SYMBOL INDEX (6370 symbols across 552 files) FILE: ludwig/accounting/used_tokens.py function get_used_tokens_for_ecd (line 4) | def get_used_tokens_for_ecd(inputs: dict[str, torch.Tensor], targets: di... function get_used_tokens_for_llm (line 25) | def get_used_tokens_for_llm(model_inputs: torch.Tensor, tokenizer) -> int: FILE: ludwig/api.py class EvaluationFrequency (line 123) | class EvaluationFrequency: # noqa F821 class TrainingStats (line 142) | class TrainingStats: # noqa F821 method __iter__ (line 154) | def __iter__(self): method __contains__ (line 157) | def __contains__(self, key): method __getitem__ (line 164) | def __getitem__(self, key): class PreprocessedDataset (line 171) | class PreprocessedDataset: # noqa F821 method __iter__ (line 178) | def __iter__(self): method __getitem__ (line 181) | def __getitem__(self, index): class TrainingResults (line 187) | class TrainingResults: # noqa F821 method __iter__ (line 192) | def __iter__(self): method __getitem__ (line 199) | def __getitem__(self, index): class LudwigModel (line 208) | class LudwigModel: method __init__ (line 277) | def __init__( method _initialize_llm (line 353) | def _initialize_llm(self, random_seed: int = default_random_seed): method train (line 364) | def train( method train_online (line 786) | def train_online( method _tune_batch_size (line 858) | def _tune_batch_size(self, trainer, dataset, random_seed: int = defaul... method save_dequantized_base_model (line 920) | def save_dequantized_base_model(self, save_path: str) -> None: method generate (line 968) | def generate( method _generate_streaming_outputs (line 1019) | def _generate_streaming_outputs( method _generate_non_streaming_outputs (line 1055) | def _generate_non_streaming_outputs( method predict (line 1082) | def predict( method evaluate (line 1187) | def evaluate( method forecast (line 1344) | def forecast( method experiment (line 1411) | def experiment( method collect_weights (line 1604) | def collect_weights(self, tensor_names: list[str] = None, **kwargs) ->... method collect_activations (line 1618) | def collect_activations( method preprocess (line 1673) | def preprocess( method load (line 1765) | def load( method load_weights (line 1863) | def load_weights( method save (line 1900) | def save(self, save_path: str) -> None: method upload_to_hf_hub (line 1933) | def upload_to_hf_hub( method save_config (line 1994) | def save_config(self, save_path: str) -> None: method to_torchscript (line 2009) | def to_torchscript( method save_torchscript (line 2039) | def save_torchscript( method _check_initialization (line 2071) | def _check_initialization(self): method free_gpu_memory (line 2075) | def free_gpu_memory(self): method create_model (line 2085) | def create_model(config_obj: ModelConfig | dict, random_seed: int = de... method set_logging_level (line 2100) | def set_logging_level(logging_level: int) -> None: method config (line 2119) | def config(self) -> ModelConfigDict: method config (line 2124) | def config(self, user_config: ModelConfigDict): method is_merge_and_unload_set (line 2132) | def is_merge_and_unload_set(self) -> bool: function kfold_cross_validate (line 2144) | def kfold_cross_validate( function _get_compute_description (line 2372) | def _get_compute_description(backend) -> dict: function get_experiment_description (line 2398) | def get_experiment_description( FILE: ludwig/api_annotations.py function PublicAPI (line 1) | def PublicAPI(*args, **kwargs): function DeveloperAPI (line 46) | def DeveloperAPI(*args, **kwargs): function Deprecated (line 68) | def Deprecated(*args, **kwargs): function _append_doc (line 104) | def _append_doc(obj, message: str, directive: str | None = None) -> str: function _mark_annotated (line 127) | def _mark_annotated(obj) -> None: function _is_annotated (line 133) | def _is_annotated(obj) -> bool: function _get_indent (line 138) | def _get_indent(docstring: str) -> int: FILE: ludwig/automl/auto_tune_config.py function get_trainingset_metadata (line 76) | def get_trainingset_metadata(config, dataset, backend): function _get_machine_memory (line 84) | def _get_machine_memory(): function _get_text_feature_max_length (line 92) | def _get_text_feature_max_length(config, training_set_metadata) -> int: function _get_text_model_memory_usage (line 113) | def _get_text_model_memory_usage(config, training_set_metadata, memory_u... function compute_memory_usage (line 119) | def compute_memory_usage(config_obj, training_set_metadata, model_catego... function sub_new_params (line 134) | def sub_new_params(config: dict, new_param_vals: dict): function get_new_params (line 143) | def get_new_params(current_param_values, hyperparam_search_space, params... function _update_text_encoder (line 153) | def _update_text_encoder(input_features: list, old_text_encoder: str, ne... function _get_text_feature_min_usable_length (line 159) | def _get_text_feature_min_usable_length(input_features: list, training_s... function reduce_text_feature_max_length (line 170) | def reduce_text_feature_max_length(config, training_set_metadata) -> bool: function _update_num_samples (line 190) | def _update_num_samples(num_samples, hyperparam_search_space): function memory_tune_config (line 203) | def memory_tune_config(config, dataset, model_category, row_count, backe... FILE: ludwig/automl/automl.py class AutoTrainResults (line 80) | class AutoTrainResults: method __init__ (line 81) | def __init__(self, experiment_analysis: ExperimentAnalysis, creds: dic... method experiment_analysis (line 86) | def experiment_analysis(self): method best_trial_id (line 90) | def best_trial_id(self) -> str: method best_model (line 94) | def best_model(self) -> LudwigModel | None: function auto_train (line 117) | def auto_train( function create_auto_config (line 167) | def create_auto_config( function create_automl_config_for_features (line 225) | def create_automl_config_for_features( function create_features_config (line 249) | def create_features_config( function train_with_config (line 257) | def train_with_config( function _model_select (line 308) | def _model_select( function _train (line 397) | def _train( function init_config (line 417) | def init_config( function cli_init_config (line 458) | def cli_init_config(sys_argv): FILE: ludwig/automl/base_config.py class DatasetInfo (line 73) | class DatasetInfo: function allocate_experiment_resources (line 79) | def allocate_experiment_resources(resources: Resources) -> dict: function get_resource_aware_hyperopt_config (line 103) | def get_resource_aware_hyperopt_config( function _get_stratify_split_config (line 123) | def _get_stratify_split_config(field_meta: FieldMetadata) -> dict: function get_default_automl_hyperopt (line 134) | def get_default_automl_hyperopt() -> dict[str, Any]: function create_default_config (line 159) | def create_default_config( function get_reference_configs (line 250) | def get_reference_configs() -> dict: function get_dataset_info (line 255) | def get_dataset_info(df: pd.DataFrame | dd.DataFrame) -> DatasetInfo: function is_field_boolean (line 267) | def is_field_boolean(source: DataSource, field: str) -> bool: function get_dataset_info_from_source (line 290) | def get_dataset_info_from_source(source: DataSource) -> DatasetInfo: function get_features_config (line 341) | def get_features_config( function convert_targets (line 360) | def convert_targets(target_name: str | list[str] = None) -> set[str]: function get_config_from_metadata (line 369) | def get_config_from_metadata(metadata: list[FieldMetadata], targets: set... function get_field_metadata (line 392) | def get_field_metadata(fields: list[FieldInfo], row_count: int, targets:... function infer_mode (line 425) | def infer_mode(field: FieldInfo, targets: set[str] = None) -> str: FILE: ludwig/backend/__init__.py function _has_ray (line 39) | def _has_ray(): function get_local_backend (line 60) | def get_local_backend(**kwargs): function create_deepspeed_backend (line 64) | def create_deepspeed_backend(**kwargs): function create_ray_backend (line 70) | def create_ray_backend(**kwargs): function create_backend (line 85) | def create_backend(type, **kwargs): function initialize_backend (line 96) | def initialize_backend(backend): function provision_preprocessing_workers (line 106) | def provision_preprocessing_workers(backend): FILE: ludwig/backend/base.py class Backend (line 59) | class Backend(ABC): method __init__ (line 60) | def __init__( method storage (line 72) | def storage(self) -> StorageManager: method cache (line 76) | def cache(self) -> CacheManager: method dataset_manager (line 80) | def dataset_manager(self) -> DatasetManager: method initialize (line 84) | def initialize(self): method initialize_pytorch (line 88) | def initialize_pytorch(self, *args, **kwargs): method create_trainer (line 93) | def create_trainer(self, config: BaseTrainerConfig, model: BaseModel, ... method sync_model (line 97) | def sync_model(self, model): method broadcast_return (line 101) | def broadcast_return(self, fn): method is_coordinator (line 105) | def is_coordinator(self): method df_engine (line 110) | def df_engine(self) -> DataFrameEngine: method supports_multiprocessing (line 115) | def supports_multiprocessing(self): method read_binary_files (line 119) | def read_binary_files(self, column: Series, map_fn: Callable | None = ... method num_nodes (line 124) | def num_nodes(self) -> int: method num_training_workers (line 129) | def num_training_workers(self) -> int: method get_available_resources (line 133) | def get_available_resources(self) -> Resources: method max_concurrent_trials (line 137) | def max_concurrent_trials(self, hyperopt_config: HyperoptConfigDict) -... method tune_batch_size (line 141) | def tune_batch_size(self, evaluator_cls: type[BatchSizeEvaluator], dat... method batch_transform (line 150) | def batch_transform( method supports_batch_size_tuning (line 156) | def supports_batch_size_tuning(self) -> bool: class LocalPreprocessingMixin (line 160) | class LocalPreprocessingMixin: method df_engine (line 162) | def df_engine(self): method supports_multiprocessing (line 166) | def supports_multiprocessing(self): method read_binary_files (line 170) | def read_binary_files(column: pd.Series, map_fn: Callable | None = Non... method batch_transform (line 194) | def batch_transform(df: DataFrame, batch_size: int, transform_fn: Call... class LocalTrainingMixin (line 203) | class LocalTrainingMixin: method initialize (line 205) | def initialize(): method initialize_pytorch (line 209) | def initialize_pytorch(*args, **kwargs): method create_predictor (line 213) | def create_predictor(model: BaseModel, **kwargs): method sync_model (line 218) | def sync_model(self, model): method broadcast_return (line 222) | def broadcast_return(fn): method is_coordinator (line 226) | def is_coordinator() -> bool: method tune_batch_size (line 230) | def tune_batch_size(evaluator_cls: type[BatchSizeEvaluator], dataset_l... class RemoteTrainingMixin (line 235) | class RemoteTrainingMixin: method sync_model (line 236) | def sync_model(self, model): method broadcast_return (line 240) | def broadcast_return(fn): method is_coordinator (line 244) | def is_coordinator() -> bool: class LocalBackend (line 249) | class LocalBackend(LocalPreprocessingMixin, LocalTrainingMixin, Backend): method shared_instance (line 255) | def shared_instance(cls) -> LocalBackend: method __init__ (line 261) | def __init__(self, **kwargs) -> None: method num_nodes (line 265) | def num_nodes(self) -> int: method num_training_workers (line 269) | def num_training_workers(self) -> int: method get_available_resources (line 272) | def get_available_resources(self) -> Resources: method max_concurrent_trials (line 275) | def max_concurrent_trials(self, hyperopt_config: HyperoptConfigDict) -... method create_trainer (line 280) | def create_trainer( class DataParallelBackend (line 298) | class DataParallelBackend(LocalPreprocessingMixin, Backend, ABC): method __init__ (line 301) | def __init__(self, **kwargs): method initialize (line 306) | def initialize(self): method initialize_pytorch (line 309) | def initialize_pytorch(self, *args, **kwargs): method create_trainer (line 314) | def create_trainer( method create_predictor (line 324) | def create_predictor(self, model: BaseModel, **kwargs): method sync_model (line 329) | def sync_model(self, model): method broadcast_return (line 334) | def broadcast_return(self, fn): method is_coordinator (line 346) | def is_coordinator(self): method num_nodes (line 350) | def num_nodes(self) -> int: method num_training_workers (line 354) | def num_training_workers(self) -> int: method get_available_resources (line 357) | def get_available_resources(self) -> Resources: method max_concurrent_trials (line 367) | def max_concurrent_trials(self, hyperopt_config: HyperoptConfigDict) -... method tune_batch_size (line 371) | def tune_batch_size(self, evaluator_cls: type[BatchSizeEvaluator], dat... FILE: ludwig/backend/datasource.py function read_binary_files_with_index (line 18) | def read_binary_files_with_index( FILE: ludwig/backend/deepspeed.py class DeepSpeedBackend (line 11) | class DeepSpeedBackend(DataParallelBackend): method __init__ (line 14) | def __init__( method initialize (line 28) | def initialize(self): method supports_batch_size_tuning (line 40) | def supports_batch_size_tuning(self) -> bool: method tune_batch_size (line 44) | def tune_batch_size(self, evaluator_cls: type[BatchSizeEvaluator], dat... FILE: ludwig/backend/ray.py function _num_nodes (line 78) | def _num_nodes() -> int: function get_trainer_kwargs (line 83) | def get_trainer_kwargs(**kwargs) -> dict[str, Any]: function _create_dask_engine (line 110) | def _create_dask_engine(**kwargs): function _create_modin_engine (line 116) | def _create_modin_engine(**kwargs): function _create_pandas_engine (line 122) | def _create_pandas_engine(**kwargs): function _get_df_engine (line 135) | def _get_df_engine(processor): function _make_picklable (line 150) | def _make_picklable(obj): function train_fn (line 168) | def train_fn( function tune_batch_size_fn (line 247) | def tune_batch_size_fn( function tune_learning_rate_fn (line 278) | def tune_learning_rate_fn( class TqdmCallback (line 308) | class TqdmCallback(rt.UserCallback): method __init__ (line 311) | def __init__(self) -> None: method after_report (line 316) | def after_report(self, run_context, metrics: list[dict], checkpoint=No... function spread_env (line 344) | def spread_env(use_gpu: bool = False, num_workers: int = 1, **kwargs): function _build_scaling_config (line 361) | def _build_scaling_config(trainer_kwargs: dict[str, Any]) -> ScalingConfig: function run_train_remote (line 370) | def run_train_remote(train_loop, trainer_kwargs: dict[str, Any], callbac... class RayTrainerV2 (line 395) | class RayTrainerV2(BaseTrainer): method __init__ (line 396) | def __init__( method get_schema_cls (line 412) | def get_schema_cls(): method train (line 415) | def train( method train_online (line 461) | def train_online(self, *args, **kwargs): method tune_batch_size (line 466) | def tune_batch_size( method tune_learning_rate (line 485) | def tune_learning_rate(self, config, training_set: RayDataset, **kwarg... method validation_field (line 500) | def validation_field(self): method validation_metric (line 504) | def validation_metric(self): method config (line 508) | def config(self) -> ECDTrainerConfig: method batch_size (line 512) | def batch_size(self) -> int: method batch_size (line 516) | def batch_size(self, value: int): method eval_batch_size (line 520) | def eval_batch_size(self) -> int: method eval_batch_size (line 524) | def eval_batch_size(self, value: int): method resources_per_worker (line 528) | def resources_per_worker(self) -> dict[str, Any]: method num_cpus (line 533) | def num_cpus(self) -> int: method num_gpus (line 537) | def num_gpus(self) -> int: method set_base_learning_rate (line 540) | def set_base_learning_rate(self, learning_rate: float): method shutdown (line 543) | def shutdown(self): function eval_fn (line 547) | def eval_fn( class RayPredictor (line 595) | class RayPredictor(BasePredictor): method __init__ (line 596) | def __init__( method get_trainer_kwargs (line 607) | def get_trainer_kwargs(self) -> dict[str, Any]: method get_resources_per_worker (line 610) | def get_resources_per_worker(self) -> tuple[int, int]: method batch_predict (line 617) | def batch_predict(self, dataset: RayDataset, *args, collect_logits: bo... method predict_single (line 655) | def predict_single(self, batch): method batch_evaluation (line 658) | def batch_evaluation( method batch_collect_activations (line 704) | def batch_collect_activations(self, model, *args, **kwargs): method _check_dataset (line 707) | def _check_dataset(self, dataset): method shutdown (line 711) | def shutdown(self): method get_batch_infer_model (line 716) | def get_batch_infer_model( class RayBackend (line 776) | class RayBackend(RemoteTrainingMixin, Backend): method __init__ (line 779) | def __init__(self, processor=None, trainer=None, loader=None, preproce... method initialize (line 788) | def initialize(self): method generate_bundles (line 798) | def generate_bundles(self, num_cpu): method provision_preprocessing_workers (line 805) | def provision_preprocessing_workers(self): method _release_preprocessing_workers (line 832) | def _release_preprocessing_workers(self): method initialize_pytorch (line 837) | def initialize_pytorch(self, **kwargs): method create_trainer (line 842) | def create_trainer(self, model: BaseModel, **kwargs) -> "BaseTrainer":... method create_predictor (line 863) | def create_predictor(self, model: BaseModel, **kwargs): method set_distributed_kwargs (line 873) | def set_distributed_kwargs(self, **kwargs): method df_engine (line 877) | def df_engine(self): method supports_multiprocessing (line 881) | def supports_multiprocessing(self): method check_lazy_load_supported (line 884) | def check_lazy_load_supported(self, feature): method read_binary_files (line 891) | def read_binary_files(self, column: Series, map_fn: Callable | None = ... method num_nodes (line 943) | def num_nodes(self) -> int: method num_training_workers (line 949) | def num_training_workers(self) -> int: method max_concurrent_trials (line 952) | def max_concurrent_trials(self, hyperopt_config) -> int | None: method tune_batch_size (line 963) | def tune_batch_size(self, evaluator_cls, dataset_len: int) -> int: method batch_transform (line 967) | def batch_transform(self, df, batch_size: int, transform_fn, name: str... method get_available_resources (line 984) | def get_available_resources(self) -> Resources: function initialize_ray (line 989) | def initialize_ray(): function init_ray_local (line 997) | def init_ray_local(): FILE: ludwig/backend/utils/storage.py class Storage (line 15) | class Storage: method __init__ (line 16) | def __init__(self, creds: dict[str, Any] | None): method use_credentials (line 20) | def use_credentials(self): method credentials (line 25) | def credentials(self) -> dict[str, Any] | None: class StorageManager (line 29) | class StorageManager: method __init__ (line 30) | def __init__( method defaults (line 48) | def defaults(self) -> Storage: method artifacts (line 52) | def artifacts(self) -> Storage: method datasets (line 57) | def datasets(self) -> Storage: method cache (line 62) | def cache(self) -> Storage: function load_creds (line 66) | def load_creds(cred: CredInputs) -> dict[str, Any]: FILE: ludwig/benchmarking/artifacts.py class BenchmarkingResult (line 11) | class BenchmarkingResult: function build_benchmarking_result (line 43) | def build_benchmarking_result(benchmarking_config: dict, experiment_idx:... FILE: ludwig/benchmarking/benchmark.py function setup_experiment (line 30) | def setup_experiment(experiment: dict[str, str]) -> dict[Any, Any]: function benchmark_one (line 57) | def benchmark_one(experiment: dict[str, str | dict[str, str]]) -> None: function benchmark (line 113) | def benchmark(benchmarking_config: dict[str, Any] | str) -> dict[str, tu... function cli (line 144) | def cli(sys_argv): FILE: ludwig/benchmarking/examples/process_config.py function process_config (line 5) | def process_config(ludwig_config: dict, experiment_dict: dict) -> dict: FILE: ludwig/benchmarking/profiler.py function get_gpu_info (line 29) | def get_gpu_info(): function monitor (line 57) | def monitor(queue: Queue, info: dict[str, Any], logging_interval: int, c... class LudwigProfiler (line 111) | class LudwigProfiler(contextlib.ContextDecorator): method __init__ (line 125) | def __init__(self, tag: str, use_torch_profiler: bool, output_dir: str... method _init_tracker_info (line 139) | def _init_tracker_info(self): method _populate_static_information (line 150) | def _populate_static_information(self) -> None: method __enter__ (line 177) | def __enter__(self): method __exit__ (line 218) | def __exit__(self, exc_type, exc_val, exc_tb) -> None: method _export_system_usage_metrics (line 239) | def _export_system_usage_metrics(self): method _reformat_torch_usage_metrics_tags (line 248) | def _reformat_torch_usage_metrics_tags( method _export_torch_metrics (line 258) | def _export_torch_metrics(self): FILE: ludwig/benchmarking/profiler_callbacks.py class LudwigProfilerCallback (line 11) | class LudwigProfilerCallback(Callback): method __init__ (line 14) | def __init__(self, experiment: dict[str, Any]): method on_preprocess_start (line 22) | def on_preprocess_start(self, *args, **kwargs): method on_preprocess_end (line 31) | def on_preprocess_end(self, *args, **kwargs): method on_train_start (line 35) | def on_train_start(self, *args, **kwargs): method on_train_end (line 44) | def on_train_end(self, *args, **kwargs): method on_evaluation_start (line 48) | def on_evaluation_start(self): method on_evaluation_end (line 57) | def on_evaluation_end(self): FILE: ludwig/benchmarking/profiler_dataclasses.py class DeviceUsageMetrics (line 8) | class DeviceUsageMetrics: class SystemResourceMetrics (line 17) | class SystemResourceMetrics: class TorchProfilerMetrics (line 71) | class TorchProfilerMetrics: function profiler_dataclass_to_flat_dict (line 85) | def profiler_dataclass_to_flat_dict(data: SystemResourceMetrics | TorchP... FILE: ludwig/benchmarking/reporting.py function initialize_stats_dict (line 13) | def initialize_stats_dict(main_function_events: list[profiler_util.Funct... function get_memory_details (line 24) | def get_memory_details(kineto_event: _KinetoEvent) -> tuple[str, int]: function get_device_memory_usage (line 37) | def get_device_memory_usage( function get_torch_op_time (line 70) | def get_torch_op_time(events: list[profiler_util.FunctionEvent], attr: s... function get_device_run_durations (line 90) | def get_device_run_durations(function_event: profiler_util.FunctionEvent... function get_num_oom_events (line 100) | def get_num_oom_events(kineto_event: _KinetoEvent, out_of_memory_events:... function get_resource_usage_report (line 108) | def get_resource_usage_report( function get_all_events (line 144) | def get_all_events(kineto_events: list[_KinetoEvent], function_events: p... function get_metrics_from_torch_profiler (line 166) | def get_metrics_from_torch_profiler(profile: torch.profiler.profiler.pro... function get_metrics_from_system_usage_profiler (line 197) | def get_metrics_from_system_usage_profiler(system_usage_info: dict) -> S... FILE: ludwig/benchmarking/summarize.py function summarize_metrics (line 19) | def summarize_metrics( function export_and_print (line 53) | def export_and_print( FILE: ludwig/benchmarking/summary_dataclasses.py class MetricDiff (line 18) | class MetricDiff: method __post_init__ (line 36) | def __post_init__(self): function build_diff (line 53) | def build_diff(name: str, base_value: float, experimental_value: float) ... class MetricsSummary (line 78) | class MetricsSummary: class MetricsDiff (line 101) | class MetricsDiff: method to_string (line 125) | def to_string(self): function export_metrics_diff_to_csv (line 160) | def export_metrics_diff_to_csv(metrics_diff: MetricsDiff, path: str): function build_metrics_summary (line 204) | def build_metrics_summary(experiment_local_directory: str) -> MetricsSum... function build_metrics_diff (line 233) | def build_metrics_diff( class ResourceUsageSummary (line 274) | class ResourceUsageSummary: class ResourceUsageDiff (line 288) | class ResourceUsageDiff: method to_string (line 303) | def to_string(self): function export_resource_usage_diff_to_csv (line 331) | def export_resource_usage_diff_to_csv(resource_usage_diff: ResourceUsage... function average_runs (line 368) | def average_runs(path_to_runs_dir: str) -> dict[str, int | float]: function summarize_resource_usage (line 387) | def summarize_resource_usage(path: str, tags: list[str] | None = None) -... function build_resource_usage_diff (line 426) | def build_resource_usage_diff( FILE: ludwig/benchmarking/utils.py function load_from_module (line 36) | def load_from_module( function export_artifacts (line 60) | def export_artifacts(experiment: dict[str, str], experiment_output_direc... function download_artifacts (line 92) | def download_artifacts( function download_one (line 130) | async def download_one( function validate_benchmarking_config (line 162) | def validate_benchmarking_config(benchmarking_config: dict[str, Any]) ->... function populate_benchmarking_config_with_defaults (line 188) | def populate_benchmarking_config_with_defaults(benchmarking_config: dict... function propagate_global_parameters (line 203) | def propagate_global_parameters(benchmarking_config: dict[str, Any]) -> ... function create_default_config (line 223) | def create_default_config(experiment: dict[str, Any]) -> str: function delete_model_checkpoints (line 244) | def delete_model_checkpoints(output_directory: str): function delete_hyperopt_outputs (line 255) | def delete_hyperopt_outputs(output_directory: str): function save_yaml (line 268) | def save_yaml(filename, dictionary): function format_time (line 273) | def format_time(time_us): function format_memory (line 287) | def format_memory(nbytes): FILE: ludwig/callbacks.py class Callback (line 26) | class Callback(ABC): method on_cmdline (line 27) | def on_cmdline(self, cmd: str, *args: list[str]): method on_preprocess_start (line 34) | def on_preprocess_start(self, config: ModelConfigDict, **kwargs): method on_preprocess_end (line 40) | def on_preprocess_end( method on_hyperopt_init (line 60) | def on_hyperopt_init(self, experiment_name: str, **kwargs): method on_hyperopt_preprocessing_start (line 66) | def on_hyperopt_preprocessing_start(self, experiment_name: str, **kwar... method on_hyperopt_preprocessing_end (line 72) | def on_hyperopt_preprocessing_end(self, experiment_name: str, **kwargs): method on_hyperopt_start (line 78) | def on_hyperopt_start(self, experiment_name: str, **kwargs): method on_hyperopt_end (line 84) | def on_hyperopt_end(self, experiment_name: str, **kwargs): method on_hyperopt_finish (line 90) | def on_hyperopt_finish(self, experiment_name: str, **kwargs): method on_hyperopt_trial_start (line 97) | def on_hyperopt_trial_start(self, parameters: HyperoptConfigDict, **kw... method on_hyperopt_trial_end (line 103) | def on_hyperopt_trial_end(self, parameters: HyperoptConfigDict, **kwar... method should_stop_hyperopt (line 109) | def should_stop_hyperopt(self): method on_resume_training (line 116) | def on_resume_training(self, is_coordinator: bool, **kwargs): method on_train_init (line 119) | def on_train_init( method on_train_start (line 139) | def on_train_start( method on_train_end (line 154) | def on_train_end(self, output_directory: str, **kwargs): method on_trainer_train_setup (line 160) | def on_trainer_train_setup(self, trainer, save_path: str, is_coordinat... method on_trainer_train_teardown (line 169) | def on_trainer_train_teardown(self, trainer, progress_tracker, save_pa... method on_batch_start (line 180) | def on_batch_start(self, trainer, progress_tracker, save_path: str, **... method on_batch_end (line 190) | def on_batch_end(self, trainer, progress_tracker, save_path: str, sync... method on_eval_start (line 201) | def on_eval_start(self, trainer, progress_tracker, save_path: str, **k... method on_eval_end (line 211) | def on_eval_end(self, trainer, progress_tracker, save_path: str, **kwa... method on_epoch_start (line 221) | def on_epoch_start(self, trainer, progress_tracker, save_path: str, **... method on_epoch_end (line 231) | def on_epoch_end(self, trainer, progress_tracker, save_path: str, **kw... method on_validation_start (line 241) | def on_validation_start(self, trainer, progress_tracker, save_path: st... method on_validation_end (line 251) | def on_validation_end(self, trainer, progress_tracker, save_path: str,... method on_test_start (line 261) | def on_test_start(self, trainer, progress_tracker, save_path: str, **k... method on_test_end (line 271) | def on_test_end(self, trainer, progress_tracker, save_path: str, **kwa... method should_early_stop (line 281) | def should_early_stop(self, trainer, progress_tracker, is_coordinator,... method on_checkpoint (line 285) | def on_checkpoint(self, trainer, progress_tracker, **kwargs): method on_save_best_checkpoint (line 289) | def on_save_best_checkpoint(self, trainer, progress_tracker, save_path... method on_build_metadata_start (line 292) | def on_build_metadata_start(self, df, mode: str, **kwargs): method on_build_metadata_end (line 300) | def on_build_metadata_end(self, df, mode, **kwargs): method on_build_data_start (line 308) | def on_build_data_start(self, df, mode, **kwargs): method on_build_data_end (line 317) | def on_build_data_end(self, df, mode, **kwargs): method on_evaluation_start (line 325) | def on_evaluation_start(self, **kwargs): method on_evaluation_end (line 328) | def on_evaluation_end(self, **kwargs): method on_visualize_figure (line 331) | def on_visualize_figure(self, fig, **kwargs): method on_ludwig_end (line 338) | def on_ludwig_end(self, **kwargs): method prepare_ray_tune (line 344) | def prepare_ray_tune( FILE: ludwig/check.py function check_install (line 16) | def check_install(logging_level: int = logging.INFO, **kwargs): function cli (line 40) | def cli(sys_argv): FILE: ludwig/cli.py class CLI (line 24) | class CLI: method __init__ (line 30) | def __init__(self): method train (line 73) | def train(self): method predict (line 78) | def predict(self): method evaluate (line 83) | def evaluate(self): method forecast (line 88) | def forecast(self): method experiment (line 93) | def experiment(self): method hyperopt (line 98) | def hyperopt(self): method benchmark (line 103) | def benchmark(self): method serve (line 108) | def serve(self): method visualize (line 113) | def visualize(self): method collect_summary (line 118) | def collect_summary(self): method collect_weights (line 123) | def collect_weights(self): method collect_activations (line 128) | def collect_activations(self): method export_torchscript (line 133) | def export_torchscript(self): method export_triton (line 138) | def export_triton(self): method export_mlflow (line 143) | def export_mlflow(self): method export_schema (line 148) | def export_schema(self): method preprocess (line 153) | def preprocess(self): method synthesize_dataset (line 158) | def synthesize_dataset(self): method init_config (line 163) | def init_config(self): method render_config (line 168) | def render_config(self): method check_install (line 173) | def check_install(self): method datasets (line 178) | def datasets(self): method upload (line 183) | def upload(self): function main (line 189) | def main(): FILE: ludwig/collect.py function collect_activations (line 38) | def collect_activations( function collect_weights (line 123) | def collect_weights(model_path: str, tensors: list[str], output_director... function save_tensors (line 156) | def save_tensors(collected_tensors, output_directory): function print_model_summary (line 167) | def print_model_summary(model_path: str, **kwargs) -> None: function pretrained_summary (line 190) | def pretrained_summary(pretrained_model: str, **kwargs) -> None: function cli_collect_activations (line 235) | def cli_collect_activations(sys_argv): function cli_collect_weights (line 363) | def cli_collect_weights(sys_argv): function cli_collect_summary (line 419) | def cli_collect_summary(sys_argv): FILE: ludwig/combiners/combiners.py class Handle (line 51) | class Handle: class Combiner (line 63) | class Combiner(LudwigModule, ABC): method __init__ (line 71) | def __init__(self, input_features: dict[str, "InputFeature"]): method concatenated_shape (line 76) | def concatenated_shape(self) -> torch.Size: method input_shape (line 86) | def input_shape(self) -> dict: method output_shape (line 94) | def output_shape(self) -> torch.Size: function register_combiner (line 109) | def register_combiner(config_cls: type[BaseCombinerConfig]): function create_combiner (line 117) | def create_combiner(config: BaseCombinerConfig, **kwargs) -> Combiner: class ConcatCombiner (line 122) | class ConcatCombiner(Combiner): method __init__ (line 123) | def __init__(self, input_features: dict[str, "InputFeature"] = None, c... method forward (line 158) | def forward(self, inputs: dict) -> dict: # encoder outputs class SequenceConcatCombiner (line 190) | class SequenceConcatCombiner(Combiner): method __init__ (line 191) | def __init__( method concatenated_shape (line 209) | def concatenated_shape(self) -> torch.Size: method forward (line 229) | def forward(self, inputs: dict) -> dict: # encoder outputs class SequenceCombiner (line 331) | class SequenceCombiner(Combiner): method __init__ (line 332) | def __init__(self, input_features: dict[str, "InputFeature"], config: ... method concatenated_shape (line 358) | def concatenated_shape(self) -> torch.Size: method forward (line 378) | def forward(self, inputs: dict) -> dict: # encoder outputs class TabNetCombiner (line 394) | class TabNetCombiner(Combiner): method __init__ (line 395) | def __init__( method concatenated_shape (line 424) | def concatenated_shape(self) -> torch.Size: method forward (line 433) | def forward( method output_shape (line 468) | def output_shape(self) -> torch.Size: class TransformerCombiner (line 473) | class TransformerCombiner(Combiner): method __init__ (line 474) | def __init__( method forward (line 534) | def forward( class TabTransformerCombiner (line 570) | class TabTransformerCombiner(Combiner): method __init__ (line 571) | def __init__( method get_flatten_size (line 690) | def get_flatten_size(output_shape: torch.Size) -> torch.Size: method output_shape (line 695) | def output_shape(self) -> torch.Size: method forward (line 698) | def forward( class ComparatorCombiner (line 776) | class ComparatorCombiner(Combiner): method __init__ (line 777) | def __init__( method get_entity_shape (line 838) | def get_entity_shape(self, entity: list) -> torch.Size: method output_shape (line 843) | def output_shape(self) -> torch.Size: method forward (line 846) | def forward( class ProjectAggregateCombiner (line 917) | class ProjectAggregateCombiner(Combiner): method __init__ (line 918) | def __init__( method forward (line 968) | def forward(self, inputs: dict) -> dict: # encoder outputs FILE: ludwig/config_sampling/explore_schema.py function explore_properties (line 21) | def explore_properties( function get_samples (line 103) | def get_samples(jsonschema_property: dict[str, Any]) -> list[ParameterBa... function merge_dq (line 118) | def merge_dq(config_options: dict[str, Any], child_config_options_dq: De... function explore_from_all_of (line 128) | def explore_from_all_of(config_options: dict[str, Any], item: dict[str, ... function get_potential_values (line 139) | def get_potential_values(item: dict[str, Any]) -> list[ParameterBaseType... function generate_possible_configs (line 172) | def generate_possible_configs(config_options: dict[str, Any]): function create_nested_dict (line 206) | def create_nested_dict(flat_dict: dict[str, float | str]) -> ModelConfig... function combine_configs (line 237) | def combine_configs( function combine_configs_for_comparator_combiner (line 259) | def combine_configs_for_comparator_combiner( function combine_configs_for_sequence_combiner (line 290) | def combine_configs_for_sequence_combiner( FILE: ludwig/config_sampling/parameter_sampling.py function handle_property_type (line 10) | def handle_property_type( function explore_array (line 48) | def explore_array(item: dict[str, Any]) -> list[list[ParameterBaseTypes]]: function explore_number (line 80) | def explore_number(item: dict[str, Any]) -> list[ParameterBaseTypes]: function explore_integer (line 102) | def explore_integer(item: dict[str, Any]) -> list[ParameterBaseTypes]: function explore_string (line 126) | def explore_string(item: dict[str, Any]) -> list[ParameterBaseTypes]: function explore_boolean (line 138) | def explore_boolean() -> list[bool]: function explore_null (line 143) | def explore_null() -> list[None]: FILE: ludwig/config_validation/checks.py class ConfigCheckRegistry (line 36) | class ConfigCheckRegistry: method __init__ (line 39) | def __init__(self): method register (line 42) | def register(self, check_fn): method check_config (line 45) | def check_config(self, config: "ModelConfig") -> None: # noqa: F821 function get_config_check_registry (line 53) | def get_config_check_registry(): function register_config_check (line 59) | def register_config_check(fn) -> Callable: class ConfigCheck (line 64) | class ConfigCheck(ABC): method check (line 69) | def check(config: "ModelConfig") -> None: # noqa: F821 function check_feature_names_unique (line 75) | def check_feature_names_unique(config: "ModelConfig") -> None: # noqa: ... function check_tied_features_valid (line 88) | def check_tied_features_valid(config: "ModelConfig") -> None: # noqa: F821 function check_training_runway (line 102) | def check_training_runway(config: "ModelConfig") -> None: # noqa: F821 function check_ray_backend_in_memory_preprocessing (line 114) | def check_ray_backend_in_memory_preprocessing(config: "ModelConfig") -> ... function check_sequence_concat_combiner_requirements (line 136) | def check_sequence_concat_combiner_requirements(config: "ModelConfig") -... function check_comparator_combiner_requirements (line 154) | def check_comparator_combiner_requirements(config: "ModelConfig") -> Non... function check_class_balance_preprocessing (line 178) | def check_class_balance_preprocessing(config: "ModelConfig") -> None: #... function check_sampling_exclusivity (line 188) | def check_sampling_exclusivity(config: "ModelConfig") -> None: # noqa: ... function check_validation_metric_exists (line 197) | def check_validation_metric_exists(config: "ModelConfig") -> None: # no... function check_splitter (line 215) | def check_splitter(config: "ModelConfig") -> None: # noqa: F821 function check_hf_tokenizer_requirements (line 224) | def check_hf_tokenizer_requirements(config: "ModelConfig") -> None: # n... function check_hf_encoder_requirements (line 237) | def check_hf_encoder_requirements(config: "ModelConfig") -> None: # noq... function check_stacked_transformer_requirements (line 250) | def check_stacked_transformer_requirements(config: "ModelConfig") -> Non... function check_hyperopt_search_algorithm_dependencies_installed (line 274) | def check_hyperopt_search_algorithm_dependencies_installed(config: "Mode... function check_hyperopt_scheduler_dependencies_installed (line 286) | def check_hyperopt_scheduler_dependencies_installed(config: "ModelConfig... function check_tagger_decoder_requirements (line 298) | def check_tagger_decoder_requirements(config: "ModelConfig") -> None: #... function check_hyperopt_parameter_dicts (line 329) | def check_hyperopt_parameter_dicts(config: "ModelConfig") -> None: # no... function check_concat_combiner_requirements (line 372) | def check_concat_combiner_requirements(config: "ModelConfig") -> None: ... function check_hyperopt_nested_parameter_dicts (line 403) | def check_hyperopt_nested_parameter_dicts(config: "ModelConfig") -> None... function check_llm_exactly_one_input_text_feature (line 439) | def check_llm_exactly_one_input_text_feature(config: "ModelConfig"): # ... function check_llm_finetuning_output_feature_config (line 450) | def check_llm_finetuning_output_feature_config(config: "ModelConfig"): ... function check_llm_finetuning_trainer_config (line 466) | def check_llm_finetuning_trainer_config(config: "ModelConfig"): # noqa:... function check_llm_finetuning_backend_config (line 484) | def check_llm_finetuning_backend_config(config: "ModelConfig"): # noqa:... function check_llm_finetuning_adalora_config (line 515) | def check_llm_finetuning_adalora_config(config: "ModelConfig"): function check_llm_finetuning_adaption_prompt_parameters (line 543) | def check_llm_finetuning_adaption_prompt_parameters(config: "ModelConfig"): function _get_llm_model_config (line 568) | def _get_llm_model_config(model_name: str) -> AutoConfig: function check_llm_quantization_backend_incompatibility (line 575) | def check_llm_quantization_backend_incompatibility(config: "ModelConfig"... function check_llm_text_encoder_is_not_used_with_ecd (line 608) | def check_llm_text_encoder_is_not_used_with_ecd(config: "ModelConfig") -... function check_qlora_requirements (line 627) | def check_qlora_requirements(config: "ModelConfig") -> None: # noqa: F821 function check_qlora_merge_and_unload_compatibility (line 637) | def check_qlora_merge_and_unload_compatibility(config: "ModelConfig") ->... function check_prompt_requirements (line 662) | def check_prompt_requirements(config: "ModelConfig") -> None: # noqa: F821 function check_sample_ratio_and_size_compatible (line 724) | def check_sample_ratio_and_size_compatible(config: "ModelConfig") -> None: FILE: ludwig/config_validation/preprocessing.py function check_global_max_sequence_length_fits_prompt_template (line 1) | def check_global_max_sequence_length_fits_prompt_template(metadata, glob... FILE: ludwig/config_validation/validation.py function get_schema (line 24) | def get_schema(model_type: str = MODEL_ECD): function get_validator (line 48) | def get_validator(): function check_schema (line 60) | def check_schema(updated_config): FILE: ludwig/contrib.py function create_load_action (line 22) | def create_load_action(contrib_loader: ContribLoader) -> argparse.Action: function add_contrib_callback_args (line 32) | def add_contrib_callback_args(parser: argparse.ArgumentParser): function preload (line 42) | def preload(argv): FILE: ludwig/contribs/__init__.py class ContribLoader (line 25) | class ContribLoader(ABC): method load (line 27) | def load(self) -> Callback: method preload (line 30) | def preload(self): class AimLoader (line 42) | class AimLoader(ContribLoader): method load (line 43) | def load(self) -> Callback: method preload (line 48) | def preload(self): class CometLoader (line 52) | class CometLoader(ContribLoader): method load (line 53) | def load(self) -> Callback: method preload (line 58) | def preload(self): class WandbLoader (line 62) | class WandbLoader(ContribLoader): method load (line 63) | def load(self) -> Callback: method preload (line 68) | def preload(self): class MlflowLoader (line 72) | class MlflowLoader(ContribLoader): method load (line 73) | def load(self) -> Callback: FILE: ludwig/contribs/aim.py class AimCallback (line 15) | class AimCallback(Callback): method __init__ (line 18) | def __init__(self, repo=None): method on_train_init (line 21) | def on_train_init( method aim_track (line 51) | def aim_track(self, progress_tracker): method on_trainer_train_teardown (line 67) | def on_trainer_train_teardown(self, trainer, progress_tracker, save_pa... method on_train_start (line 70) | def on_train_start(self, model, config, *args, **kwargs): method on_train_end (line 79) | def on_train_end(self, output_directory, *args, **kwargs): method on_eval_end (line 82) | def on_eval_end(self, trainer, progress_tracker, save_path): method on_ludwig_end (line 93) | def on_ludwig_end(self): method on_visualize_figure (line 97) | def on_visualize_figure(self, fig): method normalize_config (line 103) | def normalize_config(config): FILE: ludwig/contribs/comet.py class CometCallback (line 29) | class CometCallback(Callback): method __init__ (line 32) | def __init__(self): method on_train_init (line 35) | def on_train_init( method on_train_start (line 60) | def on_train_start(self, model, config, config_fp, *args, **kwargs): method on_train_end (line 79) | def on_train_end(self, output_directory, *args, **kwargs): method on_eval_end (line 83) | def on_eval_end(self, trainer, progress_tracker, save_path): method on_epoch_end (line 89) | def on_epoch_end(self, trainer, progress_tracker, save_path): method on_visualize_figure (line 95) | def on_visualize_figure(self, fig): method on_cmdline (line 99) | def on_cmdline(self, cmd, *args): method _save_config (line 126) | def _save_config(self, config, directory="."): method _log_html (line 131) | def _log_html(self, text): method _make_command_line (line 137) | def _make_command_line(self, cmd, args): FILE: ludwig/contribs/mlflow/__init__.py function _get_runs (line 19) | def _get_runs(experiment_id: str): function get_or_create_experiment_id (line 24) | def get_or_create_experiment_id(experiment_name, artifact_uri: str = None): class MlflowCallback (line 38) | class MlflowCallback(Callback): method __init__ (line 39) | def __init__(self, tracking_uri=None, log_artifacts: bool = True): method get_experiment_id (line 68) | def get_experiment_id(self, experiment_name): method on_preprocess_end (line 71) | def on_preprocess_end( method on_hyperopt_init (line 80) | def on_hyperopt_init(self, experiment_name): method on_hyperopt_trial_start (line 84) | def on_hyperopt_trial_start(self, parameters): method on_train_init (line 96) | def on_train_init(self, base_config, experiment_name, output_directory... method log_config (line 123) | def log_config(self, config): method on_train_start (line 127) | def on_train_start(self, config, **kwargs): method on_train_end (line 131) | def on_train_end(self, output_directory): method on_trainer_train_setup (line 139) | def on_trainer_train_setup(self, trainer, save_path, is_coordinator): method on_eval_end (line 161) | def on_eval_end(self, trainer, progress_tracker, save_path): method on_trainer_train_teardown (line 168) | def on_trainer_train_teardown(self, trainer, progress_tracker, save_pa... method on_visualize_figure (line 185) | def on_visualize_figure(self, fig): method prepare_ray_tune (line 190) | def prepare_ray_tune(self, train_fn, tune_config, tune_callbacks): method _log_params (line 210) | def _log_params(self, params): method __setstate__ (line 215) | def __setstate__(self, d): function _log_mlflow_loop (line 226) | def _log_mlflow_loop(q: queue.Queue, log_artifacts: bool = True): function _log_mlflow (line 251) | def _log_mlflow(log_metrics, steps, save_path, should_continue, log_arti... function _log_artifacts (line 266) | def _log_artifacts(output_directory): function _log_model (line 280) | def _log_model(lpath): FILE: ludwig/contribs/mlflow/model.py function get_default_conda_env (line 28) | def get_default_conda_env(): function save_model (line 43) | def save_model( function log_model (line 150) | def log_model( function _load_model (line 184) | def _load_model(path): function _load_pyfunc (line 190) | def _load_pyfunc(path): function load_model (line 198) | def load_model(model_uri): class _LudwigModelWrapper (line 220) | class _LudwigModelWrapper: method __init__ (line 221) | def __init__(self, ludwig_model): method predict (line 224) | def predict(self, dataframe): function export_model (line 229) | def export_model(model_path, output_path, registered_model_name=None): function log_saved_model (line 255) | def log_saved_model(lpath): class _CopyModel (line 266) | class _CopyModel: method __init__ (line 269) | def __init__(self, lpath): method save (line 272) | def save(self, path): method config (line 276) | def config(self): FILE: ludwig/contribs/wandb.py class WandbCallback (line 28) | class WandbCallback(Callback): method on_train_init (line 31) | def on_train_init( method on_train_start (line 49) | def on_train_start(self, model, config, *args, **kwargs): method on_eval_end (line 56) | def on_eval_end(self, trainer, progress_tracker, save_path): method on_epoch_end (line 61) | def on_epoch_end(self, trainer, progress_tracker, save_path): method on_visualize_figure (line 66) | def on_visualize_figure(self, fig): method on_train_end (line 71) | def on_train_end(self, output_directory): FILE: ludwig/data/batcher/base.py class Batcher (line 22) | class Batcher(ABC): method next_batch (line 24) | def next_batch(self) -> dict[str, np.ndarray]: method last_batch (line 28) | def last_batch(self) -> bool: method set_epoch (line 32) | def set_epoch(self, epoch: int, batch_size: int): FILE: ludwig/data/batcher/bucketed.py class BucketedBatcher (line 21) | class BucketedBatcher(Batcher): method __init__ (line 22) | def __init__( method shuffle (line 63) | def shuffle(self, buckets_idcs): method next_batch (line 67) | def next_batch(self): method last_batch (line 101) | def last_batch(self): method set_epoch (line 106) | def set_epoch(self, epoch, batch_size): method _compute_steps_per_epoch (line 113) | def _compute_steps_per_epoch(self) -> int: FILE: ludwig/data/batcher/iterable.py class IterableBatcher (line 19) | class IterableBatcher(Batcher): method __init__ (line 20) | def __init__(self, dataset, data, steps_per_epoch, ignore_last=False): method next_batch (line 29) | def next_batch(self): method last_batch (line 41) | def last_batch(self): method set_epoch (line 44) | def set_epoch(self, epoch, batch_size): FILE: ludwig/data/batcher/random_access.py class RandomAccessBatcher (line 28) | class RandomAccessBatcher(Batcher): method __init__ (line 29) | def __init__(self, dataset, sampler, batch_size=128, ignore_last=False... method next_batch (line 43) | def next_batch(self): method last_batch (line 65) | def last_batch(self): method set_epoch (line 85) | def set_epoch(self, epoch, batch_size): method _compute_steps_per_epoch (line 93) | def _compute_steps_per_epoch(self): FILE: ludwig/data/batcher/test_batcher.py function test_pandas_size (line 10) | def test_pandas_size(): function test_pandas_batcher_use_all_samples (line 48) | def test_pandas_batcher_use_all_samples(): FILE: ludwig/data/cache/manager.py class DatasetCache (line 14) | class DatasetCache: method __init__ (line 15) | def __init__(self, config, checksum, cache_map, dataset_manager): method get (line 21) | def get(self): method put (line 48) | def put(self, training_set, test_set, validation_set, training_set_met... method delete (line 83) | def delete(self): method get_cached_obj_path (line 89) | def get_cached_obj_path(self, cached_obj_name: str) -> str: class CacheManager (line 93) | class CacheManager: method __init__ (line 94) | def __init__( method get_dataset_cache (line 102) | def get_dataset_cache( method get_cache_key (line 129) | def get_cache_key(self, dataset: CacheableDataset, config: dict) -> str: method get_cache_path (line 132) | def get_cache_path(self, dataset: CacheableDataset | None, key: str, t... method get_cache_directory (line 145) | def get_cache_directory(self, dataset: CacheableDataset | None) -> str: method can_cache (line 152) | def can_cache(self, skip_save_processed_input: bool) -> bool: method data_format (line 156) | def data_format(self) -> str: FILE: ludwig/data/cache/types.py function alphanum (line 30) | def alphanum(v): class CacheableDataset (line 36) | class CacheableDataset(ABC): method get_cache_path (line 41) | def get_cache_path(self) -> str: method get_cache_directory (line 45) | def get_cache_directory(self) -> str: method unwrap (line 49) | def unwrap(self) -> str | DataFrame: class CacheableDataframe (line 55) | class CacheableDataframe(CacheableDataset): method get_cache_path (line 60) | def get_cache_path(self) -> str: method get_cache_directory (line 63) | def get_cache_directory(self) -> str: method unwrap (line 66) | def unwrap(self) -> str | DataFrame: class CacheablePath (line 72) | class CacheablePath(CacheableDataset): method name (line 76) | def name(self) -> str: method checksum (line 80) | def checksum(self) -> str: method get_cache_path (line 83) | def get_cache_path(self) -> str: method get_cache_directory (line 86) | def get_cache_directory(self) -> str: method unwrap (line 89) | def unwrap(self) -> str | DataFrame: function wrap (line 96) | def wrap(dataset: CacheInput | None) -> CacheableDataset: FILE: ludwig/data/cache/util.py function calculate_checksum (line 8) | def calculate_checksum(original_dataset: CacheableDataset, config: Model... FILE: ludwig/data/concatenate_datasets.py function concatenate_csv (line 28) | def concatenate_csv(train_csv, vali_csv, test_csv, output_csv): function concatenate_files (line 36) | def concatenate_files(train_fname, vali_fname, test_fname, read_fn, back... function concatenate_df (line 58) | def concatenate_df(train_df, vali_df, test_df, backend): function concatenate_splits (line 77) | def concatenate_splits(train_df, vali_df, test_df, backend): FILE: ludwig/data/dataframe/base.py class DataFrameEngine (line 22) | class DataFrameEngine(ABC): method df_like (line 24) | def df_like(self, df, proc_cols): method parallelize (line 28) | def parallelize(self, data): method persist (line 32) | def persist(self, data): method compute (line 36) | def compute(self, data): method from_pandas (line 40) | def from_pandas(self, df): method map_objects (line 44) | def map_objects(self, series, map_fn, meta=None): method map_partitions (line 48) | def map_partitions(self, series, map_fn, meta=None): method map_batches (line 52) | def map_batches(self, df, map_fn, enable_tensor_extension_casting=True): method apply_objects (line 56) | def apply_objects(self, series, map_fn, meta=None): method reduce_objects (line 60) | def reduce_objects(self, series, reduce_fn): method split (line 64) | def split(self, df, probabilities): method to_parquet (line 69) | def to_parquet(self, df, path, index=False): method write_predictions (line 77) | def write_predictions(self, df: DataFrame, path: str): method read_predictions (line 81) | def read_predictions(self, path: str) -> DataFrame: method to_ray_dataset (line 85) | def to_ray_dataset(self, df): method array_lib (line 90) | def array_lib(self): method df_lib (line 95) | def df_lib(self): method partitioned (line 100) | def partitioned(self): method set_parallelism (line 104) | def set_parallelism(self, parallelism): FILE: ludwig/data/dataframe/dask.py function set_scheduler (line 48) | def set_scheduler(scheduler): function reset_index_across_all_partitions (line 53) | def reset_index_across_all_partitions(df): class DaskEngine (line 72) | class DaskEngine(DataFrameEngine): method __init__ (line 73) | def __init__(self, parallelism=None, persist=True, _use_ray=True, **kw... method set_parallelism (line 81) | def set_parallelism(self, parallelism): method df_like (line 84) | def df_like(self, df: dd.DataFrame, proc_cols: dict[str, dd.Series]): method parallelize (line 127) | def parallelize(self, data): method persist (line 132) | def persist(self, data): method concat (line 137) | def concat(self, dfs): method compute (line 140) | def compute(self, data): method from_pandas (line 143) | def from_pandas(self, df): method map_objects (line 147) | def map_objects(self, series, map_fn, meta=None): method map_partitions (line 151) | def map_partitions(self, series, map_fn, meta=None): method map_batches (line 155) | def map_batches(self, series, map_fn, enable_tensor_extension_casting=... method apply_objects (line 172) | def apply_objects(self, df, apply_fn, meta=None): method reduce_objects (line 176) | def reduce_objects(self, series, reduce_fn): method split (line 184) | def split(self, df, probabilities): method remove_empty_partitions (line 201) | def remove_empty_partitions(self, df): method to_parquet (line 221) | def to_parquet(self, df, path, index=False): method write_predictions (line 232) | def write_predictions(self, df: dd.DataFrame, path: str): method read_predictions (line 241) | def read_predictions(self, path: str) -> dd.DataFrame: method to_ray_dataset (line 246) | def to_ray_dataset(self, df) -> Dataset: method from_ray_dataset (line 251) | def from_ray_dataset(self, dataset) -> dd.DataFrame: method reset_index (line 262) | def reset_index(self, df): method array_lib (line 266) | def array_lib(self): method df_lib (line 270) | def df_lib(self): method parallelism (line 274) | def parallelism(self): method partitioned (line 278) | def partitioned(self): function tensor_extension_casting (line 283) | def tensor_extension_casting(enforced: bool): FILE: ludwig/data/dataframe/modin.py class ModinEngine (line 28) | class ModinEngine(DataFrameEngine): method __init__ (line 29) | def __init__(self, **kwargs): method df_like (line 32) | def df_like(self, df, proc_cols): method parallelize (line 36) | def parallelize(self, data): method persist (line 39) | def persist(self, data): method compute (line 42) | def compute(self, data): method from_pandas (line 45) | def from_pandas(self, df): method map_objects (line 48) | def map_objects(self, series, map_fn, meta=None): method map_batches (line 51) | def map_batches(self, df, map_fn, enable_tensor_extension_casting=True): method map_partitions (line 54) | def map_partitions(self, series, map_fn, meta=None): method apply_objects (line 57) | def apply_objects(self, df, apply_fn, meta=None): method reduce_objects (line 60) | def reduce_objects(self, series, reduce_fn): method split (line 63) | def split(self, df, probabilities): method remove_empty_partitions (line 66) | def remove_empty_partitions(self, df): method to_parquet (line 69) | def to_parquet(self, df, path, index=False): method write_predictions (line 78) | def write_predictions(self, df: pd.DataFrame, path: str): method read_predictions (line 83) | def read_predictions(self, path: str) -> pd.DataFrame: method to_ray_dataset (line 88) | def to_ray_dataset(self, df): method from_ray_dataset (line 93) | def from_ray_dataset(self, dataset) -> pd.DataFrame: method reset_index (line 96) | def reset_index(self, df): method array_lib (line 100) | def array_lib(self): method df_lib (line 104) | def df_lib(self): method partitioned (line 108) | def partitioned(self): method set_parallelism (line 111) | def set_parallelism(self, parallelism): FILE: ludwig/data/dataframe/pandas.py class PandasEngine (line 27) | class PandasEngine(DataFrameEngine): method __init__ (line 28) | def __init__(self, **kwargs): method df_like (line 31) | def df_like(self, df, proc_cols): method parallelize (line 35) | def parallelize(self, data): method persist (line 38) | def persist(self, data): method compute (line 41) | def compute(self, data): method concat (line 45) | def concat(dfs) -> pd.DataFrame: method from_pandas (line 48) | def from_pandas(self, df): method map_objects (line 51) | def map_objects(self, series, map_fn, meta=None): method map_batches (line 54) | def map_batches(self, df, map_fn, enable_tensor_extension_casting=True): method map_partitions (line 57) | def map_partitions(self, series, map_fn, meta=None): method apply_objects (line 60) | def apply_objects(self, df, apply_fn, meta=None): method reduce_objects (line 63) | def reduce_objects(self, series, reduce_fn): method split (line 66) | def split(self, df, probabilities): method remove_empty_partitions (line 70) | def remove_empty_partitions(df: pd.DataFrame) -> pd.DataFrame: method to_parquet (line 73) | def to_parquet(self, df, path, index=False): method write_predictions (line 76) | def write_predictions(self, df: pd.DataFrame, path: str): method read_predictions (line 81) | def read_predictions(self, path: str) -> pd.DataFrame: method to_ray_dataset (line 86) | def to_ray_dataset(self, df): method from_ray_dataset (line 92) | def from_ray_dataset(dataset) -> pd.DataFrame: method reset_index (line 96) | def reset_index(df) -> pd.DataFrame: method array_lib (line 100) | def array_lib(self): method df_lib (line 104) | def df_lib(self): method partitioned (line 108) | def partitioned(self): method set_parallelism (line 111) | def set_parallelism(self, parallelism): FILE: ludwig/data/dataset/__init__.py function get_pandas_dataset_manager (line 18) | def get_pandas_dataset_manager(**kwargs): function get_ray_dataset_manager (line 24) | def get_ray_dataset_manager(**kwargs): function create_dataset_manager (line 37) | def create_dataset_manager(backend, cache_format, **kwargs): FILE: ludwig/data/dataset/base.py class Dataset (line 30) | class Dataset(ABC): method __len__ (line 32) | def __len__(self) -> int: method initialize_batcher (line 37) | def initialize_batcher( method to_df (line 48) | def to_df(self, features: Iterable[BaseFeature] | None = None) -> Data... method to_scalar_df (line 52) | def to_scalar_df(self, features: Iterable[BaseFeature] | None = None) ... method in_memory_size_bytes (line 56) | def in_memory_size_bytes(self) -> int: class DatasetManager (line 60) | class DatasetManager(ABC): method create (line 62) | def create(self, dataset, config, training_set_metadata) -> Dataset: method save (line 66) | def save(self, cache_path, dataset, config, training_set_metadata, tag... method can_cache (line 70) | def can_cache(self, skip_save_processed_input) -> bool: method data_format (line 75) | def data_format(self) -> str: FILE: ludwig/data/dataset/pandas.py class PandasDataset (line 43) | class PandasDataset(Dataset): method __init__ (line 44) | def __init__(self, dataset, features, data_hdf5_fp): method to_df (line 53) | def to_df(self, features: Iterable[BaseFeature] | None = None) -> Data... method to_scalar_df (line 59) | def to_scalar_df(self, features: Iterable[BaseFeature] | None = None) ... method get (line 62) | def get(self, proc_column, idx=None): method get_dataset (line 87) | def get_dataset(self) -> dict[str, np.ndarray]: method __len__ (line 90) | def __len__(self): method processed_data_fp (line 94) | def processed_data_fp(self) -> str | None: method in_memory_size_bytes (line 98) | def in_memory_size_bytes(self) -> int: method initialize_batcher (line 103) | def initialize_batcher( class PandasDatasetManager (line 125) | class PandasDatasetManager(DatasetManager): method __init__ (line 126) | def __init__(self, backend: Backend): method create (line 129) | def create(self, dataset, config, training_set_metadata) -> Dataset: method save (line 132) | def save(self, cache_path, dataset, config, training_set_metadata, tag... method can_cache (line 138) | def can_cache(self, skip_save_processed_input) -> bool: method data_format (line 142) | def data_format(self) -> str: FILE: ludwig/data/dataset/ray.py function cast_as_tensor_dtype (line 43) | def cast_as_tensor_dtype(series: Series) -> Series: function read_remote_parquet (line 47) | def read_remote_parquet(path: str): class RayDataset (line 52) | class RayDataset(Dataset): method __init__ (line 55) | def __init__( method to_ray_dataset (line 69) | def to_ray_dataset( method initialize_batcher (line 85) | def initialize_batcher(self, batch_size=128, should_shuffle=True, rand... method __len__ (line 97) | def __len__(self): method size (line 101) | def size(self): method in_memory_size_bytes (line 105) | def in_memory_size_bytes(self): method to_df (line 108) | def to_df(self, features=None): method to_scalar_df (line 111) | def to_scalar_df(self, features=None): class RayDatasetManager (line 117) | class RayDatasetManager(DatasetManager): method __init__ (line 118) | def __init__(self, backend): method create (line 121) | def create(self, dataset: str | DataFrame, config: dict[str, Any], tra... method save (line 124) | def save( method can_cache (line 135) | def can_cache(self, skip_save_processed_input): method data_format (line 139) | def data_format(self): class RayDatasetShard (line 143) | class RayDatasetShard(Dataset): method __init__ (line 146) | def __init__( method initialize_batcher (line 157) | def initialize_batcher(self, batch_size=128, should_shuffle=True, rand... method __len__ (line 167) | def __len__(self): method size (line 176) | def size(self): method to_df (line 179) | def to_df(self, features=None): method to_scalar_df (line 182) | def to_scalar_df(self, features=None): class _BaseBatcher (line 186) | class _BaseBatcher(Batcher): method __init__ (line 189) | def __init__( method next_batch (line 213) | def next_batch(self): method last_batch (line 222) | def last_batch(self): method set_epoch (line 225) | def set_epoch(self, epoch, batch_size): method step (line 232) | def step(self): method steps_per_epoch (line 236) | def steps_per_epoch(self): method _fetch_next_batch (line 239) | def _fetch_next_batch(self): method _fetch_next_epoch (line 250) | def _fetch_next_epoch(self): method _to_tensors_fn (line 253) | def _to_tensors_fn(self): method _prepare_batch (line 268) | def _prepare_batch(self, batch: pd.DataFrame) -> dict[str, np.ndarray]: class RayDatasetBatcher (line 283) | class RayDatasetBatcher(_BaseBatcher): method __init__ (line 286) | def __init__( method _fetch_next_epoch (line 298) | def _fetch_next_epoch(self): method _create_async_reader (line 304) | def _create_async_reader(self, dataset: RayNativeDataset): class RayDatasetShardBatcher (line 330) | class RayDatasetShardBatcher(_BaseBatcher): method __init__ (line 333) | def __init__( method _fetch_next_epoch (line 345) | def _fetch_next_epoch(self): method _create_async_reader (line 351) | def _create_async_reader(self): FILE: ludwig/data/dataset_synthesizer.py function _get_feature_encoder_or_decoder (line 105) | def _get_feature_encoder_or_decoder(feature): function generate_string (line 119) | def generate_string(length): function build_vocab (line 126) | def build_vocab(size): function return_none (line 133) | def return_none(feature): function assign_vocab (line 137) | def assign_vocab(feature): function build_feature_parameters (line 143) | def build_feature_parameters(features): function build_synthetic_dataset_df (line 170) | def build_synthetic_dataset_df(dataset_size: int, config: ModelConfigDic... function build_synthetic_dataset (line 181) | def build_synthetic_dataset(dataset_size: int, features: list[dict], out... function generate_datapoint (line 228) | def generate_datapoint(features: list[dict], outdir: str) -> str | int |... function generate_category (line 245) | def generate_category(feature, outdir: str | None = None) -> str: function generate_number (line 254) | def generate_number(feature, outdir: str | None = None) -> int: function generate_binary (line 262) | def generate_binary(feature, outdir: str | None = None) -> bool: function generate_sequence (line 272) | def generate_sequence(feature, outdir: str | None = None) -> str: function generate_set (line 288) | def generate_set(feature, outdir: str | None = None) -> str: function generate_bag (line 300) | def generate_bag(feature, outdir: str | None = None) -> str: function generate_text (line 312) | def generate_text(feature, outdir: str | None = None) -> str: function generate_timeseries (line 325) | def generate_timeseries(feature, max_len=10, outdir: str | None = None) ... function generate_audio (line 339) | def generate_audio(feature, outdir: str) -> str: function generate_image (line 370) | def generate_image(feature, outdir: str, save_as_numpy: bool = False) ->... function generate_datetime (line 425) | def generate_datetime(feature, outdir: str | None = None) -> str: function generate_h3 (line 448) | def generate_h3(feature, outdir: str | None = None) -> str: function generate_vector (line 466) | def generate_vector(feature, outdir: str | None = None) -> str: function generate_category_distribution (line 479) | def generate_category_distribution(feature, outdir: str | None = None) -... function cycle_category (line 512) | def cycle_category(feature): function cycle_binary (line 525) | def cycle_binary(feature): function cli_synthesize_dataset (line 538) | def cli_synthesize_dataset(dataset_size: int, features: list[dict], outp... function cli (line 583) | def cli(sys_argv): FILE: ludwig/data/negative_sampling.py function _negative_sample_user (line 12) | def _negative_sample_user(interaction_row: np.array, neg_pos_ratio: int,... function negative_sample (line 39) | def negative_sample( FILE: ludwig/data/postprocessing.py function postprocess (line 33) | def postprocess( function _save_as_numpy (line 69) | def _save_as_numpy(predictions, output_directory, saved_keys, backend): function convert_dict_to_df (line 84) | def convert_dict_to_df(predictions: dict[str, dict[str, list[Any] | torc... function convert_predictions (line 118) | def convert_predictions( function convert_to_df (line 125) | def convert_to_df( FILE: ludwig/data/preprocessing.py class DataFormatPreprocessor (line 131) | class DataFormatPreprocessor(ABC): method preprocess_for_training (line 134) | def preprocess_for_training( method preprocess_for_prediction (line 152) | def preprocess_for_prediction( method prepare_processed_data (line 159) | def prepare_processed_data( class DictPreprocessor (line 174) | class DictPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 176) | def preprocess_for_training( method preprocess_for_prediction (line 218) | def preprocess_for_prediction( class DataFramePreprocessor (line 234) | class DataFramePreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 236) | def preprocess_for_training( method preprocess_for_prediction (line 272) | def preprocess_for_prediction( class CSVPreprocessor (line 291) | class CSVPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 293) | def preprocess_for_training( method preprocess_for_prediction (line 324) | def preprocess_for_prediction( class TSVPreprocessor (line 342) | class TSVPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 344) | def preprocess_for_training( method preprocess_for_prediction (line 375) | def preprocess_for_prediction( class JSONPreprocessor (line 393) | class JSONPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 395) | def preprocess_for_training( method preprocess_for_prediction (line 426) | def preprocess_for_prediction( class JSONLPreprocessor (line 444) | class JSONLPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 446) | def preprocess_for_training( method preprocess_for_prediction (line 477) | def preprocess_for_prediction( class ExcelPreprocessor (line 495) | class ExcelPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 497) | def preprocess_for_training( method preprocess_for_prediction (line 528) | def preprocess_for_prediction( class ParquetPreprocessor (line 546) | class ParquetPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 548) | def preprocess_for_training( method preprocess_for_prediction (line 579) | def preprocess_for_prediction( method prepare_processed_data (line 597) | def prepare_processed_data( class PicklePreprocessor (line 626) | class PicklePreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 628) | def preprocess_for_training( method preprocess_for_prediction (line 659) | def preprocess_for_prediction( class FatherPreprocessor (line 677) | class FatherPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 679) | def preprocess_for_training( method preprocess_for_prediction (line 710) | def preprocess_for_prediction( class FWFPreprocessor (line 728) | class FWFPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 730) | def preprocess_for_training( method preprocess_for_prediction (line 761) | def preprocess_for_prediction( class HTMLPreprocessor (line 779) | class HTMLPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 781) | def preprocess_for_training( method preprocess_for_prediction (line 812) | def preprocess_for_prediction( class ORCPreprocessor (line 830) | class ORCPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 832) | def preprocess_for_training( method preprocess_for_prediction (line 863) | def preprocess_for_prediction( class SASPreprocessor (line 881) | class SASPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 883) | def preprocess_for_training( method preprocess_for_prediction (line 914) | def preprocess_for_prediction( class SPSSPreprocessor (line 932) | class SPSSPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 934) | def preprocess_for_training( method preprocess_for_prediction (line 965) | def preprocess_for_prediction( class StataPreprocessor (line 983) | class StataPreprocessor(DataFormatPreprocessor): method preprocess_for_training (line 985) | def preprocess_for_training( method preprocess_for_prediction (line 1016) | def preprocess_for_prediction( class HDF5Preprocessor (line 1034) | class HDF5Preprocessor(DataFormatPreprocessor): method preprocess_for_training (line 1036) | def preprocess_for_training( method preprocess_for_prediction (line 1064) | def preprocess_for_prediction( method prepare_processed_data (line 1072) | def prepare_processed_data( function build_dataset (line 1147) | def build_dataset( function embed_fixed_features (line 1376) | def embed_fixed_features( function _get_sampled_dataset_df (line 1402) | def _get_sampled_dataset_df(dataset_df, df_engine, sample_ratio, sample_... function get_features_with_cacheable_fixed_embeddings (line 1425) | def get_features_with_cacheable_fixed_embeddings( function cast_columns (line 1454) | def cast_columns(dataset_cols, features, backend) -> None: function merge_preprocessing (line 1470) | def merge_preprocessing( function build_preprocessing_parameters (line 1479) | def build_preprocessing_parameters( function is_input_feature (line 1525) | def is_input_feature(feature_config: FeatureConfigDict) -> bool: function build_metadata (line 1531) | def build_metadata( function build_data (line 1556) | def build_data( function balance_data (line 1603) | def balance_data( function precompute_fill_value (line 1645) | def precompute_fill_value( function handle_missing_values (line 1705) | def handle_missing_values(dataset_cols, feature, preprocessing_parameter... function handle_outliers (line 1712) | def handle_outliers(dataset_cols, feature, preprocessing_parameters: Pre... function _handle_missing_values (line 1729) | def _handle_missing_values( function handle_features_with_prompt_config (line 1772) | def handle_features_with_prompt_config( function _get_prompt_config (line 1841) | def _get_prompt_config(config: ModelConfigDict, input_feature_config: di... function _has_prompt_section (line 1856) | def _has_prompt_section(config: dict) -> bool: function load_hdf5 (line 1860) | def load_hdf5(hdf5_file_path, preprocessing_params, backend, split_data=... function load_metadata (line 1881) | def load_metadata(metadata_file_path: str) -> TrainingSetMetadataDict: function drop_extra_cols (line 1890) | def drop_extra_cols(features, dfs): function preprocess_for_training (line 1895) | def preprocess_for_training( function _preprocess_file_for_training (line 2077) | def _preprocess_file_for_training( function _preprocess_df_for_training (line 2182) | def _preprocess_df_for_training( function preprocess_for_prediction (line 2237) | def preprocess_for_prediction( function _get_cache_hit_message (line 2378) | def _get_cache_hit_message(cache: DatasetCache) -> str: FILE: ludwig/data/prompt.py function index_column (line 46) | def index_column( function format_input_with_prompt (line 121) | def format_input_with_prompt( function _validate_prompt_template (line 206) | def _validate_prompt_template( function _get_template_fields (line 228) | def _get_template_fields(template: str) -> tuple[set[str], dict[str, typ... function _get_dtype (line 236) | def _get_dtype(format_spec: str) -> type: FILE: ludwig/data/sampler.py class DistributedSampler (line 25) | class DistributedSampler: method __init__ (line 28) | def __init__( method __iter__ (line 44) | def __iter__(self): method __len__ (line 61) | def __len__(self): method set_epoch (line 64) | def set_epoch(self, epoch): FILE: ludwig/data/split.py class Splitter (line 51) | class Splitter(ABC): method split (line 53) | def split( method validate (line 58) | def validate(self, config: ModelConfigDict): method has_split (line 61) | def has_split(self, split_index: int) -> bool: method required_columns (line 65) | def required_columns(self) -> list[str]: function _make_divisions_ensure_minimum_rows (line 70) | def _make_divisions_ensure_minimum_rows( function _split_divisions_with_min_rows (line 89) | def _split_divisions_with_min_rows(n_rows: int, probabilities: list[floa... class RandomSplitter (line 106) | class RandomSplitter(Splitter): method __init__ (line 107) | def __init__(self, probabilities: list[float] = DEFAULT_PROBABILITIES,... method split (line 110) | def split( method has_split (line 127) | def has_split(self, split_index: int) -> bool: method get_schema_cls (line 131) | def get_schema_cls(): class FixedSplitter (line 136) | class FixedSplitter(Splitter): method __init__ (line 137) | def __init__(self, column: str = SPLIT, **kwargs): method split (line 140) | def split( method required_columns (line 149) | def required_columns(self) -> list[str]: method get_schema_cls (line 153) | def get_schema_cls(): function stratify_split_dataframe (line 157) | def stratify_split_dataframe( class StratifySplitter (line 193) | class StratifySplitter(Splitter): method __init__ (line 194) | def __init__(self, column: str, probabilities: list[float] = DEFAULT_P... method split (line 198) | def split( method validate (line 229) | def validate(self, config: "ModelConfig"): # noqa: F821 method has_split (line 240) | def has_split(self, split_index: int) -> bool: method required_columns (line 244) | def required_columns(self) -> list[str]: method get_schema_cls (line 248) | def get_schema_cls(): class DatetimeSplitter (line 253) | class DatetimeSplitter(Splitter): method __init__ (line 254) | def __init__( method split (line 267) | def split( method validate (line 298) | def validate(self, config: "ModelConfig"): # noqa: F821 method has_split (line 309) | def has_split(self, split_index: int) -> bool: method required_columns (line 313) | def required_columns(self) -> list[str]: method get_schema_cls (line 317) | def get_schema_cls(): class HashSplitter (line 322) | class HashSplitter(Splitter): method __init__ (line 323) | def __init__( method split (line 332) | def split( method has_split (line 354) | def has_split(self, split_index: int) -> bool: method required_columns (line 358) | def required_columns(self) -> list[str]: method get_schema_cls (line 362) | def get_schema_cls(): function get_splitter (line 367) | def get_splitter(type: str | None = None, **kwargs) -> Splitter: function split_dataset (line 375) | def split_dataset( FILE: ludwig/data/split_dataset.py function split (line 20) | def split(input_path, output1, output2, split): FILE: ludwig/data/utils.py function convert_to_dict (line 11) | def convert_to_dict( function set_fixed_split (line 37) | def set_fixed_split(preprocessing_params: PreprocessingConfigDict) -> Pr... function get_input_and_output_features (line 52) | def get_input_and_output_features(feature_configs): FILE: ludwig/datasets/__init__.py function _load_dataset_config (line 31) | def _load_dataset_config(config_filename: str): function _get_dataset_configs (line 39) | def _get_dataset_configs() -> dict[str, DatasetConfig]: function _get_dataset_config (line 48) | def _get_dataset_config(dataset_name) -> DatasetConfig: function get_dataset (line 57) | def get_dataset(dataset_name, cache_dir=None) -> DatasetLoader: function load_dataset_uris (line 70) | def load_dataset_uris( function _is_hf (line 129) | def _is_hf(dataset, training_set): function _load_hf_datasets (line 137) | def _load_hf_datasets( function _load_cacheable_hf_dataset (line 201) | def _load_cacheable_hf_dataset( function _load_cacheable_dataset (line 217) | def _load_cacheable_dataset(dataset: str, backend: Backend) -> Cacheable... function list_datasets (line 226) | def list_datasets() -> list[str]: function get_datasets_output_features (line 232) | def get_datasets_output_features( function describe_dataset (line 291) | def describe_dataset(dataset_name: str) -> str: function download_dataset (line 297) | def download_dataset(dataset_name: str, output_dir: str = "."): function get_buffer (line 305) | def get_buffer(dataset_name: str, kaggle_username: str = None, kaggle_ke... function _get_hf_dataset_and_subsample (line 319) | def _get_hf_dataset_and_subsample(dataset_name: str) -> tuple[str, str |... function cli (line 334) | def cli(sys_argv): function __getattr__ (line 373) | def __getattr__(name: str) -> Any: FILE: ludwig/datasets/archives.py class ArchiveType (line 29) | class ArchiveType(str, Enum): function infer_archive_type (line 41) | def infer_archive_type(archive_path): function is_archive (line 62) | def is_archive(path): function list_archive (line 67) | def list_archive(archive_path, archive_type: ArchiveType | None = None) ... function extract_archive (line 90) | def extract_archive(archive_path: str, archive_type: ArchiveType | None ... FILE: ludwig/datasets/dataset_config.py class DatasetFallbackMirror (line 22) | class DatasetFallbackMirror: class DatasetConfig (line 33) | class DatasetConfig: FILE: ludwig/datasets/kaggle.py function create_kaggle_client (line 7) | def create_kaggle_client(): function update_env (line 15) | def update_env(**kwargs): function download_kaggle_dataset (line 25) | def download_kaggle_dataset( FILE: ludwig/datasets/loaders/adult_census_income.py class AdultCensusIncomeLoader (line 20) | class AdultCensusIncomeLoader(DatasetLoader): method load_file_to_dataframe (line 21) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: method transform_dataframe (line 27) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/agnews.py class AGNewsLoader (line 20) | class AGNewsLoader(DatasetLoader): method transform_dataframe (line 21) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/allstate_claims_severity.py class AllstateClaimsSeverityLoader (line 22) | class AllstateClaimsSeverityLoader(DatasetLoader): method load_file_to_dataframe (line 23) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: FILE: ludwig/datasets/loaders/camseq.py class CamseqLoader (line 23) | class CamseqLoader(DatasetLoader): method transform_files (line 24) | def transform_files(self, file_paths: list[str]) -> list[str]: method load_unprocessed_dataframe (line 49) | def load_unprocessed_dataframe(self, file_paths: list[str]) -> pd.Data... FILE: ludwig/datasets/loaders/code_alpaca_loader.py class CodeAlpacaLoader (line 21) | class CodeAlpacaLoader(DatasetLoader): method load_file_to_dataframe (line 24) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: FILE: ludwig/datasets/loaders/consumer_complaints_loader.py class ConsumerComplaintsLoader (line 19) | class ConsumerComplaintsLoader(DatasetLoader): method load_file_to_dataframe (line 22) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: function preprocess_df (line 31) | def preprocess_df(df): FILE: ludwig/datasets/loaders/creditcard_fraud.py class CreditCardFraudLoader (line 20) | class CreditCardFraudLoader(DatasetLoader): method transform_dataframe (line 21) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/dataset_loader.py class TqdmUpTo (line 42) | class TqdmUpTo(tqdm): method update_to (line 48) | def update_to(self, b=1, bsize=1, tsize=None): function _list_of_strings (line 62) | def _list_of_strings(list_or_string: str | list[str]) -> list[str]: function _glob_multiple (line 67) | def _glob_multiple(pathnames: list[str], root_dir: str = None, recursive... function _sha256_digest (line 77) | def _sha256_digest(file_path) -> str: class DatasetState (line 89) | class DatasetState(int, Enum): class DatasetLoader (line 99) | class DatasetLoader: method __init__ (line 121) | def __init__(self, config: DatasetConfig, cache_dir: str | None = None): method name (line 127) | def name(self): method version (line 132) | def version(self): method is_kaggle_dataset (line 137) | def is_kaggle_dataset(self) -> bool: method download_dir (line 141) | def download_dir(self) -> str: method raw_dataset_dir (line 146) | def raw_dataset_dir(self) -> str: method processed_dataset_dir (line 151) | def processed_dataset_dir(self) -> str: method processed_dataset_filename (line 156) | def processed_dataset_filename(self) -> str: method processed_dataset_path (line 161) | def processed_dataset_path(self) -> str: method processed_temp_dir (line 166) | def processed_temp_dir(self) -> str: method state (line 171) | def state(self) -> DatasetState: method download_urls (line 191) | def download_urls(self) -> list[str]: method download_filenames (line 195) | def download_filenames(self) -> list[str]: method get_mirror_download_paths (line 202) | def get_mirror_download_paths(mirror: DatasetFallbackMirror): method get_mirror_download_filenames (line 206) | def get_mirror_download_filenames(self, mirror: DatasetFallbackMirror): method description (line 212) | def description(self) -> str: method model_configs (line 217) | def model_configs(self) -> dict[str, dict]: method best_model_config (line 222) | def best_model_config(self) -> dict | None: method default_model_config (line 227) | def default_model_config(self) -> dict | None: method _get_preserved_paths (line 234) | def _get_preserved_paths(self, root_dir=None): method export (line 243) | def export(self, output_directory: str) -> None: method _download_and_process (line 257) | def _download_and_process(self, kaggle_username: str | None = None, ka... method load (line 288) | def load( method download (line 309) | def download(self, kaggle_username: str | None = None, kaggle_key: str... method download_from_fallback_mirrors (line 326) | def download_from_fallback_mirrors(self): method verify (line 341) | def verify(self) -> None: method extract (line 356) | def extract(self) -> list[str]: method transform (line 373) | def transform(self) -> None: method transform_files (line 382) | def transform_files(self, file_paths: list[str]) -> list[str]: method load_file_to_dataframe (line 399) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: method load_files_to_dataframe (line 418) | def load_files_to_dataframe(self, file_paths: list[str], root_dir=None... method load_unprocessed_dataframe (line 448) | def load_unprocessed_dataframe(self, file_paths: list[str]) -> pd.Data... method _get_dataframe_with_fixed_splits_from_hf (line 468) | def _get_dataframe_with_fixed_splits_from_hf(self): method _get_dataframe_with_fixed_splits (line 482) | def _get_dataframe_with_fixed_splits(self, train_paths, validation_pat... method transform_dataframe (line 504) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: method save_processed (line 513) | def save_processed(self, dataframe: pd.DataFrame) -> None: method load_transformed_dataset (line 519) | def load_transformed_dataset(self) -> pd.DataFrame: method get_mtime (line 523) | def get_mtime(self) -> float: method split (line 528) | def split(dataset: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame, ... FILE: ludwig/datasets/loaders/ethos_binary.py class EthosBinaryLoader (line 20) | class EthosBinaryLoader(DatasetLoader): method load_file_to_dataframe (line 21) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: method transform_dataframe (line 25) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/flickr8k.py class Flickr8kLoader (line 22) | class Flickr8kLoader(DatasetLoader): method transform_files (line 23) | def transform_files(self, file_paths: list[str]) -> list[str]: FILE: ludwig/datasets/loaders/forest_cover.py class ForestCoverLoader (line 23) | class ForestCoverLoader(DatasetLoader): method __init__ (line 24) | def __init__(self, config: DatasetConfig, cache_dir: str | None = None... method transform_dataframe (line 28) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/goemotions.py class GoEmotionsLoader (line 20) | class GoEmotionsLoader(DatasetLoader): method transform_dataframe (line 21) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/higgs.py class HiggsLoader (line 22) | class HiggsLoader(DatasetLoader): method __init__ (line 23) | def __init__(self, config: DatasetConfig, cache_dir: str | None = None... method load_file_to_dataframe (line 27) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: method transform_dataframe (line 31) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/hugging_face.py class HFLoader (line 29) | class HFLoader(DatasetLoader): method load_hf_to_dict (line 38) | def load_hf_to_dict(hf_id: str | None = None, hf_subsample: str | None... method load (line 52) | def load( FILE: ludwig/datasets/loaders/ieee_fraud.py class IEEEFraudLoader (line 22) | class IEEEFraudLoader(DatasetLoader): method load_unprocessed_dataframe (line 25) | def load_unprocessed_dataframe(self, file_paths: list[str]) -> pd.Data... FILE: ludwig/datasets/loaders/insurance_lite.py class InsuranceLiteLoader (line 22) | class InsuranceLiteLoader(DatasetLoader): method transform_dataframe (line 26) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/kdd_loader.py class KDDCup2009Loader (line 23) | class KDDCup2009Loader(DatasetLoader): method __init__ (line 24) | def __init__(self, config: DatasetConfig, cache_dir: str | None = None... method load_file_to_dataframe (line 29) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: method transform_dataframe (line 33) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: function process_categorical_features (line 73) | def process_categorical_features(df, categorical_features): function process_number_features (line 79) | def process_number_features(df, categorical_features): class KDDAppetencyLoader (line 128) | class KDDAppetencyLoader(KDDCup2009Loader): method __init__ (line 134) | def __init__(self, config: DatasetConfig, cache_dir: str | None = None... class KDDChurnLoader (line 140) | class KDDChurnLoader(KDDCup2009Loader): method __init__ (line 146) | def __init__(self, config: DatasetConfig, cache_dir: str | None = None... class KDDUpsellingLoader (line 150) | class KDDUpsellingLoader(KDDCup2009Loader): method __init__ (line 156) | def __init__(self, config: DatasetConfig, cache_dir: str | None = None... FILE: ludwig/datasets/loaders/mnist.py class MNISTLoader (line 32) | class MNISTLoader(DatasetLoader): method __init__ (line 33) | def __init__(self, config: DatasetConfig, cache_dir: str | None = None): method transform_files (line 47) | def transform_files(self, file_paths: list[str]) -> list[str]: method load_unprocessed_dataframe (line 53) | def load_unprocessed_dataframe(self, file_paths: list[str]) -> pd.Data... method read_source_dataset (line 57) | def read_source_dataset(self, dataset="training", path="."): method write_output_dataset (line 83) | def write_output_dataset(self, labels, images, output_dir): method output_training_and_test_data (line 109) | def output_training_and_test_data(self): FILE: ludwig/datasets/loaders/naval.py class NavalLoader (line 20) | class NavalLoader(DatasetLoader): method load_file_to_dataframe (line 21) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: FILE: ludwig/datasets/loaders/rossman_store_sales.py class RossmanStoreSalesLoader (line 24) | class RossmanStoreSalesLoader(DatasetLoader): method load_unprocessed_dataframe (line 27) | def load_unprocessed_dataframe(self, file_paths: list[str]) -> pd.Data... function preprocess_dates (line 42) | def preprocess_dates(df): function preprocess_stores (line 56) | def preprocess_stores(df, stores_df): function preprocess_df (line 92) | def preprocess_df(df, stores_df): FILE: ludwig/datasets/loaders/santander_value_prediction.py class SantanderValuePredictionLoader (line 20) | class SantanderValuePredictionLoader(DatasetLoader): method transform_dataframe (line 26) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/sarcastic_headlines.py class SarcasticHeadlinesLoader (line 21) | class SarcasticHeadlinesLoader(DatasetLoader): method load_file_to_dataframe (line 22) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: FILE: ludwig/datasets/loaders/sarcos.py class SarcosLoader (line 24) | class SarcosLoader(DatasetLoader): method load_file_to_dataframe (line 44) | def load_file_to_dataframe(self, file_path: str) -> pd.DataFrame: method transform_dataframe (line 51) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/split_loaders.py class RandomSplitLoader (line 22) | class RandomSplitLoader(DatasetLoader): method transform_dataframe (line 31) | def transform_dataframe(self, dataframe: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/datasets/loaders/sst.py class SSTLoader (line 23) | class SSTLoader(DatasetLoader): method __init__ (line 35) | def __init__( method get_sentiment_label (line 51) | def get_sentiment_label(id2sent, phrase_id): method transform_files (line 54) | def transform_files(self, file_paths: list[str]) -> list[str]: class SST2Loader (line 160) | class SST2Loader(SSTLoader): method __init__ (line 177) | def __init__( method get_sentiment_label (line 194) | def get_sentiment_label(self, id2sent, phrase_id): class SST3Loader (line 203) | class SST3Loader(SSTLoader): method __init__ (line 219) | def __init__( method get_sentiment_label (line 235) | def get_sentiment_label(self, id2sent, phrase_id): class SST5Loader (line 246) | class SST5Loader(SSTLoader): method __init__ (line 262) | def __init__( method get_sentiment_label (line 278) | def get_sentiment_label(self, id2sent, phrase_id): function format_text (line 293) | def format_text(text: str): function convert_parentheses (line 298) | def convert_parentheses(text: str): function convert_parentheses_back (line 303) | def convert_parentheses_back(text: str): function get_sentence_idcs_in_split (line 308) | def get_sentence_idcs_in_split(datasplit: pd.DataFrame, split_id: int): function get_sentences_with_idcs (line 314) | def get_sentences_with_idcs(sentences: pd.DataFrame, sentences_idcs: set... function sentence_subtrees (line 320) | def sentence_subtrees(sentence_idx, trees_pointers, trees_phrases): function visit_postorder (line 327) | def visit_postorder(node, visit_list): class SSTTree (line 334) | class SSTTree: class Node (line 335) | class Node: method __init__ (line 336) | def __init__(self, key, val=None): method create_node (line 342) | def create_node(self, parent, i): method create_tree (line 364) | def create_tree(self, parents, tree_phrases): method __init__ (line 376) | def __init__(self, tree_pointers, tree_phrases): method subtrees (line 379) | def subtrees(self): FILE: ludwig/datasets/utils.py function model_configs_for_dataset (line 11) | def model_configs_for_dataset(dataset_name: str) -> dict[str, dict]: function _get_model_configs (line 20) | def _get_model_configs(dataset_name: str) -> dict[str, dict]: function _load_model_config (line 40) | def _load_model_config(model_config_filename: str): FILE: ludwig/decoders/base.py class Decoder (line 24) | class Decoder(LudwigModule, ABC): method forward (line 26) | def forward(self, inputs, mask=None): method name (line 30) | def name(self): FILE: ludwig/decoders/generic_decoders.py class PassthroughDecoder (line 34) | class PassthroughDecoder(Decoder): method __init__ (line 35) | def __init__(self, input_size: int = 1, num_classes: int = None, decod... method forward (line 43) | def forward(self, inputs, **kwargs): method get_schema_cls (line 47) | def get_schema_cls(): method input_shape (line 51) | def input_shape(self) -> torch.Size: method output_shape (line 55) | def output_shape(self) -> torch.Size: class Regressor (line 61) | class Regressor(Decoder): method __init__ (line 62) | def __init__( method get_schema_cls (line 87) | def get_schema_cls(): method input_shape (line 91) | def input_shape(self): method forward (line 94) | def forward(self, inputs, **kwargs): class Projector (line 100) | class Projector(Decoder): method __init__ (line 101) | def __init__( method get_schema_cls (line 143) | def get_schema_cls(): method input_shape (line 147) | def input_shape(self): method forward (line 150) | def forward(self, inputs, **kwargs): class Classifier (line 159) | class Classifier(Decoder): method __init__ (line 160) | def __init__( method get_schema_cls (line 190) | def get_schema_cls(): method input_shape (line 194) | def input_shape(self): method forward (line 197) | def forward(self, inputs, **kwargs): FILE: ludwig/decoders/image_decoders.py class UNetDecoder (line 32) | class UNetDecoder(Decoder): method __init__ (line 33) | def __init__( method forward (line 65) | def forward(self, combiner_outputs: dict[str, torch.Tensor], target: t... method get_prediction_set (line 77) | def get_prediction_set(self): method get_schema_cls (line 81) | def get_schema_cls() -> type[ImageDecoderConfig]: method output_shape (line 85) | def output_shape(self) -> torch.Size: method input_shape (line 89) | def input_shape(self) -> torch.Size: FILE: ludwig/decoders/llm_decoders.py class Matcher (line 19) | class Matcher: method __init__ (line 20) | def __init__(self, match: dict[str, dict[str, Any]]): method contains (line 23) | def contains(self, decoded_input: str, value: str) -> bool: method regex (line 26) | def regex(self, decoded_input: str, regex_pattern: str) -> bool: method __call__ (line 48) | def __call__(self, decoded_input: str) -> str | None: class TextExtractorDecoder (line 71) | class TextExtractorDecoder(Decoder): method __init__ (line 72) | def __init__( method get_schema_cls (line 103) | def get_schema_cls(): method input_shape (line 107) | def input_shape(self): method get_prediction_set (line 110) | def get_prediction_set(self): method forward (line 113) | def forward(self, inputs: list[torch.Tensor], input_lengths: list[int]... class CategoryExtractorDecoder (line 144) | class CategoryExtractorDecoder(Decoder): method __init__ (line 145) | def __init__( method get_schema_cls (line 171) | def get_schema_cls(): method input_shape (line 175) | def input_shape(self): method get_prediction_set (line 178) | def get_prediction_set(self): method forward (line 181) | def forward(self, inputs: list[torch.Tensor], input_lengths: list[int]... FILE: ludwig/decoders/registry.py function get_decoder_registry (line 9) | def get_decoder_registry() -> Registry: function register_decoder (line 14) | def register_decoder(name: str, features: str | list[str]): function get_decoder_cls (line 29) | def get_decoder_cls(feature: str, name: str) -> type[Decoder]: function get_decoder_classes (line 34) | def get_decoder_classes(feature: str) -> dict[str, type[Decoder]]: FILE: ludwig/decoders/sequence_decoder_utils.py function repeat_2D_tensor (line 9) | def repeat_2D_tensor(tensor, k): function get_rnn_init_state (line 21) | def get_rnn_init_state( function get_lstm_init_state (line 64) | def get_lstm_init_state( FILE: ludwig/decoders/sequence_decoders.py class RNNDecoder (line 33) | class RNNDecoder(nn.Module): method __init__ (line 36) | def __init__(self, hidden_size: int, vocab_size: int, cell_type: str, ... method forward (line 52) | def forward(self, input: torch.Tensor, hidden: torch.Tensor) -> tuple[... class LSTMDecoder (line 75) | class LSTMDecoder(nn.Module): method __init__ (line 78) | def __init__(self, hidden_size: int, vocab_size: int, num_layers: int ... method forward (line 91) | def forward( class SequenceRNNDecoder (line 118) | class SequenceRNNDecoder(nn.Module): method __init__ (line 121) | def __init__( method forward (line 141) | def forward(self, combiner_outputs: dict[str, torch.Tensor], target: t... class SequenceLSTMDecoder (line 189) | class SequenceLSTMDecoder(nn.Module): method __init__ (line 192) | def __init__( method forward (line 211) | def forward(self, combiner_outputs: dict[str, torch.Tensor], target: t... class SequenceGeneratorDecoder (line 260) | class SequenceGeneratorDecoder(Decoder): method __init__ (line 263) | def __init__( method forward (line 307) | def forward( method get_prediction_set (line 322) | def get_prediction_set(self): method get_schema_cls (line 326) | def get_schema_cls(): method input_shape (line 330) | def input_shape(self): method output_shape (line 335) | def output_shape(self): FILE: ludwig/decoders/sequence_tagger.py class SequenceTaggerDecoder (line 18) | class SequenceTaggerDecoder(Decoder): method __init__ (line 19) | def __init__( method forward (line 47) | def forward(self, inputs: dict[str, torch.Tensor], target: torch.Tenso... method get_prediction_set (line 78) | def get_prediction_set(self): method get_schema_cls (line 82) | def get_schema_cls(): method input_shape (line 86) | def input_shape(self): method output_shape (line 91) | def output_shape(self): FILE: ludwig/decoders/utils.py function extract_generated_tokens (line 5) | def extract_generated_tokens( FILE: ludwig/distributed/__init__.py function load_ddp (line 6) | def load_ddp(): function load_fsdp (line 12) | def load_fsdp(): function load_deepspeed (line 18) | def load_deepspeed(): function load_local (line 24) | def load_local(): function init_dist_strategy (line 39) | def init_dist_strategy(strategy: str | dict[str, Any], **kwargs) -> Dist... function get_current_dist_strategy (line 50) | def get_current_dist_strategy() -> DistributedStrategy: function get_dist_strategy (line 56) | def get_dist_strategy(strategy: str | dict[str, Any]) -> type[Distribute... function get_default_strategy_name (line 63) | def get_default_strategy_name() -> str: FILE: ludwig/distributed/base.py class DistributedStrategy (line 25) | class DistributedStrategy(ABC): method prepare (line 34) | def prepare( method prepare_for_inference (line 51) | def prepare_for_inference(self, model: nn.Module) -> nn.Module: method to_device (line 54) | def to_device(self, model: BaseModel, device: torch.device | None = No... method backward (line 57) | def backward(self, loss: torch.Tensor, model: nn.Module): method step (line 60) | def step(self, optimizer: Optimizer, *args, **kwargs): method zero_grad (line 63) | def zero_grad(self, optimizer: Optimizer): method set_batch_size (line 66) | def set_batch_size(self, model: nn.Module, batch_size: int): method size (line 70) | def size(self) -> int: method rank (line 74) | def rank(self) -> int: method local_size (line 78) | def local_size(self) -> int: method local_rank (line 82) | def local_rank(self) -> int: method is_coordinator (line 85) | def is_coordinator(self) -> bool: method barrier (line 89) | def barrier(self): method allreduce (line 93) | def allreduce(self, t: torch.Tensor) -> torch.Tensor: method broadcast (line 97) | def broadcast(self, t: torch.Tensor) -> torch.Tensor: method sync_model (line 101) | def sync_model(self, model: nn.Module): method sync_optimizer (line 105) | def sync_optimizer(self, optimizer: Optimizer): method broadcast_object (line 109) | def broadcast_object(self, v: Any, name: str | None = None) -> Any: method wait_optimizer_synced (line 113) | def wait_optimizer_synced(self, optimizer: Optimizer): method prepare_model_update (line 118) | def prepare_model_update(self, model: nn.Module, should_step: bool): method prepare_optimizer_update (line 123) | def prepare_optimizer_update(self, optimizer: Optimizer): method is_available (line 128) | def is_available(cls) -> bool: method gather_all_tensors_fn (line 133) | def gather_all_tensors_fn(cls) -> Callable | None: method get_ray_trainer_backend (line 138) | def get_ray_trainer_backend(cls, **kwargs) -> Any | None: method get_trainer_cls (line 143) | def get_trainer_cls(cls, backend_config: BackendConfig) -> tuple[type[... method shutdown (line 147) | def shutdown(self): method return_first (line 150) | def return_first(self, fn: Callable) -> Callable: method allow_gradient_accumulation (line 162) | def allow_gradient_accumulation(self) -> bool: method allow_mixed_precision (line 165) | def allow_mixed_precision(self) -> bool: method allow_clip_gradients (line 168) | def allow_clip_gradients(self) -> bool: method prepare_before_load (line 171) | def prepare_before_load(self) -> bool: method is_model_parallel (line 176) | def is_model_parallel(cls) -> bool: method create_checkpoint_handle (line 179) | def create_checkpoint_handle( method extract_model_for_serialization (line 191) | def extract_model_for_serialization(cls, model: nn.Module) -> nn.Modul... method replace_model_from_serialization (line 195) | def replace_model_from_serialization(cls, state: nn.Module | tuple[nn.... class LocalStrategy (line 200) | class LocalStrategy(DistributedStrategy): method prepare (line 201) | def prepare( method size (line 209) | def size(self) -> int: method rank (line 212) | def rank(self) -> int: method local_size (line 215) | def local_size(self) -> int: method local_rank (line 218) | def local_rank(self) -> int: method barrier (line 221) | def barrier(self): method allreduce (line 224) | def allreduce(self, t: torch.Tensor) -> torch.Tensor: method broadcast (line 227) | def broadcast(self, t: torch.Tensor) -> torch.Tensor: method sync_model (line 230) | def sync_model(self, model: nn.Module): method sync_optimizer (line 233) | def sync_optimizer(self, optimizer: Optimizer): method broadcast_object (line 236) | def broadcast_object(self, v: Any, name: str | None = None) -> Any: method wait_optimizer_synced (line 239) | def wait_optimizer_synced(self, optimizer: Optimizer): method prepare_model_update (line 243) | def prepare_model_update(self, model: nn.Module, should_step: bool): method prepare_optimizer_update (line 247) | def prepare_optimizer_update(self, optimizer: Optimizer): method is_available (line 251) | def is_available(cls) -> bool: method gather_all_tensors_fn (line 257) | def gather_all_tensors_fn(cls) -> Callable | None: method get_ray_trainer_backend (line 261) | def get_ray_trainer_backend(cls, **kwargs) -> Any | None: method get_trainer_cls (line 265) | def get_trainer_cls(cls, backend_config: BackendConfig) -> tuple[type[... method shutdown (line 268) | def shutdown(self): FILE: ludwig/distributed/ddp.py class DDPStrategy (line 29) | class DDPStrategy(DistributedStrategy): method __init__ (line 30) | def __init__(self): method _log_on_init (line 34) | def _log_on_init(self): method prepare (line 37) | def prepare( method size (line 45) | def size(self) -> int: method rank (line 48) | def rank(self) -> int: method local_size (line 51) | def local_size(self) -> int: method local_rank (line 54) | def local_rank(self) -> int: method barrier (line 57) | def barrier(self): method allreduce (line 60) | def allreduce(self, t: torch.Tensor) -> torch.Tensor: method broadcast (line 64) | def broadcast(self, t: torch.Tensor) -> torch.Tensor: method sync_model (line 68) | def sync_model(self, model: nn.Module): method sync_optimizer (line 72) | def sync_optimizer(self, optimizer: Optimizer): method broadcast_object (line 76) | def broadcast_object(self, v: Any, name: str | None = None) -> Any: method wait_optimizer_synced (line 81) | def wait_optimizer_synced(self, optimizer: Optimizer): method prepare_model_update (line 85) | def prepare_model_update(self, model: nn.Module, should_step: bool): method prepare_optimizer_update (line 94) | def prepare_optimizer_update(self, optimizer: Optimizer): method