 655) | def _get_model_type(self, variant: str | None = None): method _update_local_model_args (line 671) | def _update_local_model_args(self, local_model_args: dict) -> dict: class IMAPAModel (line 677) | class IMAPAModel(AbstractStatsForecastIntermittentDemandModel): method _get_model_type (line 705) | def _get_model_type(self, variant: str | None = None): class ZeroModel (line 711) | class ZeroModel(AbstractStatsForecastIntermittentDemandModel): method _get_model_type (line 729) | def _get_model_type(self, variant: str | None = None): method _get_point_forecast (line 733) | def _get_point_forecast( FILE: timeseries/src/autogluon/timeseries/models/multi_window/multi_window_model.py class MultiWindowBacktestingModel (line 21) | class MultiWindowBacktestingModel(AbstractTimeSeriesModel): method __init__ (line 39) | def __init__( method supports_static_features (line 66) | def supports_static_features(self) -> bool: method supports_known_covariates (line 70) | def supports_known_covariates(self) -> bool: method supports_past_covariates (line 74) | def supports_past_covariates(self) -> bool: method _get_model_base (line 77) | def _get_model_base(self): method _get_hpo_backend (line 80) | def _get_hpo_backend(self) -> str: method _is_gpu_available (line 83) | def _is_gpu_available(self) -> bool: method get_minimum_resources (line 86) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method _fit (line 89) | def _fit( method get_info (line 190) | def get_info(self) -> dict: method get_child_model (line 195) | def get_child_model(self, window_index: int) -> AbstractTimeSeriesModel: method _predict (line 200) | def _predict( method score_and_cache_oof (line 210) | def score_and_cache_oof( method _get_search_space (line 237) | def _get_search_space(self): method _initialize_transforms_and_regressor (line 240) | def _initialize_transforms_and_regressor(self) -> None: method _get_hpo_train_fn_kwargs (line 246) | def _get_hpo_train_fn_kwargs(self, **train_fn_kwargs) -> dict: method save (line 252) | def save(self, path: str | None = None, verbose: bool = True) -> str: method persist (line 263) | def persist(self) -> Self: method load (line 270) | def load( method convert_to_refit_full_template (line 283) | def convert_to_refit_full_template(self) -> AbstractTimeSeriesModel: method convert_to_refit_full_via_copy (line 290) | def convert_to_refit_full_via_copy(self) -> AbstractTimeSeriesModel: method _more_tags (line 297) | def _more_tags(self) -> dict: FILE: timeseries/src/autogluon/timeseries/models/registry.py class ModelRecord (line 7) | class ModelRecord: class ModelRegistry (line 12) | class ModelRegistry(ABCMeta): method __new__ (line 22) | def __new__(cls, name, bases, attrs): method _add (line 40) | def _add(cls, alias: str, record: ModelRecord) -> None: method _get_model_record (line 46) | def _get_model_record(cls, alias: str | type) -> ModelRecord: method get_model_class (line 55) | def get_model_class(cls, alias: str | type) -> type: method get_model_priority (line 59) | def get_model_priority(cls, alias: str | type) -> int: method available_aliases (line 63) | def available_aliases(cls) -> list[str]: FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/backbone/attention.py class AttentionAxis (line 18) | class AttentionAxis(Enum): class BaseMultiheadAttention (line 23) | class BaseMultiheadAttention(torch.nn.Module): method __init__ (line 24) | def __init__( method rearrange_inputs (line 55) | def rearrange_inputs(self, inputs: torch.Tensor) -> torch.Tensor: method get_qkv (line 64) | def get_qkv( method positional_embedding (line 82) | def positional_embedding(self, q, k, v, kv_cache, layer_idx): method rearrange_output (line 107) | def rearrange_output(self, output: torch.Tensor, batch: int, variate: ... method run_attention (line 119) | def run_attention(self, attention_mask, q, k, v, seq_pos_offset, dropo... method forward (line 149) | def forward( class TimeWiseMultiheadAttention (line 170) | class TimeWiseMultiheadAttention(BaseMultiheadAttention): class SpaceWiseMultiheadAttention (line 181) | class SpaceWiseMultiheadAttention(BaseMultiheadAttention): FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/backbone/backbone.py class TotoOutput (line 17) | class TotoOutput(NamedTuple): function patchify_id_mask (line 28) | def patchify_id_mask(id_mask: torch.Tensor, patch_size: int) -> torch.Te... class PatchEmbedding (line 36) | class PatchEmbedding(torch.nn.Module): method __init__ (line 42) | def __init__(self, patch_size: int, stride: int, embed_dim: int): method _patchify (line 49) | def _patchify(self, x: torch.Tensor) -> torch.Tensor: method forward (line 52) | def forward( class TotoBackbone (line 73) | class TotoBackbone(torch.nn.Module): method __init__ (line 121) | def __init__( method allocate_kv_cache (line 169) | def allocate_kv_cache( method backbone (line 190) | def backbone( method forward (line 242) | def forward( method device (line 261) | def device(self): FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/backbone/distribution.py class DistributionOutput (line 14) | class DistributionOutput(ABC, torch.nn.Module): class StudentTOutput (line 18) | class StudentTOutput(DistributionOutput): method __init__ (line 19) | def __init__(self, embed_dim): method forward (line 26) | def forward(self, inputs, loc=None, scale=None): class MixtureOfStudentTsOutput (line 43) | class MixtureOfStudentTsOutput(DistributionOutput): method __init__ (line 44) | def __init__( method forward (line 58) | def forward(self, inputs, loc=None, scale=None): FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/backbone/kvcache.py class KVCache (line 18) | class KVCache: method __post_init__ (line 40) | def __post_init__(self): method __getitem__ (line 81) | def __getitem__(self, layer_idx: int) -> KV: method current_len (line 90) | def current_len(self, cache_idx: int) -> int: method seq_len (line 93) | def seq_len(self, layer_idx: int) -> int: method append (line 97) | def append(self, layer_idx: int, kv: KV): method reset (line 133) | def reset(self): FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/backbone/rope.py class TimeAwareRotaryEmbedding (line 13) | class TimeAwareRotaryEmbedding(RotaryEmbedding): method __init__ (line 21) | def __init__(self, *args, **kwargs): method rotate_queries_and_keys (line 35) | def rotate_queries_and_keys( method get_scale (line 76) | def get_scale(self, t: torch.Tensor, seq_len: int | None = None, offse... FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/backbone/rotary_embedding_torch.py function exists (line 38) | def exists(val): function default (line 42) | def default(val, d): function slice_at_dim (line 46) | def slice_at_dim(t, dim_slice: slice, *, dim): function rotate_half (line 56) | def rotate_half(x): function apply_rotary_emb (line 64) | def apply_rotary_emb(freqs, t, start_index=0, scale=1.0, seq_dim=-2, fre... function apply_learned_rotations (line 98) | def apply_learned_rotations(rotations, t, start_index=0, freq_ranges=None): class RotaryEmbedding (line 110) | class RotaryEmbedding(Module): method __init__ (line 111) | def __init__( method device (line 189) | def device(self): method get_seq_pos (line 192) | def get_seq_pos(self, seq_len, device=None, dtype=None, offset=0): method rotate_queries_or_keys (line 198) | def rotate_queries_or_keys(self, t, seq_dim=None, offset=0, scale=None): method rotate_queries_with_cached_keys (line 216) | def rotate_queries_with_cached_keys(self, q, k, seq_dim=None, offset=0): method rotate_queries_and_keys (line 238) | def rotate_queries_and_keys(self, q, k, seq_dim=None): method get_scale (line 261) | def get_scale(self, t: Tensor, seq_len: int | None = None, offset=0): method get_axial_freqs (line 281) | def get_axial_freqs(self, *dims, offsets: (tuple[int | float, ...] | T... method forward (line 321) | def forward(self, t: Tensor, seq_len: int | None = None, offset=0): FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/backbone/scaler.py function compute_causal_statistics (line 14) | def compute_causal_statistics( class CausalPatchStdMeanScaler (line 198) | class CausalPatchStdMeanScaler(Scaler): method __init__ (line 239) | def __init__( method __call__ (line 257) | def __call__( # type: ignore[override] FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/backbone/transformer.py class SwiGLU (line 23) | class SwiGLU(torch.nn.Module): method forward (line 29) | def forward(self, x: torch.Tensor) -> torch.Tensor: class RMSNorm (line 35) | class RMSNorm(torch.nn.Module): method __init__ (line 36) | def __init__(self, dim: int, include_weight: bool = True, eps: float =... method forward (line 44) | def forward(self, x: torch.Tensor): method increment_and_forward_ (line 48) | def increment_and_forward_(self, x: torch.Tensor, y: torch.Tensor): function make_batched_block_mask (line 55) | def make_batched_block_mask(t: torch.Tensor) -> torch.Tensor: class TransformerLayer (line 60) | class TransformerLayer(torch.nn.Module): method __init__ (line 83) | def __init__( method forward (line 137) | def forward( class Transformer (line 151) | class Transformer(torch.nn.Module): method __init__ (line 180) | def __init__( method _get_mask (line 220) | def _get_mask( method _pad_to_multiple (line 256) | def _pad_to_multiple( method _get_layer_types (line 280) | def _get_layer_types( method forward (line 301) | def forward( FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/dataset.py function pad_array (line 15) | def pad_array( function pad_id_mask (line 43) | def pad_id_mask( class MaskedTimeseries (line 79) | class MaskedTimeseries(NamedTuple): method to (line 115) | def to(self, device: torch.device) -> "MaskedTimeseries": function is_extreme_value (line 125) | def is_extreme_value(t: torch.Tensor) -> torch.Tensor: function replace_extreme_values (line 141) | def replace_extreme_values(t: torch.Tensor, replacement: float = 0.0) ->... function freq_to_seconds (line 145) | def freq_to_seconds(freq: str | pd.offsets.BaseOffset) -> float: FILE: timeseries/src/autogluon/timeseries/models/toto/_internal/forecaster.py class Forecast (line 25) | class Forecast: method quantile (line 29) | def quantile(self, q: float | torch.Tensor) -> torch.Tensor: method median (line 40) | def median(self) -> torch.Tensor: method std (line 47) | def std(self) -> torch.Tensor: class TotoForecaster (line 55) | class TotoForecaster: method __init__ (line 84) | def __init__(self, model: TotoBackbone): method forecast (line 87) | def forecast( method generate_mean (line 184) | def generate_mean( method generate_samples (line 258) | def generate_samples( method create_affine_transformed (line 391) | def create_affine_transformed(base_distr: Distribution, loc: torch.Ten... FILE: timeseries/src/autogluon/timeseries/models/toto/dataloader.py class TotoInferenceDataset (line 14) | class TotoInferenceDataset(torch.utils.data.Dataset): method __init__ (line 15) | def __init__( method __len__ (line 30) | def __len__(self): method __getitem__ (line 33) | def __getitem__(self, idx) -> np.ndarray: class TotoDataLoader (line 43) | class TotoDataLoader: method __init__ (line 44) | def __init__( method _get_timeout_callback (line 63) | def _get_timeout_callback(seconds: float | None) -> Callable: method _collate (line 73) | def _collate(time_series: list[np.ndarray], device: Any) -> torch.Tensor: method __iter__ (line 81) | def __iter__(self) -> Iterator[MaskedTimeseries]: FILE: timeseries/src/autogluon/timeseries/models/toto/hf_pretrained_model.py class TotoConfig (line 11) | class TotoConfig(PretrainedConfig): method __init__ (line 14) | def __init__( class TotoPretrainedModel (line 50) | class TotoPretrainedModel(PreTrainedModel): method __init__ (line 54) | def __init__(self, config: TotoConfig): method _remap_state_dict_keys (line 77) | def _remap_state_dict_keys(state_dict): method load_from_checkpoint (line 101) | def load_from_checkpoint( method from_pretrained (line 151) | def from_pretrained( method forward (line 199) | def forward(self, *args, **kwargs): FILE: timeseries/src/autogluon/timeseries/models/toto/model.py class TotoModel (line 20) | class TotoModel(AbstractTimeSeriesModel): method __init__ (line 62) | def __init__( method save (line 92) | def save(self, path: str | None = None, verbose: bool = True) -> str: method load (line 101) | def load(cls, path: str, reset_paths: bool = True, load_oof: bool = Fa... method _is_gpu_available (line 108) | def _is_gpu_available(self) -> bool: method get_minimum_resources (line 113) | def get_minimum_resources(self, is_gpu_available: bool = False) -> dic... method load_forecaster (line 116) | def load_forecaster(self): method persist (line 139) | def persist(self) -> Self: method _get_default_hyperparameters (line 144) | def _get_default_hyperparameters(self) -> dict: method _get_sample_batch_size (line 153) | def _get_sample_batch_size(self) -> int: method allowed_hyperparameters (line 166) | def allowed_hyperparameters(self) -> list[str]: method _more_tags (line 176) | def _more_tags(self) -> dict: method _fit (line 183) | def _fit( method _predict (line 196) | def _predict( FILE: timeseries/src/autogluon/timeseries/predictor.py class TimeSeriesPredictor (line 34) | class TimeSeriesPredictor: method __init__ (line 146) | def __init__( method _trainer (line 228) | def _trainer(self) -> TimeSeriesTrainer: method is_fit (line 232) | def is_fit(self) -> bool: method _assert_is_fit (line 235) | def _assert_is_fit(self, method_name: str) -> None: method _setup_log_to_file (line 240) | def _setup_log_to_file(self, log_to_file: bool, log_file_path: str | P... method _to_data_frame (line 248) | def _to_data_frame( method _check_and_prepare_data_frame (line 269) | def _check_and_prepare_data_frame( method _check_and_prepare_data_frame_for_evaluation (line 322) | def _check_and_prepare_data_frame_for_evaluation( method _get_dataset_stats (line 348) | def _get_dataset_stats(self, data: TimeSeriesDataFrame) -> str: method _filter_useless_train_data (line 363) | def _filter_useless_train_data( method fit (line 407) | def fit( method _recommend_num_val_windows_auto (line 832) | def _recommend_num_val_windows_auto( method _recommend_refit_every_n_windows_auto (line 858) | def _recommend_refit_every_n_windows_auto(self, num_val_windows: tuple... method _validate_and_normalize_validation_and_ensemble_inputs (line 863) | def _validate_and_normalize_validation_and_ensemble_inputs( method _normalize_num_val_windows_input (line 889) | def _normalize_num_val_windows_input( method _reduce_num_val_windows_if_necessary (line 910) | def _reduce_num_val_windows_if_necessary( method model_names (line 960) | def model_names(self) -> list[str]: method predict (line 965) | def predict( method backtest_predictions (line 1052) | def backtest_predictions( method backtest_predictions (line 1063) | def backtest_predictions( method backtest_predictions (line 1073) | def backtest_predictions( method backtest_targets (line 1181) | def backtest_targets( method evaluate (line 1246) | def evaluate( method feature_importance (line 1316) | def feature_importance( method _load_version_file (line 1445) | def _load_version_file(cls, path: str) -> str: method load (line 1470) | def load(cls, path: str | Path, require_version_match: bool = True) ->... method _save_version_file (line 1530) | def _save_version_file(self) -> None: method save (line 1535) | def save(self) -> None: method info (line 1547) | def info(self) -> dict[str, Any]: method model_best (line 1552) | def model_best(self) -> str: method persist (line 1561) | def persist(self, models: Literal["all", "best"] | list[str] = "best",... method unpersist (line 1587) | def unpersist(self) -> list[str]: method leaderboard (line 1601) | def leaderboard( method make_future_data_frame (line 1697) | def make_future_data_frame(self, data: TimeSeriesDataFrame | pd.DataFr... method fit_summary (line 1735) | def fit_summary(self, verbosity: int = 1) -> dict[str, Any]: method refit_full (line 1786) | def refit_full(self, model: str = "all", set_best_to_refit_full: bool ... method _simulation_artifact (line 1849) | def _simulation_artifact(self, test_data: TimeSeriesDataFrame) -> dict: method plot (line 1898) | def plot( FILE: timeseries/src/autogluon/timeseries/regressor.py class CovariateRegressor (line 17) | class CovariateRegressor(Protocol): method is_fit (line 18) | def is_fit(self) -> bool: ... method fit (line 20) | def fit(self, data: TimeSeriesDataFrame, time_limit: float | None = No... method transform (line 22) | def transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFrame:... method fit_transform (line 24) | def fit_transform( method inverse_transform (line 28) | def inverse_transform( class GlobalCovariateRegressor (line 36) | class GlobalCovariateRegressor(CovariateRegressor): method __init__ (line 75) | def __init__( method is_fit (line 105) | def is_fit(self) -> bool: method fit (line 108) | def fit(self, data: TimeSeriesDataFrame, time_limit: float | None = No... method transform (line 168) | def transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFrame: method fit_transform (line 175) | def fit_transform( method inverse_transform (line 182) | def inverse_transform( method _predict (line 194) | def _predict(self, data: TimeSeriesDataFrame, static_features: pd.Data... method _get_tabular_df (line 202) | def _get_tabular_df( method _subsample_df (line 219) | def _subsample_df(self, df: pd.DataFrame) -> pd.DataFrame: function get_covariate_regressor (line 227) | def get_covariate_regressor(covariate_regressor: None, target: str, cova... function get_covariate_regressor (line 229) | def get_covariate_regressor( function get_covariate_regressor (line 232) | def get_covariate_regressor( FILE: timeseries/src/autogluon/timeseries/splitter.py class AbstractWindowSplitter (line 11) | class AbstractWindowSplitter: method __init__ (line 12) | def __init__(self, prediction_length: int, num_val_windows: int = 1): method split (line 16) | def split(self, data: TimeSeriesDataFrame) -> Iterator[tuple[TimeSerie... class ExpandingWindowSplitter (line 20) | class ExpandingWindowSplitter(AbstractWindowSplitter): method __init__ (line 44) | def __init__(self, prediction_length: int, num_val_windows: int = 1, v... method split (line 50) | def split(self, data: TimeSeriesDataFrame) -> Iterator[tuple[TimeSerie... FILE: timeseries/src/autogluon/timeseries/trainer/ensemble_composer.py class EnsembleComposer (line 27) | class EnsembleComposer: method __init__ (line 71) | def __init__( method _get_base_model_graph (line 103) | def _get_base_model_graph(source_graph: nx.DiGraph) -> nx.DiGraph: method _get_rootset (line 117) | def _get_rootset(graph: nx.DiGraph) -> list[str]: method _load_model (line 120) | def _load_model(self, model_name: str) -> Any: method _iter_models (line 126) | def _iter_models(self, layer: int) -> Iterator[tuple[str, Any]]: method iter_ensembles (line 151) | def iter_ensembles(self) -> Iterator[tuple[int, AbstractTimeSeriesEnse... method fit (line 168) | def fit( method iter_layer_models_and_hps (line 302) | def iter_layer_models_and_hps(self, layer_idx: int): method _fit_single_ensemble (line 313) | def _fit_single_ensemble( method _get_model_oof_predictions (line 366) | def _get_model_oof_predictions(self, model_name: str) -> list[TimeSeri... method _add_model (line 371) | def _add_model(self, model, base_models: list[str]): method _can_fit_ensemble (line 384) | def _can_fit_ensemble( method _get_ensemble_model_name (line 406) | def _get_ensemble_model_name(self, name: str, layer_idx: int) -> str: method _calculate_predict_time (line 417) | def _calculate_predict_time(self, model: AbstractTimeSeriesEnsembleMod... function validate_ensemble_hyperparameters (line 432) | def validate_ensemble_hyperparameters(hyperparameters: list[dict[str, di... FILE: timeseries/src/autogluon/timeseries/trainer/model_set_builder.py class TrainableModelSetBuilder (line 24) | class TrainableModelSetBuilder: method __init__ (line 34) | def __init__( method get_model_set (line 54) | def get_model_set( method _get_model_type (line 116) | def _get_model_type(self, model: ModelKey) -> Type[AbstractTimeSeriesM... method _get_default_model_init_kwargs (line 128) | def _get_default_model_init_kwargs(self) -> dict[str, Any]: method _get_model_name (line 139) | def _get_model_name(self, ag_args: dict[str, Any], model_type: Type[Ab... class HyperparameterBuilder (line 149) | class HyperparameterBuilder: method __init__ (line 154) | def __init__( method get_hyperparameters (line 164) | def get_hyperparameters(self) -> TrainerHyperparameterSpec: method _check_and_clean_hyperparameters (line 185) | def _check_and_clean_hyperparameters( method _get_excluded_models (line 214) | def _get_excluded_models(self) -> set[str]: method _normalize_model_type_name (line 227) | def _normalize_model_type_name(model_name: str) -> str: method _verify_searchspaces (line 230) | def _verify_searchspaces(self, hyperparameters: dict[str, list[ModelHy... function contains_searchspace (line 252) | def contains_searchspace(model_hyperparameters: ModelHyperparameters) ->... FILE: timeseries/src/autogluon/timeseries/trainer/prediction_cache.py class PredictionCache (line 14) | class PredictionCache(ABC): method __init__ (line 20) | def __init__(self, root_path: str): method get (line 24) | def get( method put (line 30) | def put( method clear (line 40) | def clear(self) -> None: function get_prediction_cache (line 44) | def get_prediction_cache(use_cache: bool, root_path: str) -> PredictionC... function compute_dataset_hash (line 51) | def compute_dataset_hash(data: TimeSeriesDataFrame, known_covariates: Ti... class NoOpPredictionCache (line 57) | class NoOpPredictionCache(PredictionCache): method get (line 60) | def get( method put (line 65) | def put( method clear (line 74) | def clear(self) -> None: class FileBasedPredictionCache (line 78) | class FileBasedPredictionCache(PredictionCache): method path (line 84) | def path(self) -> Path: method get (line 87) | def get( method put (line 93) | def put( method clear (line 103) | def clear(self) -> None: method _load_cached_predictions (line 108) | def _load_cached_predictions(self) -> dict[str, dict[str, dict[str, An... method _get_cached_pred_dicts (line 118) | def _get_cached_pred_dicts( method _save_cached_pred_dicts (line 136) | def _save_cached_pred_dicts( FILE: timeseries/src/autogluon/timeseries/trainer/trainer.py class TimeSeriesTrainer (line 41) | class TimeSeriesTrainer(AbstractTrainer[TimeSeriesModelBase]): method __init__ (line 46) | def __init__( method path_pkl (line 106) | def path_pkl(self) -> str: method save_train_data (line 109) | def save_train_data(self, data: TimeSeriesDataFrame, verbose: bool = T... method save_val_data (line 113) | def save_val_data(self, data: TimeSeriesDataFrame, verbose: bool = Tru... method load_train_data (line 117) | def load_train_data(self) -> TimeSeriesDataFrame: method load_val_data (line 121) | def load_val_data(self) -> TimeSeriesDataFrame | None: method load_data (line 128) | def load_data(self) -> tuple[TimeSeriesDataFrame, TimeSeriesDataFrame ... method save (line 133) | def save(self) -> None: method _get_model_oof_predictions (line 143) | def _get_model_oof_predictions(self, model_name: str) -> list[TimeSeri... method _add_model (line 148) | def _add_model( method _get_model_layers (line 183) | def _get_model_layers(self) -> dict[str, int]: method get_models_attribute_dict (line 204) | def get_models_attribute_dict(self, attribute: str, models: list[str] ... method get_model_best (line 214) | def get_model_best(self) -> str: method get_model_names (line 237) | def get_model_names(self, layer: int | None = None) -> list[str]: method get_info (line 243) | def get_info(self, include_model_info: bool = False) -> dict[str, Any]: method tune_model_hyperparameters (line 268) | def tune_model_hyperparameters( method _train_and_save (line 331) | def _train_and_save( method fit (line 401) | def fit( method _fit_ensembles (line 560) | def _fit_ensembles( method _get_validation_windows (line 592) | def _get_validation_windows(self, train_data: TimeSeriesDataFrame, val... method _get_val_splitter (line 598) | def _get_val_splitter(self, use_val_data: bool = False) -> AbstractWin... method _get_base_model_predictions (line 606) | def _get_base_model_predictions(self, model_names: list[str]) -> dict[... method leaderboard (line 613) | def leaderboard( method persist (line 706) | def persist( method unpersist (line 732) | def unpersist(self, model_names: Literal["all"] | list[str] = "all") -... method _get_model_for_prediction (line 744) | def _get_model_for_prediction(self, model: str | TimeSeriesModelBase |... method predict (line 767) | def predict( method _get_eval_metric (line 788) | def _get_eval_metric(self, metric: str | TimeSeriesScorer | None) -> T... method _score_with_predictions (line 799) | def _score_with_predictions( method score (line 812) | def score( method evaluate (line 823) | def evaluate( method get_feature_importance (line 844) | def get_feature_importance( method _model_uses_feature (line 950) | def _model_uses_feature(self, model: str | TimeSeriesModelBase, featur... method backtest_predictions (line 963) | def backtest_predictions( method backtest_targets (line 1003) | def backtest_targets( method _add_ci_to_feature_importance (line 1029) | def _add_ci_to_feature_importance( method _predict_model (line 1056) | def _predict_model( method _get_inputs_to_model (line 1072) | def _get_inputs_to_model( method get_model_pred_dict (line 1091) | def get_model_pred_dict( method _get_total_pred_time_from_marginal (line 1173) | def _get_total_pred_time_from_marginal(self, pred_time_dict_marginal: ... method _merge_refit_full_data (line 1181) | def _merge_refit_full_data( method refit_single_full (line 1190) | def refit_single_full( method refit_full (line 1239) | def refit_full(self, model: str = "all") -> dict[str, str]: method get_trainable_base_models (line 1271) | def get_trainable_base_models( FILE: timeseries/src/autogluon/timeseries/trainer/utils.py function log_scores_and_times (line 6) | def log_scores_and_times( FILE: timeseries/src/autogluon/timeseries/transforms/covariate_scaler.py class CovariateScaler (line 17) | class CovariateScaler(Protocol): method fit_transform (line 23) | def fit_transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFr... method transform (line 25) | def transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFrame:... method transform_known_covariates (line 27) | def transform_known_covariates( class GlobalCovariateScaler (line 32) | class GlobalCovariateScaler(CovariateScaler): method __init__ (line 43) | def __init__( method is_fit (line 58) | def is_fit(self) -> bool: method fit (line 61) | def fit(self, data: TimeSeriesDataFrame) -> "GlobalCovariateScaler": method fit_transform (line 80) | def fit_transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFr... method transform (line 85) | def transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFrame: method transform_known_covariates (line 107) | def transform_known_covariates( method _get_transformer_for_columns (line 120) | def _get_transformer_for_columns(self, df: pd.DataFrame, columns: list... function get_covariate_scaler (line 154) | def get_covariate_scaler(name: None, **scaler_kwargs) -> None: ... function get_covariate_scaler (line 156) | def get_covariate_scaler(name: Literal["global"], **scaler_kwargs) -> Gl... function get_covariate_scaler (line 157) | def get_covariate_scaler(name: Literal["global"] | None = None, **scaler... FILE: timeseries/src/autogluon/timeseries/transforms/target_scaler.py class TargetScaler (line 10) | class TargetScaler(Protocol): method fit_transform (line 11) | def fit_transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFr... method fit (line 13) | def fit(self, data: TimeSeriesDataFrame) -> Self: ... method transform (line 15) | def transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFrame:... method inverse_transform (line 17) | def inverse_transform(self, predictions: TimeSeriesDataFrame) -> TimeS... class LocalTargetScaler (line 20) | class LocalTargetScaler(TargetScaler): method __init__ (line 23) | def __init__( method _compute_loc_scale (line 33) | def _compute_loc_scale(self, target_series: pd.Series) -> tuple[pd.Ser... method fit_transform (line 36) | def fit_transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFr... method fit (line 39) | def fit(self, data: TimeSeriesDataFrame) -> "LocalTargetScaler": method _reindex_loc_scale (line 48) | def _reindex_loc_scale(self, item_index: pd.Index) -> tuple[np.ndarray... method transform (line 60) | def transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFrame: method inverse_transform (line 65) | def inverse_transform(self, predictions: TimeSeriesDataFrame) -> TimeS... class LocalStandardScaler (line 71) | class LocalStandardScaler(LocalTargetScaler): method _compute_loc_scale (line 77) | def _compute_loc_scale(self, target_series: pd.Series) -> tuple[pd.Ser... class LocalMeanAbsScaler (line 82) | class LocalMeanAbsScaler(LocalTargetScaler): method _compute_loc_scale (line 85) | def _compute_loc_scale(self, target_series: pd.Series) -> tuple[pd.Ser... class LocalMinMaxScaler (line 90) | class LocalMinMaxScaler(LocalTargetScaler): method _compute_loc_scale (line 96) | def _compute_loc_scale(self, target_series: pd.Series) -> tuple[pd.Ser... class LocalRobustScaler (line 103) | class LocalRobustScaler(LocalTargetScaler): method __init__ (line 109) | def __init__( method _compute_loc_scale (line 120) | def _compute_loc_scale(self, target_series: pd.Series) -> tuple[pd.Ser... function get_target_scaler (line 138) | def get_target_scaler(name: None, **scaler_kwargs) -> None: ... function get_target_scaler (line 140) | def get_target_scaler(name: Literal["standard", "mean_abs", "min_max", "... function get_target_scaler (line 141) | def get_target_scaler( FILE: timeseries/src/autogluon/timeseries/utils/datetime/base.py function norm_freq_str (line 47) | def norm_freq_str(offset: pd.DateOffset) -> str: FILE: timeseries/src/autogluon/timeseries/utils/datetime/lags.py function _make_lags (line 11) | def _make_lags(middle: int, delta: int) -> np.ndarray: function _make_lags_for_second (line 19) | def _make_lags_for_second(multiple, num_cycles=3): function _make_lags_for_minute (line 24) | def _make_lags_for_minute(multiple, num_cycles=3): function _make_lags_for_hour (line 29) | def _make_lags_for_hour(multiple, num_cycles=7): function _make_lags_for_business_hour (line 34) | def _make_lags_for_business_hour(multiple, num_cycles=7): function _make_lags_for_day (line 38) | def _make_lags_for_day(multiple, num_cycles=4, days_in_week=7, days_in_m... function _make_lags_for_week (line 46) | def _make_lags_for_week(multiple, num_cycles=3): function _make_lags_for_month (line 54) | def _make_lags_for_month(multiple, num_cycles=3): function _make_lags_for_quarter (line 59) | def _make_lags_for_quarter(multiple, num_cycles=3): function _make_lags_for_semi_month (line 63) | def _make_lags_for_semi_month(multiple, num_cycles=3): function get_lags_for_frequency (line 68) | def get_lags_for_frequency( FILE: timeseries/src/autogluon/timeseries/utils/datetime/seasonality.py function get_seasonality (line 23) | def get_seasonality(freq: str | None) -> int: FILE: timeseries/src/autogluon/timeseries/utils/datetime/time_features.py function _normalize (line 13) | def _normalize(values, num: float): function quarter_of_year (line 18) | def quarter_of_year(index: pd.DatetimeIndex) -> np.ndarray: function month_of_year (line 22) | def month_of_year(index: pd.DatetimeIndex) -> np.ndarray: function week_of_year (line 26) | def week_of_year(index: pd.DatetimeIndex) -> np.ndarray: function day_of_month (line 35) | def day_of_month(index: pd.DatetimeIndex) -> np.ndarray: function day_of_year (line 39) | def day_of_year(index: pd.DatetimeIndex) -> np.ndarray: function day_of_week (line 43) | def day_of_week(index: pd.DatetimeIndex) -> np.ndarray: function hour_of_day (line 47) | def hour_of_day(index: pd.DatetimeIndex) -> np.ndarray: function minute_of_hour (line 51) | def minute_of_hour(index: pd.DatetimeIndex) -> np.ndarray: function second_of_minute (line 55) | def second_of_minute(index: pd.DatetimeIndex) -> np.ndarray: function get_time_features_for_frequency (line 59) | def get_time_features_for_frequency(freq) -> list[Callable]: FILE: timeseries/src/autogluon/timeseries/utils/features.py class CovariateMetadata (line 24) | class CovariateMetadata: method __post_init__ (line 37) | def __post_init__(self): method static_features (line 43) | def static_features(self) -> list[str]: method known_covariates (line 47) | def known_covariates(self) -> list[str]: method past_covariates (line 51) | def past_covariates(self) -> list[str]: method covariates (line 55) | def covariates(self) -> list[str]: method covariates_real (line 59) | def covariates_real(self) -> list[str]: method covariates_cat (line 63) | def covariates_cat(self) -> list[str]: method real_features (line 67) | def real_features(self) -> list[str]: method cat_features (line 71) | def cat_features(self) -> list[str]: method all_features (line 75) | def all_features(self) -> list[str]: method to_dict (line 78) | def to_dict(self) -> dict[str, Any]: class ContinuousAndCategoricalFeatureGenerator (line 82) | class ContinuousAndCategoricalFeatureGenerator(PipelineFeatureGenerator): method __init__ (line 88) | def __init__(self, verbosity: int = 0, minimum_cat_count=2, **kwargs): method transform (line 105) | def transform(self, X: pd.DataFrame, *args, **kwargs) -> pd.DataFrame: method fit_transform (line 108) | def fit_transform(self, X: pd.DataFrame, *args, **kwargs) -> pd.DataFr... class TimeSeriesFeatureGenerator (line 117) | class TimeSeriesFeatureGenerator: method __init__ (line 142) | def __init__( method required_column_names (line 165) | def required_column_names(self) -> list[str]: method covariate_metadata (line 169) | def covariate_metadata(self) -> CovariateMetadata: method fit (line 173) | def fit(self, data: TimeSeriesDataFrame) -> None: method fit_transform (line 176) | def fit_transform(self, data: TimeSeriesDataFrame) -> TimeSeriesDataFr... method _concat_dfs (line 278) | def _concat_dfs(dfs_to_concat: list[pd.DataFrame]) -> pd.DataFrame: method _impute_covariates (line 284) | def _impute_covariates(self, ts_df: TimeSeriesDataFrame, column_names:... method _impute_static_features (line 295) | def _impute_static_features(self, static_df: pd.DataFrame | None) -> p... method transform (line 304) | def transform(self, data: TimeSeriesDataFrame, data_frame_name: str = ... method transform_future_known_covariates (line 343) | def transform_future_known_covariates( method _detect_and_log_column_types (line 362) | def _detect_and_log_column_types(transformed_df: pd.DataFrame) -> tupl... method _check_required_columns_are_present (line 377) | def _check_required_columns_are_present( method _convert_numeric_to_float_dtype (line 386) | def _convert_numeric_to_float_dtype(self, df: pd.DataFrame) -> pd.Data... class AbstractFeatureImportanceTransform (line 396) | class AbstractFeatureImportanceTransform: method __init__ (line 401) | def __init__( method _transform_static_series (line 410) | def _transform_static_series(self, feature_data: pd.Series, is_categor... method _transform_series (line 414) | def _transform_series(self, feature_data: pd.Series, is_categorical: b... method transform (line 418) | def transform(self, data: TimeSeriesDataFrame, feature_name: str, **kw... class PermutationFeatureImportanceTransform (line 450) | class PermutationFeatureImportanceTransform(AbstractFeatureImportanceTra... method __init__ (line 453) | def __init__( method _transform_static_series (line 465) | def _transform_static_series(self, feature_data: pd.Series, is_categor... method _transform_series (line 468) | def _transform_series(self, feature_data: pd.Series, is_categorical: b... class ConstantReplacementFeatureImportanceTransform (line 482) | class ConstantReplacementFeatureImportanceTransform(AbstractFeatureImpor... method __init__ (line 486) | def __init__( method _transform_static_series (line 496) | def _transform_static_series(self, feature_data: pd.Series, is_categor... method _transform_series (line 499) | def _transform_series(self, feature_data: pd.Series, is_categorical: b... FILE: timeseries/src/autogluon/timeseries/utils/forecast.py function get_forecast_horizon_index_single_time_series (line 10) | def get_forecast_horizon_index_single_time_series( function get_forecast_horizon_index_ts_dataframe (line 24) | def get_forecast_horizon_index_ts_dataframe(*args, **kwargs) -> pd.Multi... function make_future_data_frame (line 28) | def make_future_data_frame( FILE: timeseries/src/autogluon/timeseries/utils/timer.py class Timer (line 6) | class Timer: method __init__ (line 41) | def __init__( method start (line 49) | def start(self) -> Self: method time_elapsed (line 54) | def time_elapsed(self) -> float: method time_remaining (line 63) | def time_remaining(self) -> float | None: method timed_out (line 73) | def timed_out(self) -> bool: class SplitTimer (line 84) | class SplitTimer(Timer): method __init__ (line 114) | def __init__( method start (line 125) | def start(self) -> Self: method round_time_remaining (line 132) | def round_time_remaining(self) -> float | None: method round_time_elapsed (line 157) | def round_time_elapsed(self) -> float: method next_round (line 163) | def next_round(self) -> Self: FILE: timeseries/src/autogluon/timeseries/utils/warning_filters.py function warning_filter (line 17) | def warning_filter(all_warnings: bool = False): function disable_root_logger (line 33) | def disable_root_logger(root_log_level=logging.ERROR): function set_loggers_level (line 42) | def set_loggers_level(regex: str, level=logging.ERROR): function disable_tqdm (line 56) | def disable_tqdm(): function disable_stdout (line 71) | def disable_stdout(): class DuplicateLogFilter (line 78) | class DuplicateLogFilter: method __init__ (line 79) | def __init__(self, max_count: int = 1): method filter (line 83) | def filter(self, record): function disable_duplicate_logs (line 90) | def disable_duplicate_logs(logger, max_count: int = 1): FILE: timeseries/tests/conftest.py function download_and_cache_hf_hub_dependencies (line 15) | def download_and_cache_hf_hub_dependencies(): function pytest_addoption (line 22) | def pytest_addoption(parser): function pytest_configure (line 26) | def pytest_configure(config): function pytest_collection_modifyitems (line 36) | def pytest_collection_modifyitems(config, items): function pytest_sessionstart (line 46) | def pytest_sessionstart(): function temp_model_path (line 65) | def temp_model_path(tmp_path_factory): function dummy_hyperparameters (line 71) | def dummy_hyperparameters(): function df_with_static (line 83) | def df_with_static(): function df_with_covariates (line 95) | def df_with_covariates(): function df_with_covariates_and_metadata (line 109) | def df_with_covariates_and_metadata(): FILE: timeseries/tests/smoketests/test_all_models.py function generate_train_and_test_data (line 16) | def generate_train_and_test_data( function assert_leaderboard_contains_all_models (line 103) | def assert_leaderboard_contains_all_models( function test_all_models_can_handle_all_covariates (line 139) | def test_all_models_can_handle_all_covariates( function test_all_models_handle_all_pandas_frequencies (line 211) | def test_all_models_handle_all_pandas_frequencies(freq, hyperparameters): function test_when_tuning_data_and_time_limit_are_provided_then_all_models_are_trained (line 251) | def test_when_tuning_data_and_time_limit_are_provided_then_all_models_ar... FILE: timeseries/tests/unittests/common.py function get_all_pandas_frequencies (line 16) | def get_all_pandas_frequencies(): function to_supported_pandas_freq (line 90) | def to_supported_pandas_freq(freq: str) -> str: function get_data_frame_with_item_index (line 98) | def get_data_frame_with_item_index( function mask_entries (line 133) | def mask_entries(data: TimeSeriesDataFrame) -> TimeSeriesDataFrame: function get_data_frame_with_variable_lengths (line 149) | def get_data_frame_with_variable_lengths( function get_data_frame_with_covariates (line 178) | def get_data_frame_with_covariates( function get_static_features (line 210) | def get_static_features(item_ids: list[str | int], feature_names: list[s... function dict_equal_primitive (line 238) | def dict_equal_primitive(this, that): class CustomMetric (line 255) | class CustomMetric(TimeSeriesScorer): method save_past_metrics (line 256) | def save_past_metrics( method compute_metric (line 261) | def compute_metric( method clear_past_metrics (line 266) | def clear_past_metrics(self) -> None: function get_prediction_for_df (line 270) | def get_prediction_for_df(data, prediction_length=5): FILE: timeseries/tests/unittests/conftest.py function patch_naive_models (line 10) | def patch_naive_models(): FILE: timeseries/tests/unittests/models/chronos/test_chronos2.py class TestChronos2Inference (line 14) | class TestChronos2Inference: method chronos2_model (line 16) | def chronos2_model(self, tmp_path_factory): method mocked_chronos2_model (line 24) | def mocked_chronos2_model(self, tmp_path_factory): method test_when_past_only_covariates_provided_then_chronos2_uses_them (line 36) | def test_when_past_only_covariates_provided_then_chronos2_uses_them(se... method test_when_known_covariates_provided_then_chronos2_uses_them (line 58) | def test_when_known_covariates_provided_then_chronos2_uses_them(self, ... method test_when_model_persisted_then_pipeline_loaded (line 84) | def test_when_model_persisted_then_pipeline_loaded(self, mocked_chrono... method test_when_chronos2_saved_to_custom_path_then_model_can_be_loaded (line 88) | def test_when_chronos2_saved_to_custom_path_then_model_can_be_loaded(s... method test_when_predict_called_then_output_format_correct (line 97) | def test_when_predict_called_then_output_format_correct(self, chronos2... method test_when_fine_tune_disabled_then_model_does_not_call_fit (line 107) | def test_when_fine_tune_disabled_then_model_does_not_call_fit(self, mo... method test_when_revision_provided_then_from_pretrained_is_called_with_revision (line 112) | def test_when_revision_provided_then_from_pretrained_is_called_with_re... method test_when_chronos2_scores_oof_and_time_limit_is_exceeded_then_exception_is_raised (line 127) | def test_when_chronos2_scores_oof_and_time_limit_is_exceeded_then_exce... class TestChronos2FineTuning (line 134) | class TestChronos2FineTuning: method mocked_fine_tunable_chronos2_model (line 136) | def mocked_fine_tunable_chronos2_model(self, tmp_path_factory): method fine_tuned_chronos2_model (line 156) | def fine_tuned_chronos2_model(self, tmp_path_factory): method test_when_fine_tune_enabled_then_model_calls_fit_on_pipeline (line 171) | def test_when_fine_tune_enabled_then_model_calls_fit_on_pipeline(self,... method test_when_validation_data_provided_and_eval_turned_on_then_validation_inputs_passed (line 180) | def test_when_validation_data_provided_and_eval_turned_on_then_validat... method test_when_fine_tuned_then_is_fine_tuned_flag_set (line 200) | def test_when_fine_tuned_then_is_fine_tuned_flag_set(self, mocked_fine... method test_when_fine_tuned_then_output_dir_passed_to_fit (line 207) | def test_when_fine_tuned_then_output_dir_passed_to_fit(self, mocked_fi... method test_when_fine_tuned_then_model_path_returns_local_path (line 213) | def test_when_fine_tuned_then_model_path_returns_local_path(self, fine... method test_when_fine_tuned_then_local_path_has_checkpoint (line 217) | def test_when_fine_tuned_then_local_path_has_checkpoint(self, fine_tun... method test_when_fine_tuned_and_saved_then_model_can_be_loaded (line 224) | def test_when_fine_tuned_and_saved_then_model_can_be_loaded(self, fine... method test_when_fine_tuned_model_loaded_then_can_predict (line 230) | def test_when_fine_tuned_model_loaded_then_can_predict(self, fine_tune... method test_when_fine_tuned_and_moved_then_model_path_updates (line 238) | def test_when_fine_tuned_and_moved_then_model_path_updates(self, fine_... method test_when_covariates_provided_then_chronos2_is_fine_tuned_with_them (line 254) | def test_when_covariates_provided_then_chronos2_is_fine_tuned_with_the... method test_when_covariates_disabled_then_not_used_during_fit (line 291) | def test_when_covariates_disabled_then_not_used_during_fit( method test_when_covariates_disabled_then_not_used_during_predict (line 327) | def test_when_covariates_disabled_then_not_used_during_predict( FILE: timeseries/tests/unittests/models/chronos/test_model.py function chronos_model_path (line 46) | def chronos_model_path(request): function default_chronos_tiny_model (line 54) | def default_chronos_tiny_model(request, chronos_model_path) -> ChronosMo... function default_chronos_tiny_model_gpu (line 68) | def default_chronos_tiny_model_gpu(request, chronos_model_path) -> Chron... function test_when_on_cpu_then_chronos_model_can_score_and_cache_oof (line 83) | def test_when_on_cpu_then_chronos_model_can_score_and_cache_oof(data, de... function test_when_on_cpu_then_chronos_model_can_infer (line 90) | def test_when_on_cpu_then_chronos_model_can_infer(data, default_chronos_... function test_when_on_cpu_and_model_requested_from_hf_then_chronos_model_can_infer (line 97) | def test_when_on_cpu_and_model_requested_from_hf_then_chronos_model_can_... function test_given_nan_features_when_on_cpu_then_chronos_model_inferences_not_nan (line 107) | def test_given_nan_features_when_on_cpu_then_chronos_model_inferences_no... function test_when_on_gpu_then_chronos_model_can_score_and_cache_oof (line 117) | def test_when_on_gpu_then_chronos_model_can_score_and_cache_oof(data, de... function test_when_on_gpu_then_chronos_model_can_infer (line 124) | def test_when_on_gpu_then_chronos_model_can_infer(data, default_chronos_... function test_given_nan_features_when_on_gpu_then_chronos_model_inferences_not_nan (line 131) | def test_given_nan_features_when_on_gpu_then_chronos_model_inferences_no... function test_when_batch_size_provided_then_batch_size_used_to_infer (line 142) | def test_when_batch_size_provided_then_batch_size_used_to_infer(batch_si... function test_when_cpu_models_saved_then_models_can_be_loaded_and_inferred (line 164) | def test_when_cpu_models_saved_then_models_can_be_loaded_and_inferred(da... function test_when_gpu_models_saved_then_models_can_be_loaded_and_inferred (line 175) | def test_when_gpu_models_saved_then_models_can_be_loaded_and_inferred(da... function test_when_context_length_not_provided_then_context_length_set_to_dataset_length (line 188) | def test_when_context_length_not_provided_then_context_length_set_to_dat... function test_when_context_length_provided_then_context_length_set_to_capped_init_context_length (line 217) | def test_when_context_length_provided_then_context_length_set_to_capped_... function test_given_variable_length_data_when_context_length_not_provided_then_context_length_set_to_max_data_length (line 239) | def test_given_variable_length_data_when_context_length_not_provided_the... function test_when_torch_dtype_provided_then_parameters_loaded_in_torch_dtype (line 269) | def test_when_torch_dtype_provided_then_parameters_loaded_in_torch_dtype( function test_when_torch_dtype_provided_and_model_persisted_then_parameters_loaded_in_torch_dtype (line 287) | def test_when_torch_dtype_provided_and_model_persisted_then_parameters_l... function test_when_model_persisted_then_model_pipeline_can_infer (line 304) | def test_when_model_persisted_then_model_pipeline_can_infer(chronos_mode... function test_when_model_not_persisted_only_fit_then_model_pipeline_is_none (line 316) | def test_when_model_not_persisted_only_fit_then_model_pipeline_is_none(c... function test_when_model_saved_loaded_and_persisted_then_model_pipeline_can_infer (line 327) | def test_when_model_saved_loaded_and_persisted_then_model_pipeline_can_i... function test_when_chronos_fit_in_standalone_through_predictor_and_persist_called_then_chronos_pipeline_is_persisted (line 341) | def test_when_chronos_fit_in_standalone_through_predictor_and_persist_ca... function test_when_chronos_fit_with_validation_through_predictor_and_persist_called_then_chronos_pipeline_is_persisted (line 357) | def test_when_chronos_fit_with_validation_through_predictor_and_persist_... function test_when_chronos_scores_oof_and_time_limit_is_exceeded_then_exception_is_raised (line 375) | def test_when_chronos_scores_oof_and_time_limit_is_exceeded_then_excepti... function test_when_eval_during_fine_tune_is_false_then_evaluation_is_turned_off (line 389) | def test_when_eval_during_fine_tune_is_false_then_evaluation_is_turned_o... function test_fine_tune_eval_max_items_is_used (line 415) | def test_fine_tune_eval_max_items_is_used(chronos_model_path, max_items): function test_fine_tune_shuffle_buffer_size_is_used (line 444) | def test_fine_tune_shuffle_buffer_size_is_used(chronos_model_path, shuff... function test_when_search_spaces_provided_then_model_can_hpo (line 465) | def test_when_search_spaces_provided_then_model_can_hpo(): function test_when_chronos_bolt_fine_tuned_with_custom_quantiles_then_loaded_model_has_custom_quantiles (line 480) | def test_when_chronos_bolt_fine_tuned_with_custom_quantiles_then_loaded_... function test_when_chronos_bolt_no_fine_tune_with_custom_quantiles_then_original_quantiles_preserved (line 497) | def test_when_chronos_bolt_no_fine_tune_with_custom_quantiles_then_origi... function test_when_revision_provided_then_from_pretrained_is_called_with_revision (line 507) | def test_when_revision_provided_then_from_pretrained_is_called_with_revi... FILE: timeseries/tests/unittests/models/chronos/test_utils.py function test_pseudo_shuffled_iterable_dataset_shuffles_the_iterable (line 31) | def test_pseudo_shuffled_iterable_dataset_shuffles_the_iterable(iterable... function test_chronos_fine_tuning_dataset_returns_data_in_chronos_format_when_tokenizer_is_given (line 50) | def test_chronos_fine_tuning_dataset_returns_data_in_chronos_format_when... function test_chronos_fine_tuning_dataset_returns_data_in_chronos_bolt_format_when_tokenizer_is_not_given (line 91) | def test_chronos_fine_tuning_dataset_returns_data_in_chronos_bolt_format... function test_chronos_fine_tuning_dataset_shuffle_returns_shuffled_dataset (line 112) | def test_chronos_fine_tuning_dataset_shuffle_returns_shuffled_dataset(sh... function test_when_context_length_provided_then_inference_dataset_context_length_used (line 123) | def test_when_context_length_provided_then_inference_dataset_context_len... function test_when_context_length_provided_then_padding_correct (line 131) | def test_when_context_length_provided_then_padding_correct(context_length): function test_when_inference_dataset_initialized_then_indptr_set_correctly (line 150) | def test_when_inference_dataset_initialized_then_indptr_set_correctly(it... function test_when_chronos_inference_dataloader_used_and_time_limit_exceeded_then_exception_is_raised (line 161) | def test_when_chronos_inference_dataloader_used_and_time_limit_exceeded_... FILE: timeseries/tests/unittests/models/common.py function get_default_hyperparameters (line 119) | def get_default_hyperparameters(model_type: Callable[..., AbstractTimeSe... function get_multi_window_deepar (line 132) | def get_multi_window_deepar(hyperparameters=None, **kwargs): function patch_constructor (line 140) | def patch_constructor( FILE: timeseries/tests/unittests/models/conftest.py function local_model_class (line 25) | def local_model_class(request): function seasonal_local_model_class (line 30) | def seasonal_local_model_class(request): function intermittent_local_model_class (line 35) | def intermittent_local_model_class(request): function gluonts_model_class (line 40) | def gluonts_model_class(request): function gluonts_model_with_static_features_class (line 45) | def gluonts_model_with_static_features_class(request): function gluonts_model_with_known_covariates_class (line 50) | def gluonts_model_with_known_covariates_class(request): function gluonts_model_with_known_covariates_and_static_features_class (line 55) | def gluonts_model_with_known_covariates_and_static_features_class(request): function mlforecast_model_class (line 60) | def mlforecast_model_class(request): function per_step_tabular_model_class (line 65) | def per_step_tabular_model_class(): function multi_window_deepar_model_class (line 70) | def multi_window_deepar_model_class(): function chronos_zero_shot_model_class (line 75) | def chronos_zero_shot_model_class(request): function chronos_model_class (line 87) | def chronos_model_class(request): function patch_toto_constructor (line 95) | def patch_toto_constructor(): function model_class (line 148) | def model_class(request): function inference_only_model_class (line 166) | def inference_only_model_class(request): FILE: timeseries/tests/unittests/models/ensemble/array_based/conftest.py function ensemble_data (line 9) | def ensemble_data(): function ensemble_test_data (line 26) | def ensemble_test_data(): FILE: timeseries/tests/unittests/models/ensemble/array_based/test_abstract.py class DummyEnsembleRegressor (line 14) | class DummyEnsembleRegressor(EnsembleRegressor): method __init__ (line 15) | def __init__(self, *args, **kwargs): method fit (line 19) | def fit( method predict (line 29) | def predict( class DummyArrayBasedEnsembleModel (line 40) | class DummyArrayBasedEnsembleModel(ArrayBasedTimeSeriesEnsembleModel): method __init__ (line 41) | def __init__(self, *args, **kwargs): method model_names (line 46) | def model_names(self) -> list[str]: method _fit (line 49) | def _fit(self, predictions_per_window, data_per_window, model_scores=N... method _get_ensemble_regressor (line 53) | def _get_ensemble_regressor(self) -> EnsembleRegressor: class TestArrayBasedTimeSeriesEnsembleModel (line 57) | class TestArrayBasedTimeSeriesEnsembleModel: method model (line 59) | def model(self): method ensemble_data (line 63) | def ensemble_data(self, request): method test_given_model_when_initialized_then_default_hyperparameters_set (line 80) | def test_given_model_when_initialized_then_default_hyperparameters_set... method test_given_model_when_initialized_then_ensemble_regressor_is_none (line 87) | def test_given_model_when_initialized_then_ensemble_regressor_is_none(... method test_given_dataframe_when_to_array_called_then_array_has_correct_shape (line 90) | def test_given_dataframe_when_to_array_called_then_array_has_correct_s... method test_given_model_when_fit_called_then_ensemble_regressor_created_and_fitted (line 97) | def test_given_model_when_fit_called_then_ensemble_regressor_created_a... method test_given_model_when_get_base_model_predictions_called_with_empty_dict_then_error_raised (line 105) | def test_given_model_when_get_base_model_predictions_called_with_empty... method test_given_model_when_get_base_model_predictions_called_then_correct_array_shape_returned (line 109) | def test_given_model_when_get_base_model_predictions_called_then_corre... method test_given_model_when_get_base_model_predictions_called_with_single_window_then_correct_array_shape_returned (line 126) | def test_given_model_when_get_base_model_predictions_called_with_singl... method test_given_unfitted_model_when_predict_called_then_error_raised (line 142) | def test_given_unfitted_model_when_predict_called_then_error_raised(se... method test_given_model_when_fit_called_then_regressor_receives_correct_array (line 148) | def test_given_model_when_fit_called_then_regressor_receives_correct_a... method test_given_model_when_isotonize_called_with_sort_then_quantiles_sorted (line 163) | def test_given_model_when_isotonize_called_with_sort_then_quantiles_so... method test_given_model_when_remap_base_models_called_then_model_names_updated (line 181) | def test_given_model_when_remap_base_models_called_then_model_names_up... method test_given_model_when_detect_and_ignore_failures_enabled_then_nan_models_filtered (line 192) | def test_given_model_when_detect_and_ignore_failures_enabled_then_nan_... method test_given_model_when_detect_and_ignore_failures_disabled_then_all_models_kept (line 209) | def test_given_model_when_detect_and_ignore_failures_disabled_then_all... method test_given_model_when_all_models_failed_then_error_raised (line 224) | def test_given_model_when_all_models_failed_then_error_raised(self, mo... method test_given_model_when_fit_with_failed_models_then_only_good_models_used (line 237) | def test_given_model_when_fit_with_failed_models_then_only_good_models... method test_given_model_when_model_has_loss_10x_median_then_filtered_out (line 257) | def test_given_model_when_model_has_loss_10x_median_then_filtered_out(... FILE: timeseries/tests/unittests/models/ensemble/array_based/test_linear_stacker.py class TestLinearStackerEnsembleRegressor (line 9) | class TestLinearStackerEnsembleRegressor: method sample_data (line 11) | def sample_data(self): method test_given_weights_per_when_ensemble_regressor_fit_then_can_predict_correct_shape (line 30) | def test_given_weights_per_when_ensemble_regressor_fit_then_can_predic... method test_given_weights_per_when_regressor_fit_then_weights_have_correct_shape (line 52) | def test_given_weights_per_when_regressor_fit_then_weights_have_correc... method test_given_weights_per_when_regressor_fit_then_weights_sum_to_one_per_model (line 76) | def test_given_weights_per_when_regressor_fit_then_weights_sum_to_one_... method test_per_when_regressor_initialized_with_weights_then_predictions_correct (line 92) | def test_per_when_regressor_initialized_with_weights_then_predictions_... class TestLinearStackerEnsembleRegressionSparsification (line 113) | class TestLinearStackerEnsembleRegressionSparsification: method sample_data_with_low_weight_model (line 115) | def sample_data_with_low_weight_model(self): method test_when_prune_below_zero_then_no_sparsification (line 141) | def test_when_prune_below_zero_then_no_sparsification(self, sample_dat... method test_when_prune_below_set_then_low_weight_models_dropped (line 158) | def test_when_prune_below_set_then_low_weight_models_dropped(self, sam... method test_when_sparsified_then_weights_sum_to_one (line 176) | def test_when_sparsified_then_weights_sum_to_one(self, sample_data_wit... method test_when_all_models_below_threshold_then_keeps_highest (line 192) | def test_when_all_models_below_threshold_then_keeps_highest(self, samp... method test_when_sparsified_then_correct_model_dropped (line 210) | def test_when_sparsified_then_correct_model_dropped(self, sample_data_... method test_when_sparsified_then_predictions_correct_with_kept_models_only (line 245) | def test_when_sparsified_then_predictions_correct_with_kept_models_only( class TestLinearStackerEnsembleModelSparsification (line 277) | class TestLinearStackerEnsembleModelSparsification: method sparsification_data (line 279) | def sparsification_data(self, ensemble_data): method test_when_sparsified_then_model_names_updated (line 291) | def test_when_sparsified_then_model_names_updated(self, sparsification... method test_when_sparsified_then_predictions_use_correct_models (line 308) | def test_when_sparsified_then_predictions_use_correct_models(self, spa... FILE: timeseries/tests/unittests/models/ensemble/array_based/test_models.py class TestEnsembleModels (line 24) | class TestEnsembleModels: method model_class (line 26) | def model_class(self, request): method test_given_model_when_initialized_then_ensemble_regressor_is_none (line 29) | def test_given_model_when_initialized_then_ensemble_regressor_is_none(... method test_given_model_when_fit_called_then_ensemble_regressor_created_and_fitted (line 32) | def test_given_model_when_fit_called_then_ensemble_regressor_created_a... method test_given_fitted_model_when_predict_called_then_prediction_returned (line 40) | def test_given_fitted_model_when_predict_called_then_prediction_returned( FILE: timeseries/tests/unittests/models/ensemble/array_based/test_tabular.py class TestTabularEnsembleCommon (line 19) | class TestTabularEnsembleCommon: method ensemble_model_class (line 23) | def ensemble_model_class(self, request): method test_given_model_hyperparameters_when_fit_called_then_correct_hyperparameters_are_used (line 27) | def test_given_model_hyperparameters_when_fit_called_then_correct_hype... method test_given_fitted_ensemble_when_deleted_and_loaded_then_can_predict (line 40) | def test_given_fitted_ensemble_when_deleted_and_loaded_then_can_predict( method test_given_fitted_ensemble_saved_when_moved_and_loaded_then_can_predict (line 56) | def test_given_fitted_ensemble_saved_when_moved_and_loaded_then_can_pr... method test_given_ensemble_when_predict_without_fit_then_error_raised (line 83) | def test_given_ensemble_when_predict_without_fit_then_error_raised( class TestTabularEnsemble (line 94) | class TestTabularEnsemble: method test_given_quantile_levels_when_fit_called_then_correct_quantile_levels_used (line 95) | def test_given_quantile_levels_when_fit_called_then_correct_quantile_l... method test_given_quantile_levels_when_get_median_quantile_index_called_then_correct_index_returned (line 116) | def test_given_quantile_levels_when_get_median_quantile_index_called_t... method test_when_get_feature_df_called_then_columns_in_correct_order (line 127) | def test_when_get_feature_df_called_then_columns_in_correct_order( method test_given_tabular_ensemble_when_fitted_then_model_is_fit (line 167) | def test_given_tabular_ensemble_when_fitted_then_model_is_fit(self, en... class TestPerQuantileTabularEnsemble (line 175) | class TestPerQuantileTabularEnsemble: method test_given_quantile_levels_when_fit_called_then_correct_number_of_models_created (line 176) | def test_given_quantile_levels_when_fit_called_then_correct_number_of_... method test_given_per_quantile_ensemble_when_fitted_then_separate_models_created (line 188) | def test_given_per_quantile_ensemble_when_fitted_then_separate_models_... method test_given_per_quantile_ensemble_when_predict_called_then_correct_features_passed_to_models (line 201) | def test_given_per_quantile_ensemble_when_predict_called_then_correct_... FILE: timeseries/tests/unittests/models/ensemble/conftest.py function predictions_data_and_prediction_length (line 11) | def predictions_data_and_prediction_length(request, temp_model_path): FILE: timeseries/tests/unittests/models/ensemble/test_abstract.py class DummyEnsembleModel (line 11) | class DummyEnsembleModel(AbstractTimeSeriesEnsembleModel): method __init__ (line 12) | def __init__(self, *args, **kwargs): method _fit (line 16) | def _fit(self, predictions_per_window, data_per_window, model_scores=N... method _predict (line 19) | def _predict(self, data, **kwargs): method remap_base_models (line 22) | def remap_base_models(self, model_refit_map: dict[str, str]) -> None: method model_names (line 26) | def model_names(self) -> list[str]: class TestAbstractTimeSeriesEnsembleModel (line 30) | class TestAbstractTimeSeriesEnsembleModel: method model (line 34) | def model(self): method ensemble_data (line 38) | def ensemble_data(self): method test_given_model_when_fit_called_with_small_time_limit_then_exception_raised (line 48) | def test_given_model_when_fit_called_with_small_time_limit_then_except... method test_given_model_when_fit_called_with_missing_data_windows_then_value_error_raised (line 53) | def test_given_model_when_fit_called_with_missing_data_windows_then_va... method test_given_model_when_fit_called_with_single_data_frame_then_value_error_raised (line 60) | def test_given_model_when_fit_called_with_single_data_frame_then_value... method test_given_model_when_fit_called_then_internal_fit_method_called_correctly (line 65) | def test_given_model_when_fit_called_then_internal_fit_method_called_c... FILE: timeseries/tests/unittests/models/ensemble/test_per_item_greedy.py class TestPerItemGreedyEnsemble (line 12) | class TestPerItemGreedyEnsemble: method fitted_model (line 14) | def fitted_model(self, predictions_data_and_prediction_length): method test_when_fit_called_then_weights_df_has_correct_structure (line 20) | def test_when_fit_called_then_weights_df_has_correct_structure(self, f... method test_when_fit_called_then_average_weights_are_computed_correctly (line 26) | def test_when_fit_called_then_average_weights_are_computed_correctly(s... method test_when_fit_called_then_weights_sum_to_one_per_item (line 32) | def test_when_fit_called_then_weights_sum_to_one_per_item(self, fitted... method test_when_predict_called_then_predictions_can_be_scored (line 36) | def test_when_predict_called_then_predictions_can_be_scored(self, fitt... method test_when_predict_with_unseen_items_then_average_weight_is_used (line 45) | def test_when_predict_with_unseen_items_then_average_weight_is_used(se... method test_when_remap_base_models_called_then_columns_are_renamed (line 63) | def test_when_remap_base_models_called_then_columns_are_renamed(self, ... method test_when_n_jobs_exceeds_num_items_then_n_jobs_is_reduced (line 69) | def test_when_n_jobs_exceeds_num_items_then_n_jobs_is_reduced(self, pr... method test_when_model_names_called_then_returns_non_zero_weight_models (line 80) | def test_when_model_names_called_then_returns_non_zero_weight_models(s... FILE: timeseries/tests/unittests/models/ensemble/test_weighted.py function ensemble_data_with_varying_scores (line 28) | def ensemble_data_with_varying_scores(request): class TestAllTimeSeriesWeightedEnsembleModels (line 41) | class TestAllTimeSeriesWeightedEnsembleModels: method model_constructor (line 51) | def model_constructor(self, request): method test_ensemble_models_can_be_initialized (line 54) | def test_ensemble_models_can_be_initialized(self, model_constructor): method test_ensemble_models_can_fit_and_predict (line 60) | def test_ensemble_models_can_fit_and_predict(self, model_constructor, ... method test_when_ensemble_models_predict_then_prediction_horizon_aligns_with_input (line 76) | def test_when_ensemble_models_predict_then_prediction_horizon_aligns_w... method test_when_ensemble_models_predict_then_prediction_contains_no_nans (line 92) | def test_when_ensemble_models_predict_then_prediction_contains_no_nans( method test_given_model_when_fit_called_then_internal_fit_method_called_correctly (line 107) | def test_given_model_when_fit_called_then_internal_fit_method_called_c... method test_when_some_base_models_fail_during_prediction_then_ensemble_raises_runtime_error (line 127) | def test_when_some_base_models_fail_during_prediction_then_ensemble_ra... method test_when_predict_called_then_predictions_can_be_scored (line 138) | def test_when_predict_called_then_predictions_can_be_scored( class TestSimpleAverageEnsemble (line 155) | class TestSimpleAverageEnsemble: method test_when_fit_called_then_weights_are_equal_and_correct (line 156) | def test_when_fit_called_then_weights_are_equal_and_correct(self, ense... class TestPerformanceWeightedEnsemble (line 165) | class TestPerformanceWeightedEnsemble: method test_when_fit_called_then_scores_are_correct (line 167) | def test_when_fit_called_then_scores_are_correct(self, ensemble_data_w... method test_when_fit_called_then_higher_scores_are_given_to_higher_scores (line 189) | def test_when_fit_called_then_higher_scores_are_given_to_higher_scores( method test_when_fit_called_then_raises_error_without_model_scores (line 203) | def test_when_fit_called_then_raises_error_without_model_scores(self, ... FILE: timeseries/tests/unittests/models/test_abstract.py class ConcreteTimeSeriesModel (line 15) | class ConcreteTimeSeriesModel(AbstractTimeSeriesModel): method _fit (line 28) | def _fit( method _predict (line 41) | def _predict( function train_data (line 59) | def train_data(): function test_when_model_is_initialized_then_key_fields_set_correctly (line 63) | def test_when_model_is_initialized_then_key_fields_set_correctly(temp_mo... function test_when_model_receives_median_then_must_not_drop_median_set_to_false (line 79) | def test_when_model_receives_median_then_must_not_drop_median_set_to_fal... function test_when_model_does_not_receive_median_then_must_not_drop_median_set_to_true (line 87) | def test_when_model_does_not_receive_median_then_must_not_drop_median_se... function test_when_model_saved_and_loaded_with_load_oof_then_load_oof_called (line 95) | def test_when_model_saved_and_loaded_with_load_oof_then_load_oof_called(... function test_when_support_model_covariate_properties_are_accessed_then_their_values_are_correct (line 103) | def test_when_support_model_covariate_properties_are_accessed_then_their... function test_when_model_is_initialized_with_ag_args_fit_then_they_are_included_in_get_params (line 111) | def test_when_model_is_initialized_with_ag_args_fit_then_they_are_includ... function test_when_create_covariate_regressor_is_called_then_covariate_regressor_is_constructed (line 127) | def test_when_create_covariate_regressor_is_called_then_covariate_regres... function test_when_hyperparameter_tune_called_with_empty_search_space_then_skip_hpo_called (line 148) | def test_when_hyperparameter_tune_called_with_empty_search_space_then_sk... function test_when_time_limit_is_capped_with_aux_params_then_time_limit_is_adjusted (line 160) | def test_when_time_limit_is_capped_with_aux_params_then_time_limit_is_ad... function test_when_max_time_limit_ratio_is_provided_with_aux_params_then_time_limit_is_adjusted (line 171) | def test_when_max_time_limit_ratio_is_provided_with_aux_params_then_time... function test_when_model_is_fit_with_time_limit_less_than_zero_then_error_is_raised (line 184) | def test_when_model_is_fit_with_time_limit_less_than_zero_then_error_is_... function test_when_convert_to_refit_full_via_copy_called_then_output_is_correct (line 190) | def test_when_convert_to_refit_full_via_copy_called_then_output_is_corre... function test_when_model_predicts_then_columns_have_correct_order (line 200) | def test_when_model_predicts_then_columns_have_correct_order(temp_model_... FILE: timeseries/tests/unittests/models/test_gluonts.py function test_when_context_length_is_not_set_then_default_context_length_is_used (line 24) | def test_when_context_length_is_not_set_then_default_context_length_is_u... function test_when_context_length_is_set_then_provided_context_length_is_used (line 33) | def test_when_context_length_is_set_then_provided_context_length_is_used... function test_given_time_limit_when_fit_called_then_models_train_correctly (line 42) | def test_given_time_limit_when_fit_called_then_models_train_correctly( function test_given_low_time_limit_when_fit_called_then_model_training_does_not_exceed_time_limit (line 60) | def test_given_low_time_limit_when_fit_called_then_model_training_does_n... function test_when_models_saved_then_gluonts_predictors_can_be_loaded (line 75) | def test_when_models_saved_then_gluonts_predictors_can_be_loaded(gluonts... function test_when_static_features_present_then_they_are_passed_to_dataset (line 92) | def test_when_static_features_present_then_they_are_passed_to_dataset( function test_given_fit_with_static_features_when_predicting_then_static_features_are_used (line 110) | def test_given_fit_with_static_features_when_predicting_then_static_feat... function test_when_static_features_present_then_model_attributes_set_correctly (line 129) | def test_when_static_features_present_then_model_attributes_set_correctly( function test_when_disable_static_features_set_to_true_then_static_features_are_not_used (line 141) | def test_when_disable_static_features_set_to_true_then_static_features_a... function test_when_known_covariates_present_then_they_are_passed_to_dataset (line 161) | def test_when_known_covariates_present_then_they_are_passed_to_dataset( function test_when_known_covariates_present_then_model_attributes_set_correctly (line 177) | def test_when_known_covariates_present_then_model_attributes_set_correctly( function test_when_known_covariates_present_for_predict_then_covariates_have_correct_shape (line 186) | def test_when_known_covariates_present_for_predict_then_covariates_have_... function test_when_disable_known_covariates_set_to_true_then_known_covariates_are_not_used (line 209) | def test_when_disable_known_covariates_set_to_true_then_known_covariates... function test_when_static_and_dynamic_covariates_present_then_model_trains_normally (line 227) | def test_when_static_and_dynamic_covariates_present_then_model_trains_no... function test_given_custom_predict_batch_size_then_predictor_uses_correct_batch_size (line 248) | def test_given_custom_predict_batch_size_then_predictor_uses_correct_bat... function catch_trainer_kwargs (line 257) | def catch_trainer_kwargs(model): function test_when_custom_callbacks_passed_via_trainer_kwargs_then_trainer_receives_them (line 267) | def test_when_custom_callbacks_passed_via_trainer_kwargs_then_trainer_re... function test_when_early_stopping_patience_provided_then_early_stopping_callback_created (line 279) | def test_when_early_stopping_patience_provided_then_early_stopping_callb... function test_when_early_stopping_patience_is_none_then_early_stopping_callback_not_created (line 293) | def test_when_early_stopping_patience_is_none_then_early_stopping_callba... function test_when_custom_trainer_kwargs_given_then_trainer_receives_them (line 305) | def test_when_custom_trainer_kwargs_given_then_trainer_receives_them(): function test_when_model_finishes_training_then_logs_are_removed (line 316) | def test_when_model_finishes_training_then_logs_are_removed(temp_model_p... function test_when_keep_lightning_logs_set_then_logs_are_not_removed (line 325) | def test_when_keep_lightning_logs_set_then_logs_are_not_removed(keep_lig... function test_given_features_present_when_model_is_fit_then_feature_transformer_is_present (line 338) | def test_given_features_present_when_model_is_fit_then_feature_transform... function test_when_model_is_initialized_then_covariate_scaler_is_created (line 374) | def test_when_model_is_initialized_then_covariate_scaler_is_created(gluo... function test_when_distr_output_passed_to_tft_then_model_can_fit_and_predict (line 381) | def test_when_distr_output_passed_to_tft_then_model_can_fit_and_predict(): function test_when_categorical_covariate_has_new_value_in_validation_then_model_trains_without_error (line 396) | def test_when_categorical_covariate_has_new_value_in_validation_then_mod... FILE: timeseries/tests/unittests/models/test_local.py function test_when_local_model_is_saved_and_loaded_then_model_can_predict (line 32) | def test_when_local_model_is_saved_and_loaded_then_model_can_predict(loc... function test_when_local_model_saved_then_local_model_args_are_saved (line 43) | def test_when_local_model_saved_then_local_model_args_are_saved(local_mo... function get_seasonal_period_from_fitted_local_model (line 52) | def get_seasonal_period_from_fitted_local_model(model): function test_when_seasonal_period_is_set_to_none_then_inferred_period_is_used (line 70) | def test_when_seasonal_period_is_set_to_none_then_inferred_period_is_used( function test_when_seasonal_period_is_provided_then_inferred_period_is_overridden (line 97) | def test_when_seasonal_period_is_provided_then_inferred_period_is_overri... function test_when_invalid_model_arguments_provided_then_model_ignores_them (line 118) | def test_when_invalid_model_arguments_provided_then_model_ignores_them(m... function test_when_n_jobs_hyperparameter_provided_then_joblib_receives_it (line 138) | def test_when_n_jobs_hyperparameter_provided_then_joblib_receives_it(inp... function failing_predict (line 149) | def failing_predict(*args, **kwargs): function test_when_fallback_model_disabled_and_model_fails_then_exception_is_raised (line 153) | def test_when_fallback_model_disabled_and_model_fails_then_exception_is_... function test_when_fallback_model_enabled_and_model_fails_then_no_exception_is_raised (line 163) | def test_when_fallback_model_enabled_and_model_fails_then_no_exception_i... function test_when_seasonal_period_equals_one_then_average_and_seasonal_average_are_equivalent (line 174) | def test_when_seasonal_period_equals_one_then_average_and_seasonal_avera... function test_when_data_shorter_than_seasonal_period_then_average_forecast_is_used (line 198) | def test_when_data_shorter_than_seasonal_period_then_average_forecast_is... function test_when_npts_fit_with_default_seasonal_features_then_predictions_match_gluonts (line 220) | def test_when_npts_fit_with_default_seasonal_features_then_predictions_m... class MockConformalModel (line 250) | class MockConformalModel(AbstractConformalizedStatsForecastModel): method _get_point_forecast (line 251) | def _get_point_forecast(self, time_series: pd.Series, local_model_args... method _get_nonconformity_scores (line 254) | def _get_nonconformity_scores(self, time_series: pd.Series, local_mode... function test_when_conformalized_model_called_then_nonconformity_score_shapes_correct (line 276) | def test_when_conformalized_model_called_then_nonconformity_score_shapes... function test_when_conformalized_model_called_then_nonconformity_score_values_correct (line 307) | def test_when_conformalized_model_called_then_nonconformity_score_values... function test_when_intermittent_models_fit_then_values_are_lower_bounded (line 335) | def test_when_intermittent_models_fit_then_values_are_lower_bounded( function test_when_local_models_fit_then_quantiles_are_present_and_ranked (line 357) | def test_when_local_models_fit_then_quantiles_are_present_and_ranked( function test_when_leading_nans_are_present_then_seasonal_naive_can_forecast (line 376) | def test_when_leading_nans_are_present_then_seasonal_naive_can_forecast(... function test_when_variant_hyperparameter_provided_to_croston_model_then_correct_model_class_is_created (line 397) | def test_when_variant_hyperparameter_provided_to_croston_model_then_corr... FILE: timeseries/tests/unittests/models/test_mlforecast.py function test_when_covariates_and_features_present_then_train_and_val_dfs_have_correct_shape (line 30) | def test_when_covariates_and_features_present_then_train_and_val_dfs_hav... function test_when_covariates_and_features_are_varied_and_metric_provided_then_models_can_predict (line 77) | def test_when_covariates_and_features_are_varied_and_metric_provided_the... function test_when_covariates_and_features_present_then_model_can_predict (line 122) | def test_when_covariates_and_features_present_then_model_can_predict(tem... function test_when_eval_metric_is_changed_then_model_can_predict (line 145) | def test_when_eval_metric_is_changed_then_model_can_predict(temp_model_p... function test_given_long_time_series_passed_to_model_then_preprocess_receives_shortened_time_series (line 154) | def test_given_long_time_series_passed_to_model_then_preprocess_receives... function test_given_some_time_series_are_too_short_then_forecast_doesnt_contain_nans_and_index_correct (line 178) | def test_given_some_time_series_are_too_short_then_forecast_doesnt_conta... function test_given_some_time_series_are_too_short_then_seasonal_naive_forecast_is_used (line 204) | def test_given_some_time_series_are_too_short_then_seasonal_naive_foreca... function test_when_point_forecast_metric_is_used_then_per_item_residuals_are_used_for_prediction (line 232) | def test_when_point_forecast_metric_is_used_then_per_item_residuals_are_... function test_when_mlf_model_is_used_then_predictions_have_correct_scale (line 259) | def test_when_mlf_model_is_used_then_predictions_have_correct_scale(temp... function test_given_train_data_has_nans_when_fit_called_then_nan_rows_removed_from_train_df (line 277) | def test_given_train_data_has_nans_when_fit_called_then_nan_rows_removed... function test_when_trained_model_moved_to_different_folder_then_loaded_model_can_predict (line 294) | def test_when_trained_model_moved_to_different_folder_then_loaded_model_... function test_when_target_transform_provided_then_scaler_is_used_inside_mlforecast (line 317) | def test_when_target_transform_provided_then_scaler_is_used_inside_mlfor... function test_when_deprecated_scaler_hyperparameter_is_provided_then_correct_scaler_is_created (line 335) | def test_when_deprecated_scaler_hyperparameter_is_provided_then_correct_... function test_when_lag_transforms_provided_then_model_can_fit_and_predict (line 359) | def test_when_lag_transforms_provided_then_model_can_fit_and_predict( FILE: timeseries/tests/unittests/models/test_models.py class TestAllModelsInitialization (line 31) | class TestAllModelsInitialization: method test_models_can_be_initialized (line 43) | def test_models_can_be_initialized(self, model_class, temp_model_path): method test_when_model_created_then_model_has_all_required_tags (line 47) | def test_when_model_created_then_model_has_all_required_tags(self, mod... method test_when_get_hyperparameters_called_then_copy_is_returned (line 54) | def test_when_get_hyperparameters_called_then_copy_is_returned(self, m... class TestAllModelsPostTraining (line 60) | class TestAllModelsPostTraining: method trained_model (line 62) | def trained_model(self, request, model_class, tmp_path_factory): method test_when_score_called_then_scores_can_be_computed (line 74) | def test_when_score_called_then_scores_can_be_computed(self, trained_m... method test_when_val_score_accessed_then_value_is_returned (line 78) | def test_when_val_score_accessed_then_value_is_returned(self, trained_... method test_when_predict_time_accessed_then_value_is_returned (line 81) | def test_when_predict_time_accessed_then_value_is_returned(self, train... method test_given_score_and_cache_oof_called_when_get_oof_predictions_called_then_oof_predictions_are_available (line 84) | def test_given_score_and_cache_oof_called_when_get_oof_predictions_cal... method test_when_score_called_then_model_receives_truncated_data (line 96) | def test_when_score_called_then_model_receives_truncated_data(self, tr... method test_when_models_saved_then_they_can_be_loaded (line 110) | def test_when_models_saved_then_they_can_be_loaded(self, trained_model): method test_when_predict_called_then_predictions_align_index_aligns_with_expected_index (line 131) | def test_when_predict_called_then_predictions_align_index_aligns_with_... method test_when_context_has_one_observation_then_model_can_predict (line 146) | def