is_available (line 98) | def is_available(cls) -> bool: method gather_all_tensors_fn (line 102) | def gather_all_tensors_fn(cls) -> Callable | None: method get_ray_trainer_backend (line 106) | def get_ray_trainer_backend(cls, **kwargs) -> Any | None: method get_trainer_cls (line 112) | def get_trainer_cls(cls, backend_config: BackendConfig) -> tuple[type[... method shutdown (line 115) | def shutdown(self): method create_checkpoint_handle (line 123) | def create_checkpoint_handle( method to_device (line 134) | def to_device(self, model: Union["BaseModel", DDP], device: torch.devi... function local_rank_and_size (line 142) | def local_rank_and_size() -> tuple[int, int]: FILE: ludwig/distributed/deepspeed.py class DeepSpeedStrategy (line 44) | class DeepSpeedStrategy(DDPStrategy): method __init__ (line 45) | def __init__( method _log_on_init (line 72) | def _log_on_init(self): method prepare (line 75) | def prepare( method prepare_for_inference (line 125) | def prepare_for_inference(self, model: nn.Module) -> nn.Module: method to_device (line 130) | def to_device(self, model: nn.Module, device: torch.device | None = No... method backward (line 133) | def backward(self, loss: torch.Tensor, model: nn.Module): method step (line 150) | def step(self, optimizer: Optimizer, *args, **kwargs): method zero_grad (line 154) | def zero_grad(self, optimizer: Optimizer): method set_batch_size (line 158) | def set_batch_size(self, model: nn.Module, batch_size: int): method barrier (line 164) | def barrier(self): method allow_gradient_accumulation (line 167) | def allow_gradient_accumulation(self) -> bool: method allow_mixed_precision (line 171) | def allow_mixed_precision(self) -> bool: method allow_clip_gradients (line 175) | def allow_clip_gradients(self) -> bool: method prepare_before_load (line 179) | def prepare_before_load(self) -> bool: method is_model_parallel (line 187) | def is_model_parallel(cls) -> bool: method create_checkpoint_handle (line 190) | def create_checkpoint_handle( method extract_model_for_serialization (line 200) | def extract_model_for_serialization(cls, model: nn.Module) -> nn.Modul... method replace_model_from_serialization (line 204) | def replace_model_from_serialization(cls, state: nn.Module | tuple[nn.... class DeepSpeedCheckpoint (line 211) | class DeepSpeedCheckpoint(Checkpoint): method prepare (line 212) | def prepare(self, directory: str): method load (line 217) | def load(self, save_path: str, device: torch.device | None = None) -> ... method save (line 238) | def save(self, save_path: str, global_step: int): method get_state_for_inference (line 251) | def get_state_for_inference(self, save_path: str, device: torch.device... FILE: ludwig/distributed/fsdp.py class FSDPStrategy (line 16) | class FSDPStrategy(DDPStrategy): method _log_on_init (line 17) | def _log_on_init(self): method prepare (line 20) | def prepare( method to_device (line 28) | def to_device(self, model: nn.Module, device: torch.device | None = No... method is_model_parallel (line 32) | def is_model_parallel(cls) -> bool: FILE: ludwig/encoders/bag_encoders.py class BagEmbedWeightedEncoder (line 36) | class BagEmbedWeightedEncoder(Encoder): method __init__ (line 37) | def __init__( method get_schema_cls (line 92) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 96) | def input_shape(self) -> torch.Size: method output_shape (line 100) | def output_shape(self) -> torch.Size: method forward (line 103) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: FILE: ludwig/encoders/base.py class Encoder (line 26) | class Encoder(LudwigModule, ABC): method forward (line 28) | def forward(self, inputs, training=None, mask=None): method get_embedding_layer (line 31) | def get_embedding_layer(self) -> nn.Module: method name (line 40) | def name(self) -> str: FILE: ludwig/encoders/category_encoders.py class CategoricalPassthroughEncoder (line 40) | class CategoricalPassthroughEncoder(Encoder): method __init__ (line 41) | def __init__(self, input_size=1, encoder_config=None, **kwargs): method forward (line 49) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 57) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 61) | def input_shape(self) -> torch.Size: method output_shape (line 65) | def output_shape(self) -> torch.Size: method get_embedding_layer (line 68) | def get_embedding_layer(self) -> nn.Module: class CategoricalEmbedEncoder (line 74) | class CategoricalEmbedEncoder(Encoder): method __init__ (line 75) | def __init__( method forward (line 105) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 116) | def get_schema_cls() -> type[BaseEncoderConfig]: method output_shape (line 120) | def output_shape(self) -> torch.Size: method input_shape (line 124) | def input_shape(self) -> torch.Size: class CategoricalSparseEncoder (line 130) | class CategoricalSparseEncoder(Encoder): method __init__ (line 131) | def __init__( method forward (line 160) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 171) | def get_schema_cls() -> type[BaseEncoderConfig]: method output_shape (line 175) | def output_shape(self) -> torch.Size: method input_shape (line 179) | def input_shape(self) -> torch.Size: class CategoricalOneHotEncoder (line 185) | class CategoricalOneHotEncoder(Encoder): method __init__ (line 186) | def __init__( method forward (line 199) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 211) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 215) | def input_shape(self) -> torch.Size: method output_shape (line 219) | def output_shape(self) -> torch.Size: method get_embedding_layer (line 222) | def get_embedding_layer(self) -> nn.Module: FILE: ludwig/encoders/date_encoders.py class DateEmbed (line 40) | class DateEmbed(Encoder): method __init__ (line 41) | def __init__( method forward (line 228) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 269) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 273) | def input_shape(self) -> torch.Size: method output_shape (line 277) | def output_shape(self) -> torch.Size: class DateWave (line 283) | class DateWave(Encoder): method __init__ (line 284) | def __init__( method forward (line 362) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 402) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 406) | def input_shape(self) -> torch.Size: method output_shape (line 410) | def output_shape(self) -> torch.Size: FILE: ludwig/encoders/generic_encoders.py class PassthroughEncoder (line 33) | class PassthroughEncoder(Encoder): method __init__ (line 34) | def __init__(self, input_size=1, encoder_config=None, **kwargs): method forward (line 41) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 49) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 53) | def input_shape(self) -> torch.Size: method output_shape (line 57) | def output_shape(self) -> torch.Size: class DenseEncoder (line 63) | class DenseEncoder(Encoder): method __init__ (line 64) | def __init__( method forward (line 101) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 109) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 113) | def input_shape(self) -> torch.Size: method output_shape (line 117) | def output_shape(self) -> torch.Size: FILE: ludwig/encoders/h3_encoders.py class H3Embed (line 42) | class H3Embed(Encoder): method __init__ (line 43) | def __init__( method forward (line 171) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 208) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 212) | def input_shape(self) -> torch.Size: method output_shape (line 216) | def output_shape(self) -> torch.Size: class H3WeightedSum (line 222) | class H3WeightedSum(Encoder): method __init__ (line 223) | def __init__( method forward (line 297) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 322) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 326) | def input_shape(self) -> torch.Size: method output_shape (line 330) | def output_shape(self) -> torch.Size: class H3RNN (line 336) | class H3RNN(Encoder): method __init__ (line 337) | def __init__( method forward (line 442) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 458) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 462) | def input_shape(self) -> torch.Size: method output_shape (line 466) | def output_shape(self) -> torch.Size: FILE: ludwig/encoders/image/base.py class ImageEncoder (line 43) | class ImageEncoder(Encoder): class Stacked2DCNN (line 49) | class Stacked2DCNN(ImageEncoder): method __init__ (line 50) | def __init__( method forward (line 147) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 160) | def get_schema_cls() -> type[ImageEncoderConfig]: method output_shape (line 164) | def output_shape(self) -> torch.Size: method input_shape (line 168) | def input_shape(self) -> torch.Size: class ResNetEncoder (line 174) | class ResNetEncoder(ImageEncoder): method __init__ (line 175) | def __init__( method forward (line 243) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 251) | def get_schema_cls() -> type[ImageEncoderConfig]: method output_shape (line 255) | def output_shape(self) -> torch.Size: method input_shape (line 259) | def input_shape(self) -> torch.Size: class MLPMixerEncoder (line 265) | class MLPMixerEncoder(ImageEncoder): method __init__ (line 266) | def __init__( method forward (line 311) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 316) | def get_schema_cls() -> type[ImageEncoderConfig]: method input_shape (line 320) | def input_shape(self) -> torch.Size: method output_shape (line 324) | def output_shape(self) -> torch.Size: class ViTEncoder (line 330) | class ViTEncoder(ImageEncoder): method __init__ (line 331) | def __init__( method forward (line 413) | def forward(self, inputs: torch.Tensor, head_mask: torch.Tensor | None... method get_schema_cls (line 418) | def get_schema_cls() -> type[ImageEncoderConfig]: method input_shape (line 422) | def input_shape(self) -> torch.Size: method output_shape (line 426) | def output_shape(self) -> torch.Size: class UNetEncoder (line 432) | class UNetEncoder(ImageEncoder): method __init__ (line 433) | def __init__( method forward (line 456) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 461) | def get_schema_cls() -> type[ImageEncoderConfig]: method output_shape (line 465) | def output_shape(self) -> torch.Size: method input_shape (line 469) | def input_shape(self) -> torch.Size: FILE: ludwig/encoders/image/timm.py function _get_timm (line 21) | def _get_timm(): class TimmEncoder (line 31) | class TimmEncoder(ImageEncoder): method __init__ (line 45) | def __init__( method forward (line 83) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 87) | def get_schema_cls() -> type[BaseEncoderConfig]: method output_shape (line 91) | def output_shape(self) -> torch.Size: method input_shape (line 95) | def input_shape(self) -> torch.Size: class TimmCAFormerEncoder (line 101) | class TimmCAFormerEncoder(TimmEncoder): method __init__ (line 107) | def __init__(self, model_name: str = "caformer_s18", **kwargs): method get_schema_cls (line 111) | def get_schema_cls() -> type[BaseEncoderConfig]: class TimmConvFormerEncoder (line 117) | class TimmConvFormerEncoder(TimmEncoder): method __init__ (line 120) | def __init__(self, model_name: str = "convformer_s18", **kwargs): method get_schema_cls (line 124) | def get_schema_cls() -> type[BaseEncoderConfig]: class TimmPoolFormerEncoder (line 130) | class TimmPoolFormerEncoder(TimmEncoder): method __init__ (line 133) | def __init__(self, model_name: str = "poolformerv2_s12", **kwargs): method get_schema_cls (line 137) | def get_schema_cls() -> type[BaseEncoderConfig]: FILE: ludwig/encoders/image/torchvision.py class TVBaseEncoder (line 41) | class TVBaseEncoder(ImageEncoder): method __init__ (line 42) | def __init__( method forward (line 118) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method _remove_softmax_layer (line 122) | def _remove_softmax_layer(self): method output_shape (line 133) | def output_shape(self) -> torch.Size: method input_shape (line 140) | def input_shape(self) -> torch.Size: class TVAlexNetEncoder (line 155) | class TVAlexNetEncoder(TVBaseEncoder): method __init__ (line 159) | def __init__( method _remove_softmax_layer (line 171) | def _remove_softmax_layer(self): method get_schema_cls (line 175) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVConvNeXtEncoder (line 197) | class TVConvNeXtEncoder(TVBaseEncoder): method __init__ (line 201) | def __init__( method _remove_softmax_layer (line 208) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 212) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVDenseNetEncoder (line 226) | class TVDenseNetEncoder(TVBaseEncoder): method __init__ (line 230) | def __init__( method _remove_softmax_layer (line 237) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 241) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVEfficientNetEncoder (line 262) | class TVEfficientNetEncoder(TVBaseEncoder): method __init__ (line 266) | def __init__( method _remove_softmax_layer (line 273) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 277) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVGoogLeNetEncoder (line 288) | class TVGoogLeNetEncoder(TVBaseEncoder): method __init__ (line 292) | def __init__( method _remove_softmax_layer (line 307) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 311) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVInceptionV3Encoder (line 322) | class TVInceptionV3Encoder(TVBaseEncoder): method __init__ (line 326) | def __init__( method _remove_softmax_layer (line 340) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 344) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVMaxVitEncoder (line 355) | class TVMaxVitEncoder(TVBaseEncoder): method __init__ (line 359) | def __init__( method _remove_softmax_layer (line 366) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 370) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVMNASNetEncoder (line 384) | class TVMNASNetEncoder(TVBaseEncoder): method __init__ (line 388) | def __init__( method _remove_softmax_layer (line 395) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 399) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVMobileNetV2Encoder (line 410) | class TVMobileNetV2Encoder(TVBaseEncoder): method __init__ (line 414) | def __init__( method _remove_softmax_layer (line 421) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 425) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVMobileNetV3Encoder (line 437) | class TVMobileNetV3Encoder(TVBaseEncoder): method __init__ (line 441) | def __init__( method _remove_softmax_layer (line 448) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 452) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVRegNetEncoder (line 477) | class TVRegNetEncoder(TVBaseEncoder): method __init__ (line 481) | def __init__( method _remove_softmax_layer (line 488) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 492) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVResNetEncoder (line 507) | class TVResNetEncoder(TVBaseEncoder): method __init__ (line 511) | def __init__( method _remove_softmax_layer (line 518) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 522) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVResNeXtEncoder (line 535) | class TVResNeXtEncoder(TVBaseEncoder): method __init__ (line 539) | def __init__( method _remove_softmax_layer (line 546) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 550) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVShuffleNetV2Encoder (line 564) | class TVShuffleNetV2Encoder(TVBaseEncoder): method __init__ (line 568) | def __init__( method _remove_softmax_layer (line 575) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 579) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVSqueezeNetEncoder (line 591) | class TVSqueezeNetEncoder(TVBaseEncoder): method __init__ (line 595) | def __init__( method _remove_softmax_layer (line 602) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 609) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVSwinTransformerEncoder (line 622) | class TVSwinTransformerEncoder(TVBaseEncoder): method __init__ (line 626) | def __init__( method _remove_softmax_layer (line 633) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 637) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVVGGEncoder (line 655) | class TVVGGEncoder(TVBaseEncoder): method __init__ (line 659) | def __init__( method _remove_softmax_layer (line 666) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 670) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVViTEncoder (line 685) | class TVViTEncoder(TVBaseEncoder): method __init__ (line 689) | def __init__( method _remove_softmax_layer (line 708) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 712) | def get_schema_cls() -> type[BaseEncoderConfig]: class TVWideResNetEncoder (line 724) | class TVWideResNetEncoder(TVBaseEncoder): method __init__ (line 728) | def __init__( method _remove_softmax_layer (line 735) | def _remove_softmax_layer(self) -> None: method get_schema_cls (line 739) | def get_schema_cls() -> type[BaseEncoderConfig]: FILE: ludwig/encoders/registry.py function get_encoder_registry (line 10) | def get_encoder_registry() -> Registry: function get_sequence_encoder_registry (line 15) | def get_sequence_encoder_registry() -> Registry: function register_sequence_encoder (line 19) | def register_sequence_encoder(name: str): function register_encoder (line 27) | def register_encoder(name: str, features: str | list[str]): function get_encoder_cls (line 44) | def get_encoder_cls(feature: str, name: str) -> type[Encoder]: function get_encoder_classes (line 48) | def get_encoder_classes(feature: str) -> dict[str, type[Encoder]]: FILE: ludwig/encoders/sequence_encoders.py class SequenceEncoder (line 47) | class SequenceEncoder(Encoder): class SequencePassthroughEncoder (line 53) | class SequencePassthroughEncoder(SequenceEncoder): method __init__ (line 54) | def __init__( method forward (line 86) | def forward(self, input_sequence: torch.Tensor, mask: torch.Tensor | N... method get_schema_cls (line 104) | def get_schema_cls() -> type[SequenceEncoderConfig]: method input_shape (line 108) | def input_shape(self) -> torch.Size: method output_shape (line 112) | def output_shape(self) -> torch.Size: class SequenceEmbedEncoder (line 118) | class SequenceEmbedEncoder(SequenceEncoder): method __init__ (line 119) | def __init__( method forward (line 231) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 242) | def get_schema_cls() -> type[SequenceEncoderConfig]: method input_shape (line 246) | def input_shape(self) -> torch.Size: method output_shape (line 250) | def output_shape(self) -> torch.Size: class ParallelCNN (line 257) | class ParallelCNN(SequenceEncoder): method __init__ (line 258) | def __init__( method forward (line 510) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 541) | def get_schema_cls() -> type[SequenceEncoderConfig]: method input_shape (line 545) | def input_shape(self) -> torch.Size: method output_shape (line 549) | def output_shape(self) -> torch.Size: class StackedCNN (line 558) | class StackedCNN(SequenceEncoder): method __init__ (line 559) | def __init__( method get_schema_cls (line 848) | def get_schema_cls() -> type[SequenceEncoderConfig]: method input_shape (line 852) | def input_shape(self) -> torch.Size: method output_shape (line 856) | def output_shape(self) -> torch.Size: method forward (line 861) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... class StackedParallelCNN (line 896) | class StackedParallelCNN(SequenceEncoder): method __init__ (line 897) | def __init__( method get_schema_cls (line 1161) | def get_schema_cls() -> type[SequenceEncoderConfig]: method input_shape (line 1165) | def input_shape(self) -> torch.Size: method output_shape (line 1169) | def output_shape(self) -> torch.Size: method forward (line 1174) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... class StackedRNN (line 1209) | class StackedRNN(SequenceEncoder): method __init__ (line 1210) | def __init__( method get_schema_cls (line 1431) | def get_schema_cls() -> type[SequenceEncoderConfig]: method input_shape (line 1435) | def input_shape(self) -> torch.Size: method output_shape (line 1439) | def output_shape(self) -> torch.Size: method input_dtype (line 1444) | def input_dtype(self): method forward (line 1447) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... class StackedCNNRNN (line 1480) | class StackedCNNRNN(SequenceEncoder): method __init__ (line 1481) | def __init__( method get_schema_cls (line 1714) | def get_schema_cls() -> type[SequenceEncoderConfig]: method input_shape (line 1718) | def input_shape(self) -> torch.Size: method output_shape (line 1722) | def output_shape(self) -> torch.Size: method forward (line 1727) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... class StackedTransformer (line 1767) | class StackedTransformer(SequenceEncoder): method __init__ (line 1768) | def __init__( method get_schema_cls (line 1986) | def get_schema_cls() -> type[SequenceEncoderConfig]: method input_shape (line 1990) | def input_shape(self) -> torch.Size: method output_shape (line 1994) | def output_shape(self) -> torch.Size: method forward (line 1999) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... FILE: ludwig/encoders/set_encoders.py class SetSparseEncoder (line 36) | class SetSparseEncoder(Encoder): method __init__ (line 37) | def __init__( method forward (line 93) | def forward(self, inputs: torch.Tensor) -> EncoderOutputDict: method get_schema_cls (line 108) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 112) | def input_shape(self) -> torch.Size: method output_shape (line 116) | def output_shape(self) -> torch.Size: FILE: ludwig/encoders/text_encoders.py function _cls_pooled_error_message (line 72) | def _cls_pooled_error_message(encoder: str): class HFTextEncoder (line 78) | class HFTextEncoder(Encoder): method _init_config (line 79) | def _init_config(self, transformer, schema_keys: list[str], encoder_co... method _init_transformer_from_scratch (line 101) | def _init_transformer_from_scratch( method _maybe_resize_token_embeddings (line 120) | def _maybe_resize_token_embeddings(self, transformer, vocab_size: int)... method _wrap_transformer (line 140) | def _wrap_transformer( method get_embedding_layer (line 158) | def get_embedding_layer(self) -> nn.Module: class HFTextEncoderImpl (line 167) | class HFTextEncoderImpl(HFTextEncoder): method __init__ (line 168) | def __init__( method forward (line 208) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method input_shape (line 224) | def input_shape(self) -> torch.Size: method output_shape (line 228) | def output_shape(self) -> torch.Size: method input_dtype (line 243) | def input_dtype(self) -> torch.dtype: class ALBERTEncoder (line 249) | class ALBERTEncoder(HFTextEncoder): method __init__ (line 252) | def __init__( method forward (line 331) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 348) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 352) | def input_shape(self) -> torch.Size: method output_shape (line 356) | def output_shape(self) -> torch.Size: method input_dtype (line 371) | def input_dtype(self) -> torch.dtype: class MT5Encoder (line 377) | class MT5Encoder(HFTextEncoder): method __init__ (line 380) | def __init__( method forward (line 458) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 470) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 474) | def input_shape(self) -> torch.Size: method output_shape (line 478) | def output_shape(self) -> torch.Size: method input_dtype (line 493) | def input_dtype(self) -> torch.dtype: class XLMRoBERTaEncoder (line 499) | class XLMRoBERTaEncoder(HFTextEncoder): method __init__ (line 502) | def __init__( method forward (line 555) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 572) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 576) | def input_shape(self) -> torch.Size: method output_shape (line 580) | def output_shape(self) -> torch.Size: method input_dtype (line 595) | def input_dtype(self) -> torch.dtype: class BERTEncoder (line 601) | class BERTEncoder(HFTextEncoder): method __init__ (line 604) | def __init__( method forward (line 677) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 694) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 698) | def input_shape(self) -> torch.Size: method output_shape (line 703) | def output_shape(self) -> torch.Size: method input_dtype (line 718) | def input_dtype(self) -> torch.dtype: class XLMEncoder (line 724) | class XLMEncoder(HFTextEncoder): method __init__ (line 727) | def __init__( method forward (line 820) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 833) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 837) | def input_shape(self) -> torch.Size: method output_shape (line 842) | def output_shape(self) -> torch.Size: method input_dtype (line 857) | def input_dtype(self) -> torch.dtype: class GPTEncoder (line 863) | class GPTEncoder(HFTextEncoder): method __init__ (line 866) | def __init__( method forward (line 932) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 945) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 949) | def input_shape(self) -> torch.Size: method output_shape (line 953) | def output_shape(self) -> torch.Size: method input_dtype (line 961) | def input_dtype(self) -> torch.dtype: class GPT2Encoder (line 967) | class GPT2Encoder(HFTextEncoder): method __init__ (line 970) | def __init__( method forward (line 1037) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1050) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 1054) | def input_shape(self) -> torch.Size: method output_shape (line 1058) | def output_shape(self) -> torch.Size: method input_dtype (line 1066) | def input_dtype(self) -> torch.dtype: class DeBERTaEncoder (line 1072) | class DeBERTaEncoder(HFTextEncoderImpl): method __init__ (line 1073) | def __init__(self, *args, **kwargs): method get_schema_cls (line 1080) | def get_schema_cls() -> type[BaseEncoderConfig]: class RoBERTaEncoder (line 1086) | class RoBERTaEncoder(HFTextEncoder): method __init__ (line 1089) | def __init__( method forward (line 1139) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1155) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 1159) | def input_shape(self) -> torch.Size: method output_shape (line 1163) | def output_shape(self) -> torch.Size: method input_dtype (line 1172) | def input_dtype(self) -> torch.dtype: class TransformerXLEncoder (line 1178) | class TransformerXLEncoder(HFTextEncoder): method __init__ (line 1181) | def __init__( method forward (line 1274) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1281) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 1285) | def input_shape(self) -> torch.Size: method output_shape (line 1289) | def output_shape(self) -> torch.Size: method input_dtype (line 1298) | def input_dtype(self) -> torch.dtype: class XLNetEncoder (line 1304) | class XLNetEncoder(HFTextEncoder): method __init__ (line 1307) | def __init__( method forward (line 1401) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1414) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 1418) | def input_shape(self) -> torch.Size: method output_shape (line 1422) | def output_shape(self) -> torch.Size: method input_dtype (line 1430) | def input_dtype(self) -> torch.dtype: class DistilBERTEncoder (line 1436) | class DistilBERTEncoder(HFTextEncoder): method __init__ (line 1439) | def __init__( method forward (line 1509) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1524) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 1528) | def input_shape(self) -> torch.Size: method output_shape (line 1532) | def output_shape(self) -> torch.Size: method input_dtype (line 1542) | def input_dtype(self) -> torch.dtype: class CTRLEncoder (line 1548) | class CTRLEncoder(HFTextEncoder): method __init__ (line 1551) | def __init__( method forward (line 1617) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1630) | def get_schema_cls(): method input_shape (line 1634) | def input_shape(self) -> torch.Size: method output_shape (line 1638) | def output_shape(self) -> torch.Size: method input_dtype (line 1647) | def input_dtype(self) -> torch.dtype: class CamemBERTEncoder (line 1653) | class CamemBERTEncoder(HFTextEncoder): method __init__ (line 1656) | def __init__( method forward (line 1729) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1746) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 1750) | def input_shape(self) -> torch.Size: method output_shape (line 1754) | def output_shape(self) -> torch.Size: method input_dtype (line 1769) | def input_dtype(self) -> torch.dtype: class T5Encoder (line 1775) | class T5Encoder(HFTextEncoder): method __init__ (line 1778) | def __init__( method forward (line 1842) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1855) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 1859) | def input_shape(self) -> torch.Size: method output_shape (line 1863) | def output_shape(self) -> torch.Size: method input_dtype (line 1878) | def input_dtype(self) -> torch.dtype: class FlauBERTEncoder (line 1884) | class FlauBERTEncoder(HFTextEncoder): method __init__ (line 1887) | def __init__( method forward (line 1981) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 1994) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 1998) | def input_shape(self) -> torch.Size: method output_shape (line 2002) | def output_shape(self) -> torch.Size: method input_dtype (line 2017) | def input_dtype(self) -> torch.dtype: class ELECTRAEncoder (line 2023) | class ELECTRAEncoder(HFTextEncoder): method __init__ (line 2026) | def __init__( method forward (line 2096) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 2109) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 2113) | def input_shape(self) -> torch.Size: method output_shape (line 2117) | def output_shape(self) -> torch.Size: method input_dtype (line 2132) | def input_dtype(self) -> torch.dtype: class LongformerEncoder (line 2138) | class LongformerEncoder(HFTextEncoder): method __init__ (line 2141) | def __init__( method forward (line 2190) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 2206) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 2210) | def input_shape(self) -> torch.Size: method output_shape (line 2214) | def output_shape(self) -> torch.Size: method input_dtype (line 2229) | def input_dtype(self) -> torch.dtype: class AutoTransformerEncoder (line 2235) | class AutoTransformerEncoder(HFTextEncoder): method __init__ (line 2238) | def __init__( method _maybe_resize_token_embeddings (line 2274) | def _maybe_resize_token_embeddings(self, transformer, vocab_size: int ... method forward (line 2282) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method get_schema_cls (line 2307) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 2311) | def input_shape(self) -> torch.Size: method output_shape (line 2315) | def output_shape(self) -> torch.Size: method input_dtype (line 2331) | def input_dtype(self) -> torch.dtype: class TfIdfEncoder (line 2337) | class TfIdfEncoder(Encoder): method __init__ (line 2338) | def __init__( method forward (line 2360) | def forward(self, t: torch.Tensor, mask: torch.Tensor | None = None) -... method get_schema_cls (line 2373) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 2377) | def input_shape(self) -> torch.Size: method output_shape (line 2381) | def output_shape(self) -> torch.Size: method get_embedding_layer (line 2384) | def get_embedding_layer(self) -> nn.Module: class LLMEncoder (line 2390) | class LLMEncoder(Encoder): method __init__ (line 2399) | def __init__(self, encoder_config: LLMEncoderConfig = None, **kwargs): method get_schema_cls (line 2444) | def get_schema_cls() -> type[BaseEncoderConfig]: method input_shape (line 2448) | def input_shape(self) -> torch.Size: method output_shape (line 2452) | def output_shape(self) -> torch.Size: method get_embedding_layer (line 2455) | def get_embedding_layer(self) -> nn.Module: method initialize_adapter (line 2458) | def initialize_adapter(self): method prepare_for_training (line 2471) | def prepare_for_training(self): method prepare_for_quantized_training (line 2477) | def prepare_for_quantized_training(self): method forward (line 2482) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method _save_to_state_dict (line 2488) | def _save_to_state_dict(self, destination: dict, prefix: str, keep_var... method state_dict (line 2504) | def state_dict(self, *args, destination=None, prefix="", keep_vars=Fal... method _load_from_state_dict (line 2516) | def _load_from_state_dict( method remove_missing_non_adapter_keys (line 2537) | def remove_missing_non_adapter_keys(self, module, incompatible_keys): FILE: ludwig/encoders/types.py class EncoderOutputDict (line 6) | class EncoderOutputDict(TypedDict, total=False): FILE: ludwig/error.py class LudwigError (line 20) | class LudwigError(Exception): method __reduce__ (line 23) | def __reduce__(self): class InputDataError (line 32) | class InputDataError(LudwigError, ValueError): method __init__ (line 44) | def __init__(self, column_name: str, feature_type: str, message: str): method __str__ (line 50) | def __str__(self): method __reduce__ (line 53) | def __reduce__(self): class ConfigValidationError (line 58) | class ConfigValidationError(LudwigError, ValueError): method __init__ (line 67) | def __init__(self, message: str): method __reduce__ (line 71) | def __reduce__(self): FILE: ludwig/evaluate.py function evaluate_cli (line 33) | def evaluate_cli( function cli (line 129) | def cli(sys_argv): FILE: ludwig/experiment.py function experiment_cli (line 36) | def experiment_cli( function kfold_cross_validate_cli (line 245) | def kfold_cross_validate_cli( function cli (line 285) | def cli(sys_argv): FILE: ludwig/explain/captum.py class ExplanationRunConfig (line 49) | class ExplanationRunConfig: function retry_with_halved_batch_size (line 59) | def retry_with_halved_batch_size(run_config: ExplanationRunConfig): class WrapperModule (line 96) | class WrapperModule(torch.nn.Module): method __init__ (line 105) | def __init__(self, model: ECD, target: str): method forward (line 118) | def forward(self, *args): class IntegratedGradientsExplainer (line 155) | class IntegratedGradientsExplainer(Explainer): method explain (line 156) | def explain(self) -> ExplanationsResult: function get_input_tensors (line 269) | def get_input_tensors( function get_baseline (line 345) | def get_baseline(model: LudwigModel, sample_encoded: list[Variable]) -> ... function get_total_attribution (line 373) | def get_total_attribution( function get_token_attributions (line 494) | def get_token_attributions( FILE: ludwig/explain/captum_ray.py class RayIntegratedGradientsExplainer (line 26) | class RayIntegratedGradientsExplainer(IntegratedGradientsExplainer): method __init__ (line 27) | def __init__(self, *args, resources_per_task: dict[str, Any] = None, n... method explain (line 32) | def explain(self) -> ExplanationsResult: function get_input_tensors_task (line 164) | def get_input_tensors_task( function get_total_attribution_task (line 177) | def get_total_attribution_task( function split_list (line 206) | def split_list(v, n): FILE: ludwig/explain/explainer.py class Explainer (line 13) | class Explainer(metaclass=ABCMeta): method __init__ (line 14) | def __init__( method is_binary_target (line 46) | def is_binary_target(self) -> bool: method is_category_target (line 51) | def is_category_target(self) -> bool: method vocab_size (line 56) | def vocab_size(self) -> int: method explain (line 68) | def explain(self) -> ExplanationsResult: FILE: ludwig/explain/explanation.py class FeatureAttribution (line 11) | class FeatureAttribution: class LabelExplanation (line 26) | class LabelExplanation: method add (line 32) | def add(self, feature_name: str, attribution: float, token_attribution... method to_array (line 36) | def to_array(self) -> npt.NDArray[np.float64]: class Explanation (line 43) | class Explanation: method add (line 54) | def add( method to_array (line 81) | def to_array(self) -> npt.NDArray[np.float64]: class ExplanationsResult (line 88) | class ExplanationsResult: FILE: ludwig/explain/util.py function filter_cols (line 12) | def filter_cols(df, cols): function prepare_data (line 18) | def prepare_data(model: LudwigModel, inputs_df: pd.DataFrame, sample_df:... function get_pred_col (line 34) | def get_pred_col(preds, target): function get_feature_name (line 45) | def get_feature_name(model: LudwigModel, target: str) -> str: function get_absolute_module_key_from_submodule (line 53) | def get_absolute_module_key_from_submodule(module: torch.nn.Module, subm... function replace_layer_with_copy (line 72) | def replace_layer_with_copy(feat: BaseFeature, target_layer: torch.nn.Mo... FILE: ludwig/export.py function export_torchscript (line 30) | def export_torchscript( function export_triton (line 60) | def export_triton(model_path, output_path="model_repository", model_name... function export_mlflow (line 89) | def export_mlflow(model_path, output_path="mlflow", registered_model_nam... function cli_export_torchscript (line 115) | def cli_export_torchscript(sys_argv): function cli_export_triton (line 182) | def cli_export_triton(sys_argv): function cli_export_mlflow (line 229) | def cli_export_mlflow(sys_argv): FILE: ludwig/features/audio_feature.py class _AudioPreprocessing (line 54) | class _AudioPreprocessing(torch.nn.Module): method __init__ (line 57) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 69) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class AudioFeatureMixin (line 88) | class AudioFeatureMixin(BaseFeatureMixin): method type (line 90) | def type(): method cast_column (line 94) | def cast_column(column, backend): method get_feature_meta (line 98) | def get_feature_meta( method _get_feature_dim (line 121) | def _get_feature_dim(preprocessing_parameters: PreprocessingConfigDict... method _process_in_memory (line 144) | def _process_in_memory( method _transform_to_feature (line 224) | def _transform_to_feature( method _get_stats (line 264) | def _get_stats(audio, sampling_rate_in_hz, max_length_in_s): method _merge_stats (line 276) | def _merge_stats(merged_stats, audio_stats): method _get_2D_feature (line 285) | def _get_2D_feature( method add_feature_data (line 346) | def add_feature_data( method _get_max_length_feature (line 413) | def _get_max_length_feature( class AudioInputFeature (line 439) | class AudioInputFeature(AudioFeatureMixin, SequenceInputFeature): method __init__ (line 440) | def __init__(self, input_feature_config: AudioInputFeatureConfig, enco... method forward (line 448) | def forward(self, inputs, mask=None): method input_shape (line 458) | def input_shape(self) -> torch.Size: method input_dtype (line 462) | def input_dtype(self): method update_config_with_metadata (line 466) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method create_preproc_module (line 472) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... method get_schema_cls (line 476) | def get_schema_cls(): FILE: ludwig/features/bag_feature.py class BagFeatureMixin (line 33) | class BagFeatureMixin(BaseFeatureMixin): method type (line 35) | def type(): method cast_column (line 39) | def cast_column(column, backend): method get_feature_meta (line 43) | def get_feature_meta( method feature_data (line 66) | def feature_data(column, metadata, preprocessing_parameters: Preproces... method add_feature_data (line 77) | def add_feature_data( class BagInputFeature (line 95) | class BagInputFeature(BagFeatureMixin, InputFeature): method __init__ (line 96) | def __init__(self, input_feature_config: BagInputFeatureConfig, encode... method forward (line 104) | def forward(self, inputs): method input_shape (line 113) | def input_shape(self) -> torch.Size: method output_shape (line 117) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 121) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 125) | def get_schema_cls(): method create_preproc_module (line 129) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... FILE: ludwig/features/base_feature.py class BaseFeatureMixin (line 57) | class BaseFeatureMixin(ABC): method type (line 64) | def type() -> str: method cast_column (line 69) | def cast_column(column: DataFrame, backend) -> DataFrame: method get_feature_meta (line 79) | def get_feature_meta( method add_feature_data (line 97) | def add_feature_data( class ModuleWrapper (line 121) | class ModuleWrapper: class PredictModule (line 131) | class PredictModule(torch.nn.Module): method __init__ (line 138) | def __init__(self): class BaseFeature (line 145) | class BaseFeature: method __init__ (line 153) | def __init__(self, feature: BaseFeatureConfig): class InputFeature (line 167) | class InputFeature(BaseFeature, LudwigModule, ABC): method create_sample_input (line 170) | def create_sample_input(self, batch_size: int = 2): method unskip (line 174) | def unskip(self) -> "InputFeature": method update_config_with_metadata (line 180) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method update_config_after_module_init (line 183) | def update_config_after_module_init(self, feature_config): method initialize_encoder (line 186) | def initialize_encoder(self, encoder_config): method get_preproc_input_dtype (line 193) | def get_preproc_input_dtype(cls, metadata: TrainingSetMetadataDict) ->... method create_preproc_module (line 197) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... class OutputFeature (line 201) | class OutputFeature(BaseFeature, LudwigModule, ABC): method __init__ (line 204) | def __init__( method create_sample_output (line 260) | def create_sample_output(self, batch_size: int = 2): method get_prediction_set (line 266) | def get_prediction_set(self): method get_output_dtype (line 275) | def get_output_dtype(cls): method initialize_decoder (line 278) | def initialize_decoder(self, decoder_config): method train_loss (line 286) | def train_loss(self, targets: Tensor, predictions: dict[str, Tensor], ... method eval_loss (line 291) | def eval_loss(self, targets: Tensor, predictions: dict[str, Tensor]): method _setup_loss (line 301) | def _setup_loss(self): method _setup_metrics (line 305) | def _setup_metrics(self): method create_calibration_module (line 318) | def create_calibration_module(self, feature: BaseOutputFeatureConfig) ... method eval_loss_metric (line 323) | def eval_loss_metric(self) -> LudwigMetric: method calibration_module (line 327) | def calibration_module(self) -> torch.nn.Module: method create_predict_module (line 332) | def create_predict_module(self) -> PredictModule: method prediction_module (line 340) | def prediction_module(self) -> PredictModule: method predictions (line 344) | def predictions(self, all_decoder_outputs: dict[str, torch.Tensor], fe... method logits (line 358) | def logits(self, combiner_outputs: dict[str, torch.Tensor], target=Non... method metric_kwargs (line 371) | def metric_kwargs(self) -> dict[str, Any]: method update_metrics (line 375) | def update_metrics(self, targets: Tensor, predictions: dict[str, Tenso... method get_metrics (line 387) | def get_metrics(self): method reset_metrics (line 407) | def reset_metrics(self): method forward (line 412) | def forward( method postprocess_predictions (line 463) | def postprocess_predictions( method get_postproc_output_dtype (line 471) | def get_postproc_output_dtype(cls, metadata: TrainingSetMetadataDict) ... method create_postproc_module (line 475) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... method update_config_with_metadata (line 480) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 485) | def calculate_overall_stats(predictions, targets, train_set_metadata): method output_specific_fully_connected (line 488) | def output_specific_fully_connected(self, inputs, mask=None): method prepare_decoder_inputs (line 507) | def prepare_decoder_inputs( class PassthroughPreprocModule (line 537) | class PassthroughPreprocModule(torch.nn.Module): method __init__ (line 545) | def __init__(self, preproc: torch.nn.Module, encoder: torch.nn.Module): method forward (line 549) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: function create_passthrough_input_feature (line 554) | def create_passthrough_input_feature(feature: InputFeature, config: Base... FILE: ludwig/features/binary_feature.py class _BinaryPreprocessing (line 46) | class _BinaryPreprocessing(torch.nn.Module): method __init__ (line 47) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 53) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class _BinaryPostprocessing (line 70) | class _BinaryPostprocessing(torch.nn.Module): method __init__ (line 71) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 78) | def forward(self, preds: dict[str, torch.Tensor], feature_name: str) -... class _BinaryPredict (line 94) | class _BinaryPredict(PredictModule): method __init__ (line 95) | def __init__(self, threshold, calibration_module=None): method forward (line 100) | def forward(self, inputs: dict[str, torch.Tensor], feature_name: str) ... class BinaryFeatureMixin (line 116) | class BinaryFeatureMixin(BaseFeatureMixin): method type (line 118) | def type(): method cast_column (line 122) | def cast_column(column, backend): method get_feature_meta (line 144) | def get_feature_meta( method add_feature_data (line 180) | def add_feature_data( class BinaryInputFeature (line 204) | class BinaryInputFeature(BinaryFeatureMixin, InputFeature): method __init__ (line 205) | def __init__(self, input_feature_config: BinaryInputFeatureConfig, enc... method forward (line 214) | def forward(self, inputs): method input_dtype (line 232) | def input_dtype(self): method input_shape (line 236) | def input_shape(self) -> torch.Size: method output_shape (line 240) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 244) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 248) | def get_schema_cls(): method create_sample_input (line 251) | def create_sample_input(self, batch_size: int = 2): method get_preproc_input_dtype (line 255) | def get_preproc_input_dtype(cls, metadata: TrainingSetMetadataDict) ->... method create_preproc_module (line 259) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... class BinaryOutputFeature (line 263) | class BinaryOutputFeature(BinaryFeatureMixin, OutputFeature): method __init__ (line 264) | def __init__( method logits (line 276) | def logits(self, inputs, **kwargs): method create_calibration_module (line 280) | def create_calibration_module(self, feature: BinaryOutputFeatureConfig... method create_predict_module (line 291) | def create_predict_module(self) -> PredictModule: method get_prediction_set (line 297) | def get_prediction_set(self): method get_output_dtype (line 301) | def get_output_dtype(cls): method output_shape (line 305) | def output_shape(self) -> torch.Size: method input_shape (line 309) | def input_shape(self) -> torch.Size: method update_config_with_metadata (line 313) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 317) | def calculate_overall_stats(predictions, targets, train_set_metadata): method postprocess_predictions (line 347) | def postprocess_predictions( method get_schema_cls (line 383) | def get_schema_cls(): method get_postproc_output_dtype (line 387) | def get_postproc_output_dtype(cls, metadata: TrainingSetMetadataDict) ... method create_postproc_module (line 391) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... method metric_kwargs (line 394) | def metric_kwargs(self) -> dict: FILE: ludwig/features/category_feature.py class _CategoryPreprocessing (line 61) | class _CategoryPreprocessing(torch.nn.Module): method __init__ (line 62) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 72) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class _CategoryPostprocessing (line 80) | class _CategoryPostprocessing(torch.nn.Module): method __init__ (line 81) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 88) | def forward(self, preds: dict[str, torch.Tensor], feature_name: str) -... class _CategoryPredict (line 100) | class _CategoryPredict(PredictModule): method __init__ (line 101) | def __init__(self, calibration_module=None, use_cumulative_probs=False): method forward (line 110) | def forward(self, inputs: dict[str, torch.Tensor], feature_name: str) ... class CategoryFeatureMixin (line 138) | class CategoryFeatureMixin(BaseFeatureMixin): method type (line 140) | def type(): method cast_column (line 144) | def cast_column(column, backend): method get_feature_meta (line 148) | def get_feature_meta( method feature_data (line 196) | def feature_data(backend, column, metadata): method add_feature_data (line 236) | def add_feature_data( class CategoryDistributionFeatureMixin (line 254) | class CategoryDistributionFeatureMixin(VectorFeatureMixin): method type (line 256) | def type(): method get_feature_meta (line 260) | def get_feature_meta( class CategoryInputFeature (line 277) | class CategoryInputFeature(CategoryFeatureMixin, InputFeature): method __init__ (line 278) | def __init__(self, input_feature_config: CategoryInputFeatureConfig, e... method forward (line 286) | def forward(self, inputs): method input_dtype (line 299) | def input_dtype(self): method input_shape (line 303) | def input_shape(self) -> torch.Size: method output_shape (line 307) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 311) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 316) | def get_schema_cls(): method create_preproc_module (line 320) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... class CategoryOutputFeature (line 324) | class CategoryOutputFeature(CategoryFeatureMixin, OutputFeature): method __init__ (line 325) | def __init__( method logits (line 344) | def logits(self, inputs, **kwargs): # hidden method create_calibration_module (line 352) | def create_calibration_module(self, feature: CategoryOutputFeatureConf... method create_predict_module (line 363) | def create_predict_module(self) -> PredictModule: method get_prediction_set (line 368) | def get_prediction_set(self): method input_shape (line 372) | def input_shape(self) -> torch.Size: method get_output_dtype (line 376) | def get_output_dtype(cls): method output_shape (line 380) | def output_shape(self) -> torch.Size: method metric_kwargs (line 383) | def metric_kwargs(self): method update_config_with_metadata (line 387) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 480) | def calculate_overall_stats(predictions, targets, train_set_metadata): method postprocess_predictions (line 489) | def postprocess_predictions( method get_schema_cls (line 524) | def get_schema_cls(): method create_postproc_module (line 528) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... class CategoryDistributionOutputFeature (line 532) | class CategoryDistributionOutputFeature(CategoryDistributionFeatureMixin... method input_shape (line 534) | def input_shape(self) -> torch.Size: method get_output_dtype (line 538) | def get_output_dtype(cls): method output_shape (line 542) | def output_shape(self) -> torch.Size: method get_schema_cls (line 546) | def get_schema_cls(): FILE: ludwig/features/date_feature.py class _DatePreprocessing (line 40) | class _DatePreprocessing(torch.nn.Module): method __init__ (line 41) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 44) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class DateFeatureMixin (line 54) | class DateFeatureMixin(BaseFeatureMixin): method type (line 56) | def type(): method cast_column (line 60) | def cast_column(column, backend): method get_feature_meta (line 64) | def get_feature_meta( method date_to_list (line 74) | def date_to_list(date_value, datetime_format, preprocessing_parameters): method add_feature_data (line 106) | def add_feature_data( class DateInputFeature (line 125) | class DateInputFeature(DateFeatureMixin, InputFeature): method __init__ (line 126) | def __init__(self, input_feature_config: DateInputFeatureConfig, encod... method forward (line 134) | def forward(self, inputs): method input_dtype (line 141) | def input_dtype(self): method input_shape (line 145) | def input_shape(self) -> torch.Size: method output_shape (line 149) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 153) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method create_sample_input (line 156) | def create_sample_input(self, batch_size: int = 2): method get_schema_cls (line 161) | def get_schema_cls(): method create_preproc_module (line 165) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... FILE: ludwig/features/feature_registries.py function get_base_type_registry (line 61) | def get_base_type_registry() -> dict: function get_input_type_registry (line 81) | def get_input_type_registry() -> dict: function get_output_type_registry (line 100) | def get_output_type_registry() -> dict: function update_config_with_metadata (line 115) | def update_config_with_metadata(config_obj: "ModelConfig", training_set_... function update_config_with_model (line 127) | def update_config_with_model(config_obj: "ModelConfig", model: "BaseMode... FILE: ludwig/features/feature_utils.py function should_regularize (line 30) | def should_regularize(regularize_layers): function set_str_to_idx (line 39) | def set_str_to_idx(set_string, feature_dict, tokenizer_name): function compute_token_probabilities (line 50) | def compute_token_probabilities( function compute_sequence_probability (line 83) | def compute_sequence_probability( function sanitize (line 106) | def sanitize(name): function compute_feature_hash (line 111) | def compute_feature_hash(feature: dict) -> str: function get_input_size_with_dependencies (line 127) | def get_input_size_with_dependencies( function get_module_dict_key_from_name (line 147) | def get_module_dict_key_from_name(name: str, feature_name_suffix: str = ... function get_name_from_module_dict_key (line 153) | def get_name_from_module_dict_key(key: str, feature_name_suffix_length: ... class LudwigFeatureDict (line 159) | class LudwigFeatureDict(torch.nn.Module): method __init__ (line 173) | def __init__(self): method get (line 178) | def get(self, key) -> torch.nn.Module: method set (line 181) | def set(self, key: str, module: torch.nn.Module) -> None: method __len__ (line 186) | def __len__(self) -> int: method __next__ (line 189) | def __next__(self) -> None: method __iter__ (line 192) | def __iter__(self) -> None: method keys (line 195) | def keys(self) -> list[str]: method values (line 201) | def values(self) -> list[torch.nn.Module]: method items (line 204) | def items(self) -> list[tuple[str, torch.nn.Module]]: method update (line 209) | def update(self, modules: dict[str, torch.nn.Module]) -> None: FILE: ludwig/features/h3_feature.py class _H3Preprocessing (line 34) | class _H3Preprocessing(torch.nn.Module): method __init__ (line 35) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 41) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class H3FeatureMixin (line 67) | class H3FeatureMixin(BaseFeatureMixin): method type (line 69) | def type(): method cast_column (line 73) | def cast_column(column, backend): method get_feature_meta (line 81) | def get_feature_meta( method h3_to_list (line 91) | def h3_to_list(h3_int): method add_feature_data (line 98) | def add_feature_data( class H3InputFeature (line 118) | class H3InputFeature(H3FeatureMixin, InputFeature): method __init__ (line 119) | def __init__(self, input_feature_config: H3InputFeatureConfig, encoder... method forward (line 127) | def forward(self, inputs): method input_dtype (line 137) | def input_dtype(self): method input_shape (line 141) | def input_shape(self) -> torch.Size: method output_shape (line 145) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 149) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method create_preproc_module (line 153) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... method get_schema_cls (line 157) | def get_schema_cls(): FILE: ludwig/features/image_feature.py class AutoAugment (line 110) | class AutoAugment(torch.nn.Module): method __init__ (line 111) | def __init__(self, config: AutoAugmentationConfig): method get_augmentation_method (line 116) | def get_augmentation_method(self): method forward (line 125) | def forward(self, imgs: torch.Tensor) -> torch.Tensor: class RandomVFlip (line 134) | class RandomVFlip(torch.nn.Module): method __init__ (line 135) | def __init__( method forward (line 141) | def forward(self, imgs): class RandomHFlip (line 149) | class RandomHFlip(torch.nn.Module): method __init__ (line 150) | def __init__( method forward (line 156) | def forward(self, imgs): class RandomRotate (line 164) | class RandomRotate(torch.nn.Module): method __init__ (line 165) | def __init__(self, config: RandomRotateConfig): method forward (line 169) | def forward(self, imgs): class RandomContrast (line 179) | class RandomContrast(torch.nn.Module): method __init__ (line 180) | def __init__(self, config: RandomContrastConfig): method forward (line 185) | def forward(self, imgs): class RandomBrightness (line 195) | class RandomBrightness(torch.nn.Module): method __init__ (line 196) | def __init__(self, config: RandomBrightnessConfig): method forward (line 201) | def forward(self, imgs): class RandomBlur (line 211) | class RandomBlur(torch.nn.Module): method __init__ (line 212) | def __init__(self, config: RandomBlurConfig): method forward (line 216) | def forward(self, imgs): class ImageAugmentation (line 223) | class ImageAugmentation(torch.nn.Module): method __init__ (line 224) | def __init__( method forward (line 249) | def forward(self, imgs): method _convert_back_to_uint8 (line 266) | def _convert_back_to_uint8(self, images): method _renormalize_image (line 277) | def _renormalize_image(self, images): class ImageTransformMetadata (line 287) | class ImageTransformMetadata: function _get_torchvision_transform (line 293) | def _get_torchvision_transform( function _get_torchvision_parameters (line 320) | def _get_torchvision_parameters(model_type: str, model_variant: str) -> ... function is_torchvision_encoder (line 324) | def is_torchvision_encoder(encoder_obj: Encoder) -> bool: class _ImagePreprocessing (line 331) | class _ImagePreprocessing(torch.nn.Module): method __init__ (line 334) | def __init__( method forward (line 355) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class _ImagePostprocessing (line 416) | class _ImagePostprocessing(torch.nn.Module): method __init__ (line 417) | def __init__(self): method forward (line 422) | def forward(self, preds: dict[str, torch.Tensor], feature_name: str) -... class _ImagePredict (line 429) | class _ImagePredict(PredictModule): method forward (line 430) | def forward(self, inputs: dict[str, torch.Tensor], feature_name: str) ... class ImageFeatureMixin (line 437) | class ImageFeatureMixin(BaseFeatureMixin): method type (line 439) | def type(): method cast_column (line 443) | def cast_column(column, backend): method get_feature_meta (line 447) | def get_feature_meta( method _read_image_if_bytes_obj_and_resize (line 457) | def _read_image_if_bytes_obj_and_resize( method _read_image_with_pretrained_transform (line 559) | def _read_image_with_pretrained_transform( method _set_image_and_height_equal_for_encoder (line 581) | def _set_image_and_height_equal_for_encoder( method _infer_image_size (line 611) | def _infer_image_size( method _infer_number_of_channels (line 647) | def _infer_number_of_channels(image_sample: list[torch.Tensor]): method _infer_image_num_classes (line 684) | def _infer_image_num_classes( method _finalize_preprocessing_parameters (line 719) | def _finalize_preprocessing_parameters( method add_feature_data (line 853) | def add_feature_data( class ImageInputFeature (line 1002) | class ImageInputFeature(ImageFeatureMixin, InputFeature): method __init__ (line 1003) | def __init__(self, input_feature_config: ImageInputFeatureConfig, enco... method forward (line 1034) | def forward(self, inputs: torch.Tensor) -> torch.Tensor: method input_dtype (line 1043) | def input_dtype(self): method input_shape (line 1047) | def input_shape(self) -> torch.Size: method output_shape (line 1051) | def output_shape(self) -> torch.Size: method update_config_after_module_init (line 1054) | def update_config_after_module_init(self, feature_config): method update_config_with_metadata (line 1066) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 1072) | def get_schema_cls(): method create_preproc_module (line 1076) | def create_preproc_module(metadata: dict[str, Any]) -> torch.nn.Module: method get_augmentation_pipeline (line 1094) | def get_augmentation_pipeline(self): class ImageOutputFeature (line 1098) | class ImageOutputFeature(ImageFeatureMixin, OutputFeature): method __init__ (line 1099) | def __init__( method logits (line 1110) | def logits(self, inputs: dict[str, torch.Tensor], target=None, **kwargs): method metric_kwargs (line 1113) | def metric_kwargs(self): method create_predict_module (line 1116) | def create_predict_module(self) -> PredictModule: method get_prediction_set (line 1119) | def get_prediction_set(self): method get_output_dtype (line 1123) | def get_output_dtype(cls): method output_shape (line 1127) | def output_shape(self) -> torch.Size: method input_shape (line 1131) | def input_shape(self) -> torch.Size: method update_config_with_metadata (line 1135) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 1141) | def calculate_overall_stats(predictions, targets, metadata): method postprocess_predictions (line 1145) | def postprocess_predictions( method create_postproc_module (line 1166) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... method get_schema_cls (line 1170) | def get_schema_cls(): FILE: ludwig/features/number_feature.py class NumberTransformer (line 43) | class NumberTransformer(nn.Module, ABC): method transform (line 45) | def transform(self, x: np.ndarray) -> np.ndarray: method inverse_transform (line 49) | def inverse_transform(self, x: np.ndarray) -> np.ndarray: method transform_inference (line 53) | def transform_inference(self, x: torch.Tensor) -> torch.Tensor: method inverse_transform_inference (line 57) | def inverse_transform_inference(self, x: torch.Tensor) -> torch.Tensor: method fit_transform_params (line 62) | def fit_transform_params(column: np.ndarray, backend: Any) -> dict[str... class ZScoreTransformer (line 66) | class ZScoreTransformer(NumberTransformer): method __init__ (line 67) | def __init__(self, mean: float = None, std: float = None, **kwargs: di... method transform (line 80) | def transform(self, x: np.ndarray) -> np.ndarray: method inverse_transform (line 83) | def inverse_transform(self, x: np.ndarray) -> np.ndarray: method transform_inference (line 86) | def transform_inference(self, x: torch.Tensor) -> torch.Tensor: method inverse_transform_inference (line 89) | def inverse_transform_inference(self, x: torch.Tensor) -> torch.Tensor: method fit_transform_params (line 93) | def fit_transform_params(column: np.ndarray, backend: "Backend") -> di... class MinMaxTransformer (line 101) | class MinMaxTransformer(NumberTransformer): method __init__ (line 102) | def __init__(self, min: float = None, max: float = None, **kwargs: dict): method transform (line 111) | def transform(self, x: np.ndarray) -> np.ndarray: method inverse_transform (line 114) | def inverse_transform(self, x: np.ndarray) -> np.ndarray: method transform_inference (line 119) | def transform_inference(self, x: torch.Tensor) -> torch.Tensor: method inverse_transform_inference (line 122) | def inverse_transform_inference(self, x: torch.Tensor) -> torch.Tensor: method fit_transform_params (line 128) | def fit_transform_params(column: np.ndarray, backend: "Backend") -> di... class InterQuartileTransformer (line 136) | class InterQuartileTransformer(NumberTransformer): method __init__ (line 137) | def __init__(self, q1: float = None, q2: float = None, q3: float = Non... method transform (line 153) | def transform(self, x: np.ndarray) -> np.ndarray: method inverse_transform (line 156) | def inverse_transform(self, x: np.ndarray) -> np.ndarray: method transform_inference (line 159) | def transform_inference(self, x: torch.Tensor) -> torch.Tensor: method inverse_transform_inference (line 162) | def inverse_transform_inference(self, x: torch.Tensor) -> torch.Tensor: method fit_transform_params (line 166) | def fit_transform_params(column: np.ndarray, backend: "Backend") -> di... class Log1pTransformer (line 176) | class Log1pTransformer(NumberTransformer): method __init__ (line 177) | def __init__(self, **kwargs: dict): method transform (line 181) | def transform(self, x: np.ndarray) -> np.ndarray: method inverse_transform (line 189) | def inverse_transform(self, x: np.ndarray) -> np.ndarray: method transform_inference (line 192) | def transform_inference(self, x: torch.Tensor) -> torch.Tensor: method inverse_transform_inference (line 195) | def inverse_transform_inference(self, x: torch.Tensor) -> torch.Tensor: method fit_transform_params (line 199) | def fit_transform_params(column: np.ndarray, backend: "Backend") -> di... class IdentityTransformer (line 203) | class IdentityTransformer(NumberTransformer): method __init__ (line 204) | def __init__(self, **kwargs): method transform (line 207) | def transform(self, x: np.ndarray) -> np.ndarray: method inverse_transform (line 210) | def inverse_transform(self, x: np.ndarray) -> np.ndarray: method transform_inference (line 213) | def transform_inference(self, x: torch.Tensor) -> torch.Tensor: method inverse_transform_inference (line 216) | def inverse_transform_inference(self, x: torch.Tensor) -> torch.Tensor: method fit_transform_params (line 220) | def fit_transform_params(column: np.ndarray, backend: "Backend") -> di... function get_transformer (line 233) | def get_transformer(metadata, preprocessing_parameters) -> NumberTransfo... class _OutlierReplacer (line 240) | class _OutlierReplacer(torch.nn.Module): method __init__ (line 241) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 247) | def forward(self, v: torch.Tensor) -> torch.Tensor: class _NumberPreprocessing (line 255) | class _NumberPreprocessing(torch.nn.Module): method __init__ (line 256) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 266) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class _NumberPostprocessing (line 280) | class _NumberPostprocessing(torch.nn.Module): method __init__ (line 281) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 286) | def forward(self, preds: dict[str, torch.Tensor], feature_name: str) -... class _NumberPredict (line 292) | class _NumberPredict(PredictModule): method __init__ (line 293) | def __init__(self, clip): method forward (line 297) | def forward(self, inputs: dict[str, torch.Tensor], feature_name: str) ... class NumberFeatureMixin (line 308) | class NumberFeatureMixin(BaseFeatureMixin): method type (line 310) | def type(): method cast_column (line 314) | def cast_column(column, backend): method get_feature_meta (line 318) | def get_feature_meta( method add_feature_data (line 340) | def add_feature_data( class NumberInputFeature (line 379) | class NumberInputFeature(NumberFeatureMixin, InputFeature): method __init__ (line 380) | def __init__(self, input_feature_config: NumberInputFeatureConfig, enc... method forward (line 389) | def forward(self, inputs): method input_shape (line 401) | def input_shape(self) -> torch.Size: method output_shape (line 405) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 409) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 413) | def get_schema_cls(): method create_sample_input (line 416) | def create_sample_input(self, batch_size: int = 2): method get_preproc_input_dtype (line 420) | def get_preproc_input_dtype(cls, metadata: TrainingSetMetadataDict) ->... method create_preproc_module (line 424) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... class NumberOutputFeature (line 428) | class NumberOutputFeature(NumberFeatureMixin, OutputFeature): method __init__ (line 429) | def __init__( method logits (line 441) | def logits(self, inputs, **kwargs): # hidden method create_predict_module (line 445) | def create_predict_module(self) -> PredictModule: method get_prediction_set (line 453) | def get_prediction_set(self): method input_shape (line 457) | def input_shape(self) -> torch.Size: method get_output_dtype (line 461) | def get_output_dtype(cls): method output_shape (line 465) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 469) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 473) | def calculate_overall_stats(predictions, targets, metadata): method postprocess_predictions (line 477) | def postprocess_predictions( method get_schema_cls (line 496) | def get_schema_cls(): method get_postproc_output_dtype (line 500) | def get_postproc_output_dtype(cls, metadata: TrainingSetMetadataDict) ... method create_postproc_module (line 504) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... FILE: ludwig/features/sequence_feature.py class _SequencePreprocessing (line 60) | class _SequencePreprocessing(torch.nn.Module): method __init__ (line 63) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 82) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: method _process_sequence (line 102) | def _process_sequence(self, sequence: str) -> torch.Tensor: class _SequencePostprocessing (line 143) | class _SequencePostprocessing(torch.nn.Module): method __init__ (line 144) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 153) | def forward(self, preds: dict[str, torch.Tensor], feature_name: str) -... class _SequencePredict (line 179) | class _SequencePredict(PredictModule): method forward (line 180) | def forward(self, inputs: dict[str, torch.Tensor], feature_name: str) ... class SequenceFeatureMixin (line 191) | class SequenceFeatureMixin(BaseFeatureMixin): method type (line 193) | def type(): method cast_column (line 197) | def cast_column(column, backend): method get_feature_meta (line 201) | def get_feature_meta( method feature_data (line 255) | def feature_data(column, metadata, preprocessing_parameters: Preproces... method add_feature_data (line 271) | def add_feature_data( class SequenceInputFeature (line 290) | class SequenceInputFeature(SequenceFeatureMixin, InputFeature): method __init__ (line 291) | def __init__(self, input_feature_config: SequenceInputFeatureConfig, e... method forward (line 299) | def forward(self, inputs: torch.Tensor, mask=None): method input_dtype (line 311) | def input_dtype(self): method update_config_with_metadata (line 315) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 321) | def get_schema_cls(): method input_shape (line 325) | def input_shape(self) -> torch.Size: method output_shape (line 329) | def output_shape(self) -> torch.Size: method create_preproc_module (line 333) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... class SequenceOutputFeature (line 337) | class SequenceOutputFeature(SequenceFeatureMixin, OutputFeature): method __init__ (line 338) | def __init__( method logits (line 349) | def logits(self, inputs: dict[str, torch.Tensor], target=None): method create_predict_module (line 352) | def create_predict_module(self) -> PredictModule: method get_prediction_set (line 355) | def get_prediction_set(self): method get_output_dtype (line 359) | def get_output_dtype(cls): method input_shape (line 363) | def input_shape(self) -> torch.Size: method output_shape (line 368) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 372) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 456) | def calculate_overall_stats(predictions, targets, train_set_metadata): method postprocess_predictions (line 461) | def postprocess_predictions( method create_postproc_module (line 513) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... method get_schema_cls (line 517) | def get_schema_cls(): FILE: ludwig/features/set_feature.py class _SetPreprocessing (line 41) | class _SetPreprocessing(torch.nn.Module): method __init__ (line 48) | def __init__(self, metadata: TrainingSetMetadataDict, is_bag: bool = F... method forward (line 63) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class _SetPostprocessing (line 95) | class _SetPostprocessing(torch.nn.Module): method __init__ (line 98) | def __init__(self, metadata: TrainingSetMetadataDict): method forward (line 105) | def forward(self, preds: dict[str, torch.Tensor], feature_name: str) -... class _SetPredict (line 129) | class _SetPredict(PredictModule): method __init__ (line 130) | def __init__(self, threshold): method forward (line 134) | def forward(self, inputs: dict[str, torch.Tensor], feature_name: str) ... class SetFeatureMixin (line 144) | class SetFeatureMixin(BaseFeatureMixin): method type (line 146) | def type(): method cast_column (line 150) | def cast_column(column, backend): method get_feature_meta (line 154) | def get_feature_meta( method feature_data (line 178) | def feature_data(column, metadata, preprocessing_parameters: Preproces... method add_feature_data (line 189) | def add_feature_data( class SetInputFeature (line 207) | class SetInputFeature(SetFeatureMixin, InputFeature): method __init__ (line 208) | def __init__(self, input_feature_config: SetInputFeatureConfig, encode... method forward (line 216) | def forward(self, inputs): method input_dtype (line 225) | def input_dtype(self): method input_shape (line 229) | def input_shape(self) -> torch.Size: method update_config_with_metadata (line 233) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 237) | def get_schema_cls(): method output_shape (line 241) | def output_shape(self) -> torch.Size: method create_preproc_module (line 245) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... class SetOutputFeature (line 249) | class SetOutputFeature(SetFeatureMixin, OutputFeature): method __init__ (line 250) | def __init__( method logits (line 262) | def logits(self, inputs, **kwargs): # hidden method metric_kwargs (line 266) | def metric_kwargs(self) -> dict[str, Any]: method create_predict_module (line 269) | def create_predict_module(self) -> PredictModule: method get_prediction_set (line 272) | def get_prediction_set(self): method get_output_dtype (line 276) | def get_output_dtype(cls): method input_shape (line 280) | def input_shape(self) -> torch.Size: method output_shape (line 284) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 288) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 316) | def calculate_overall_stats(predictions, targets, train_set_metadata): method postprocess_predictions (line 320) | def postprocess_predictions( method create_postproc_module (line 345) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... method get_schema_cls (line 349) | def get_schema_cls(): FILE: ludwig/features/text_feature.py function get_decoded_targets_and_predictions (line 62) | def get_decoded_targets_and_predictions( function _get_metadata_reconciled_max_sequence_length (line 83) | def _get_metadata_reconciled_max_sequence_length( class TextFeatureMixin (line 119) | class TextFeatureMixin(BaseFeatureMixin): method type (line 121) | def type(): method cast_column (line 125) | def cast_column(column, backend): method get_feature_meta (line 129) | def get_feature_meta( method feature_data (line 191) | def feature_data(column, metadata, preprocessing_parameters: Preproces... method add_feature_data (line 230) | def add_feature_data( class TextInputFeature (line 248) | class TextInputFeature(TextFeatureMixin, SequenceInputFeature): method __init__ (line 249) | def __init__(self, input_feature_config: TextInputFeatureConfig, encod... method forward (line 252) | def forward(self, inputs, mask=None): method input_dtype (line 272) | def input_dtype(self): method input_shape (line 276) | def input_shape(self): method update_config_after_module_init (line 279) | def update_config_after_module_init(self, feature_config): method update_config_with_metadata (line 283) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 294) | def get_schema_cls(): method output_shape (line 298) | def output_shape(self) -> torch.Size: method create_preproc_module (line 302) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... class TextOutputFeature (line 306) | class TextOutputFeature(TextFeatureMixin, SequenceOutputFeature): method __init__ (line 307) | def __init__( method get_output_dtype (line 316) | def get_output_dtype(cls): method output_shape (line 320) | def output_shape(self) -> torch.Size: method update_metrics (line 323) | def update_metrics( method update_config_with_metadata (line 368) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 410) | def calculate_overall_stats( method postprocess_predictions (line 417) | def postprocess_predictions( method create_postproc_module (line 509) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... method get_schema_cls (line 513) | def get_schema_cls(): FILE: ludwig/features/timeseries_feature.py function create_time_delay_embedding (line 37) | def create_time_delay_embedding( class _TimeseriesPreprocessing (line 68) | class _TimeseriesPreprocessing(torch.nn.Module): method __init__ (line 71) | def __init__(self, metadata: TrainingSetMetadataDict): method _process_str_sequence (line 84) | def _process_str_sequence(self, sequence: list[str], limit: int) -> to... method _nan_to_fill_value (line 88) | def _nan_to_fill_value(self, v: torch.Tensor) -> torch.Tensor: method forward_list_of_tensors (line 96) | def forward_list_of_tensors(self, v: list[torch.Tensor]) -> torch.Tensor: method forward_list_of_strs (line 110) | def forward_list_of_strs(self, v: list[str]) -> torch.Tensor: method forward (line 129) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class TimeseriesFeatureMixin (line 138) | class TimeseriesFeatureMixin(BaseFeatureMixin): method type (line 140) | def type(): method cast_column (line 144) | def cast_column(column, backend): method get_feature_meta (line 148) | def get_feature_meta( method build_matrix (line 171) | def build_matrix(timeseries, tokenizer_name, length_limit, padding_val... method feature_data (line 195) | def feature_data(column, metadata, preprocessing_parameters: Preproces... method add_feature_data (line 215) | def add_feature_data( class TimeseriesInputFeature (line 233) | class TimeseriesInputFeature(TimeseriesFeatureMixin, SequenceInputFeature): method __init__ (line 234) | def __init__(self, input_feature_config: TimeseriesInputFeatureConfig,... method forward (line 242) | def forward(self, inputs, mask=None): method input_shape (line 253) | def input_shape(self) -> torch.Size: method input_dtype (line 257) | def input_dtype(self): method update_config_with_metadata (line 261) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method get_schema_cls (line 266) | def get_schema_cls(): method create_preproc_module (line 270) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... class TimeseriesOutputFeature (line 274) | class TimeseriesOutputFeature(TimeseriesFeatureMixin, OutputFeature): method __init__ (line 275) | def __init__( method logits (line 289) | def logits(self, inputs, **kwargs): # hidden method loss_kwargs (line 293) | def loss_kwargs(self): method metric_kwargs (line 296) | def metric_kwargs(self): method create_predict_module (line 299) | def create_predict_module(self) -> PredictModule: method get_prediction_set (line 302) | def get_prediction_set(self): method get_output_dtype (line 306) | def get_output_dtype(cls): method output_shape (line 310) | def output_shape(self) -> torch.Size: method input_shape (line 314) | def input_shape(self) -> torch.Size: method update_config_with_metadata (line 318) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 322) | def calculate_overall_stats(predictions, targets, train_set_metadata): method postprocess_predictions (line 326) | def postprocess_predictions( method create_postproc_module (line 337) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... method get_schema_cls (line 341) | def get_schema_cls(): FILE: ludwig/features/vector_feature.py class _VectorPreprocessing (line 37) | class _VectorPreprocessing(torch.nn.Module): method forward (line 38) | def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor: class _VectorPostprocessing (line 57) | class _VectorPostprocessing(torch.nn.Module): method __init__ (line 58) | def __init__(self): method forward (line 63) | def forward(self, preds: dict[str, torch.Tensor], feature_name: str) -... class _VectorPredict (line 70) | class _VectorPredict(PredictModule): method forward (line 71) | def forward(self, inputs: dict[str, torch.Tensor], feature_name: str) ... class VectorFeatureMixin (line 77) | class VectorFeatureMixin: method type (line 79) | def type(): method cast_column (line 83) | def cast_column(column, backend): method get_feature_meta (line 87) | def get_feature_meta( method add_feature_data (line 97) | def add_feature_data( class VectorInputFeature (line 144) | class VectorInputFeature(VectorFeatureMixin, InputFeature): method __init__ (line 145) | def __init__(self, input_feature_config: VectorInputFeatureConfig, enc... method forward (line 154) | def forward(self, inputs: torch.Tensor) -> torch.Tensor: method input_shape (line 164) | def input_shape(self) -> torch.Size: method output_shape (line 168) | def output_shape(self) -> torch.Size: method update_config_with_metadata (line 172) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method create_preproc_module (line 176) | def create_preproc_module(metadata: TrainingSetMetadataDict) -> torch.... method get_schema_cls (line 180) | def get_schema_cls(): class VectorOutputFeature (line 184) | class VectorOutputFeature(VectorFeatureMixin, OutputFeature): method __init__ (line 185) | def __init__( method logits (line 199) | def logits(self, inputs, **kwargs): # hidden method metric_kwargs (line 203) | def metric_kwargs(self): method create_predict_module (line 206) | def create_predict_module(self) -> PredictModule: method get_prediction_set (line 209) | def get_prediction_set(self): method get_output_dtype (line 213) | def get_output_dtype(cls): method output_shape (line 217) | def output_shape(self) -> torch.Size: method input_shape (line 221) | def input_shape(self) -> torch.Size: method update_config_with_metadata (line 225) | def update_config_with_metadata(feature_config, feature_metadata, *arg... method calculate_overall_stats (line 229) | def calculate_overall_stats(predictions, targets, train_set_metadata): method postprocess_predictions (line 233) | def postprocess_predictions( method create_postproc_module (line 244) | def create_postproc_module(metadata: TrainingSetMetadataDict) -> torch... method get_schema_cls (line 248) | def get_schema_cls(): FILE: ludwig/forecast.py function forecast_cli (line 17) | def forecast_cli( function cli (line 68) | def cli(sys_argv): FILE: ludwig/globals.py function set_disable_progressbar (line 42) | def set_disable_progressbar(value): function is_progressbar_disabled (line 47) | def is_progressbar_disabled(): FILE: ludwig/hyperopt/execution.py function _patch_bohb_configspace_conversion (line 48) | def _patch_bohb_configspace_conversion(): function _is_ray_backend (line 154) | def _is_ray_backend(backend) -> bool: class RayBackend (line 165) | class RayBackend: function _is_ray_backend (line 168) | def _is_ray_backend(backend) -> bool: function identity (line 172) | def identity(x): function _get_relative_checkpoints_dir_parts (line 176) | def _get_relative_checkpoints_dir_parts(path: Path): function ray_resource_allocation_function (line 181) | def ray_resource_allocation_function( function _create_tune_checkpoint (line 202) | def _create_tune_checkpoint(save_path): class RayTuneExecutor (line 222) | class RayTuneExecutor: method __init__ (line 223) | def __init__( method _get_search_space (line 265) | def _get_search_space(self, parameters: dict) -> tuple[dict, dict]: method encode_values (line 292) | def encode_values(param: str, values: dict, ctx: dict) -> dict: method decode_values (line 307) | def decode_values(config: dict, ctx: dict) -> dict: method _has_metric (line 314) | def _has_metric(self, stats, split): method _has_eval_metric (line 332) | def _has_eval_metric(self, stats): method get_metric_score (line 346) | def get_metric_score(self, train_stats) -> float: method get_metric_score_from_eval_stats (line 356) | def get_metric_score_from_eval_stats(self, eval_stats) -> float | list: method get_metric_score_from_train_stats (line 371) | def get_metric_score_from_train_stats(self, train_stats, select_split=... method sort_hyperopt_results (line 387) | def sort_hyperopt_results(self, hyperopt_results): method _cpu_resources_per_trial_non_none (line 393) | def _cpu_resources_per_trial_non_none(self): method _gpu_resources_per_trial_non_none (line 397) | def _gpu_resources_per_trial_non_none(self): method _get_remote_checkpoint_dir (line 400) | def _get_remote_checkpoint_dir(self, trial_dir: Path) -> str | tuple[s... method _get_kubernetes_node_address_by_ip (line 425) | def _get_kubernetes_node_address_by_ip(self) -> Callable: method _remove_partial_checkpoints (line 437) | def _remove_partial_checkpoints(trial_path: str): method _get_best_model_path (line 451) | def _get_best_model_path(self, trial_or_path, analysis: ExperimentAnal... method _evaluate_best_model (line 496) | def _evaluate_best_model( method _run_experiment (line 552) | def _run_experiment( method execute (line 774) | def execute( class CallbackStopper (line 1063) | class CallbackStopper(Stopper): method __init__ (line 1066) | def __init__(self, callbacks: list[Callback] | None): method __call__ (line 1069) | def __call__(self, trial_id, result): method stop_all (line 1072) | def stop_all(self): function get_build_hyperopt_executor (line 1079) | def get_build_hyperopt_executor(executor_type): function set_values (line 1086) | def set_values(params: dict[str, Any], model_dict: dict[str, Any]): function run_experiment (line 1097) | def run_experiment( function _run_experiment_unary (line 1176) | def _run_experiment_unary(kwargs): FILE: ludwig/hyperopt/results.py class TrialResults (line 16) | class TrialResults: class HyperoptResults (line 24) | class HyperoptResults: FILE: ludwig/hyperopt/run.py class RayBackend (line 55) | class RayBackend: function hyperopt (line 62) | def hyperopt( FILE: ludwig/hyperopt/search_algos.py function _is_package_installed (line 11) | def _is_package_installed(package_name: str, search_algo_name: str) -> b... class SearchAlgorithm (line 22) | class SearchAlgorithm(ABC): method __init__ (line 23) | def __init__(self, search_alg_dict: dict) -> None: method check_for_random_seed (line 27) | def check_for_random_seed(self, ludwig_random_seed: int) -> None: class BasicVariantSA (line 32) | class BasicVariantSA(SearchAlgorithm): method __init__ (line 33) | def __init__(self, search_alg_dict: dict) -> None: class HyperoptSA (line 38) | class HyperoptSA(SearchAlgorithm): method __init__ (line 39) | def __init__(self, search_alg_dict: dict) -> None: class BOHBSA (line 45) | class BOHBSA(SearchAlgorithm): method __init__ (line 46) | def __init__(self, search_alg_dict: dict) -> None: class AxSA (line 53) | class AxSA(SearchAlgorithm): method __init__ (line 54) | def __init__(self, search_alg_dict: dict) -> None: method check_for_random_seed (line 61) | def check_for_random_seed(self, ludwig_random_seed: int) -> None: class BayesOptSA (line 65) | class BayesOptSA(SearchAlgorithm): method __init__ (line 66) | def __init__(self, search_alg_dict: dict) -> None: class BlendsearchSA (line 72) | class BlendsearchSA(SearchAlgorithm): method __init__ (line 73) | def __init__(self, search_alg_dict: dict) -> None: method check_for_random_seed (line 79) | def check_for_random_seed(self, ludwig_random_seed: int) -> None: class CFOSA (line 83) | class CFOSA(SearchAlgorithm): method __init__ (line 84) | def __init__(self, search_alg_dict: dict) -> None: method check_for_random_seed (line 91) | def check_for_random_seed(self, ludwig_random_seed: int) -> None: class DragonflySA (line 95) | class DragonflySA(SearchAlgorithm): method __init__ (line 96) | def __init__(self, search_alg_dict: dict) -> None: class HEBOSA (line 102) | class HEBOSA(SearchAlgorithm): method __init__ (line 103) | def __init__(self, search_alg_dict: dict) -> None: class SkoptSA (line 109) | class SkoptSA(SearchAlgorithm): method __init__ (line 110) | def __init__(self, search_alg_dict: dict) -> None: method check_for_random_seed (line 116) | def check_for_random_seed(self, ludwig_random_seed: int) -> None: class NevergradSA (line 120) | class NevergradSA(SearchAlgorithm): method __init__ (line 121) | def __init__(self, search_alg_dict: dict) -> None: method check_for_random_seed (line 127) | def check_for_random_seed(self, ludwig_random_seed: int) -> None: class OptunaSA (line 131) | class OptunaSA(SearchAlgorithm): method __init__ (line 132) | def __init__(self, search_alg_dict: dict) -> None: class ZooptSA (line 138) | class ZooptSA(SearchAlgorithm): method __init__ (line 139) | def __init__(self, search_alg_dict: dict) -> None: method check_for_random_seed (line 145) | def check_for_random_seed(self, ludwig_random_seed: int) -> None: function get_search_algorithm (line 149) | def get_search_algorithm(search_algo): FILE: ludwig/hyperopt/utils.py function print_hyperopt_results (line 49) | def print_hyperopt_results(hyperopt_results: HyperoptResults): function save_hyperopt_stats (line 57) | def save_hyperopt_stats(hyperopt_stats, hyperopt_dir_name): function load_json_value (line 62) | def load_json_value(v): function load_json_values (line 74) | def load_json_values(d): function should_tune_preprocessing (line 82) | def should_tune_preprocessing(config): function parameter_to_dict (line 90) | def parameter_to_dict(name, value): function feature_list_to_dict (line 108) | def feature_list_to_dict(config: ModelConfigDict) -> ModelConfigDict: function feature_dict_to_list (line 123) | def feature_dict_to_list(config: ModelConfigDict) -> ModelConfigDict: function substitute_parameters (line 140) | def substitute_parameters( function get_num_duplicate_trials (line 178) | def get_num_duplicate_trials(hyperopt_config: HyperoptConfigDict) -> int: function log_warning_if_all_grid_type_parameters (line 196) | def log_warning_if_all_grid_type_parameters(hyperopt_config: HyperoptCon... function update_hyperopt_params_with_defaults (line 212) | def update_hyperopt_params_with_defaults(hyperopt_params: HyperoptConfig... FILE: ludwig/hyperopt_cli.py function hyperopt_cli (line 32) | def hyperopt_cli( function cli (line 194) | def cli(sys_argv): FILE: ludwig/model_export/base_model_exporter.py class LudwigTorchWrapper (line 6) | class LudwigTorchWrapper(torch.nn.Module): method __init__ (line 9) | def __init__(self, model): method forward (line 13) | def forward(self, x): class BaseModelExporter (line 17) | class BaseModelExporter(ABC): method export (line 19) | def export(self, model_path, export_path, export_args_override): method check_model_export (line 23) | def check_model_export(self, path): FILE: ludwig/model_export/onnx_exporter.py class OnnxExporter (line 23) | class OnnxExporter(BaseModelExporter): method export (line 26) | def export(self, model_path, export_path, output_model_name): method check_model_export (line 46) | def check_model_export(self, path): FILE: ludwig/models/base.py class BaseModel (line 29) | class BaseModel(LudwigModule, metaclass=ABCMeta): method type (line 39) | def type() -> str: method __init__ (line 42) | def __init__(self, random_seed: int = None): method create_feature_dict (line 62) | def create_feature_dict(self) -> LudwigFeatureDict: method to_device (line 66) | def to_device(self, device): method metrics_to_device (line 69) | def metrics_to_device(self, device: str): method build_inputs (line 76) | def build_inputs(cls, input_feature_configs: FeatureCollection[BaseInp... method build_single_input (line 87) | def build_single_input( method build_outputs (line 102) | def build_outputs( method build_single_output (line 119) | def build_single_output( method get_model_inputs (line 128) | def get_model_inputs(self): method get_model_size (line 137) | def get_model_size(self) -> int: method to_torchscript (line 145) | def to_torchscript(self, device: TorchDevice | None = None): method save_torchscript (line 158) | def save_torchscript(self, save_path, device: TorchDevice | None = None): method input_shape (line 167) | def input_shape(self): method forward (line 173) | def forward( method predictions (line 193) | def predictions(self, inputs): method outputs_to_predictions (line 198) | def outputs_to_predictions(self, outputs: dict[str, torch.Tensor]) -> ... method evaluation_step (line 205) | def evaluation_step(self, inputs, targets): method predict_step (line 211) | def predict_step(self, inputs): method train_loss (line 215) | def train_loss( method eval_loss (line 251) | def eval_loss(self, targets, predictions): method update_metrics (line 273) | def update_metrics(self, targets, predictions): method eval_loss_metric (line 283) | def eval_loss_metric(self) -> LudwigMetric: method eval_loss_metric (line 287) | def eval_loss_metric(self, value: LudwigMetric) -> None: method eval_additional_losses_metrics (line 291) | def eval_additional_losses_metrics(self) -> LudwigMetric: method get_metrics (line 294) | def get_metrics(self) -> dict[str, dict[str, float]]: method reset_metrics (line 305) | def reset_metrics(self): method collect_weights (line 311) | def collect_weights(self, tensor_names=None, **kwargs): method unskip (line 326) | def unskip(self): method save (line 330) | def save(self, save_path: str): method load (line 334) | def load(self, save_path: str): method get_args (line 338) | def get_args(self): method use_generation_config (line 342) | def use_generation_config(self, generation_config: dict[str, Any]): method _activate_forward_hooks (line 347) | def _activate_forward_hooks(self): method _deactivate_forward_hooks (line 350) | def _deactivate_forward_hooks(self) -> None: function create_input_feature (line 356) | def create_input_feature(feature_config: BaseInputFeatureConfig, encoder... FILE: ludwig/models/calibrator.py class Calibrator (line 23) | class Calibrator: method __init__ (line 26) | def __init__(self, model: ECD, backend: Backend, batch_size: int = 128): method calibration_enabled (line 31) | def calibration_enabled(self): method train_calibration (line 38) | def train_calibration(self, dataset, dataset_name: str): FILE: ludwig/models/ecd.py class ECD (line 23) | class ECD(BaseModel): method type (line 25) | def type() -> str: method __init__ (line 28) | def __init__( method prepare_for_training (line 59) | def prepare_for_training(self): method encode (line 71) | def encode( method combine (line 92) | def combine(self, encoder_outputs): method decode (line 95) | def decode(self, combiner_outputs, targets, mask): method forward (line 114) | def forward( method unskip (line 155) | def unskip(self): method save (line 159) | def save(self, save_path): method load (line 167) | def load(self, save_path): method get_args (line 175) | def get_args(self): method get_augmentation_pipelines (line 184) | def get_augmentation_pipelines(self) -> AugmentationPipelines: FILE: ludwig/models/embedder.py class Embedder (line 22) | class Embedder(LudwigModule): method __init__ (line 23) | def __init__(self, feature_configs: list[FeatureConfigDict], metadata:... method forward (line 53) | def forward(self, inputs: dict[str, torch.Tensor]): function create_embed_batch_size_evaluator (line 63) | def create_embed_batch_size_evaluator( function create_embed_transform_fn (line 85) | def create_embed_transform_fn( function _prepare_batch (line 117) | def _prepare_batch( FILE: ludwig/models/inference.py class InferenceModule (line 31) | class InferenceModule(nn.Module): method __init__ (line 34) | def __init__( method preprocessor_forward (line 50) | def preprocessor_forward(self, inputs: dict[str, TorchscriptPreprocess... method predictor_forward (line 54) | def predictor_forward(self, preproc_inputs: dict[str, torch.Tensor]) -... method postprocessor_forward (line 66) | def postprocessor_forward(self, predictions_flattened: dict[str, torch... method forward (line 73) | def forward(self, inputs: dict[str, TorchscriptPreprocessingInput]) ->... method predict (line 80) | def predict(self, dataset: pd.DataFrame, return_type: dict | pd.DataFr... method from_ludwig_model (line 92) | def from_ludwig_model( method from_directory (line 118) | def from_directory( class _InferencePreprocessor (line 145) | class _InferencePreprocessor(nn.Module): method __init__ (line 152) | def __init__(self, config: ModelConfigDict, training_set_metadata: Tra... method forward (line 162) | def forward(self, inputs: dict[str, TorchscriptPreprocessingInput]) ->... class _InferencePredictor (line 170) | class _InferencePredictor(nn.Module): method __init__ (line 179) | def __init__(self, model: "BaseModel", device: TorchDevice): method forward (line 189) | def forward(self, preproc_inputs: dict[str, torch.Tensor]) -> dict[str... class _InferencePostprocessor (line 202) | class _InferencePostprocessor(nn.Module): method __init__ (line 211) | def __init__(self, model: "BaseModel", training_set_metadata: Training... method forward (line 219) | def forward(self, predictions_flattened: dict[str, torch.Tensor]) -> d... function save_ludwig_model_for_inference (line 231) | def save_ludwig_model_for_inference( function _init_inference_stages_from_directory (line 269) | def _init_inference_stages_from_directory( function _init_inference_stages_from_ludwig_model (line 285) | def _init_inference_stages_from_ludwig_model( function _unflatten_dict_by_feature_name (line 308) | def _unflatten_dict_by_feature_name(flattened_dict: dict[str, Any]) -> d... FILE: ludwig/models/llm.py class DictWrapper (line 42) | class DictWrapper: method __init__ (line 49) | def __init__(self, obj: LudwigFeatureDict): method get (line 52) | def get(self, key) -> torch.nn.Module: method set (line 55) | def set(self, key: str, module: torch.nn.Module) -> None: method __len__ (line 58) | def __len__(self) -> int: method __next__ (line 61) | def __next__(self) -> None: method __iter__ (line 64) | def __iter__(self) -> None: method keys (line 67) | def keys(self) -> list[str]: method values (line 70) | def values(self) -> list[torch.nn.Module]: method items (line 73) | def items(self) -> list[tuple[str, torch.nn.Module]]: method update (line 76) | def update(self, modules: dict[str, torch.nn.Module]) -> None: class LLM (line 80) | class LLM(BaseModel): method type (line 82) | def type() -> str: method __init__ (line 85) | def __init__( method create_feature_dict (line 163) | def create_feature_dict(self) -> DictWrapper: method use_generation_config (line 167) | def use_generation_config(self, generation_config_dict: dict[str, Any]... method _set_generation_config (line 182) | def _set_generation_config(self, new_generation_config_dict: dict[str,... method output_feature_decoder (line 194) | def output_feature_decoder(self) -> OutputFeature: method initialize_adapter (line 197) | def initialize_adapter(self): method prepare_for_training (line 214) | def prepare_for_training(self): method prepare_for_quantized_training (line 220) | def prepare_for_quantized_training(self): method to_device (line 225) | def to_device(self, device): method build_outputs (line 234) | def build_outputs( method forward (line 251) | def forward( method generate (line 311) | def generate( method is_merge_and_unload_set (line 362) | def is_merge_and_unload_set(self) -> bool: method merge_and_unload (line 376) | def merge_and_unload(self, progressbar: bool = False) -> None: method _unpack_inputs (line 391) | def _unpack_inputs( method get_input_ids (line 416) | def get_input_ids( method get_target_ids (line 425) | def get_target_ids(self, outputs: dict[str, torch.Tensor]) -> torch.Te... method update_metrics (line 429) | def update_metrics(self, targets, predictions): method update_metrics_finetune_llm (line 449) | def update_metrics_finetune_llm(self, targets, predictions): method train_loss (line 470) | def train_loss( method eval_loss (line 522) | def eval_loss(self, targets, predictions): method outputs_to_predictions (line 553) | def outputs_to_predictions(self, outputs: dict[str, torch.Tensor]) -> ... method save (line 561) | def save(self, save_path): method save_base_model (line 573) | def save_base_model(self, save_path): method save_dequantized_base_model (line 587) | def save_dequantized_base_model(self, save_path: str) -> None: method load (line 635) | def load(self, save_path): method get_args (line 663) | def get_args(self): method _update_target_tensor_for_finetuning (line 671) | def _update_target_tensor_for_finetuning( method _activate_forward_hooks (line 727) | def _activate_forward_hooks(self): method get_augmentation_pipelines (line 744) | def get_augmentation_pipelines() -> AugmentationPipelines: FILE: ludwig/models/predictor.py class BasePredictor (line 35) | class BasePredictor(ABC): method batch_predict (line 37) | def batch_predict(self, dataset, dataset_name=None): method predict_single (line 41) | def predict_single(self, batch): method batch_evaluation (line 45) | def batch_evaluation(self, dataset, collect_predictions=False, collect... method batch_collect_activations (line 49) | def batch_collect_activations(self, layer_names, dataset, bucketing_fi... method shutdown (line 53) | def shutdown(self): method __enter__ (line 57) | def __enter__(self): method __exit__ (line 60) | def __exit__(self, exc_type, exc_val, exc_tb): function register_predictor (line 67) | def register_predictor(model_types: list[str]): function get_predictor_cls (line 76) | def get_predictor_cls(model_type: str) -> type[BasePredictor]: class Predictor (line 81) | class Predictor(BasePredictor): method __init__ (line 84) | def __init__( method batch_predict (line 120) | def batch_predict(self, dataset: Dataset, dataset_name: str = None, co... method predict_single (line 152) | def predict_single(self, batch, collect_logits: bool = False): method _predict (line 168) | def _predict(self, batch: dict[str, np.ndarray]) -> dict[str, np.ndarr... method _accumulate_preds (line 186) | def _accumulate_preds(self, preds, predictions, exclude_pred_set=EXCLU... method _concat_preds (line 194) | def _concat_preds(self, predictions): method batch_evaluation (line 200) | def batch_evaluation(self, dataset, collect_predictions=False, collect... method batch_collect_activations (line 290) | def batch_collect_activations(self, layer_names, dataset, bucketing_fi... method _predict_on_inputs (line 330) | def _predict_on_inputs(self, inputs: dict) -> dict: method is_coordinator (line 333) | def is_coordinator(self): class LlmPredictor (line 338) | class LlmPredictor(Predictor): method _predict_on_inputs (line 339) | def _predict_on_inputs(self, inputs: dict) -> dict: class LlmFineTunePredictor (line 343) | class LlmFineTunePredictor(Predictor): method batch_evaluation (line 344) | def batch_evaluation(self, dataset, collect_predictions=False, collect... function calculate_overall_stats (line 456) | def calculate_overall_stats(output_features, predictions, dataset, train... function save_prediction_outputs (line 483) | def save_prediction_outputs( function save_evaluation_stats (line 501) | def save_evaluation_stats(test_stats, output_directory): function print_evaluation_stats (line 506) | def print_evaluation_stats(test_stats): function get_output_columns (line 520) | def get_output_columns(output_features, include_logits: bool = False): FILE: ludwig/models/retrieval.py function df_checksum (line 23) | def df_checksum(df: pd.DataFrame) -> str: function df_to_row_strs (line 27) | def df_to_row_strs(df: pd.DataFrame) -> list[str]: class RetrievalModel (line 33) | class RetrievalModel(ABC): method create_dataset_index (line 35) | def create_dataset_index(self, df: pd.DataFrame, backend: "Backend", c... method search (line 43) | def search( method save_index (line 52) | def save_index(self, name: str, cache_directory: str): method load_index (line 56) | def load_index(self, name: str, cache_directory: str): class RandomRetrieval (line 60) | class RandomRetrieval(RetrievalModel): method __init__ (line 66) | def __init__(self, **kwargs): method create_dataset_index (line 70) | def create_dataset_index(self, df: pd.DataFrame, backend: "Backend", c... method search (line 74) | def search( method save_index (line 88) | def save_index(self, name: str, cache_directory: str): method load_index (line 98) | def load_index(self, name: str, cache_directory: str): class SemanticRetrieval (line 106) | class SemanticRetrieval(RetrievalModel): method __init__ (line 112) | def __init__(self, model_name, **kwargs): method create_dataset_index (line 121) | def create_dataset_index(self, df: pd.DataFrame, backend: "Backend", c... method _encode (line 132) | def _encode(self, row_strs: list[str], backend: "Backend") -> np.ndarray: method search (line 146) | def search( method save_index (line 163) | def save_index(self, name: str, cache_directory: str): method load_index (line 170) | def load_index(self, name: str, cache_directory: str): function create_semantic_retrieval_model_evaluator (line 178) | def create_semantic_retrieval_model_evaluator( function create_semantic_retrieval_model_fn (line 192) | def create_semantic_retrieval_model_fn( function get_semantic_retrieval_model (line 209) | def get_semantic_retrieval_model(model_name: str) -> "SentenceTransformer": function get_retrieval_model (line 215) | def get_retrieval_model(type: str, **kwargs) -> RetrievalModel: FILE: ludwig/modules/attention_modules.py class FeedForwardAttentionReducer (line 26) | class FeedForwardAttentionReducer(LudwigModule): method __init__ (line 27) | def __init__(self, input_size, hidden_size=256, activation="tanh"): method forward (line 35) | def forward(self, inputs, mask=None): method input_shape (line 47) | def input_shape(self) -> torch.Size: method output_shape (line 51) | def output_shape(self) -> torch.Size: class MultiHeadSelfAttention (line 55) | class MultiHeadSelfAttention(LudwigModule): method __init__ (line 56) | def __init__(self, input_size, hidden_size, num_heads=8): method separate_heads (line 71) | def separate_heads(self, inputs, batch_size): method forward (line 75) | def forward(self, inputs: torch.Tensor, mask=None): method output_shape (line 92) | def output_shape(self): class TransformerBlock (line 96) | class TransformerBlock(LudwigModule): method __init__ (line 97) | def __init__( method input_shape (line 121) | def input_shape(self) -> torch.Size: method forward (line 124) | def forward(self, inputs, mask=None): method output_shape (line 134) | def output_shape(self) -> torch.Size: class TransformerStack (line 138) | class TransformerStack(LudwigModule): method __init__ (line 139) | def __init__( method input_shape (line 175) | def input_shape(self) -> torch.Size: method forward (line 178) | def forward(self, inputs, mask=None): method output_shape (line 185) | def output_shape(self) -> torch.Size: FILE: ludwig/modules/convolutional_modules.py class Conv1DLayer (line 28) | class Conv1DLayer(LudwigModule): method __init__ (line 29) | def __init__( method input_shape (line 106) | def input_shape(self): method forward (line 