test_when_context_has_one_observation_then_model_can_predict(self,... method test_when_itemid_has_arrow_string_dtype_then_model_can_predict (line 158) | def test_when_itemid_has_arrow_string_dtype_then_model_can_predict(sel... method test_when_get_info_is_called_then_all_keys_are_present (line 167) | def test_when_get_info_is_called_then_all_keys_are_present(self, train... class TestAllModelsWhenHyperparameterTuning (line 185) | class TestAllModelsWhenHyperparameterTuning: method test_when_hyperparameter_tune_called_then_tuning_output_correct (line 190) | def test_when_hyperparameter_tune_called_then_tuning_output_correct(se... method test_given_searcher_when_ray_backend_used_in_hpo_then_correct_searcher_used (line 220) | def test_given_searcher_when_ray_backend_used_in_hpo_then_correct_sear... method test_when_custom_metric_passed_to_model_then_model_can_hyperparameter_tune (line 257) | def test_when_custom_metric_passed_to_model_then_model_can_hyperparame... method test_when_hyperparameter_spaces_provided_to_init_and_fit_called_then_error_is_raised (line 286) | def test_when_hyperparameter_spaces_provided_to_init_and_fit_called_th... class TestAllModelsWhenCustomProblemSpecificationsProvided (line 303) | class TestAllModelsWhenCustomProblemSpecificationsProvided: method test_when_fit_called_then_models_train_and_returned_predictions_have_mean_and_correct_quantiles (line 315) | def test_when_fit_called_then_models_train_and_returned_predictions_ha... method test_when_predict_called_with_custom_frequency_then_predicted_timestamps_align_with_time (line 335) | def test_when_predict_called_with_custom_frequency_then_predicted_time... method test_when_custom_metric_passed_to_model_then_model_can_score (line 363) | def test_when_custom_metric_passed_to_model_then_model_can_score(self,... method test_when_median_not_in_quantile_levels_then_median_is_present_in_raw_predictions (line 374) | def test_when_median_not_in_quantile_levels_then_median_is_present_in_... method test_when_median_not_in_quantile_levels_then_median_is_dropped_at_prediction_time (line 389) | def test_when_median_not_in_quantile_levels_then_median_is_dropped_at_... class TestAllModelsWhenPreprocessingAndTransformsRequested (line 401) | class TestAllModelsWhenPreprocessingAndTransformsRequested: method test_when_fit_and_predict_called_then_train_val_and_test_data_is_preprocessed (line 402) | def test_when_fit_and_predict_called_then_train_val_and_test_data_is_p... method test_given_model_doesnt_support_nan_when_model_fits_then_nans_are_filled (line 429) | def test_given_model_doesnt_support_nan_when_model_fits_then_nans_are_... method test_when_target_scaler_is_used_then_model_can_fit_and_predict (line 450) | def test_when_target_scaler_is_used_then_model_can_fit_and_predict( method test_when_covariate_regressor_is_used_then_model_can_fit_and_predict (line 462) | def test_when_covariate_regressor_is_used_then_model_can_fit_and_predict( class TestInferenceOnlyModels (line 494) | class TestInferenceOnlyModels: method test_when_inference_only_model_scores_oof_then_time_limit_is_passed_to_predict (line 495) | def test_when_inference_only_model_scores_oof_then_time_limit_is_passe... FILE: timeseries/tests/unittests/models/test_multi_window_model.py function test_when_model_base_kwargs_passed_to_mw_model_then_kwargs_passed_to_base_model (line 15) | def test_when_model_base_kwargs_passed_to_mw_model_then_kwargs_passed_to... function test_when_mw_model_trained_then_oof_predictions_and_stats_are_saved (line 28) | def test_when_mw_model_trained_then_oof_predictions_and_stats_are_saved( function test_when_val_data_passed_to_mw_model_fit_then_exception_is_raised (line 47) | def test_when_val_data_passed_to_mw_model_fit_then_exception_is_raised( function test_when_saved_model_moved_then_model_can_be_loaded_with_updated_path (line 55) | def test_when_saved_model_moved_then_model_can_be_loaded_with_updated_pa... function test_when_multi_window_model_created_then_regressor_and_scaler_are_created_only_for_base_model (line 70) | def test_when_multi_window_model_created_then_regressor_and_scaler_are_c... function test_when_score_and_cache_oof_called_then_val_data_predictions_appended (line 87) | def test_when_score_and_cache_oof_called_then_val_data_predictions_appen... FILE: timeseries/tests/unittests/models/test_per_step_tabular.py function test_when_seasonal_and_trailing_lags_are_provided_then_each_model_receives_correct_lags (line 25) | def test_when_seasonal_and_trailing_lags_are_provided_then_each_model_re... function test_when_model_predicts_then_tabular_models_receive_correct_data_for_inference (line 52) | def test_when_model_predicts_then_tabular_models_receive_correct_data_fo... function test_when_n_jobs_provided_via_hyperparameters_then_it_is_stored_as_attribute (line 85) | def test_when_n_jobs_provided_via_hyperparameters_then_it_is_stored_as_a... function test_when_models_are_fitted_then_time_limit_is_distributed_evenly (line 99) | def test_when_models_are_fitted_then_time_limit_is_distributed_evenly( function test_when_per_step_models_are_fit_then_each_model_receives_correct_features (line 129) | def test_when_per_step_models_are_fit_then_each_model_receives_correct_f... function test_when_invalid_lags_are_passed_then_exception_is_raised_during_fit (line 180) | def test_when_invalid_lags_are_passed_then_exception_is_raised_during_fit( function test_when_model_is_copied_to_new_folder_then_loaded_model_can_still_predict (line 192) | def test_when_model_is_copied_to_new_folder_then_loaded_model_can_still_... function test_when_max_num_samples_provided_then_train_df_is_shortened (line 217) | def test_when_max_num_samples_provided_then_train_df_is_shortened(per_st... function test_when_max_num_items_provided_then_train_df_removes_items (line 232) | def test_when_max_num_items_provided_then_train_df_removes_items(per_ste... function test_when_validation_fraction_is_nonzero_then_validation_set_is_created (line 244) | def test_when_validation_fraction_is_nonzero_then_validation_set_is_crea... function test_when_validation_fraction_is_zero_then_no_validation_set_created (line 261) | def test_when_validation_fraction_is_zero_then_no_validation_set_created( function test_when_model_is_fit_then_internal_model_receives_correct_hyperparameters (line 277) | def test_when_model_is_fit_then_internal_model_receives_correct_hyperpar... function test_when_model_does_not_support_required_problem_type_then_exception_raised (line 302) | def test_when_model_does_not_support_required_problem_type_then_exceptio... function test_when_eval_metric_is_chosen_then_tabular_model_receives_correct_problem_type_and_eval_metric (line 323) | def test_when_eval_metric_is_chosen_then_tabular_model_receives_correct_... function test_when_regression_mode_is_used_then_residuals_are_saved (line 343) | def test_when_regression_mode_is_used_then_residuals_are_saved(per_step_... function test_when_regression_mode_predicts_then_quantile_columns_are_strictly_increasing (line 358) | def test_when_regression_mode_predicts_then_quantile_columns_are_strictl... FILE: timeseries/tests/unittests/models/test_registry.py function register_classes (line 7) | def register_classes(): function test_when_models_initialized_then_models_are_registered (line 22) | def test_when_models_initialized_then_models_are_registered(register_cla... function test_when_models_initialized_without_priority_then_model_priority_is_zero (line 27) | def test_when_models_initialized_without_priority_then_model_priority_is... function test_when_models_initialized_then_model_class_is_given (line 32) | def test_when_models_initialized_then_model_class_is_given(register_clas... function test_when_models_registered_with_no_model_suffix_then_it_is_registered (line 37) | def test_when_models_registered_with_no_model_suffix_then_it_is_register... function test_when_models_registered_with_priority_then_priority_is_correct (line 44) | def test_when_models_registered_with_priority_then_priority_is_correct(r... function test_when_models_registered_with_aliases_then_aliases_registered (line 52) | def test_when_models_registered_with_aliases_then_aliases_registered(reg... function test_when_multiple_models_with_same_alias_registered_then_value_error_raised (line 60) | def test_when_multiple_models_with_same_alias_registered_then_value_erro... function test_when_unknown_model_requested_then_value_error_raised (line 71) | def test_when_unknown_model_requested_then_value_error_raised(register_c... FILE: timeseries/tests/unittests/models/test_toto.py function noop (line 17) | def noop(): class MockTotoForecaster (line 22) | class MockTotoForecaster: method __init__ (line 23) | def __init__(self): method forecast (line 27) | def forecast(self, inputs, prediction_length, num_samples=None, sample... class TestTotoDataset (line 40) | class TestTotoDataset: method test_when_dataset_created_then_frequency_set_correctly (line 49) | def test_when_dataset_created_then_frequency_set_correctly(self, input... method test_when_dataset_iterated_then_context_has_correct_length (line 64) | def test_when_dataset_iterated_then_context_has_correct_length(self, i... method test_when_dataset_with_uneven_lengths_iterated_then_items_have_correct_length (line 73) | def test_when_dataset_with_uneven_lengths_iterated_then_items_have_cor... class TestTotoDataloader (line 84) | class TestTotoDataloader: method dataset (line 86) | def dataset(self): method test_when_dataloader_iterated_then_batches_are_on_correct_device (line 91) | def test_when_dataloader_iterated_then_batches_are_on_correct_device(s... method test_when_dataset_with_uneven_lengths_iterated_then_context_is_correctly_padded (line 109) | def test_when_dataset_with_uneven_lengths_iterated_then_context_is_cor... method test_when_long_data_loaded_then_max_context_is_enforced (line 132) | def test_when_long_data_loaded_then_max_context_is_enforced(self, inpu... method test_when_dataloader_iterated_then_batches_have_correct_shape (line 146) | def test_when_dataloader_iterated_then_batches_have_correct_shape(self... class TestTotoModel (line 172) | class TestTotoModel: method test_predict_returns_correct_format (line 175) | def test_predict_returns_correct_format(self, num_items, batch_size): FILE: timeseries/tests/unittests/test_learner.py function trained_learners (line 33) | def trained_learners(): function test_learner_can_be_initialized (line 56) | def test_learner_can_be_initialized(temp_model_path): function test_when_learner_called_then_training_is_performed (line 63) | def test_when_learner_called_then_training_is_performed(hyperparameters,... function test_given_hyperparameters_when_learner_called_then_leaderboard_is_correct (line 72) | def test_given_hyperparameters_when_learner_called_then_leaderboard_is_c... function test_given_hyperparameters_when_learner_called_then_model_can_predict (line 89) | def test_given_hyperparameters_when_learner_called_then_model_can_predict( function test_given_hyperparameters_with_spaces_when_learner_called_then_hpo_is_performed (line 105) | def test_given_hyperparameters_with_spaces_when_learner_called_then_hpo_... function test_given_hyperparameters_and_custom_models_when_learner_called_then_leaderboard_is_correct (line 144) | def test_given_hyperparameters_and_custom_models_when_learner_called_the... function test_given_hyperparameters_when_learner_called_and_loaded_back_then_all_models_can_predict (line 165) | def test_given_hyperparameters_when_learner_called_and_loaded_back_then_... function test_when_static_features_in_tuning_data_are_missing_then_exception_is_raised (line 189) | def test_when_static_features_in_tuning_data_are_missing_then_exception_... function test_when_static_features_columns_in_tuning_data_are_missing_then_exception_is_raised (line 199) | def test_when_static_features_columns_in_tuning_data_are_missing_then_ex... function test_when_train_data_has_no_static_features_but_val_data_has_static_features_then_val_data_features_get_removed (line 211) | def test_when_train_data_has_no_static_features_but_val_data_has_static_... function test_when_train_data_static_features_are_subset_of_val_data_static_features_then_columns_are_correct (line 225) | def test_when_train_data_static_features_are_subset_of_val_data_static_f... function test_when_static_features_are_preprocessed_then_dtypes_are_correct (line 243) | def test_when_static_features_are_preprocessed_then_dtypes_are_correct(t... function test_when_train_data_has_static_feat_but_pred_data_has_no_static_feat_then_exception_is_raised (line 254) | def test_when_train_data_has_static_feat_but_pred_data_has_no_static_fea... function test_given_expected_known_covariates_missing_from_train_data_when_learner_fits_then_exception_is_raised (line 269) | def test_given_expected_known_covariates_missing_from_train_data_when_le... function test_given_expected_known_covariates_missing_from_data_when_learner_predicts_then_exception_is_raised (line 278) | def test_given_expected_known_covariates_missing_from_data_when_learner_... function test_given_extra_covariates_are_present_in_dataframe_when_learner_predicts_then_they_are_ignored (line 297) | def test_given_extra_covariates_are_present_in_dataframe_when_learner_pr... function test_given_extra_items_and_timestamps_are_present_in_dataframe_when_learner_predicts_then_correct_subset_is_selected (line 317) | def test_given_extra_items_and_timestamps_are_present_in_dataframe_when_... function test_when_train_data_has_static_or_dynamic_feat_then_leaderboard_works (line 352) | def test_when_train_data_has_static_or_dynamic_feat_then_leaderboard_works( function test_when_features_are_all_nan_and_learner_is_loaded_then_mode_or_median_are_imputed (line 388) | def test_when_features_are_all_nan_and_learner_is_loaded_then_mode_or_me... FILE: timeseries/tests/unittests/test_metrics.py function get_ag_and_gts_metrics (line 35) | def get_ag_and_gts_metrics() -> list[tuple[str, GluonTSMetric]]: function deepar_trained (line 55) | def deepar_trained() -> AbstractGluonTSModel: function deepar_trained_zero_data (line 68) | def deepar_trained_zero_data() -> AbstractGluonTSModel: function to_gluonts_forecast (line 83) | def to_gluonts_forecast(forecast_df, freq): function to_gluonts_test_set (line 97) | def to_gluonts_test_set(data, prediction_length): function check_gluonts_parity (line 106) | def check_gluonts_parity(ag_metric_name, gts_metric, data, model, zero_f... function test_when_metric_evaluated_then_output_equal_to_gluonts (line 129) | def test_when_metric_evaluated_then_output_equal_to_gluonts(ag_metric_na... function test_given_all_zero_data_when_metric_evaluated_then_output_equal_to_gluonts (line 139) | def test_given_all_zero_data_when_metric_evaluated_then_output_equal_to_... function test_given_zero_forecasts_when_metric_evaluated_then_output_equal_to_gluonts (line 152) | def test_given_zero_forecasts_when_metric_evaluated_then_output_equal_to... function test_given_missing_target_values_when_metric_evaluated_then_output_equal_to_gluonts (line 165) | def test_given_missing_target_values_when_metric_evaluated_then_output_e... function test_given_missing_target_values_when_metric_evaluated_then_metric_is_not_nan (line 177) | def test_given_missing_target_values_when_metric_evaluated_then_metric_i... function test_given_predictions_contain_nan_when_metric_evaluated_then_exception_is_raised (line 186) | def test_given_predictions_contain_nan_when_metric_evaluated_then_except... function test_available_metrics_have_coefficients (line 195) | def test_available_metrics_have_coefficients(): function test_given_correct_input_check_get_eval_metric_output_correct (line 208) | def test_given_correct_input_check_get_eval_metric_output_correct(check_... function test_given_unavailable_input_and_raise_check_get_eval_metric_raises (line 212) | def test_given_unavailable_input_and_raise_check_get_eval_metric_raises(): function test_given_historic_data_not_cached_when_scoring_then_exception_is_raised (line 218) | def test_given_historic_data_not_cached_when_scoring_then_exception_is_r... function test_when_eval_metric_seasonal_period_is_longer_than_ts_then_abs_seasonal_error_is_set_to_1 (line 227) | def test_when_eval_metric_seasonal_period_is_longer_than_ts_then_abs_sea... function test_when_eval_metric_seasonal_period_is_longer_than_ts_then_squared_seasonal_error_is_set_to_1 (line 235) | def test_when_eval_metric_seasonal_period_is_longer_than_ts_then_squared... function test_RMSSE (line 244) | def test_RMSSE(prediction_length, seasonal_period, expected_result): function test_RMSLE (line 273) | def test_RMSLE(prediction_length, expected_result): function test_given_metric_is_optimized_by_median_when_model_predicts_then_median_is_pasted_to_mean_forecast (line 294) | def test_given_metric_is_optimized_by_median_when_model_predicts_then_me... function test_when_perfect_predictions_passed_to_metric_then_score_equals_optimum (line 305) | def test_when_perfect_predictions_passed_to_metric_then_score_equals_opt... function test_when_better_predictions_passed_to_metric_then_score_improves (line 317) | def test_when_better_predictions_passed_to_metric_then_score_improves(me... function test_when_experimental_metric_name_used_then_predictor_can_score (line 330) | def test_when_experimental_metric_name_used_then_predictor_can_score(met... function test_when_horizon_weight_contains_invalid_values_then_exception_is_raised (line 348) | def test_when_horizon_weight_contains_invalid_values_then_exception_is_r... function test_when_horizon_weight_is_all_ones_then_metric_value_does_not_change (line 354) | def test_when_horizon_weight_is_all_ones_then_metric_value_does_not_chan... function test_when_horizon_weight_is_non_uniform_then_metric_value_changes (line 366) | def test_when_horizon_weight_is_non_uniform_then_metric_value_changes(me... function test_when_horizon_weight_is_checked_then_values_are_normalized (line 385) | def test_when_horizon_weight_is_checked_then_values_are_normalized(input... function test_when_horizon_weight_is_checked_then_horizon_weight_has_correct_shape (line 398) | def test_when_horizon_weight_is_checked_then_horizon_weight_has_correct_... function partially_matching_predictions (line 406) | def partially_matching_predictions(): function test_when_horizon_weight_is_zero_for_wrong_predictions_then_metric_value_is_zero (line 420) | def test_when_horizon_weight_is_zero_for_wrong_predictions_then_metric_v... function test_when_horizon_weight_is_zero_for_correct_predictions_then_error_increases (line 429) | def test_when_horizon_weight_is_zero_for_correct_predictions_then_error_... FILE: timeseries/tests/unittests/test_predictor.py function test_predictor_can_be_initialized (line 45) | def test_predictor_can_be_initialized(temp_model_path): function test_when_predictor_called_then_training_is_performed (line 51) | def test_when_predictor_called_then_training_is_performed(temp_model_path): function test_given_hyperparameters_when_predictor_called_then_model_can_predict (line 64) | def test_given_hyperparameters_when_predictor_called_then_model_can_pred... function test_when_pathlib_path_provided_to_predictor_then_loaded_predictor_can_predict (line 81) | def test_when_pathlib_path_provided_to_predictor_then_loaded_predictor_c... function test_given_different_target_name_when_predictor_called_then_model_can_predict (line 96) | def test_given_different_target_name_when_predictor_called_then_model_ca... function test_given_no_tuning_data_when_predictor_called_then_model_can_predict (line 121) | def test_given_no_tuning_data_when_predictor_called_then_model_can_predi... function test_given_hyperparameters_and_quantiles_when_predictor_called_then_model_can_predict (line 139) | def test_given_hyperparameters_and_quantiles_when_predictor_called_then_... function test_given_hyperparameters_and_custom_models_when_predictor_called_then_leaderboard_is_correct (line 174) | def test_given_hyperparameters_and_custom_models_when_predictor_called_t... function test_given_hyperparameters_when_predictor_called_and_loaded_back_then_all_models_can_predict (line 193) | def test_given_hyperparameters_when_predictor_called_and_loaded_back_the... function test_given_hp_spaces_and_custom_target_when_predictor_called_predictor_can_predict (line 227) | def test_given_hp_spaces_and_custom_target_when_predictor_called_predict... function test_given_hyperparameters_when_predictor_called_and_loaded_back_then_loaded_learner_can_predict (line 271) | def test_given_hyperparameters_when_predictor_called_and_loaded_back_the... function test_given_enable_ensemble_true_when_predictor_called_then_ensemble_is_fitted (line 295) | def test_given_enable_ensemble_true_when_predictor_called_then_ensemble_... function test_given_enable_ensemble_true_and_only_one_model_when_predictor_called_then_ensemble_is_not_fitted (line 310) | def test_given_enable_ensemble_true_and_only_one_model_when_predictor_ca... function test_given_enable_ensemble_false_when_predictor_called_then_ensemble_is_not_fitted (line 324) | def test_given_enable_ensemble_false_when_predictor_called_then_ensemble... function test_given_model_fails_when_predictor_predicts_then_exception_is_raised (line 337) | def test_given_model_fails_when_predictor_predicts_then_exception_is_rai... function test_given_model_fails_when_predictor_scores_then_exception_is_raised (line 346) | def test_given_model_fails_when_predictor_scores_then_exception_is_raise... function test_given_no_searchspace_and_hyperparameter_tune_kwargs_when_predictor_fits_then_exception_is_raised (line 355) | def test_given_no_searchspace_and_hyperparameter_tune_kwargs_when_predic... function test_given_searchspace_and_no_hyperparameter_tune_kwargs_when_predictor_fits_then_exception_is_raised (line 367) | def test_given_searchspace_and_no_hyperparameter_tune_kwargs_when_predic... function test_given_mixed_searchspace_and_hyperparameter_tune_kwargs_when_predictor_fits_then_no_exception_is_raised (line 381) | def test_given_mixed_searchspace_and_hyperparameter_tune_kwargs_when_pre... function test_when_target_included_in_known_covariates_then_exception_is_raised (line 397) | def test_when_target_included_in_known_covariates_then_exception_is_rais... function test_when_fit_summary_is_called_then_all_keys_and_models_are_included (line 423) | def test_when_fit_summary_is_called_then_all_keys_and_models_are_included( function test_when_info_is_called_then_all_keys_and_models_are_included (line 455) | def test_when_info_is_called_then_all_keys_and_models_are_included(temp_... function test_when_predictor_is_loaded_then_info_works (line 465) | def test_when_predictor_is_loaded_then_info_works(temp_model_path): function test_when_train_data_contains_nans_then_predictor_can_fit (line 478) | def test_when_train_data_contains_nans_then_predictor_can_fit(temp_model... function test_when_prediction_data_contains_nans_then_predictor_can_predict (line 489) | def test_when_prediction_data_contains_nans_then_predictor_can_predict(t... function test_when_some_train_time_series_contain_only_nans_then_they_are_removed_from_train_data (line 499) | def test_when_some_train_time_series_contain_only_nans_then_they_are_rem... function test_when_all_train_time_series_contain_only_nans_then_exception_is_raised (line 513) | def test_when_all_train_time_series_contain_only_nans_then_exception_is_... function test_when_all_nan_data_passed_to_predict_then_predictor_can_predict (line 521) | def test_when_all_nan_data_passed_to_predict_then_predictor_can_predict(... function test_when_scoring_method_receives_only_future_data_then_exception_is_raised (line 531) | def test_when_scoring_method_receives_only_future_data_then_exception_is... function test_when_fit_receives_only_future_data_as_tuning_data_then_exception_is_raised (line 540) | def test_when_fit_receives_only_future_data_as_tuning_data_then_exceptio... function test_given_data_is_in_dataframe_format_then_predictor_works (line 548) | def test_given_data_is_in_dataframe_format_then_predictor_works(temp_mod... function test_given_data_is_in_str_format_then_predictor_works (line 559) | def test_given_data_is_in_str_format_then_predictor_works(temp_model_pat... function test_given_data_cannot_be_interpreted_as_tsdf_then_exception_raised (line 579) | def test_given_data_cannot_be_interpreted_as_tsdf_then_exception_raised(... function test_given_data_is_not_sorted_then_predictor_can_fit_and_predict (line 587) | def test_given_data_is_not_sorted_then_predictor_can_fit_and_predict(tem... function test_given_data_is_not_sorted_then_preprocessed_data_is_sorted (line 597) | def test_given_data_is_not_sorted_then_preprocessed_data_is_sorted(temp_... function test_when_data_passed_to_predictor_contains_infs_then_they_are_replaced_with_nans (line 607) | def test_when_data_passed_to_predictor_contains_infs_then_they_are_repla... function test_when_both_argument_aliases_are_passed_to_init_then_exception_is_raised (line 616) | def test_when_both_argument_aliases_are_passed_to_init_then_exception_is... function test_when_invalid_argument_passed_to_init_then_exception_is_raised (line 621) | def test_when_invalid_argument_passed_to_init_then_exception_is_raised(t... function test_when_invalid_argument_passed_to_fit_then_exception_is_raised (line 626) | def test_when_invalid_argument_passed_to_fit_then_exception_is_raised(te... function test_when_refit_full_called_then_best_model_is_updated (line 633) | def test_when_refit_full_called_then_best_model_is_updated(temp_model_pa... function test_when_refit_full_is_passed_to_fit_then_refit_full_is_skipped (line 652) | def test_when_refit_full_is_passed_to_fit_then_refit_full_is_skipped(tem... function test_when_excluded_model_names_provided_then_excluded_models_are_not_trained (line 667) | def test_when_excluded_model_names_provided_then_excluded_models_are_not... function irregular_timestamp_data_frame (line 738) | def irregular_timestamp_data_frame(request): function test_given_irregular_time_series_when_predictor_called_with_freq_then_predictor_can_predict (line 748) | def test_given_irregular_time_series_when_predictor_called_with_freq_the... function test_given_irregular_time_series_and_no_tuning_when_predictor_called_with_freq_then_predictor_can_predict (line 768) | def test_given_irregular_time_series_and_no_tuning_when_predictor_called... function test_given_regular_time_series_when_predictor_called_with_freq_then_predictions_have_predictor_freq (line 788) | def test_given_regular_time_series_when_predictor_called_with_freq_then_... function test_given_irregular_time_series_when_predictor_called_without_freq_then_training_fails (line 806) | def test_given_irregular_time_series_when_predictor_called_without_freq_... function test_given_regular_time_series_when_predictor_called_without_freq_then_freq_is_inferred (line 820) | def test_given_regular_time_series_when_predictor_called_without_freq_th... function test_given_regular_time_series_when_predictor_loaded_from_disk_then_inferred_freq_persists (line 833) | def test_given_regular_time_series_when_predictor_loaded_from_disk_then_... function test_given_short_and_long_series_in_train_data_when_fit_called_then_trainer_receives_only_long_series (line 853) | def test_given_short_and_long_series_in_train_data_when_fit_called_then_... function test_given_short_and_long_series_in_train_data_and_tuning_data_when_fit_called_then_trainer_receives_only_long_series (line 880) | def test_given_short_and_long_series_in_train_data_and_tuning_data_when_... function test_given_only_short_series_in_train_data_when_fit_called_then_exception_is_raised (line 902) | def test_given_only_short_series_in_train_data_when_fit_called_then_exce... function test_given_only_short_series_in_train_data_then_exception_is_raised (line 920) | def test_given_only_short_series_in_train_data_then_exception_is_raised( function test_given_refit_every_n_windows_when_fit_then_model_is_fit_correct_number_of_times (line 941) | def test_given_refit_every_n_windows_when_fit_then_model_is_fit_correct_... function test_given_custom_metric_when_creating_predictor_then_predictor_can_evaluate (line 958) | def test_given_custom_metric_when_creating_predictor_then_predictor_can_... function test_when_custom_metric_passed_to_score_then_predictor_can_evaluate (line 965) | def test_when_custom_metric_passed_to_score_then_predictor_can_evaluate(... function test_given_invalid_cutoff_when_evaluate_called_then_exception_is_raised (line 984) | def test_given_invalid_cutoff_when_evaluate_called_then_exception_is_rai... function test_metric_with_non_default_cutoff_is_different_from_metric_without_cutoff (line 997) | def test_metric_with_non_default_cutoff_is_different_from_metric_without... function test_metric_with_cutoff_is_same_as_slicing_and_evaluating (line 1013) | def test_metric_with_cutoff_is_same_as_slicing_and_evaluating(temp_model... function test_when_evaluate_receives_multiple_metrics_then_score_dict_contains_all_keys (line 1043) | def test_when_evaluate_receives_multiple_metrics_then_score_dict_contain... function test_given_time_limit_is_not_none_then_first_model_doesnt_receive_full_time_limit (line 1066) | def test_given_time_limit_is_not_none_then_first_model_doesnt_receive_fu... function test_given_time_limit_is_not_none_then_time_is_distributed_across_windows_for_global_models (line 1087) | def test_given_time_limit_is_not_none_then_time_is_distributed_across_wi... function test_when_log_to_file_set_then_predictor_logs_to_file (line 1111) | def test_when_log_to_file_set_then_predictor_logs_to_file(temp_model_path): function test_when_log_file_set_then_predictor_logs_to_custom_file (line 1123) | def test_when_log_file_set_then_predictor_logs_to_custom_file(temp_model... function test_when_log_file_set_with_pathlib_then_predictor_logs_to_custom_file (line 1144) | def test_when_log_file_set_with_pathlib_then_predictor_logs_to_custom_fi... function test_when_log_to_file_set_to_false_then_predictor_does_not_log_to_file (line 1165) | def test_when_log_to_file_set_to_false_then_predictor_does_not_log_to_fi... function test_when_predictor_init_with_verbosity_then_verbosity_propagates_to_all_loggers (line 1173) | def test_when_predictor_init_with_verbosity_then_verbosity_propagates_to... function test_when_predictor_fit_with_verbosity_then_verbosity_overridden_and_propagates_to_all_loggers (line 1185) | def test_when_predictor_fit_with_verbosity_then_verbosity_overridden_and... function test_when_predictor_predict_called_with_random_seed_then_torch_seed_set_for_all_predictions (line 1199) | def test_when_predictor_predict_called_with_random_seed_then_torch_seed_... function test_when_plot_called_then_figure_contains_correct_number_of_subplots (line 1241) | def test_when_plot_called_then_figure_contains_correct_number_of_subplots( function test_when_not_all_quantile_forecasts_available_then_predictor_can_plot (line 1268) | def test_when_not_all_quantile_forecasts_available_then_predictor_can_pl... function test_when_predictions_for_plot_have_incorrect_format_then_exception_is_raised (line 1288) | def test_when_predictions_for_plot_have_incorrect_format_then_exception_... function test_given_skip_model_selection_when_multiple_models_provided_then_exception_is_raised (line 1293) | def test_given_skip_model_selection_when_multiple_models_provided_then_e... function test_given_skip_model_selection_when_search_space_provided_then_exception_is_raised (line 1300) | def test_given_skip_model_selection_when_search_space_provided_then_exce... function test_given_skip_model_selection_then_predictor_can_fit_predict (line 1312) | def test_given_skip_model_selection_then_predictor_can_fit_predict(temp_... function test_given_skip_model_selection_then_all_predictor_methods_work (line 1321) | def test_given_skip_model_selection_then_all_predictor_methods_work(temp... function test_when_persist_called_then_at_least_one_model_persisted (line 1343) | def test_when_persist_called_then_at_least_one_model_persisted(temp_mode... function test_when_predictor_saved_loaded_and_persist_called_then_at_least_one_model_persisted (line 1355) | def test_when_predictor_saved_loaded_and_persist_called_then_at_least_on... function test_when_persist_not_called_then_no_models_persisted (line 1372) | def test_when_persist_not_called_then_no_models_persisted(temp_model_pat... function test_when_predictor_saved_loaded_and_persist_not_called_then_no_models_persisted (line 1383) | def test_when_predictor_saved_loaded_and_persist_not_called_then_no_mode... function test_when_predictor_persisted_saved_loaded_and_persist_not_called_then_no_models_persisted (line 1397) | def test_when_predictor_persisted_saved_loaded_and_persist_not_called_th... function test_when_persist_called_then_persisted_models_names_are_returned (line 1417) | def test_when_persist_called_then_persisted_models_names_are_returned(te... function test_when_persist_and_unpersisted_called_then_persisted_and_unpersisted_models_names_are_returned (line 1429) | def test_when_persist_and_unpersisted_called_then_persisted_and_unpersis... function _add_ensemble_to_predictor (line 1447) | def _add_ensemble_to_predictor(predictor, hyperparameters, make_best_mod... function test_given_single_model_with_ensemble_when_predictor_persisted_then_only_one_model_persisted (line 1464) | def test_given_single_model_with_ensemble_when_predictor_persisted_then_... function test_given_multiple_models_with_ensemble_when_predictor_all_persisted_then_all_models_persisted (line 1488) | def test_given_multiple_models_with_ensemble_when_predictor_all_persiste... function test_given_multiple_models_with_ensemble_when_predictor_persisted_then_ensemble_and_dependencies_persisted (line 1507) | def test_given_multiple_models_with_ensemble_when_predictor_persisted_th... function test_given_multiple_models_with_ensemble_when_ensemble_persisted_then_persist_obeys_with_ancestors (line 1527) | def test_given_multiple_models_with_ensemble_when_ensemble_persisted_the... function test_given_multiple_models_with_ensemble_when_predictor_persisted_saved_loaded_then_ensemble_and_dependencies_persisted (line 1546) | def test_given_multiple_models_with_ensemble_when_predictor_persisted_sa... function test_given_multiple_models_with_ensemble_when_single_model_persisted_then_single_model_persisted (line 1569) | def test_given_multiple_models_with_ensemble_when_single_model_persisted... function importance_dataset_and_predictors (line 1594) | def importance_dataset_and_predictors(request, tmp_path_factory): function test_when_feature_importance_called_with_improvements_then_improvements_are_correct (line 1648) | def test_when_feature_importance_called_with_improvements_then_improveme... function test_given_predictor_takes_no_features_when_feature_importance_called_with_improvements_then_improvements_are_zero (line 1695) | def test_given_predictor_takes_no_features_when_feature_importance_calle... function test_given_predictor_takes_known_only_when_feature_importance_called_with_improvements_then_past_and_static_improvements_are_zero (line 1738) | def test_given_predictor_takes_known_only_when_feature_importance_called... function test_when_predictor_saved_to_same_directory_then_leaderboard_works (line 1769) | def test_when_predictor_saved_to_same_directory_then_leaderboard_works(t... function test_when_predictor_saved_to_same_directory_and_loaded_then_number_of_models_matches (line 1778) | def test_when_predictor_saved_to_same_directory_and_loaded_then_number_o... function test_when_invalid_path_provided_to_load_then_correct_exception_is_raised (line 1793) | def test_when_invalid_path_provided_to_load_then_correct_exception_is_ra... function test_when_extra_info_is_true_then_leaderboard_returns_concrete_hyperparameters (line 1799) | def test_when_extra_info_is_true_then_leaderboard_returns_concrete_hyper... function test_when_extra_metrics_provided_then_leaderboard_contains_metric_values (line 1814) | def test_when_extra_metrics_provided_then_leaderboard_contains_metric_va... function test_when_extra_metrics_provided_and_data_missing_then_exception_is_raised (line 1823) | def test_when_extra_metrics_provided_and_data_missing_then_exception_is_... function test_when_extra_metrics_and_extra_info_provided_then_leaderboard_contains_correct_columns (line 1830) | def test_when_extra_metrics_and_extra_info_provided_then_leaderboard_con... function test_when_leaky_feature_provided_then_model_with_regressor_achieves_good_accuracy (line 1841) | def test_when_leaky_feature_provided_then_model_with_regressor_achieves_... function test_when_invalid_model_provided_then_informative_error_is_raised (line 1860) | def test_when_invalid_model_provided_then_informative_error_is_raised(me... function test_when_freq_is_none_and_predictor_is_not_fit_then_make_future_data_frame_raises_an_error (line 1867) | def test_when_freq_is_none_and_predictor_is_not_fit_then_make_future_dat... function test_when_predictor_predicts_then_forecast_index_matches_the_make_future_data_frame_output (line 1873) | def test_when_predictor_predicts_then_forecast_index_matches_the_make_fu... function test_when_freq_is_set_and_predictor_is_not_fit_then_make_future_data_frame_returns_correct_index (line 1883) | def test_when_freq_is_set_and_predictor_is_not_fit_then_make_future_data... function test_when_make_future_data_frame_output_is_used_to_set_the_known_covariates_then_prediction_works (line 1891) | def test_when_make_future_data_frame_output_is_used_to_set_the_known_cov... function test_when_horizon_weight_is_provided_to_predictor_then_eval_metric_uses_it_during_training (line 1903) | def test_when_horizon_weight_is_provided_to_predictor_then_eval_metric_u... function test_when_seasonal_period_is_provided_to_predictor_then_eval_metric_uses_it_during_training (line 1923) | def test_when_seasonal_period_is_provided_to_predictor_then_eval_metric_... function test_when_backtest_predictions_and_targets_called_then_metrics_can_be_computed (line 1948) | def test_when_backtest_predictions_and_targets_called_then_metrics_can_b... function test_when_backtest_predictions_called_with_multiple_models_then_dict_is_returned (line 1969) | def test_when_backtest_predictions_called_with_multiple_models_then_dict... function test_when_method_called_before_fit_then_exception_is_raised (line 1995) | def test_when_method_called_before_fit_then_exception_is_raised(temp_mod... class TestMultilayerValidationAndNormalization (line 2002) | class TestMultilayerValidationAndNormalization: method test_given_int_num_val_windows_when_normalized_then_converted_to_tuple (line 2003) | def test_given_int_num_val_windows_when_normalized_then_converted_to_t... method test_given_negative_values_when_normalized_then_validation_error_raised (line 2008) | def test_given_negative_values_when_normalized_then_validation_error_r... method test_given_empty_tuple_when_normalized_then_validation_error_raised (line 2013) | def test_given_empty_tuple_when_normalized_then_validation_error_raise... method test_given_list_instead_of_tuple_when_normalized_then_type_error_raised (line 2018) | def test_given_list_instead_of_tuple_when_normalized_then_type_error_r... method test_given_short_time_series_when_reduced_then_num_val_windows_reduced (line 2027) | def test_given_short_time_series_when_reduced_then_num_val_windows_red... method test_given_very_short_series_when_reduced_then_layers_trimmed_to_max_allowed (line 2041) | def test_given_very_short_series_when_reduced_then_layers_trimmed_to_m... method test_given_very_short_series_when_reduced_then_layers_deleted_to_max_allowed (line 2049) | def test_given_very_short_series_when_reduced_then_layers_deleted_to_m... method test_given_mismatched_lengths_when_explicitly_provided_then_value_error_raised (line 2061) | def test_given_mismatched_lengths_when_explicitly_provided_then_value_... method test_given_dict_ensemble_hyperparameters_when_validated_then_converted_to_list (line 2071) | def test_given_dict_ensemble_hyperparameters_when_validated_then_conve... method test_given_reduced_layers_when_validated_then_ensemble_hyperparameters_trimmed (line 2083) | def test_given_reduced_layers_when_validated_then_ensemble_hyperparame... method test_given_none_ensemble_hyperparameters_when_validated_then_none_returned (line 2100) | def test_given_none_ensemble_hyperparameters_when_validated_then_none_... method test_given_tuning_data_when_fit_called_then_num_val_windows_is_set_to_one (line 2113) | def test_given_tuning_data_when_fit_called_then_num_val_windows_is_set... method test_given_num_val_windows_too_high_for_given_data_then_num_val_windows_is_reduced (line 2125) | def test_given_num_val_windows_too_high_for_given_data_then_num_val_wi... method test_given_tuning_data_provided_when_normalized_then_last_element_set_to_one (line 2142) | def test_given_tuning_data_provided_when_normalized_then_last_element_... method test_given_tuning_data_provided_when_reduced_then_last_element_ignored_in_calculation (line 2147) | def test_given_tuning_data_provided_when_reduced_then_last_element_ign... method test_given_tuning_data_when_validated_then_last_num_val_windows_set_to_one (line 2157) | def test_given_tuning_data_when_validated_then_last_num_val_windows_se... class TestPredictorMultilayerEnsemble (line 2169) | class TestPredictorMultilayerEnsemble: method multilayer_hyperparameters (line 2171) | def multilayer_hyperparameters(self): method multilayer_ensemble_hyperparameters (line 2175) | def multilayer_ensemble_hyperparameters(self): method fitted_predictor (line 2182) | def fitted_predictor( method test_given_multilayer_params_when_predictor_fit_then_multiple_ensembles_created (line 2194) | def test_given_multilayer_params_when_predictor_fit_then_multiple_ense... method test_given_multilayer_ensembles_when_predict_called_then_predictions_returned (line 2199) | def test_given_multilayer_ensembles_when_predict_called_then_predictio... method test_given_multilayer_ensembles_when_saved_and_loaded_then_loaded_predictor_can_predict (line 2203) | def test_given_multilayer_ensembles_when_saved_and_loaded_then_loaded_... method test_given_multilayer_ensembles_when_leaderboard_called_then_all_ensembles_included (line 2215) | def test_given_multilayer_ensembles_when_leaderboard_called_then_all_e... method test_given_multilayer_ensembles_when_leaderboard_called_then_all_ensembles_have_layer_ids (line 2221) | def test_given_multilayer_ensembles_when_leaderboard_called_then_all_e... method test_given_multilayer_ensembles_when_saved_and_loaded_then_loaded_predictor_computes_new_leaderboard (line 2228) | def test_given_multilayer_ensembles_when_saved_and_loaded_then_loaded_... method test_given_multilayer_ensembles_when_model_names_called_then_all_ensembles_included (line 2239) | def test_given_multilayer_ensembles_when_model_names_called_then_all_e... method test_given_tuning_data_and_multilayer_when_fit_called_then_predictor_can_fit_and_predict (line 2245) | def test_given_tuning_data_and_multilayer_when_fit_called_then_predict... method test_given_short_series_and_multilayer_when_fit_called_then_layers_reduced_with_warning (line 2260) | def test_given_short_series_and_multilayer_when_fit_called_then_layers... method test_given_tuning_data_and_multilayer_when_fit_called_then_correct_num_val_windows_passed_to_learner (line 2275) | def test_given_tuning_data_and_multilayer_when_fit_called_then_correct... method test_given_multilayer_ensembles_when_fit_called_then_top_layer_ensemble_has_best_score (line 2299) | def test_given_multilayer_ensembles_when_fit_called_then_top_layer_ens... class TestAutoValidationSettings (line 2319) | class TestAutoValidationSettings: method test_when_num_val_windows_auto_then_correct_distribution (line 2336) | def test_when_num_val_windows_auto_then_correct_distribution( method test_when_refit_auto_then_follows_sqrt_formula (line 2369) | def test_when_refit_auto_then_follows_sqrt_formula(self, temp_model_pa... method test_when_tuning_data_with_auto_then_last_window_one (line 2391) | def test_when_tuning_data_with_auto_then_last_window_one( method test_when_both_auto_then_refit_matches_sqrt (line 2417) | def test_when_both_auto_then_refit_matches_sqrt(self, temp_model_path,... method test_when_val_step_size_varies_then_auto_adjusts_windows (line 2441) | def test_when_val_step_size_varies_then_auto_adjusts_windows( FILE: timeseries/tests/unittests/test_regressor.py function get_multi_window_zero_model (line 19) | def get_multi_window_zero_model(hyperparameters=None, **kwargs): function get_model_with_regressor (line 32) | def get_model_with_regressor(dummy_hyperparameters, df_with_covariates_a... function test_when_refit_during_predict_is_true_then_regressor_is_trained_during_predict (line 61) | def test_when_refit_during_predict_is_true_then_regressor_is_trained_dur... function test_when_model_is_used_with_regressor_then_regressor_methods_are_called_the_expected_number_of_times (line 78) | def test_when_model_is_used_with_regressor_then_regressor_methods_are_ca... function test_when_data_contains_no_known_covariates_or_static_features_then_regressor_is_not_used (line 107) | def test_when_data_contains_no_known_covariates_or_static_features_then_... function test_when_regressor_is_used_then_tabular_df_contains_correct_features (line 128) | def test_when_regressor_is_used_then_tabular_df_contains_correct_features( function test_when_target_scaler_and_regressor_are_used_then_regressor_receives_scaled_data_as_input (line 158) | def test_when_target_scaler_and_regressor_are_used_then_regressor_receiv... function test_when_covariate_regressor_used_then_residuals_are_subtracted_before_forecaster_fits (line 181) | def test_when_covariate_regressor_used_then_residuals_are_subtracted_bef... function test_when_validation_fraction_is_set_then_tabular_model_uses_val_data (line 207) | def test_when_validation_fraction_is_set_then_tabular_model_uses_val_dat... function test_when_validation_fraction_is_none_then_tabular_model_doesnt_use_val_data (line 218) | def test_when_validation_fraction_is_none_then_tabular_model_doesnt_use_... function test_when_not_enough_time_is_left_to_predict_then_regressor_is_disabled (line 229) | def test_when_not_enough_time_is_left_to_predict_then_regressor_is_disab... function test_when_regressor_is_disabled_then_data_is_not_modified_during_transform (line 245) | def test_when_regressor_is_disabled_then_data_is_not_modified_during_tra... function test_when_all_features_are_constant_then_regressor_is_not_fit (line 261) | def test_when_all_features_are_constant_then_regressor_is_not_fit(): FILE: timeseries/tests/unittests/test_splitter.py function test_when_splitter_splits_then_underlying_data_is_not_copied (line 8) | def test_when_splitter_splits_then_underlying_data_is_not_copied(): function test_when_static_features_are_present_then_static_features_are_preserved (line 16) | def test_when_static_features_are_present_then_static_features_are_prese... function test_when_covariates_are_present_then_covariates_are_preserved (line 25) | def test_when_covariates_are_present_then_covariates_are_preserved(): function test_when_splitter_splits_then_all_folds_have_expected_length (line 38) | def test_when_splitter_splits_then_all_folds_have_expected_length(predic... FILE: timeseries/tests/unittests/test_transforms.py function test_when_scaler_transforms_then_input_data_is_not_modified (line 31) | def test_when_scaler_transforms_then_input_data_is_not_modified(scaler_n... function test_when_scaler_transforms_then_no_new_nans_appear (line 41) | def test_when_scaler_transforms_then_no_new_nans_appear(scaler_name): function test_when_inverse_transform_applied_then_output_matches_input (line 49) | def test_when_inverse_transform_applied_then_output_matches_input(scaler... function test_when_model_fits_then_fit_transform_called_as_many_times_as_expected (line 64) | def test_when_model_fits_then_fit_transform_called_as_many_times_as_expe... function test_when_model_predicts_then_fit_transform_called_once (line 84) | def test_when_model_predicts_then_fit_transform_called_once(model_class): function test_given_target_scaler_param_set_when_model_fits_then_target_scaler_created (line 113) | def test_given_target_scaler_param_set_when_model_fits_then_target_scale... function test_given_invalid_scaler_name_when_model_fits_then_exception_is_raised (line 124) | def test_given_invalid_scaler_name_when_model_fits_then_exception_is_rai... function test_given_no_scaler_name_when_model_fits_then_no_scaler_is_added (line 131) | def test_given_no_scaler_name_when_model_fits_then_no_scaler_is_added(hy... function test_when_global_covariate_scaler_used_then_correct_feature_types_are_detected (line 142) | def test_when_global_covariate_scaler_used_then_correct_feature_types_ar... function test_when_covariate_scaler_is_used_then_original_data_is_not_modified (line 159) | def test_when_covariate_scaler_is_used_then_original_data_is_not_modified( function test_when_global_covariate_scaler_is_fit_then_column_transformers_are_created (line 179) | def test_when_global_covariate_scaler_is_fit_then_column_transformers_ar... function test_when_global_covariate_scaler_transforms_then_real_columns_are_standardized (line 188) | def test_when_global_covariate_scaler_transforms_then_real_columns_are_s... function test_when_hyperparameter_spaces_of_transforms_provided_to_init_then_model_can_tune (line 201) | def test_when_hyperparameter_spaces_of_transforms_provided_to_init_then_... FILE: timeseries/tests/unittests/test_ts_dataset.py function _build_ts_dataframe (line 30) | def _build_ts_dataframe(item_ids, datetime_index, target, static_feature... function test_from_iterable (line 63) | def test_from_iterable(): function test_validate_data_frame (line 75) | def test_validate_data_frame(): function test_validate_multi_index_data_frame (line 88) | def test_validate_multi_index_data_frame(): function test_from_gluonts_list_dataset (line 102) | def test_from_gluonts_list_dataset(): function test_from_data_frame (line 124) | def test_from_data_frame(): function test_split_by_time (line 161) | def test_split_by_time( function test_slice_by_time (line 210) | def test_slice_by_time(start_timestamp, end_timestamp, item_ids, datetim... function test_when_dataset_constructed_from_dataframe_without_freq_then_freq_is_inferred (line 225) | def test_when_dataset_constructed_from_dataframe_without_freq_then_freq_... function test_when_dataset_constructed_from_iterable_with_freq_then_freq_is_inferred (line 250) | def test_when_dataset_constructed_from_iterable_with_freq_then_freq_is_i... function test_when_dataset_constructed_via_constructor_with_freq_then_freq_is_inferred (line 263) | def test_when_dataset_constructed_via_constructor_with_freq_then_freq_is... function test_when_dataset_constructed_with_irregular_timestamps_then_freq_call_returns_none (line 305) | def test_when_dataset_constructed_with_irregular_timestamps_then_freq_ca... function test_when_dataset_constructed_with_irregular_timestamps_then_irregular_freqstr_is_inferred (line 320) | def test_when_dataset_constructed_with_irregular_timestamps_then_irregul... function test_given_raise_if_irregular_is_true_when_frequency_inferred_then_error_is_raised (line 335) | def test_given_raise_if_irregular_is_true_when_frequency_inferred_then_e... function test_when_dataset_sliced_by_step_then_output_times_and_values_correct (line 430) | def test_when_dataset_sliced_by_step_then_output_times_and_values_correct( function test_when_slice_by_timestep_used_with_different_inputs_then_output_selects_correct_indices (line 479) | def test_when_slice_by_timestep_used_with_different_inputs_then_output_s... function test_when_dataframe_copy_called_on_instance_then_output_correct (line 487) | def test_when_dataframe_copy_called_on_instance_then_output_correct(inpu... function test_when_dataframe_stdlib_copy_called_then_output_correct (line 495) | def test_when_dataframe_stdlib_copy_called_then_output_correct(input_df): function test_when_dataframe_class_copy_called_then_output_correct (line 503) | def test_when_dataframe_class_copy_called_then_output_correct(input_df): function test_when_dataframe_class_rename_called_then_output_correct (line 512) | def test_when_dataframe_class_rename_called_then_output_correct(input_df... function test_when_dataframe_instance_rename_called_then_output_correct (line 526) | def test_when_dataframe_instance_rename_called_then_output_correct(input... function test_when_dataframe_read_pickle_called_then_output_correct (line 540) | def test_when_dataframe_read_pickle_called_then_output_correct(input_df,... function test_when_dataframe_read_pickle_called_then_static_features_are_correct (line 553) | def test_when_dataframe_read_pickle_called_then_static_features_are_corr... function test_when_dataframe_copy_called_on_instance_then_static_features_are_correct (line 567) | def test_when_dataframe_copy_called_on_instance_then_static_features_are... function test_when_dataframe_stdlib_copy_called_then_static_features_are_correct (line 575) | def test_when_dataframe_stdlib_copy_called_then_static_features_are_corr... function test_when_dataframe_class_rename_called_then_static_features_are_correct (line 584) | def test_when_dataframe_class_rename_called_then_static_features_are_cor... function test_when_dataframe_instance_rename_called_then_static_features_are_correct (line 599) | def test_when_dataframe_instance_rename_called_then_static_features_are_... function test_when_dataset_sliced_by_step_then_static_features_are_correct (line 615) | def test_when_dataset_sliced_by_step_then_static_features_are_correct(): function test_when_static_features_index_has_wrong_name_then_its_renamed_to_item_id (line 625) | def test_when_static_features_index_has_wrong_name_then_its_renamed_to_i... function test_when_dataset_sliced_by_time_then_static_features_are_correct (line 634) | def test_when_dataset_sliced_by_time_then_static_features_are_correct(): function test_when_dataset_split_by_time_then_static_features_are_correct (line 644) | def test_when_dataset_split_by_time_then_static_features_are_correct(): function test_given_correct_static_feature_index_when_constructing_data_frame_then_error_not_raised (line 655) | def test_given_correct_static_feature_index_when_constructing_data_frame... function test_given_wrong_static_feature_index_when_constructing_data_frame_then_error_raised (line 681) | def test_given_wrong_static_feature_index_when_constructing_data_frame_t... function test_when_dataframe_sliced_by_item_array_then_static_features_stay_consistent (line 703) | def test_when_dataframe_sliced_by_item_array_then_static_features_stay_c... function test_given_wrong_ids_stored_in_item_id_column_when_constructing_tsdf_then_exception_is_raised (line 711) | def test_given_wrong_ids_stored_in_item_id_column_when_constructing_tsdf... function test_given_static_features_have_multiindex_when_constructing_tsdf_then_exception_is_raised (line 720) | def test_given_static_features_have_multiindex_when_constructing_tsdf_th... function test_given_item_id_is_stored_as_column_and_not_index_in_static_features_then_tsdf_is_constructed_correctly (line 728) | def test_given_item_id_is_stored_as_column_and_not_index_in_static_featu... function test_given_item_id_stored_in_both_index_and_column_when_constructing_tsdf_then_values_in_index_are_used (line 737) | def test_given_item_id_stored_in_both_index_and_column_when_constructing... function test_when_item_id_index_has_mixed_dtype_then_value_error_is_raised (line 764) | def test_when_item_id_index_has_mixed_dtype_then_value_error_is_raised(i... function test_when_static_features_are_modified_on_shallow_copy_then_original_df_doesnt_change (line 769) | def test_when_static_features_are_modified_on_shallow_copy_then_original... function test_when_dataset_constructed_from_dataframe_then_timestamp_column_is_converted_to_datetime (line 777) | def test_when_dataset_constructed_from_dataframe_then_timestamp_column_i... function test_when_path_is_given_to_constructor_then_tsdf_is_constructed_correctly (line 790) | def test_when_path_is_given_to_constructor_then_tsdf_is_constructed_corr... function test_given_custom_id_column_when_data_and_static_are_loaded_from_path_them_tsdf_is_constructed_correctly (line 802) | def test_given_custom_id_column_when_data_and_static_are_loaded_from_pat... function test_given_static_features_are_missing_when_loading_from_path_then_tsdf_can_be_constructed (line 828) | def test_given_static_features_are_missing_when_loading_from_path_then_t... function test_when_fill_missing_values_called_then_gaps_are_filled_and_index_is_unchanged (line 843) | def test_when_fill_missing_values_called_then_gaps_are_filled_and_index_... function test_when_fill_missing_values_called_then_leading_nans_are_filled_and_index_is_unchanged (line 852) | def test_when_fill_missing_values_called_then_leading_nans_are_filled_an... function test_when_fill_missing_values_called_then_trailing_nans_are_filled_and_index_is_unchanged (line 863) | def test_when_fill_missing_values_called_then_trailing_nans_are_filled_a... function test_when_dropna_called_then_missing_values_are_dropped (line 873) | def test_when_dropna_called_then_missing_values_are_dropped(): function test_given_static_features_dont_contain_custom_id_column_when_from_data_frame_called_then_exception_is_raised (line 880) | def test_given_static_features_dont_contain_custom_id_column_when_from_d... function test_when_data_contains_item_id_column_that_is_unused_then_column_is_renamed (line 888) | def test_when_data_contains_item_id_column_that_is_unused_then_column_is... function test_when_static_features_contain_item_id_column_that_is_unused_then_column_is_renamed (line 895) | def test_when_static_features_contain_item_id_column_that_is_unused_then... function test_when_data_contains_timestamp_column_that_is_unused_then_column_is_renamed (line 906) | def test_when_data_contains_timestamp_column_that_is_unused_then_column_... function test_given_index_is_irregular_when_convert_frequency_called_then_result_has_regular_index (line 914) | def test_given_index_is_irregular_when_convert_frequency_called_then_res... function test_given_index_is_irregular_when_convert_frequency_called_then_new_index_has_desired_frequency (line 929) | def test_given_index_is_irregular_when_convert_frequency_called_then_new... function test_given_index_is_regular_when_convert_frequency_called_the_df_doesnt_change (line 942) | def test_given_index_is_regular_when_convert_frequency_called_the_df_doe... function test_when_convert_frequency_called_with_different_freq_then_original_df_is_not_modified (line 948) | def test_when_convert_frequency_called_with_different_freq_then_original... function test_when_convert_frequency_called_then_static_features_are_kept (line 957) | def test_when_convert_frequency_called_then_static_features_are_kept(): function test_given_index_is_regular_when_convert_frequency_is_called_then_new_index_has_desired_frequency (line 965) | def test_given_index_is_regular_when_convert_frequency_is_called_then_ne... function test_when_aggregation_method_is_changed_then_aggregated_result_is_correct (line 995) | def test_when_aggregation_method_is_changed_then_aggregated_result_is_co... function test_when_convert_frequency_called_then_categorical_columns_are_preserved (line 1010) | def test_when_convert_frequency_called_then_categorical_columns_are_pres... function test_when_timestamps_have_datetime64_type_then_tsdf_can_be_constructed (line 1024) | def test_when_timestamps_have_datetime64_type_then_tsdf_can_be_construct... function test_when_to_data_frame_called_then_return_values_is_a_pandas_df (line 1031) | def test_when_to_data_frame_called_then_return_values_is_a_pandas_df(): function test_when_resampling_timestamps_with_different_dtypes_then_no_nat_values_in_index (line 1039) | def test_when_resampling_timestamps_with_different_dtypes_then_no_nat_va... FILE: timeseries/tests/unittests/trainer/test_ensemble_composer.py class TestSingleLayerEnsemble (line 17) | class TestSingleLayerEnsemble: method trainer (line 19) | def trainer(self, tmp_path_factory, patch_naive_models): method test_when_ensemble_composer_created_then_can_train_single_layer_ensembles (line 48) | def test_when_ensemble_composer_created_then_can_train_single_layer_en... method test_when_single_layer_then_ensemble_names_have_no_suffix (line 74) | def test_when_single_layer_then_ensemble_names_have_no_suffix(self, tr... class TestTwoLayerStacking (line 99) | class TestTwoLayerStacking: method fitted_composer_and_expected_count (line 117) | def fitted_composer_and_expected_count(self, tmp_path_factory, request... method test_when_two_layers_then_correct_number_of_ensembles_created (line 146) | def test_when_two_layers_then_correct_number_of_ensembles_created(self... method test_when_two_layers_then_layer_indices_correct (line 151) | def test_when_two_layers_then_layer_indices_correct(self, fitted_compo... method test_when_two_layers_then_every_layer_has_correct_oof_predictions (line 158) | def test_when_two_layers_then_every_layer_has_correct_oof_predictions(... method test_when_two_layers_then_l3_uses_l2_as_base (line 168) | def test_when_two_layers_then_l3_uses_l2_as_base(self, fitted_composer... method test_when_two_layers_then_graph_structure_correct (line 178) | def test_when_two_layers_then_graph_structure_correct(self, fitted_com... method test_when_two_layers_then_ensemble_names_have_layer_suffix (line 190) | def test_when_two_layers_then_ensemble_names_have_layer_suffix(self, f... class TestThreeLayerStacking (line 199) | class TestThreeLayerStacking: method fitted_composer (line 201) | def fitted_composer(self, tmp_path_factory, patch_naive_models, request): method test_when_three_layers_then_correct_number_of_ensembles_created (line 238) | def