110) | def forward(self, inputs, training=None, mask=None): class Conv1DStack (line 127) | class Conv1DStack(LudwigModule): method __init__ (line 128) | def __init__( method input_shape (line 254) | def input_shape(self): method forward (line 258) | def forward(self, inputs, mask=None): class ParallelConv1D (line 279) | class ParallelConv1D(LudwigModule): method __init__ (line 280) | def __init__( method input_shape (line 381) | def input_shape(self) -> torch.Size: method forward (line 385) | def forward(self, inputs, mask=None): class ParallelConv1DStack (line 410) | class ParallelConv1DStack(LudwigModule): method __init__ (line 411) | def __init__( method input_shape (line 506) | def input_shape(self): method forward (line 510) | def forward(self, inputs, mask=None): class Conv2DLayer (line 530) | class Conv2DLayer(LudwigModule): method __init__ (line 531) | def __init__( method forward (line 609) | def forward(self, inputs): method output_shape (line 618) | def output_shape(self) -> torch.Size: method input_shape (line 622) | def input_shape(self) -> torch.Size: class Conv2DStack (line 626) | class Conv2DStack(LudwigModule): method __init__ (line 627) | def __init__( method forward (line 761) | def forward(self, inputs): method _check_in_channels (line 769) | def _check_in_channels(self, first_in_channels: int | None, layers: li... method output_shape (line 781) | def output_shape(self) -> torch.Size: method input_shape (line 785) | def input_shape(self) -> torch.Size: class Conv2DLayerFixedPadding (line 789) | class Conv2DLayerFixedPadding(LudwigModule): method __init__ (line 790) | def __init__( method forward (line 837) | def forward(self, inputs): method input_shape (line 846) | def input_shape(self) -> torch.Size: method output_shape (line 850) | def output_shape(self) -> torch.Size: class ResNetBlock (line 854) | class ResNetBlock(LudwigModule): method __init__ (line 855) | def __init__( method forward (line 919) | def forward(self, inputs): method input_shape (line 934) | def input_shape(self) -> torch.Size: method output_shape (line 938) | def output_shape(self) -> torch.Size: class ResNetBottleneckBlock (line 943) | class ResNetBottleneckBlock(LudwigModule): method __init__ (line 944) | def __init__( method forward (line 1031) | def forward(self, inputs): method output_shape (line 1049) | def output_shape(self) -> torch.Size: method input_shape (line 1053) | def input_shape(self) -> torch.Size: class ResNetBlockLayer (line 1057) | class ResNetBlockLayer(LudwigModule): method __init__ (line 1058) | def __init__( method forward (line 1121) | def forward(self, inputs): method output_shape (line 1128) | def output_shape(self) -> torch.Size: method input_shape (line 1132) | def input_shape(self) -> torch.Size: class ResNet (line 1136) | class ResNet(LudwigModule): method __init__ (line 1137) | def __init__( method get_is_bottleneck (line 1239) | def get_is_bottleneck(self, resnet_size: int, block_sizes: list[int]) ... method get_block_fn (line 1244) | def get_block_fn(self, is_bottleneck: bool) -> ResNetBlock | ResNetBot... method get_blocks (line 1249) | def get_blocks(self, resnet_size: int, block_sizes: list[int], block_s... method forward (line 1256) | def forward(self, inputs: torch.Tensor) -> torch.Tensor: method output_shape (line 1264) | def output_shape(self) -> torch.Size: method input_shape (line 1268) | def input_shape(self) -> torch.Size: function get_resnet_block_sizes (line 1292) | def get_resnet_block_sizes(resnet_size): class UNetDoubleConvLayer (line 1314) | class UNetDoubleConvLayer(LudwigModule): method __init__ (line 1315) | def __init__( method forward (line 1349) | def forward(self, inputs): method output_shape (line 1358) | def output_shape(self) -> torch.Size: method input_shape (line 1362) | def input_shape(self) -> torch.Size: class UNetDownStack (line 1366) | class UNetDownStack(LudwigModule): method __init__ (line 1367) | def __init__( method forward (line 1414) | def forward(self, inputs): method output_shape (line 1427) | def output_shape(self) -> torch.Size: method input_shape (line 1431) | def input_shape(self) -> torch.Size: class UNetUpStack (line 1435) | class UNetUpStack(LudwigModule): method __init__ (line 1436) | def __init__( method forward (line 1483) | def forward(self, inputs, skips): method output_shape (line 1496) | def output_shape(self) -> torch.Size: method input_shape (line 1500) | def input_shape(self) -> torch.Size: FILE: ludwig/modules/embedding_modules.py function embedding_matrix (line 30) | def embedding_matrix( function embedding_matrix_on_device (line 86) | def embedding_matrix_on_device( class Embed (line 113) | class Embed(LudwigModule): method __init__ (line 116) | def __init__( method forward (line 148) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method input_shape (line 160) | def input_shape(self) -> torch.Size: method output_shape (line 164) | def output_shape(self) -> torch.Size: class EmbedSet (line 168) | class EmbedSet(LudwigModule): method __init__ (line 171) | def __init__( method forward (line 213) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method input_shape (line 232) | def input_shape(self) -> torch.Size: method output_shape (line 236) | def output_shape(self) -> torch.Size: method input_dtype (line 240) | def input_dtype(self): class EmbedWeighted (line 244) | class EmbedWeighted(LudwigModule): method __init__ (line 247) | def __init__( method forward (line 280) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method input_shape (line 300) | def input_shape(self) -> torch.Size: method output_shape (line 304) | def output_shape(self) -> torch.Size: class EmbedSequence (line 308) | class EmbedSequence(LudwigModule): method __init__ (line 309) | def __init__( method forward (line 343) | def forward(self, inputs: torch.Tensor, mask: torch.Tensor | None = No... method input_shape (line 355) | def input_shape(self) -> torch.Size: method output_shape (line 359) | def output_shape(self) -> torch.Size: class TokenAndPositionEmbedding (line 363) | class TokenAndPositionEmbedding(LudwigModule): method __init__ (line 364) | def __init__( method input_shape (line 398) | def input_shape(self) -> torch.Size: method output_shape (line 402) | def output_shape(self) -> torch.Size: method forward (line 405) | def forward(self, inputs, mask: torch.Tensor | None = None): FILE: ludwig/modules/fully_connected_modules.py class FCLayer (line 27) | class FCLayer(LudwigModule): method __init__ (line 36) | def __init__( method forward (line 75) | def forward(self, inputs, mask=None): method input_shape (line 83) | def input_shape(self) -> torch.Size: method output_shape (line 87) | def output_shape(self) -> torch.Size: class FCStack (line 91) | class FCStack(LudwigModule): method __init__ (line 109) | def __init__( method forward (line 189) | def forward(self, inputs, mask=None): method num_layers (line 209) | def num_layers(self) -> int: method input_shape (line 213) | def input_shape(self) -> torch.Size: method output_shape (line 217) | def output_shape(self) -> torch.Size: FILE: ludwig/modules/initializer_modules.py function _create_and_init (line 23) | def _create_and_init(init_fn, init_kwargs, *args, **kwargs): function get_initializer (line 29) | def get_initializer(parameters): FILE: ludwig/modules/loss_implementations/corn.py function corn_loss (line 12) | def corn_loss(logits, y_train, num_classes): FILE: ludwig/modules/loss_modules.py function register_loss (line 50) | def register_loss(config_cls: type[BaseLossConfig]): function create_loss (line 58) | def create_loss(config: BaseLossConfig) -> nn.Module: class LogitsInputsMixin (line 62) | class LogitsInputsMixin: method get_loss_inputs (line 64) | def get_loss_inputs(cls): class MSELoss (line 70) | class MSELoss(_MSELoss, LogitsInputsMixin): method __init__ (line 73) | def __init__(self, config: MSELossConfig): class MAELoss (line 78) | class MAELoss(L1Loss, LogitsInputsMixin): method __init__ (line 81) | def __init__(self, config: MAELossConfig): class MAPELoss (line 86) | class MAPELoss(nn.Module, LogitsInputsMixin): method __init__ (line 89) | def __init__(self, config: MAPELossConfig): method forward (line 92) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: class RMSELoss (line 97) | class RMSELoss(nn.Module, LogitsInputsMixin): method __init__ (line 100) | def __init__(self, config: RMSELossConfig): method forward (line 104) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: class RMSPELoss (line 109) | class RMSPELoss(nn.Module, LogitsInputsMixin): method __init__ (line 112) | def __init__(self, config: RMSPELossConfig): method forward (line 115) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: class BWCEWLoss (line 121) | class BWCEWLoss(nn.Module, LogitsInputsMixin): method __init__ (line 124) | def __init__(self, config: BWCEWLossConfig): method forward (line 133) | def forward(self, preds: torch.Tensor, target: torch.Tensor): class SoftmaxCrossEntropyLoss (line 151) | class SoftmaxCrossEntropyLoss(nn.Module, LogitsInputsMixin): method __init__ (line 152) | def __init__(self, config: SoftmaxCrossEntropyLossConfig): method forward (line 163) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: class SequenceSoftmaxCrossEntropyLoss (line 181) | class SequenceSoftmaxCrossEntropyLoss(nn.Module, LogitsInputsMixin): method __init__ (line 182) | def __init__(self, config: SequenceSoftmaxCrossEntropyLossConfig): method forward (line 195) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: class NextTokenSoftmaxCrossEntropyLoss (line 206) | class NextTokenSoftmaxCrossEntropyLoss(nn.Module, LogitsInputsMixin): method __init__ (line 207) | def __init__(self, config: NextTokenSoftmaxCrossEntropyLossConfig): method forward (line 211) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: class SigmoidCrossEntropyLoss (line 231) | class SigmoidCrossEntropyLoss(nn.Module, LogitsInputsMixin): method __init__ (line 232) | def __init__(self, config: SigmoidCrossEntropyLossConfig): method forward (line 243) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: class HuberLoss (line 251) | class HuberLoss(_HuberLoss, LogitsInputsMixin): method __init__ (line 254) | def __init__(self, config: HuberLossConfig): class CORNLoss (line 259) | class CORNLoss(nn.Module, LogitsInputsMixin): method __init__ (line 262) | def __init__(self, config: CORNLossConfig): method forward (line 265) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: FILE: ludwig/modules/lr_scheduler.py class ReduceLROnPLateauCappedDecreases (line 18) | class ReduceLROnPLateauCappedDecreases(ReduceLROnPlateau): method __init__ (line 19) | def __init__(self, optimizer: Optimizer, mode: str, reduce_limit: int,... method step (line 24) | def step(self, metrics): method num_reduce_lr (line 32) | def num_reduce_lr(self) -> int: method _reduce_lr (line 35) | def _reduce_lr(self, epoch=None): method apply_lr (line 40) | def apply_lr(self): class LRScheduler (line 52) | class LRScheduler: method __init__ (line 53) | def __init__( method step (line 79) | def step(self): method eval_step (line 89) | def eval_step(self, progress_tracker: ProgressTracker, validation_fiel... method state_dict (line 120) | def state_dict(self) -> dict[str, Any]: method load_state_dict (line 126) | def load_state_dict(self, d: dict[str, Any]): class StepInfo (line 132) | class StepInfo: method __init__ (line 139) | def __init__(self, steps_per_checkpoint: int, total_steps: int, config... function get_schedule_with_warmup_and_decay (line 158) | def get_schedule_with_warmup_and_decay( function no_decay (line 188) | def no_decay(current_step: int, num_training_steps: int, num_warmup_step... function linear_decay (line 192) | def linear_decay(current_step: int, num_training_steps: int, num_warmup_... function exponential_decay (line 200) | def exponential_decay(current_step: int, num_training_steps: int, num_wa... function wrap_decay_fn (line 210) | def wrap_decay_fn(decay_fn: Callable) -> Callable: function init_cosine_decay (line 222) | def init_cosine_decay( FILE: ludwig/modules/metric_modules.py class LudwigMetric (line 108) | class LudwigMetric(Metric, ABC): method can_report (line 110) | def can_report(cls, feature: "OutputFeature") -> bool: # noqa: F821 method sync_context (line 114) | def sync_context( class RMSEMetric (line 137) | class RMSEMetric(MeanSquaredError, LudwigMetric): method __init__ (line 140) | def __init__(self, **kwargs): class PrecisionMetric (line 145) | class PrecisionMetric(BinaryPrecision, LudwigMetric): method __init__ (line 148) | def __init__(self, **kwargs): class RecallMetric (line 153) | class RecallMetric(BinaryRecall, LudwigMetric): method __init__ (line 156) | def __init__(self, **kwargs): class BinaryAUROCMetric (line 161) | class BinaryAUROCMetric(BinaryAUROC, LudwigMetric): method __init__ (line 164) | def __init__(self, **kwargs): method update (line 167) | def update(self, preds: Tensor, target: Tensor) -> None: class CategoryAUROCMetric (line 172) | class CategoryAUROCMetric(MulticlassAUROC, LudwigMetric): method __init__ (line 175) | def __init__(self, num_classes: int, **kwargs): method update (line 178) | def update(self, preds: Tensor, target: Tensor) -> None: class SpecificityMetric (line 185) | class SpecificityMetric(BinarySpecificity, LudwigMetric): method __init__ (line 188) | def __init__(self, **kwargs): class MeanMetric (line 192) | class MeanMetric(LudwigMetric): method __init__ (line 195) | def __init__(self, **kwargs): method update (line 199) | def update(self, preds: Tensor, target: Tensor) -> None: method compute (line 202) | def compute(self) -> Tensor: method reset (line 205) | def reset(self): method get_current_value (line 210) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: class RMSPEMetric (line 215) | class RMSPEMetric(MeanMetric): method __init__ (line 216) | def __init__(self, **kwargs): method get_current_value (line 221) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: class R2Score (line 226) | class R2Score(LudwigMetric): method __init__ (line 234) | def __init__( method update (line 257) | def update(self, preds: Tensor, target: Tensor) -> None: method compute (line 271) | def compute(self) -> Tensor: class LossMetric (line 287) | class LossMetric(MeanMetric, ABC): method __init__ (line 288) | def __init__(self): method get_current_value (line 292) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: method can_report (line 296) | def can_report(cls, feature: "OutputFeature") -> bool: # noqa: F821 class BWCEWLMetric (line 301) | class BWCEWLMetric(LossMetric): method __init__ (line 304) | def __init__(self, config: BWCEWLossConfig, **kwargs): method get_current_value (line 308) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: class SoftmaxCrossEntropyMetric (line 313) | class SoftmaxCrossEntropyMetric(LossMetric): method __init__ (line 314) | def __init__(self, config: SoftmaxCrossEntropyLossConfig, **kwargs): method get_current_value (line 318) | def get_current_value(self, preds: Tensor, target: Tensor): class SequenceSoftmaxCrossEntropyMetric (line 323) | class SequenceSoftmaxCrossEntropyMetric(LossMetric): method __init__ (line 324) | def __init__(self, config: SequenceSoftmaxCrossEntropyLossConfig, **kw... method get_current_value (line 328) | def get_current_value(self, preds: Tensor, target: Tensor): class NextTokenSoftmaxCrossEntropyMetric (line 333) | class NextTokenSoftmaxCrossEntropyMetric(LossMetric): method __init__ (line 334) | def __init__(self, config: SequenceSoftmaxCrossEntropyLossConfig, **kw... method get_current_value (line 338) | def get_current_value(self, preds: Tensor, target: Tensor): class SigmoidCrossEntropyMetric (line 343) | class SigmoidCrossEntropyMetric(LossMetric): method __init__ (line 344) | def __init__(self, config: SigmoidCrossEntropyLossConfig, **kwargs): method get_current_value (line 348) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: class TokenAccuracyMetric (line 353) | class TokenAccuracyMetric(MeanMetric): method __init__ (line 354) | def __init__(self, **kwargs): method get_current_value (line 357) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: class SequenceAccuracyMetric (line 365) | class SequenceAccuracyMetric(MeanMetric): method __init__ (line 366) | def __init__(self, **kwargs): method get_current_value (line 369) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: class PerplexityMetric (line 374) | class PerplexityMetric(Perplexity, LudwigMetric): method __init__ (line 375) | def __init__(self, **kwargs): method update (line 378) | def update(self, preds: Tensor, target: Tensor) -> None: class NextTokenPerplexityMetric (line 383) | class NextTokenPerplexityMetric(MeanMetric): method __init__ (line 384) | def __init__(self, **kwargs): method get_current_value (line 388) | def get_current_value(self, preds: Tensor, target: Tensor): class BLEUScoreMetric (line 399) | class BLEUScoreMetric(BLEUScore, LudwigMetric): method __init__ (line 400) | def __init__(self, **kwargs): class ROUGEScoreMetric (line 406) | class ROUGEScoreMetric(ROUGEScore, LudwigMetric): method __init__ (line 407) | def __init__(self, **kwargs): class WordErrorRateMetric (line 413) | class WordErrorRateMetric(WordErrorRate, LudwigMetric): method __init__ (line 414) | def __init__(self, **kwargs): class CharErrorRateMetric (line 420) | class CharErrorRateMetric(CharErrorRate, LudwigMetric): method __init__ (line 421) | def __init__(self, **kwargs): class Accuracy (line 426) | class Accuracy(BinaryAccuracy, LudwigMetric): method __init__ (line 429) | def __init__(self, **kwargs): class CategoryAccuracy (line 434) | class CategoryAccuracy(MulticlassAccuracy, LudwigMetric): method __init__ (line 435) | def __init__(self, num_classes: int, **kwargs): method update (line 438) | def update(self, preds: Tensor, target: Tensor) -> None: class CategoryAccuracyMicro (line 445) | class CategoryAccuracyMicro(MulticlassAccuracy, LudwigMetric): method __init__ (line 446) | def __init__(self, num_classes: int, **kwargs): method update (line 449) | def update(self, preds: Tensor, target: Tensor) -> None: class HitsAtKMetric (line 456) | class HitsAtKMetric(MulticlassAccuracy, LudwigMetric): method __init__ (line 457) | def __init__(self, num_classes: int, top_k: int, **kwargs): method update (line 460) | def update(self, preds: Tensor, target: Tensor) -> None: method can_report (line 466) | def can_report(cls, feature: "OutputFeature") -> bool: # noqa: F821 class MAEMetric (line 471) | class MAEMetric(MeanAbsoluteError, LudwigMetric): method __init__ (line 472) | def __init__(self, **kwargs): method update (line 475) | def update(self, preds: Tensor, target: Tensor) -> None: class MSEMetric (line 480) | class MSEMetric(MeanSquaredError, LudwigMetric): method __init__ (line 481) | def __init__(self, **kwargs): method update (line 484) | def update(self, preds: Tensor, target: Tensor) -> None: class MAPEMetric (line 489) | class MAPEMetric(MeanAbsolutePercentageError, LudwigMetric): method __init__ (line 490) | def __init__(self, **kwargs): method update (line 493) | def update(self, preds: Tensor, target: Tensor) -> None: class JaccardMetric (line 498) | class JaccardMetric(MeanMetric): method __init__ (line 499) | def __init__(self, threshold: float = 0.5, **kwargs): method get_current_value (line 503) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: class HuberMetric (line 517) | class HuberMetric(LossMetric): method __init__ (line 518) | def __init__( method get_current_value (line 526) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: class CORNMetric (line 531) | class CORNMetric(LossMetric): method __init__ (line 532) | def __init__( method get_current_value (line 540) | def get_current_value(self, preds: Tensor, target: Tensor) -> Tensor: function get_metric_cls (line 544) | def get_metric_cls(metric_name: str) -> type[LudwigMetric]: function get_improved_fn (line 548) | def get_improved_fn(metric: str) -> Callable: function get_initial_validation_value (line 555) | def get_initial_validation_value(metric: str) -> float: function get_best_function (line 565) | def get_best_function(metric: str) -> Callable: FILE: ludwig/modules/metric_registry.py function register_metric (line 17) | def register_metric( function get_metric_classes (line 48) | def get_metric_classes(feature_type: str) -> dict[str, "LudwigMetric"]: function get_metric_cls (line 52) | def get_metric_cls(feature_type: str, name: str) -> "LudwigMetric": function get_metric_feature_type_registry (line 57) | def get_metric_feature_type_registry() -> Registry: function get_metric_registry (line 62) | def get_metric_registry() -> Registry: function get_metric (line 67) | def get_metric(metric_name: str) -> "LudwigMetric": # noqa function get_metrics_for_type (line 72) | def get_metrics_for_type(feature_type: str) -> dict[str, "LudwigMetric"]... function get_metric_names_for_type (line 77) | def get_metric_names_for_type(feature_type: str) -> list[str]: function get_metric_objective (line 82) | def get_metric_objective(metric_name: str) -> Literal[MINIMIZE, MAXIMIZE]: function get_metric_tensor_input (line 87) | def get_metric_tensor_input(metric_name: str) -> Literal[PREDICTIONS, PR... FILE: ludwig/modules/mlp_mixer_modules.py class MLP (line 22) | class MLP(LudwigModule): method __init__ (line 23) | def __init__( method forward (line 42) | def forward(self, inputs, **kwargs): method input_shape (line 47) | def input_shape(self) -> torch.Size: method output_shape (line 51) | def output_shape(self) -> torch.Size: class MixerBlock (line 55) | class MixerBlock(LudwigModule): method __init__ (line 56) | def __init__(self, embed_size: int, n_patches: int, token_dim: int, ch... method forward (line 68) | def forward(self, inputs: torch.Tensor, **kwargs): method input_shape (line 85) | def input_shape(self) -> torch.Size: method output_shape (line 89) | def output_shape(self) -> torch.Size: class MLPMixer (line 93) | class MLPMixer(LudwigModule): method __init__ (line 101) | def __init__( method forward (line 145) | def forward(self, inputs: torch.Tensor) -> torch.Tensor: method input_shape (line 162) | def input_shape(self) -> torch.Size: method output_shape (line 166) | def output_shape(self) -> torch.Size: FILE: ludwig/modules/normalization_modules.py class GhostBatchNormalization (line 13) | class GhostBatchNormalization(LudwigModule): method __init__ (line 14) | def __init__( method forward (line 22) | def forward(self, inputs): method moving_mean (line 55) | def moving_mean(self) -> torch.Tensor: method moving_variance (line 59) | def moving_variance(self) -> torch.Tensor: method output_shape (line 63) | def output_shape(self) -> torch.Size: method input_shape (line 67) | def input_shape(self) -> torch.Size: class BatchNorm1dOrIdentity (line 71) | class BatchNorm1dOrIdentity(BatchNorm1d): method forward (line 77) | def forward(self, input: torch.Tensor) -> torch.Tensor: class BatchNorm2dOrIdentity (line 87) | class BatchNorm2dOrIdentity(BatchNorm2d): method forward (line 93) | def forward(self, input: torch.Tensor) -> torch.Tensor: function create_norm_layer (line 111) | def create_norm_layer(norm: str, input_rank: int, num_features: int, **n... FILE: ludwig/modules/optimization_modules.py function create_clipper (line 27) | def create_clipper(gradient_clipping_config: Optional["GradientClippingC... function get_optimizer_class_and_kwargs (line 37) | def get_optimizer_class_and_kwargs( function create_optimizer (line 62) | def create_optimizer( FILE: ludwig/modules/recurrent_modules.py class RecurrentStack (line 32) | class RecurrentStack(LudwigModule): method __init__ (line 33) | def __init__( method input_shape (line 59) | def input_shape(self) -> torch.Size: method output_shape (line 65) | def output_shape(self) -> torch.Size: method forward (line 71) | def forward(self, inputs: torch.Tensor, mask=None): FILE: ludwig/modules/reduction_modules.py class SequenceReducer (line 26) | class SequenceReducer(LudwigModule): method __init__ (line 39) | def __init__(self, reduce_mode: str = None, max_sequence_length: int =... method forward (line 52) | def forward(self, inputs, mask=None): method input_shape (line 63) | def input_shape(self) -> torch.Size: method output_shape (line 71) | def output_shape(self) -> torch.Size: class ReduceLast (line 84) | class ReduceLast(torch.nn.Module): method forward (line 85) | def forward(self, inputs, mask=None): class ReduceSum (line 96) | class ReduceSum(torch.nn.Module): method forward (line 97) | def forward(self, inputs, mask=None): class ReduceMean (line 101) | class ReduceMean(torch.nn.Module): method forward (line 102) | def forward(self, inputs, mask=None): class ReduceMax (line 106) | class ReduceMax(torch.nn.Module): method forward (line 107) | def forward(self, inputs, mask=None): class ReduceConcat (line 111) | class ReduceConcat(torch.nn.Module): method forward (line 112) | def forward(self, inputs, mask=None): class ReduceNone (line 118) | class ReduceNone(torch.nn.Module): method forward (line 119) | def forward(self, inputs, mask=None): FILE: ludwig/modules/tabnet_modules.py class TabNet (line 9) | class TabNet(LudwigModule): method __init__ (line 10) | def __init__( method forward (line 92) | def forward(self, features: torch.Tensor) -> tuple[torch.Tensor, torch... method input_shape (line 165) | def input_shape(self) -> torch.Size: method output_shape (line 169) | def output_shape(self) -> torch.Size: class FeatureBlock (line 173) | class FeatureBlock(LudwigModule): method __init__ (line 174) | def __init__( method forward (line 200) | def forward(self, inputs): method input_shape (line 215) | def input_shape(self) -> torch.Size: class AttentiveTransformer (line 219) | class AttentiveTransformer(LudwigModule): method __init__ (line 220) | def __init__( method forward (line 255) | def forward(self, inputs, prior_scales): method input_shape (line 277) | def input_shape(self) -> torch.Size: method output_shape (line 281) | def output_shape(self) -> torch.Size: class FeatureTransformer (line 287) | class FeatureTransformer(LudwigModule): method __init__ (line 288) | def __init__( method forward (line 327) | def forward(self, inputs: torch.Tensor) -> torch.Tensor: method shared_fc_layers (line 340) | def shared_fc_layers(self): method input_shape (line 344) | def input_shape(self) -> torch.Size: method output_shape (line 348) | def output_shape(self) -> torch.Size: FILE: ludwig/modules/training_hooks.py class TrainingHook (line 9) | class TrainingHook(ABC): method __init__ (line 20) | def __init__(self, *args, **kwargs) -> None: method hook_fn (line 24) | def hook_fn(self, module: torch.nn.Module, inputs: torch.tensor, outpu... method activate_hook (line 40) | def activate_hook(self, module: torch.nn.Module) -> torch.nn.Module: method deactivate_hook (line 52) | def deactivate_hook(self): class NEFTuneHook (line 59) | class NEFTuneHook(TrainingHook): method __init__ (line 60) | def __init__(self, *args, **kwargs) -> None: method hook_fn (line 64) | def hook_fn(self, module: torch.nn.Module, input: torch.Tensor, output... method activate_hook (line 80) | def activate_hook(self, module: torch.nn.Module) -> torch.nn.Module: FILE: ludwig/predict.py function predict_cli (line 34) | def predict_cli( function cli (line 126) | def cli(sys_argv): FILE: ludwig/preprocess.py function preprocess_cli (line 35) | def preprocess_cli( function cli (line 159) | def cli(sys_argv): FILE: ludwig/progress_bar.py class LudwigProgressBarActions (line 11) | class LudwigProgressBarActions: class LudwigProgressBar (line 17) | class LudwigProgressBar: method __init__ (line 42) | def __init__( method set_postfix (line 97) | def set_postfix(self, ordered_dict: dict = None, **kwargs) -> None: method update (line 102) | def update(self, steps: int) -> None: method close (line 128) | def close(self) -> None: FILE: ludwig/schema/combiners/base.py class BaseCombinerConfig (line 8) | class BaseCombinerConfig(schema_utils.BaseMarshmallowConfig): FILE: ludwig/schema/combiners/common_transformer_options.py class CommonTransformerConfig (line 12) | class CommonTransformerConfig: FILE: ludwig/schema/combiners/comparator.py class ComparatorCombinerConfig (line 16) | class ComparatorCombinerConfig(BaseCombinerConfig): method __post_init__ (line 19) | def __post_init__(self): FILE: ludwig/schema/combiners/concat.py class ConcatCombinerConfig (line 15) | class ConcatCombinerConfig(BaseCombinerConfig): FILE: ludwig/schema/combiners/project_aggregate.py class ProjectAggregateCombinerConfig (line 14) | class ProjectAggregateCombinerConfig(BaseCombinerConfig): FILE: ludwig/schema/combiners/sequence.py class SequenceCombinerConfig (line 22) | class SequenceCombinerConfig(BaseCombinerConfig): FILE: ludwig/schema/combiners/sequence_concat.py class SequenceConcatCombinerConfig (line 22) | class SequenceConcatCombinerConfig(BaseCombinerConfig): method module_name (line 26) | def module_name(): FILE: ludwig/schema/combiners/tab_transformer.py class TabTransformerCombinerConfig (line 13) | class TabTransformerCombinerConfig(BaseCombinerConfig, CommonTransformer... FILE: ludwig/schema/combiners/tabnet.py class TabNetCombinerConfig (line 12) | class TabNetCombinerConfig(BaseCombinerConfig): FILE: ludwig/schema/combiners/transformer.py class TransformerCombinerConfig (line 13) | class TransformerCombinerConfig(BaseCombinerConfig, CommonTransformerCon... FILE: ludwig/schema/combiners/utils.py function register_combiner_config (line 18) | def register_combiner_config(name: str): function get_combiner_registry (line 27) | def get_combiner_registry(): function get_combiner_jsonschema (line 32) | def get_combiner_jsonschema(): function get_combiner_descriptions (line 64) | def get_combiner_descriptions(): function get_combiner_conds (line 79) | def get_combiner_conds() -> list[dict[str, Any]]: class CombinerSelection (line 95) | class CombinerSelection(schema_utils.TypeSelection): method __init__ (line 96) | def __init__(self): method get_schema_from_registry (line 102) | def get_schema_from_registry(self, key: str) -> type[schema_utils.Base... method _jsonschema_type_mapping (line 105) | def _jsonschema_type_mapping(self): FILE: ludwig/schema/common_fields.py function DropoutField (line 9) | def DropoutField(default: float = 0.0, description: str = None, paramete... function ResidualField (line 25) | def ResidualField( function NumFCLayersField (line 40) | def NumFCLayersField( function NormField (line 64) | def NormField( function NormParamsField (line 78) | def NormParamsField(description: str = None, parameter_metadata: Paramet... function FCLayersField (line 87) | def FCLayersField(description: str = None, parameter_metadata: Parameter... function BiasInitializerField (line 110) | def BiasInitializerField( function WeightsInitializerField (line 124) | def WeightsInitializerField( function EmbeddingInitializerField (line 138) | def EmbeddingInitializerField( function EmbeddingSizeField (line 152) | def EmbeddingSizeField( function EmbeddingsOnCPUField (line 169) | def EmbeddingsOnCPUField( function EmbeddingsTrainableField (line 187) | def EmbeddingsTrainableField( function PretrainedEmbeddingsField (line 203) | def PretrainedEmbeddingsField( function MaxSequenceLengthField (line 224) | def MaxSequenceLengthField( function VocabField (line 237) | def VocabField( function VocabSizeField (line 249) | def VocabSizeField( function RepresentationField (line 262) | def RepresentationField( function ReduceOutputField (line 278) | def ReduceOutputField( FILE: ludwig/schema/decoders/base.py class BaseDecoderConfig (line 14) | class BaseDecoderConfig(schema_utils.BaseMarshmallowConfig, ABC): class PassthroughDecoderConfig (line 88) | class PassthroughDecoderConfig(BaseDecoderConfig): method module_name (line 92) | def module_name(cls): class RegressorConfig (line 112) | class RegressorConfig(BaseDecoderConfig): method module_name (line 116) | def module_name(cls): class ProjectorConfig (line 152) | class ProjectorConfig(BaseDecoderConfig): method module_name (line 156) | def module_name(cls): class ClassifierConfig (line 228) | class ClassifierConfig(BaseDecoderConfig): method module_name (line 230) | def module_name(cls): FILE: ludwig/schema/decoders/image_decoders.py class ImageDecoderConfig (line 15) | class ImageDecoderConfig(BaseDecoderConfig): method set_fixed_preprocessing_params (line 16) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... class UNetDecoderConfig (line 25) | class UNetDecoderConfig(ImageDecoderConfig): method module_name (line 27) | def module_name(): FILE: ludwig/schema/decoders/llm_decoders.py class BaseExtractorDecoderConfig (line 13) | class BaseExtractorDecoderConfig(BaseMarshmallowConfig): class TextExtractorDecoderConfig (line 44) | class TextExtractorDecoderConfig(BaseExtractorDecoderConfig, BaseDecoder... method module_name (line 46) | def module_name(cls): class CategoryExtractorDecoderConfig (line 55) | class CategoryExtractorDecoderConfig(BaseExtractorDecoderConfig, BaseDec... method module_name (line 57) | def module_name(cls): FILE: ludwig/schema/decoders/sequence_decoders.py class SequenceGeneratorDecoderConfig (line 14) | class SequenceGeneratorDecoderConfig(BaseDecoderConfig): method module_name (line 16) | def module_name(): class SequenceTaggerDecoderConfig (line 59) | class SequenceTaggerDecoderConfig(BaseDecoderConfig): method module_name (line 61) | def module_name(cls): FILE: ludwig/schema/decoders/utils.py function register_decoder_config (line 19) | def register_decoder_config(name: str, features: str | list[str], model_... function get_decoder_cls (line 39) | def get_decoder_cls(model_type: str, feature: str, name: str): function get_decoder_classes (line 44) | def get_decoder_classes(model_type: str, feature: str) -> dict[str, type... function get_decoder_descriptions (line 49) | def get_decoder_descriptions(model_type: str, feature_type: str): function get_decoder_conds (line 77) | def get_decoder_conds(decoder_classes: dict[str, type["BaseDecoderConfig... function DecoderDataclassField (line 92) | def DecoderDataclassField(model_type: str, feature_type: str, default: s... FILE: ludwig/schema/defaults/base.py class BaseDefaultsConfig (line 8) | class BaseDefaultsConfig(schema_utils.BaseMarshmallowConfig): FILE: ludwig/schema/defaults/ecd.py class ECDDefaultsConfig (line 26) | class ECDDefaultsConfig(BaseDefaultsConfig): class ECDDefaultsField (line 55) | class ECDDefaultsField(schema_utils.DictMarshmallowField): method __init__ (line 56) | def __init__(self): FILE: ludwig/schema/defaults/llm.py class LLMDefaultsConfig (line 13) | class LLMDefaultsConfig(BaseDefaultsConfig): class LLMDefaultsField (line 18) | class LLMDefaultsField(schema_utils.DictMarshmallowField): method __init__ (line 19) | def __init__(self): FILE: ludwig/schema/defaults/utils.py function DefaultsDataclassField (line 11) | def DefaultsDataclassField(feature_type: str, defaults_registry: Registr... FILE: ludwig/schema/encoders/bag_encoders.py class BagEmbedWeightedConfig (line 13) | class BagEmbedWeightedConfig(BaseEncoderConfig): method module_name (line 15) | def module_name(): FILE: ludwig/schema/encoders/base.py class BaseEncoderConfig (line 18) | class BaseEncoderConfig(schema_utils.BaseMarshmallowConfig, ABC): method set_fixed_preprocessing_params (line 29) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... method is_pretrained (line 32) | def is_pretrained(self) -> bool: method can_cache_embeddings (line 35) | def can_cache_embeddings(self) -> bool: class PassthroughEncoderConfig (line 43) | class PassthroughEncoderConfig(BaseEncoderConfig): method module_name (line 47) | def module_name(): class DenseEncoderConfig (line 59) | class DenseEncoderConfig(BaseEncoderConfig): method