test_when_three_layers_then_correct_number_of_ensembles_created(se... method test_when_three_layers_then_layer_indices_correct (line 242) | def test_when_three_layers_then_layer_indices_correct(self, fitted_com... method test_when_three_layers_then_oof_predictions_correct (line 247) | def test_when_three_layers_then_oof_predictions_correct(self, fitted_c... method test_when_three_layers_then_ensemble_names_have_layer_suffix (line 255) | def test_when_three_layers_then_ensemble_names_have_layer_suffix(self,... class TestMultilayerStackingValidationScoreComputation (line 263) | class TestMultilayerStackingValidationScoreComputation: method test_when_fit_called_then_all_ensembles_are_scored_on_last_layers_data (line 272) | def test_when_fit_called_then_all_ensembles_are_scored_on_last_layers_... class TestWindowSlicing (line 330) | class TestWindowSlicing: method get_trainer_and_composer (line 331) | def get_trainer_and_composer( method test_when_ensemble_composer_called_then_window_indices_correct (line 394) | def test_when_ensemble_composer_called_then_window_indices_correct( function test_when_time_limit_exceeded_then_training_stops_early (line 458) | def test_when_time_limit_exceeded_then_training_stops_early(tmp_path_fac... class TestValidateEnsembleHyperparameters (line 501) | class TestValidateEnsembleHyperparameters: method test_given_valid_hyperparameters_when_validate_called_then_does_not_raise (line 502) | def test_given_valid_hyperparameters_when_validate_called_then_does_no... method test_given_invalid_ensemble_name_when_validate_called_then_error_raised (line 509) | def test_given_invalid_ensemble_name_when_validate_called_then_error_r... method test_given_non_dict_input_when_validate_called_then_error_raised (line 515) | def test_given_non_dict_input_when_validate_called_then_error_raised(s... class TestEnsemblePredictTime (line 520) | class TestEnsemblePredictTime: method ensemble_composer (line 529) | def ensemble_composer(self, request, tmp_path_factory, patch_naive_mod... method test_when_ensemble_trained_then_predict_time_marginal_set (line 559) | def test_when_ensemble_trained_then_predict_time_marginal_set(self, en... method test_when_ensemble_trained_then_predict_time_includes_base_models (line 565) | def test_when_ensemble_trained_then_predict_time_includes_base_models(... FILE: timeseries/tests/unittests/trainer/test_model_set_builder.py function model_set_builder (line 73) | def model_set_builder(): function test_when_hp_builder_called_then_hyperparameters_built_correctly (line 87) | def test_when_hp_builder_called_then_hyperparameters_built_correctly( function test_when_hp_builder_called_with_tune_but_no_spaces_then_error_is_raised (line 103) | def test_when_hp_builder_called_with_tune_but_no_spaces_then_error_is_ra... function test_when_hp_builder_called_with_no_tune_but_has_spaces_then_error_is_raised (line 114) | def test_when_hp_builder_called_with_no_tune_but_has_spaces_then_error_i... function test_when_model_set_builder_called_then_hyperparameters_built_correctly (line 126) | def test_when_model_set_builder_called_then_hyperparameters_built_correc... function test_when_non_model_class_provided_to_model_set_builder_then_error_is_raised (line 139) | def test_when_non_model_class_provided_to_model_set_builder_then_error_i... FILE: timeseries/tests/unittests/trainer/test_prediction_cache.py class TestDatasetHashFunction (line 12) | class TestDatasetHashFunction: method _get_known_covariates_for_df (line 13) | def _get_known_covariates_for_df(self, df: TimeSeriesDataFrame) -> Tim... method test_when_dfs_are_identical_then_identical_hash_is_computed (line 18) | def test_when_dfs_are_identical_then_identical_hash_is_computed(self): method test_when_dfs_and_known_covariates_are_identical_then_identical_hash_is_computed (line 26) | def test_when_dfs_and_known_covariates_are_identical_then_identical_ha... method test_when_different_dfs_then_different_hashes_are_computed (line 38) | def test_when_different_dfs_then_different_hashes_are_computed(self): method test_when_identical_dfs_and_different_known_covariates_are_identical_then_different_hashes_are_computed (line 44) | def test_when_identical_dfs_and_different_known_covariates_are_identic... method test_when_different_static_features_then_different_hashes_are_computed (line 57) | def test_when_different_static_features_then_different_hashes_are_comp... class TestFileBasedPredictionCache (line 67) | class TestFileBasedPredictionCache: method test_when_identical_dfs_are_given_then_cache_hit_returns_true (line 68) | def test_when_identical_dfs_are_given_then_cache_hit_returns_true(self... method test_when_different_dfs_are_given_then_cache_hit_returns_false (line 84) | def test_when_different_dfs_are_given_then_cache_hit_returns_false(sel... method test_when_cache_cleared_then_file_is_removed (line 99) | def test_when_cache_cleared_then_file_is_removed(self, tmp_path): FILE: timeseries/tests/unittests/trainer/test_trainer.py function trained_trainers (line 43) | def trained_trainers(): function test_trainer_can_be_initialized (line 66) | def test_trainer_can_be_initialized(temp_model_path): function test_when_trainer_called_then_training_is_performed (line 73) | def test_when_trainer_called_then_training_is_performed(trained_trainers... function test_given_hyperparameters_when_trainer_called_then_leaderboard_is_correct (line 82) | def test_given_hyperparameters_when_trainer_called_then_leaderboard_is_c... function test_given_test_data_when_trainer_called_then_leaderboard_is_correct (line 102) | def test_given_test_data_when_trainer_called_then_leaderboard_is_correct( function test_given_hyperparameters_when_trainer_called_then_model_can_predict (line 122) | def test_given_hyperparameters_when_trainer_called_then_model_can_predict( function test_given_hyperparameters_when_get_trainable_base_models_called_then_hyperparameters_set_correctly (line 146) | def test_given_hyperparameters_when_get_trainable_base_models_called_the... function test_given_hyperparameters_when_trainer_fit_then_freq_set_correctly (line 170) | def test_given_hyperparameters_when_trainer_fit_then_freq_set_correctly(... function test_given_hyperparameters_with_spaces_when_trainer_called_then_hpo_is_performed (line 184) | def test_given_hyperparameters_with_spaces_when_trainer_called_then_hpo_... function test_given_hyperparameters_with_lists_when_trainer_called_then_multiple_models_are_trained (line 214) | def test_given_hyperparameters_with_lists_when_trainer_called_then_multi... function test_given_hyperparameters_and_custom_models_when_trainer_called_then_leaderboard_is_correct (line 238) | def test_given_hyperparameters_and_custom_models_when_trainer_called_the... function test_given_repeating_model_when_trainer_called_incrementally_then_name_collisions_are_prevented (line 284) | def test_given_repeating_model_when_trainer_called_incrementally_then_na... function test_when_trainer_fit_and_deleted_models_load_back_correctly_and_can_predict (line 318) | def test_when_trainer_fit_and_deleted_models_load_back_correctly_and_can... function test_when_trainer_fit_and_deleted_then_oof_predictions_can_be_loaded (line 347) | def test_when_trainer_fit_and_deleted_then_oof_predictions_can_be_loaded... function test_when_known_covariates_present_then_all_ensemble_base_models_can_predict (line 373) | def test_when_known_covariates_present_then_all_ensemble_base_models_can... function trained_and_refit_trainers (line 401) | def trained_and_refit_trainers(): function test_when_refit_full_called_then_all_models_are_retrained (line 427) | def test_when_refit_full_called_then_all_models_are_retrained(trained_an... function test_when_refit_full_called_multiple_times_then_no_new_models_are_trained (line 437) | def test_when_refit_full_called_multiple_times_then_no_new_models_are_tr... function test_when_refit_full_called_then_all_models_can_predict (line 445) | def test_when_refit_full_called_then_all_models_can_predict(trained_and_... function test_when_refit_full_called_with_model_name_then_single_model_is_updated (line 453) | def test_when_refit_full_called_with_model_name_then_single_model_is_upd... function test_given_quantile_levels_is_empty_when_refit_full_is_used_then_all_models_can_predict (line 466) | def test_given_quantile_levels_is_empty_when_refit_full_is_used_then_all... function test_when_some_models_have_incorrect_suffix_then_correct_model_are_trained (line 491) | def test_when_some_models_have_incorrect_suffix_then_correct_model_are_t... function test_when_excluded_model_names_provided_then_excluded_models_are_not_trained (line 501) | def test_when_excluded_model_names_provided_then_excluded_models_are_not... function test_when_get_model_pred_dict_called_then_it_contains_all_required_keys (line 518) | def test_when_get_model_pred_dict_called_then_it_contains_all_required_k... function test_when_get_model_pred_dict_called_then_pred_time_dict_contains_all_required_keys (line 525) | def test_when_get_model_pred_dict_called_then_pred_time_dict_contains_al... function test_given_cache_predictions_is_true_when_calling_get_model_pred_dict_then_predictions_are_cached (line 531) | def test_given_cache_predictions_is_true_when_calling_get_model_pred_dic... function test_given_cache_predictions_is_true_when_predicting_multiple_times_then_cached_predictions_are_updated (line 545) | def test_given_cache_predictions_is_true_when_predicting_multiple_times_... function test_given_cache_predictions_is_true_when_predicting_multiple_times_then_cached_predictions_are_used (line 560) | def test_given_cache_predictions_is_true_when_predicting_multiple_times_... function test_given_cache_predictions_is_false_when_calling_get_model_pred_dict_then_predictions_are_not_cached (line 576) | def test_given_cache_predictions_is_false_when_calling_get_model_pred_di... function test_when_use_cache_is_set_to_false_then_cached_predictions_are_ignored (line 590) | def test_when_use_cache_is_set_to_false_then_cached_predictions_are_igno... function test_given_cached_predictions_cannot_be_loaded_when_predict_call_then_new_predictions_are_generated (line 604) | def test_given_cached_predictions_cannot_be_loaded_when_predict_call_the... function test_given_no_models_trained_during_fit_then_empty_leaderboard_returned (line 626) | def test_given_no_models_trained_during_fit_then_empty_leaderboard_retur... function test_given_skip_model_selection_when_trainer_fits_then_val_score_is_not_computed (line 646) | def test_given_skip_model_selection_when_trainer_fits_then_val_score_is_... function test_when_add_ci_to_feature_importance_called_then_confidence_bands_correct (line 657) | def test_when_add_ci_to_feature_importance_called_then_confidence_bands_... class TestEnsembleTraining (line 691) | class TestEnsembleTraining: method test_given_multiple_ensemble_hyperparameters_when_trainer_fit_then_multiple_ensembles_created (line 692) | def test_given_multiple_ensemble_hyperparameters_when_trainer_fit_then... method test_given_default_hyperparameters_when_trainer_fit_then_single_ensemble_created (line 713) | def test_given_default_hyperparameters_when_trainer_fit_then_single_en... method test_given_multiple_ensembles_with_mixed_hyperparameters_when_trainer_fit_then_all_ensembles_can_get_hyperparameters (line 726) | def test_given_multiple_ensembles_with_mixed_hyperparameters_when_trai... method test_given_empty_ensemble_hyperparameters_when_trainer_fit_then_ensemble_training_disabled (line 745) | def test_given_empty_ensemble_hyperparameters_when_trainer_fit_then_en... method test_given_enable_ensemble_false_when_trainer_initialized_then_ensemble_training_disabled (line 761) | def test_given_enable_ensemble_false_when_trainer_initialized_then_ens... class TestMultilayerEnsembleTraining (line 790) | class TestMultilayerEnsembleTraining: method train_and_val_data (line 792) | def train_and_val_data(self): method trainer_and_params (line 810) | def trainer_and_params(self, tmp_path_factory, patch_naive_models, req... method test_when_trainer_fit_then_number_of_ensembles_correct (line 832) | def test_when_trainer_fit_then_number_of_ensembles_correct(self, train... method test_when_trainer_fit_models_are_not_wrapped_only_when_not_necessary (line 840) | def test_when_trainer_fit_models_are_not_wrapped_only_when_not_necessa... method test_when_trainer_fit_then_base_model_validation_scores_use_last_layer_windows (line 857) | def test_when_trainer_fit_then_base_model_validation_scores_use_last_l... method test_when_trainer_fit_then_last_window_dates_are_correct (line 874) | def test_when_trainer_fit_then_last_window_dates_are_correct(self, tra... method test_when_trainer_fit_then_base_models_have_complete_oof_predictions (line 887) | def test_when_trainer_fit_then_base_models_have_complete_oof_predictio... method test_when_trainer_fit_then_leaderboard_sorted_by_validation_score (line 910) | def test_when_trainer_fit_then_leaderboard_sorted_by_validation_score(... method test_when_trainer_fit_then_model_graph_has_correct_structure (line 926) | def test_when_trainer_fit_then_model_graph_has_correct_structure(self,... method test_when_trainer_fit_then_predictions_are_consistent (line 944) | def test_when_trainer_fit_then_predictions_are_consistent(self, traine... FILE: timeseries/tests/unittests/utils/test_features.py function test_when_covariates_present_in_data_then_they_are_included_in_metadata (line 21) | def test_when_covariates_present_in_data_then_they_are_included_in_metad... function test_when_transform_applied_then_numeric_features_are_converted_to_float32 (line 48) | def test_when_transform_applied_then_numeric_features_are_converted_to_f... function test_when_data_transformed_then_original_data_is_not_modified (line 71) | def test_when_data_transformed_then_original_data_is_not_modified(): function test_when_known_covariates_transformed_then_original_known_covariates_not_modified (line 92) | def test_when_known_covariates_transformed_then_original_known_covariate... function test_when_duplicate_columns_provided_during_fit_then_they_are_removed (line 107) | def test_when_duplicate_columns_provided_during_fit_then_they_are_remove... function test_when_duplicate_columns_provided_during_fit_then_they_can_be_omitted_during_transform (line 119) | def test_when_duplicate_columns_provided_during_fit_then_they_can_be_omi... function test_when_known_covariates_have_non_numeric_non_cat_dtypes_then_they_can_be_omitted_at_predict_time (line 130) | def test_when_known_covariates_have_non_numeric_non_cat_dtypes_then_they... function test_when_covariates_contain_missing_values_then_they_are_filled_during_transform (line 140) | def test_when_covariates_contain_missing_values_then_they_are_filled_dur... function test_when_static_features_contain_missing_values_then_they_are_filled_during_transform (line 167) | def test_when_static_features_contain_missing_values_then_they_are_fille... function test_when_feature_importance_transforms_called_then_they_can_transform_all_features (line 199) | def test_when_feature_importance_transforms_called_then_they_can_transfo... function test_given_past_features_when_feature_importance_transforms_called_then_they_dont_change_forecast_horizon (line 248) | def test_given_past_features_when_feature_importance_transforms_called_t... function test_given_past_features_when_permutation_transform_called_then_shuffled_values_are_same (line 297) | def test_given_past_features_when_permutation_transform_called_then_shuf... function test_given_past_features_when_permutation_transform_called_then_values_change_order (line 352) | def test_given_past_features_when_permutation_transform_called_then_valu... function test_given_fixed_seed_when_permutation_transform_called_then_shuffle_indices_are_same (line 397) | def test_given_fixed_seed_when_permutation_transform_called_then_shuffle... function test_given_past_features_when_constant_transform_called_then_values_all_equal (line 442) | def test_given_past_features_when_constant_transform_called_then_values_... function test_if_categorical_feature_has_all_nan_values_then_feature_generator_works (line 484) | def test_if_categorical_feature_has_all_nan_values_then_feature_generato... FILE: timeseries/tests/unittests/utils/test_timer.py class TestTimer (line 10) | class TestTimer: method test_when_timer_not_started_then_time_elapsed_raises_error (line 11) | def test_when_timer_not_started_then_time_elapsed_raises_error(self): method test_when_timer_not_started_then_time_remaining_raises_error (line 16) | def test_when_timer_not_started_then_time_remaining_raises_error(self): method test_when_timer_not_started_then_timed_out_raises_error (line 21) | def test_when_timer_not_started_then_timed_out_raises_error(self): method test_when_timer_started_then_time_elapsed_returns_positive (line 26) | def test_when_timer_started_then_time_elapsed_returns_positive(self): method test_when_timer_started_then_time_remaining_decreases (line 31) | def test_when_timer_started_then_time_remaining_decreases(self): method test_when_time_limit_none_then_time_remaining_returns_none (line 38) | def test_when_time_limit_none_then_time_remaining_returns_none(self): method test_when_time_limit_none_then_timed_out_returns_false (line 42) | def test_when_time_limit_none_then_timed_out_returns_false(self): method test_when_time_not_exceeded_then_timed_out_returns_false (line 47) | def test_when_time_not_exceeded_then_timed_out_returns_false(self): method test_when_time_exceeded_then_timed_out_returns_true (line 51) | def test_when_time_exceeded_then_timed_out_returns_true(self): method test_when_start_called_twice_then_timer_resets (line 56) | def test_when_start_called_twice_then_timer_resets(self): class TestSplitTimer (line 67) | class TestSplitTimer: method test_when_timer_not_started_then_round_time_remaining_raises_error (line 68) | def test_when_timer_not_started_then_round_time_remaining_raises_error... method test_when_timer_not_started_then_time_elapsed_raises_error (line 73) | def test_when_timer_not_started_then_time_elapsed_raises_error(self): method test_when_timer_not_started_then_round_time_elapsed_raises_error (line 78) | def test_when_timer_not_started_then_round_time_elapsed_raises_error(s... method test_when_timer_not_started_then_next_round_raises_error (line 83) | def test_when_timer_not_started_then_next_round_raises_error(self): method test_when_time_limit_is_none_then_round_time_remaining_returns_none (line 88) | def test_when_time_limit_is_none_then_round_time_remaining_returns_non... method test_when_timer_started_then_round_time_remaining_returns_split_time (line 92) | def test_when_timer_started_then_round_time_remaining_returns_split_ti... method test_when_timer_next_round_then_remaining_time_adjusts (line 99) | def test_when_timer_next_round_then_remaining_time_adjusts(self): method test_when_all_rounds_used_then_round_time_remaining_returns_zero (line 106) | def test_when_all_rounds_used_then_round_time_remaining_returns_zero(s... method test_when_time_elapsed_then_returns_correct_duration (line 112) | def test_when_time_elapsed_then_returns_correct_duration(self): method test_when_round_time_elapsed_then_returns_correct_duration (line 118) | def test_when_round_time_elapsed_then_returns_correct_duration(self): method test_when_next_round_then_round_time_elapsed_resets (line 124) | def test_when_next_round_then_round_time_elapsed_resets(self): method test_when_start_called_then_timer_resets (line 134) | def test_when_start_called_then_timer_resets(self): method test_when_time_exceeded_then_returns_negative (line 143) | def test_when_time_exceeded_then_returns_negative(self): method test_when_round_uses_less_time_then_next_round_gets_more (line 149) | def test_when_round_uses_less_time_then_next_round_gets_more(self): method test_when_single_round_then_gets_all_time (line 172) | def test_when_single_round_then_gets_all_time(self): FILE: timeseries/tests/unittests/utils/test_utils.py function test_when_start_times_dont_match_freq_then_forecast_timestamps_are_correct (line 17) | def test_when_start_times_dont_match_freq_then_forecast_timestamps_are_c... function test_when_computing_seasonality_then_all_pandas_frequencies_are_supported (line 44) | def test_when_computing_seasonality_then_all_pandas_frequencies_are_supp... function test_when_computing_lags_then_all_pandas_frequencies_are_supported (line 53) | def test_when_computing_lags_then_all_pandas_frequencies_are_supported(f... function test_when_computing_time_features_then_all_pandas_frequencies_are_supported (line 61) | def test_when_computing_time_features_then_all_pandas_frequencies_are_su...