module_name (line 63) | def module_name(): FILE: ludwig/schema/encoders/category_encoders.py class CategoricalPassthroughEncoderConfig (line 14) | class CategoricalPassthroughEncoderConfig(BaseEncoderConfig): method module_name (line 19) | def module_name(): class CategoricalEmbedConfig (line 31) | class CategoricalEmbedConfig(BaseEncoderConfig): method module_name (line 33) | def module_name(): class CategoricalSparseConfig (line 67) | class CategoricalSparseConfig(BaseEncoderConfig): method module_name (line 69) | def module_name(): class CategoricalOneHotEncoderConfig (line 95) | class CategoricalOneHotEncoderConfig(BaseEncoderConfig): method can_cache_embeddings (line 106) | def can_cache_embeddings(self) -> bool: FILE: ludwig/schema/encoders/date_encoders.py class DateEmbedConfig (line 13) | class DateEmbedConfig(BaseEncoderConfig): method module_name (line 15) | def module_name(): class DateWaveConfig (line 104) | class DateWaveConfig(BaseEncoderConfig): method module_name (line 106) | def module_name(): FILE: ludwig/schema/encoders/h3_encoders.py class H3EmbedConfig (line 13) | class H3EmbedConfig(BaseEncoderConfig): method module_name (line 15) | def module_name(): class H3WeightedSumConfig (line 110) | class H3WeightedSumConfig(BaseEncoderConfig): method module_name (line 112) | def module_name(): class H3RNNConfig (line 207) | class H3RNNConfig(BaseEncoderConfig): method module_name (line 209) | def module_name(): FILE: ludwig/schema/encoders/image/base.py class ImageEncoderConfig (line 16) | class ImageEncoderConfig(BaseEncoderConfig): method set_fixed_preprocessing_params (line 17) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... class Stacked2DCNNConfig (line 26) | class Stacked2DCNNConfig(ImageEncoderConfig): method module_name (line 28) | def module_name(): class ResNetConfig (line 314) | class ResNetConfig(ImageEncoderConfig): method module_name (line 316) | def module_name(): class MLPMixerConfig (line 497) | class MLPMixerConfig(ImageEncoderConfig): method module_name (line 499) | def module_name(): class ViTConfig (line 579) | class ViTConfig(ImageEncoderConfig): method module_name (line 581) | def module_name(): method set_fixed_preprocessing_params (line 696) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... method requires_equal_dimensions (line 708) | def requires_equal_dimensions(cls) -> bool: method required_width (line 712) | def required_width(cls) -> int | None: method required_height (line 716) | def required_height(cls) -> int | None: method is_pretrained (line 719) | def is_pretrained(self) -> bool: class UNetEncoderConfig (line 726) | class UNetEncoderConfig(ImageEncoderConfig): method module_name (line 728) | def module_name(): FILE: ludwig/schema/encoders/image/timm.py class TimmBaseConfig (line 11) | class TimmBaseConfig(BaseEncoderConfig): method is_pretrained (line 30) | def is_pretrained(self) -> bool: class TimmEncoderConfig (line 37) | class TimmEncoderConfig(TimmBaseConfig): class TimmCAFormerEncoderConfig (line 99) | class TimmCAFormerEncoderConfig(TimmBaseConfig): class TimmConvFormerEncoderConfig (line 118) | class TimmConvFormerEncoderConfig(TimmBaseConfig): class TimmPoolFormerEncoderConfig (line 136) | class TimmPoolFormerEncoderConfig(TimmBaseConfig): FILE: ludwig/schema/encoders/image/torchvision.py class TVBaseEncoderConfig (line 11) | class TVBaseEncoderConfig(BaseEncoderConfig): method is_pretrained (line 37) | def is_pretrained(self) -> bool: class TVAlexNetEncoderConfig (line 44) | class TVAlexNetEncoderConfig(TVBaseEncoderConfig): class TVConvNeXtEncoderConfig (line 59) | class TVConvNeXtEncoderConfig(TVBaseEncoderConfig): class TVDenseNetEncoderConfig (line 74) | class TVDenseNetEncoderConfig(TVBaseEncoderConfig): class TVEfficientNetEncoderConfig (line 89) | class TVEfficientNetEncoderConfig(TVBaseEncoderConfig): class TVGoogLeNetEncoderConfig (line 116) | class TVGoogLeNetEncoderConfig(TVBaseEncoderConfig): class TVInceptionV3EncoderConfig (line 131) | class TVInceptionV3EncoderConfig(TVBaseEncoderConfig): class TVMaxVitEncoderConfig (line 146) | class TVMaxVitEncoderConfig(TVBaseEncoderConfig): class TVMNASNetEncoderConfig (line 161) | class TVMNASNetEncoderConfig(TVBaseEncoderConfig): class TVMobileNetV2EncoderConfig (line 176) | class TVMobileNetV2EncoderConfig(TVBaseEncoderConfig): class TVMobileNetV3EncoderConfig (line 191) | class TVMobileNetV3EncoderConfig(TVBaseEncoderConfig): class TVRegNetEncoderConfig (line 209) | class TVRegNetEncoderConfig(TVBaseEncoderConfig): class TVResNetEncoderConfig (line 240) | class TVResNetEncoderConfig(TVBaseEncoderConfig): class TVResNeXtEncoderConfig (line 255) | class TVResNeXtEncoderConfig(TVBaseEncoderConfig): class TVShuffleNetV2EncoderConfig (line 270) | class TVShuffleNetV2EncoderConfig(TVBaseEncoderConfig): class TVSqueezeNetEncoderConfig (line 290) | class TVSqueezeNetEncoderConfig(TVBaseEncoderConfig): class TVSwinTransformerEncoderConfig (line 308) | class TVSwinTransformerEncoderConfig(TVBaseEncoderConfig): class TVViTEncoderConfig (line 327) | class TVViTEncoderConfig(TVBaseEncoderConfig): class TVVGGEncoderConfig (line 348) | class TVVGGEncoderConfig(TVBaseEncoderConfig): class TVWideResNetEncoderConfig (line 384) | class TVWideResNetEncoderConfig(TVBaseEncoderConfig): FILE: ludwig/schema/encoders/sequence_encoders.py function NumFiltersField (line 31) | def NumFiltersField(default: int = 256) -> Field: function FilterSizeField (line 39) | def FilterSizeField(default: int = 3) -> Field: function PoolFunctionField (line 47) | def PoolFunctionField(default: str = "max") -> Field: function PoolSizeField (line 58) | def PoolSizeField(default: int | None = None) -> Field: class SequenceEncoderConfig (line 73) | class SequenceEncoderConfig(BaseEncoderConfig): method set_fixed_preprocessing_params (line 76) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... class SequencePassthroughConfig (line 86) | class SequencePassthroughConfig(SequenceEncoderConfig): method module_name (line 88) | def module_name(): class SequenceEmbedConfig (line 111) | class SequenceEmbedConfig(SequenceEncoderConfig): method module_name (line 113) | def module_name(): class ParallelCNNConfig (line 145) | class ParallelCNNConfig(SequenceEncoderConfig): method module_name (line 147) | def module_name(): class StackedCNNConfig (line 251) | class StackedCNNConfig(SequenceEncoderConfig): method module_name (line 253) | def module_name(): class StackedParallelCNNConfig (line 390) | class StackedParallelCNNConfig(SequenceEncoderConfig): method module_name (line 392) | def module_name(): class StackedRNNConfig (line 500) | class StackedRNNConfig(SequenceEncoderConfig): method module_name (line 502) | def module_name(): class StackedCNNRNNConfig (line 622) | class StackedCNNRNNConfig(SequenceEncoderConfig): method module_name (line 624) | def module_name(): class StackedTransformerConfig (line 813) | class StackedTransformerConfig(SequenceEncoderConfig): method module_name (line 815) | def module_name(): FILE: ludwig/schema/encoders/set_encoders.py class SetSparseEncoderConfig (line 13) | class SetSparseEncoderConfig(BaseEncoderConfig): method module_name (line 15) | def module_name(): FILE: ludwig/schema/encoders/text/encoders.py class HFEncoderConfig (line 23) | class HFEncoderConfig(SequenceEncoderConfig): method set_fixed_preprocessing_params (line 29) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... method is_pretrained (line 42) | def is_pretrained(self) -> bool: method can_cache_embeddings (line 45) | def can_cache_embeddings(self) -> bool: class HFEncoderImplConfig (line 52) | class HFEncoderImplConfig(HFEncoderConfig): class ALBERTConfig (line 102) | class ALBERTConfig(HFEncoderConfig): method module_name (line 106) | def module_name(): class MT5Config (line 303) | class MT5Config(HFEncoderConfig): method module_name (line 307) | def module_name(): class XLMRoBERTaConfig (line 495) | class XLMRoBERTaConfig(HFEncoderConfig): method module_name (line 499) | def module_name(): class BERTConfig (line 608) | class BERTConfig(HFEncoderConfig): method module_name (line 612) | def module_name(): class DebertaV2Config (line 784) | class DebertaV2Config(HFEncoderImplConfig, DebertaModelParams): method module_name (line 788) | def module_name(): class XLMConfig (line 814) | class XLMConfig(HFEncoderConfig): method module_name (line 818) | def module_name(): class GPTConfig (line 1065) | class GPTConfig(HFEncoderConfig): method module_name (line 1069) | def module_name(): class GPT2Config (line 1209) | class GPT2Config(HFEncoderConfig): method module_name (line 1213) | def module_name(): class RoBERTaConfig (line 1365) | class RoBERTaConfig(HFEncoderConfig): method module_name (line 1369) | def module_name(): class TransformerXLConfig (line 1459) | class TransformerXLConfig(HFEncoderConfig): method module_name (line 1463) | def module_name(): class XLNetConfig (line 1690) | class XLNetConfig(HFEncoderConfig): method module_name (line 1694) | def module_name(): class DistilBERTConfig (line 1933) | class DistilBERTConfig(HFEncoderConfig): method module_name (line 1937) | def module_name(): class CTRLConfig (line 2092) | class CTRLConfig(HFEncoderConfig): method module_name (line 2096) | def module_name(): class CamemBERTConfig (line 2237) | class CamemBERTConfig(HFEncoderConfig): method module_name (line 2241) | def module_name(): class T5Config (line 2412) | class T5Config(HFEncoderConfig): method module_name (line 2416) | def module_name(): class FlauBERTConfig (line 2561) | class FlauBERTConfig(HFEncoderConfig): method module_name (line 2565) | def module_name(): class ELECTRAConfig (line 2804) | class ELECTRAConfig(HFEncoderConfig): method module_name (line 2808) | def module_name(): class LongformerConfig (line 2974) | class LongformerConfig(HFEncoderConfig): method module_name (line 2978) | def module_name(): class AutoTransformerConfig (line 3081) | class AutoTransformerConfig(HFEncoderConfig): method __post_init__ (line 3084) | def __post_init__(self): method module_name (line 3091) | def module_name(): method use_pretrained (line 3095) | def use_pretrained(self) -> bool: class TfIdfEncoderConfig (line 3159) | class TfIdfEncoderConfig(SequenceEncoderConfig): method set_fixed_preprocessing_params (line 3170) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... method can_cache_embeddings (line 3173) | def can_cache_embeddings(self) -> bool: class LLMEncoderConfig (line 3180) | class LLMEncoderConfig(SequenceEncoderConfig): FILE: ludwig/schema/encoders/text/hf_model_params.py class DebertaModelParams (line 22) | class DebertaModelParams(schema_utils.BaseMarshmallowConfig): method get_hf_config_param_names (line 24) | def get_hf_config_param_names(cls) -> set[str]: FILE: ludwig/schema/encoders/text_encoders.py class HFEncoderConfig (line 23) | class HFEncoderConfig(SequenceEncoderConfig): method set_fixed_preprocessing_params (line 29) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... method is_pretrained (line 42) | def is_pretrained(self) -> bool: method can_cache_embeddings (line 45) | def can_cache_embeddings(self) -> bool: class HFEncoderImplConfig (line 52) | class HFEncoderImplConfig(HFEncoderConfig): class ALBERTConfig (line 102) | class ALBERTConfig(HFEncoderConfig): method module_name (line 106) | def module_name(): class MT5Config (line 303) | class MT5Config(HFEncoderConfig): method module_name (line 307) | def module_name(): class XLMRoBERTaConfig (line 495) | class XLMRoBERTaConfig(HFEncoderConfig): method module_name (line 499) | def module_name(): class BERTConfig (line 608) | class BERTConfig(HFEncoderConfig): method module_name (line 612) | def module_name(): class DebertaV2Config (line 784) | class DebertaV2Config(HFEncoderImplConfig, DebertaModelParams): method module_name (line 788) | def module_name(): class XLMConfig (line 814) | class XLMConfig(HFEncoderConfig): method module_name (line 818) | def module_name(): class GPTConfig (line 1065) | class GPTConfig(HFEncoderConfig): method module_name (line 1069) | def module_name(): class GPT2Config (line 1209) | class GPT2Config(HFEncoderConfig): method module_name (line 1213) | def module_name(): class RoBERTaConfig (line 1365) | class RoBERTaConfig(HFEncoderConfig): method module_name (line 1369) | def module_name(): class TransformerXLConfig (line 1459) | class TransformerXLConfig(HFEncoderConfig): method module_name (line 1463) | def module_name(): class XLNetConfig (line 1690) | class XLNetConfig(HFEncoderConfig): method module_name (line 1694) | def module_name(): class DistilBERTConfig (line 1933) | class DistilBERTConfig(HFEncoderConfig): method module_name (line 1937) | def module_name(): class CTRLConfig (line 2092) | class CTRLConfig(HFEncoderConfig): method module_name (line 2096) | def module_name(): class CamemBERTConfig (line 2237) | class CamemBERTConfig(HFEncoderConfig): method module_name (line 2241) | def module_name(): class T5Config (line 2412) | class T5Config(HFEncoderConfig): method module_name (line 2416) | def module_name(): class FlauBERTConfig (line 2561) | class FlauBERTConfig(HFEncoderConfig): method module_name (line 2565) | def module_name(): class ELECTRAConfig (line 2804) | class ELECTRAConfig(HFEncoderConfig): method module_name (line 2808) | def module_name(): class LongformerConfig (line 2974) | class LongformerConfig(HFEncoderConfig): method module_name (line 2978) | def module_name(): class AutoTransformerConfig (line 3081) | class AutoTransformerConfig(HFEncoderConfig): method __post_init__ (line 3084) | def __post_init__(self): method module_name (line 3093) | def module_name(): class TfIdfEncoderConfig (line 3161) | class TfIdfEncoderConfig(SequenceEncoderConfig): method set_fixed_preprocessing_params (line 3172) | def set_fixed_preprocessing_params(self, model_type: str, preprocessin... method can_cache_embeddings (line 3175) | def can_cache_embeddings(self) -> bool: class LLMEncoderConfig (line 3182) | class LLMEncoderConfig(SequenceEncoderConfig): FILE: ludwig/schema/encoders/utils.py function register_encoder_config (line 19) | def register_encoder_config(name: str, features: str | list[str], model_... function get_encoder_cls (line 39) | def get_encoder_cls(model_type: str, feature: str, name: str): function get_encoder_classes (line 44) | def get_encoder_classes(model_type: str, feature: str) -> dict[str, type... function get_encoder_descriptions (line 49) | def get_encoder_descriptions(model_type: str, feature_type: str) -> dict... function get_encoder_conds (line 77) | def get_encoder_conds(encoder_classes: dict[str, type["BaseEncoderConfig... function EncoderDataclassField (line 92) | def EncoderDataclassField( FILE: ludwig/schema/export_schema.py function _strip_parameter_metadata (line 20) | def _strip_parameter_metadata(obj): function export_schema (line 35) | def export_schema(model_type: str = MODEL_ECD, *, strip_metadata: bool =... function export_combined_schema (line 47) | def export_combined_schema(*, strip_metadata: bool = True) -> dict: function main (line 72) | def main(sys_argv=None): FILE: ludwig/schema/features/audio_feature.py class AudioInputFeatureConfigMixin (line 17) | class AudioInputFeatureConfigMixin(BaseMarshmallowConfig): class AudioInputFeatureConfig (line 33) | class AudioInputFeatureConfig(AudioInputFeatureConfigMixin, BaseInputFea... FILE: ludwig/schema/features/augmentation/base.py class BaseAugmentationConfig (line 8) | class BaseAugmentationConfig(schema_utils.BaseMarshmallowConfig): FILE: ludwig/schema/features/augmentation/image.py class AutoAugmentationConfig (line 13) | class AutoAugmentationConfig(BaseAugmentationConfig): class RandomHorizontalFlipConfig (line 30) | class RandomHorizontalFlipConfig(BaseAugmentationConfig): class RandomVerticalFlipConfig (line 42) | class RandomVerticalFlipConfig(BaseAugmentationConfig): class RandomRotateConfig (line 54) | class RandomRotateConfig(BaseAugmentationConfig): class RandomBlurConfig (line 71) | class RandomBlurConfig(BaseAugmentationConfig): class RandomBrightnessConfig (line 88) | class RandomBrightnessConfig(BaseAugmentationConfig): class RandomContrastConfig (line 112) | class RandomContrastConfig(BaseAugmentationConfig): FILE: ludwig/schema/features/augmentation/utils.py function get_augmentation_config_registry (line 16) | def get_augmentation_config_registry() -> Registry: function register_augmentation_config (line 21) | def register_augmentation_config(name: str, features: str | list[str]): function get_augmentation_cls (line 36) | def get_augmentation_cls(feature: str, name: str): function get_augmentation_classes (line 41) | def get_augmentation_classes(feature: str): function AugmentationDataclassField (line 46) | def AugmentationDataclassField( function get_augmentation_list_jsonschema (line 137) | def get_augmentation_list_jsonschema(feature_type: str, default: list[di... function get_augmentation_list_conds (line 172) | def get_augmentation_list_conds(feature_type: str): FILE: ludwig/schema/features/bag_feature.py class BagInputFeatureConfigMixin (line 17) | class BagInputFeatureConfigMixin(BaseMarshmallowConfig): class BagInputFeatureConfig (line 33) | class BagInputFeatureConfig(BagInputFeatureConfigMixin, BaseInputFeature... FILE: ludwig/schema/features/base.py class BaseFeatureConfig (line 46) | class BaseFeatureConfig(schema_utils.BaseMarshmallowConfig): method __post_init__ (line 49) | def __post_init__(self): method enable (line 85) | def enable(self): method disable (line 99) | def disable(self): class BaseInputFeatureConfig (line 116) | class BaseInputFeatureConfig(BaseFeatureConfig): method has_augmentation (line 128) | def has_augmentation(self) -> bool: class ECDInputFeatureConfig (line 134) | class ECDInputFeatureConfig(BaseFeatureConfig): class BaseOutputFeatureConfig (line 140) | class BaseOutputFeatureConfig(BaseFeatureConfig): class FeatureCollection (line 184) | class FeatureCollection(Generic[T], schema_utils.ListSerializable): method __init__ (line 185) | def __init__(self, features: list[T]): method to_list (line 191) | def to_list(self) -> list[dict[str, Any]]: method items (line 197) | def items(self) -> Iterable[tuple[str, T]]: method __iter__ (line 200) | def __iter__(self): method __len__ (line 203) | def __len__(self): method __getitem__ (line 206) | def __getitem__(self, i) -> T: class FeatureList (line 213) | class FeatureList(schema_utils.LudwigSchemaField): method __init__ (line 219) | def __init__( method _deserialize (line 233) | def _deserialize(self, value, attr, data, **kwargs) -> FeatureCollection: method _jsonschema_type_mapping (line 249) | def _jsonschema_type_mapping(self): class FeaturesTypeSelection (line 262) | class FeaturesTypeSelection(schema_utils.TypeSelection): method __init__ (line 263) | def __init__( method get_list_field (line 276) | def get_list_field(self): class ECDInputFeatureSelection (line 298) | class ECDInputFeatureSelection(FeaturesTypeSelection): method __init__ (line 299) | def __init__(self): method _jsonschema_type_mapping (line 306) | def _jsonschema_type_mapping(self): class LLMInputFeatureSelection (line 310) | class LLMInputFeatureSelection(FeaturesTypeSelection): method __init__ (line 311) | def __init__(self): method _jsonschema_type_mapping (line 314) | def _jsonschema_type_mapping(self): class ECDOutputFeatureSelection (line 318) | class ECDOutputFeatureSelection(FeaturesTypeSelection): method __init__ (line 319) | def __init__(self): method _jsonschema_type_mapping (line 322) | def _jsonschema_type_mapping(self): class LLMOutputFeatureSelection (line 326) | class LLMOutputFeatureSelection(FeaturesTypeSelection): method __init__ (line 327) | def __init__(self): method _jsonschema_type_mapping (line 331) | def _jsonschema_type_mapping(self): FILE: ludwig/schema/features/binary_feature.py class BinaryInputFeatureConfigMixin (line 28) | class BinaryInputFeatureConfigMixin(BaseMarshmallowConfig): class BinaryInputFeatureConfig (line 37) | class BinaryInputFeatureConfig(BinaryInputFeatureConfigMixin, BaseInputF... class ECDBinaryInputFeatureConfig (line 48) | class ECDBinaryInputFeatureConfig(BinaryInputFeatureConfig): class BinaryOutputFeatureConfigMixin (line 59) | class BinaryOutputFeatureConfigMixin(BaseMarshmallowConfig): class BinaryOutputFeatureConfig (line 73) | class BinaryOutputFeatureConfig(BinaryOutputFeatureConfigMixin, BaseOutp... class ECDBinaryOutputFeatureConfig (line 125) | class ECDBinaryOutputFeatureConfig(BinaryOutputFeatureConfig): class BinaryDefaultsConfig (line 136) | class BinaryDefaultsConfig(BinaryInputFeatureConfigMixin, BinaryOutputFe... FILE: ludwig/schema/features/category_feature.py class CategoryInputFeatureConfigMixin (line 29) | class CategoryInputFeatureConfigMixin(BaseMarshmallowConfig): class CategoryInputFeatureConfig (line 38) | class CategoryInputFeatureConfig(CategoryInputFeatureConfigMixin, BaseIn... class ECDCategoryInputFeatureConfig (line 50) | class ECDCategoryInputFeatureConfig(CategoryInputFeatureConfig): class CategoryOutputFeatureConfigMixin (line 61) | class CategoryOutputFeatureConfigMixin(BaseMarshmallowConfig): class CategoryOutputFeatureConfig (line 75) | class CategoryOutputFeatureConfig(CategoryOutputFeatureConfigMixin, Base... class ECDCategoryOutputFeatureConfig (line 127) | class ECDCategoryOutputFeatureConfig(CategoryOutputFeatureConfig): class CategoryDistributionOutputFeatureConfig (line 138) | class CategoryDistributionOutputFeatureConfig(CategoryOutputFeatureConfig): class CategoryDefaultsConfig (line 156) | class CategoryDefaultsConfig(CategoryInputFeatureConfigMixin, CategoryOu... class CategoryDistributionDefaultsConfig (line 173) | class CategoryDistributionDefaultsConfig(CategoryOutputFeatureConfigMixin): class LLMCategoryOutputFeatureConfig (line 180) | class LLMCategoryOutputFeatureConfig(CategoryOutputFeatureConfig): FILE: ludwig/schema/features/date_feature.py class DateInputFeatureConfigMixin (line 17) | class DateInputFeatureConfigMixin(BaseMarshmallowConfig): class DateInputFeatureConfig (line 33) | class DateInputFeatureConfig(DateInputFeatureConfigMixin, BaseInputFeatu... FILE: ludwig/schema/features/h3_feature.py class H3InputFeatureConfigMixin (line 17) | class H3InputFeatureConfigMixin(BaseMarshmallowConfig): class H3InputFeatureConfig (line 33) | class H3InputFeatureConfig(H3InputFeatureConfigMixin, BaseInputFeatureCo... FILE: ludwig/schema/features/image_feature.py class ImageInputFeatureConfigMixin (line 38) | class ImageInputFeatureConfigMixin(BaseMarshmallowConfig): method has_augmentation (line 57) | def has_augmentation(self) -> bool: class ImageInputFeatureConfig (line 65) | class ImageInputFeatureConfig(ImageInputFeatureConfigMixin, BaseInputFea... class ImageOutputFeatureConfigMixin (line 74) | class ImageOutputFeatureConfigMixin(BaseMarshmallowConfig): class ImageOutputFeatureConfig (line 93) | class ImageOutputFeatureConfig(ImageOutputFeatureConfigMixin, BaseOutput... class ImageDefaultsConfig (line 130) | class ImageDefaultsConfig(ImageInputFeatureConfigMixin, ImageOutputFeatu... FILE: ludwig/schema/features/loss/loss.py class BaseLossConfig (line 67) | class BaseLossConfig(schema_utils.BaseMarshmallowConfig): method name (line 75) | def name(cls) -> str: function get_loss_schema_registry (line 84) | def get_loss_schema_registry() -> Registry[type[BaseLossConfig]]: function get_loss_cls (line 89) | def get_loss_cls(feature: str, name: str) -> type[BaseLossConfig]: function get_loss_classes (line 94) | def get_loss_classes(feature: str) -> dict[str, type[BaseLossConfig]]: function register_loss (line 98) | def register_loss(features: str | list[str]): class MSELossConfig (line 116) | class MSELossConfig(BaseLossConfig): method name (line 129) | def name(self) -> str: class MAELossConfig (line 136) | class MAELossConfig(BaseLossConfig): method name (line 149) | def name(self) -> str: class MAPELossConfig (line 156) | class MAPELossConfig(BaseLossConfig): method name (line 169) | def name(self) -> str: class RMSELossConfig (line 176) | class RMSELossConfig(BaseLossConfig): method name (line 189) | def name(self) -> str: class RMSPELossConfig (line 196) | class RMSPELossConfig(BaseLossConfig): method name (line 209) | def name(self) -> str: class BWCEWLossConfig (line 216) | class BWCEWLossConfig(BaseLossConfig): method name (line 248) | def name(self) -> str: class SoftmaxCrossEntropyLossConfig (line 255) | class SoftmaxCrossEntropyLossConfig(BaseLossConfig): method name (line 303) | def name(self) -> str: class SequenceSoftmaxCrossEntropyLossConfig (line 310) | class SequenceSoftmaxCrossEntropyLossConfig(BaseLossConfig): method name (line 364) | def name(self) -> str: class NextTokenSoftmaxCrossEntropyLossConfig (line 371) | class NextTokenSoftmaxCrossEntropyLossConfig(SequenceSoftmaxCrossEntropy... method name (line 378) | def name(self) -> str: class SigmoidCrossEntropyLossConfig (line 385) | class SigmoidCrossEntropyLossConfig(BaseLossConfig): method name (line 408) | def name(self) -> str: class HuberLossConfig (line 415) | class HuberLossConfig(BaseLossConfig): method name (line 439) | def name(self) -> str: class CORNLossConfig (line 446) | class CORNLossConfig(BaseLossConfig): method name (line 467) | def name(self) -> str: method class_weights (line 471) | def class_weights(self) -> int: method class_similarities_temperature (line 475) | def class_similarities_temperature(self) -> int: FILE: ludwig/schema/features/loss/utils.py function get_loss_conds (line 9) | def get_loss_conds(feature_type: str): function LossDataclassField (line 25) | def LossDataclassField(feature_type: str, default: str) -> Field: FILE: ludwig/schema/features/number_feature.py class NumberInputFeatureConfigMixin (line 28) | class NumberInputFeatureConfigMixin(BaseMarshmallowConfig): class NumberInputFeatureConfig (line 37) | class NumberInputFeatureConfig(NumberInputFeatureConfigMixin, BaseInputF... class ECDNumberInputFeatureConfig (line 48) | class ECDNumberInputFeatureConfig(NumberInputFeatureConfig): class NumberOutputFeatureConfigMixin (line 59) | class NumberOutputFeatureConfigMixin(BaseMarshmallowConfig): class NumberOutputFeatureConfig (line 73) | class NumberOutputFeatureConfig(NumberOutputFeatureConfigMixin, BaseOutp... class ECDNumberOutputFeatureConfig (line 121) | class ECDNumberOutputFeatureConfig(NumberOutputFeatureConfig): class NumberDefaultsConfig (line 132) | class NumberDefaultsConfig(NumberInputFeatureConfigMixin, NumberOutputFe... FILE: ludwig/schema/features/preprocessing/audio.py class AudioPreprocessingConfig (line 13) | class AudioPreprocessingConfig(BasePreprocessingConfig): FILE: ludwig/schema/features/preprocessing/bag.py class BagPreprocessingConfig (line 15) | class BagPreprocessingConfig(BasePreprocessingConfig): FILE: ludwig/schema/features/preprocessing/base.py class BasePreprocessingConfig (line 6) | class BasePreprocessingConfig(schema_utils.BaseMarshmallowConfig): FILE: ludwig/schema/features/preprocessing/binary.py class BinaryPreprocessingConfig (line 23) | class BinaryPreprocessingConfig(BasePreprocessingConfig): class BinaryOutputPreprocessingConfig (line 71) | class BinaryOutputPreprocessingConfig(BinaryPreprocessingConfig): FILE: ludwig/schema/features/preprocessing/category.py class CategoryPreprocessingConfig (line 15) | class CategoryPreprocessingConfig(BasePreprocessingConfig): class CategoryOutputPreprocessingConfig (line 72) | class CategoryOutputPreprocessingConfig(CategoryPreprocessingConfig): class CategoryDistributionOutputPreprocessingConfig (line 99) | class CategoryDistributionOutputPreprocessingConfig(BasePreprocessingCon... method __post_init__ (line 100) | def __post_init__(self): class LLMCategoryOutputPreprocessingConfig (line 118) | class LLMCategoryOutputPreprocessingConfig(CategoryOutputPreprocessingCo... method __post_init__ (line 119) | def __post_init__(self): FILE: ludwig/schema/features/preprocessing/date.py class DatePreprocessingConfig (line 13) | class DatePreprocessingConfig(BasePreprocessingConfig): FILE: ludwig/schema/features/preprocessing/h3.py class H3PreprocessingConfig (line 13) | class H3PreprocessingConfig(BasePreprocessingConfig): FILE: ludwig/schema/features/preprocessing/image.py class ImagePreprocessingConfig (line 13) | class ImagePreprocessingConfig(BasePreprocessingConfig): class ImageOutputPreprocessingConfig (line 161) | class ImageOutputPreprocessingConfig(ImagePreprocessingConfig): FILE: ludwig/schema/features/preprocessing/number.py class NumberPreprocessingConfig (line 20) | class NumberPreprocessingConfig(BasePreprocessingConfig): class NumberOutputPreprocessingConfig (line 97) | class NumberOutputPreprocessingConfig(NumberPreprocessingConfig): FILE: ludwig/schema/features/preprocessing/sequence.py class SequencePreprocessingConfig (line 14) | class SequencePreprocessingConfig(BasePreprocessingConfig): class SequenceOutputPreprocessingConfig (line 126) | class SequenceOutputPreprocessingConfig(SequencePreprocessingConfig): FILE: ludwig/schema/features/preprocessing/set.py class SetPreprocessingConfig (line 14) | class SetPreprocessingConfig(BasePreprocessingConfig): class SetOutputPreprocessingConfig (line 66) | class SetOutputPreprocessingConfig(SetPreprocessingConfig): FILE: ludwig/schema/features/preprocessing/text.py class BaseTextPreprocessingConfig (line 16) | class BaseTextPreprocessingConfig(BasePreprocessingConfig): class TextPreprocessingConfig (line 147) | class TextPreprocessingConfig(BaseTextPreprocessingConfig): class LLMTextInputPreprocessingConfig (line 156) | class LLMTextInputPreprocessingConfig(BaseTextPreprocessingConfig): class TextOutputPreprocessingConfig (line 172) | class TextOutputPreprocessingConfig(BaseTextPreprocessingConfig): class LLMTextOutputPreprocessingConfig (line 232) | class LLMTextOutputPreprocessingConfig(TextOutputPreprocessingConfig): FILE: ludwig/schema/features/preprocessing/timeseries.py class BaseTimeseriesPreprocessingConfig (line 12) | class BaseTimeseriesPreprocessingConfig(BasePreprocessingConfig): class TimeseriesPreprocessingConfig (line 78) | class TimeseriesPreprocessingConfig(BaseTimeseriesPreprocessingConfig): class TimeseriesOutputPreprocessingConfig (line 103) | class TimeseriesOutputPreprocessingConfig(BaseTimeseriesPreprocessingCon... FILE: ludwig/schema/features/preprocessing/utils.py function register_preprocessor (line 13) | def register_preprocessor(name: str): function PreprocessingDataclassField (line 22) | def PreprocessingDataclassField(feature_type: str): FILE: ludwig/schema/features/preprocessing/vector.py class VectorPreprocessingConfig (line 13) | class VectorPreprocessingConfig(BasePreprocessingConfig): class VectorOutputPreprocessingConfig (line 50) | class VectorOutputPreprocessingConfig(VectorPreprocessingConfig): FILE: ludwig/schema/features/sequence_feature.py class SequenceInputFeatureConfigMixin (line 28) | class SequenceInputFeatureConfigMixin(BaseMarshmallowConfig): class SequenceInputFeatureConfig (line 44) | class SequenceInputFeatureConfig(SequenceInputFeatureConfigMixin, BaseIn... class SequenceOutputFeatureConfigMixin (line 54) | class SequenceOutputFeatureConfigMixin(BaseMarshmallowConfig): class SequenceOutputFeatureConfig (line 73) | class SequenceOutputFeatureConfig(SequenceOutputFeatureConfigMixin, Base... class SequenceDefaultsConfig (line 111) | class SequenceDefaultsConfig(SequenceInputFeatureConfigMixin, SequenceOu... FILE: ludwig/schema/features/set_feature.py class SetInputFeatureConfigMixin (line 28) | class SetInputFeatureConfigMixin(BaseMarshmallowConfig): class SetInputFeatureConfig (line 44) | class SetInputFeatureConfig(SetInputFeatureConfigMixin, BaseInputFeature... class SetOutputFeatureConfigMixin (line 53) | class SetOutputFeatureConfigMixin(BaseMarshmallowConfig): class SetOutputFeatureConfig (line 72) | class SetOutputFeatureConfig(SetOutputFeatureConfigMixin, BaseOutputFeat... class SetDefaultsConfig (line 118) | class SetDefaultsConfig(SetInputFeatureConfigMixin, SetOutputFeatureConf... FILE: ludwig/schema/features/text_feature.py class TextInputFeatureConfigMixin (line 38) | class TextInputFeatureConfigMixin(BaseMarshmallowConfig): class TextInputFeatureConfig (line 47) | class TextInputFeatureConfig(TextInputFeatureConfigMixin, BaseInputFeatu... class ECDTextInputFeatureConfig (line 58) | class ECDTextInputFeatureConfig(TextInputFeatureConfig): class LLMTextInputFeatureConfig (line 69) | class LLMTextInputFeatureConfig(TextInputFeatureConfig): class TextOutputFeatureConfigMixin (line 82) | class TextOutputFeatureConfigMixin(BaseMarshmallowConfig): class TextOutputFeatureConfig (line 96) | class TextOutputFeatureConfig(TextOutputFeatureConfigMixin, BaseOutputFe... class ECDTextOutputFeatureConfig (line 141) | class ECDTextOutputFeatureConfig(TextOutputFeatureConfig): class LLMTextOutputFeatureConfig (line 152) | class LLMTextOutputFeatureConfig(TextOutputFeatureConfig): class ECDTextDefaultsConfig (line 177) | class ECDTextDefaultsConfig(TextInputFeatureConfigMixin, TextOutputFeatu... class LLMTextDefaultsConfig (line 199) | class LLMTextDefaultsConfig(TextInputFeatureConfigMixin, TextOutputFeatu... FILE: ludwig/schema/features/timeseries_feature.py class TimeseriesInputFeatureConfigMixin (line 28) | class TimeseriesInputFeatureConfigMixin(BaseMarshmallowConfig): class TimeseriesInputFeatureConfig (line 44) | class TimeseriesInputFeatureConfig(TimeseriesInputFeatureConfigMixin, Ba... class TimeseriesOutputFeatureConfigMixin (line 54) | class TimeseriesOutputFeatureConfigMixin(BaseMarshmallowConfig): class TimeseriesOutputFeatureConfig (line 73) | class TimeseriesOutputFeatureConfig(BaseOutputFeatureConfig, TimeseriesO... class TimeseriesDefaultsConfig (line 117) | class TimeseriesDefaultsConfig(TimeseriesInputFeatureConfigMixin, Timese... FILE: ludwig/schema/features/utils.py function input_config_registry (line 29) | def input_config_registry(model_type: str) -> Registry: function output_config_registry (line 33) | def output_config_registry(model_type: str) -> Registry: function get_input_feature_cls (line 38) | def get_input_feature_cls(model_type: str, name: str): function get_output_feature_cls (line 44) | def get_output_feature_cls(model_type: str, name: str): function get_input_feature_jsonschema (line 50) | def get_input_feature_jsonschema(model_type: str): function get_input_feature_conds (line 79) | def get_input_feature_conds(model_type: str): function get_output_feature_jsonschema (line 99) | def get_output_feature_jsonschema(model_type: str): function get_output_feature_conds (line 128) | def get_output_feature_conds(model_type: str): FILE: ludwig/schema/features/vector_feature.py class VectorInputFeatureConfigMixin (line 28) | class VectorInputFeatureConfigMixin(BaseMarshmallowConfig): class VectorInputFeatureConfig (line 44) | class VectorInputFeatureConfig(VectorInputFeatureConfigMixin, BaseInputF... class VectorOutputFeatureConfigMixin (line 53) | class VectorOutputFeatureConfigMixin(BaseMarshmallowConfig): class VectorOutputFeatureConfig (line 72) | class VectorOutputFeatureConfig(VectorOutputFeatureConfigMixin, BaseOutp... class VectorDefaultsConfig (line 123) | class VectorDefaultsConfig(VectorInputFeatureConfigMixin, VectorOutputFe... FILE: ludwig/schema/hyperopt/__init__.py class HyperoptConfig (line 16) | class HyperoptConfig(schema_utils.BaseMarshmallowConfig, ABC): function get_hyperopt_jsonschema (line 96) | def get_hyperopt_jsonschema(): class HyperoptField (line 108) | class HyperoptField(schema_utils.DictMarshmallowField): method __init__ (line 109) | def __init__(self): method _jsonschema_type_mapping (line 112) | def _jsonschema_type_mapping(self): FILE: ludwig/schema/hyperopt/executor.py class ExecutorConfig (line 13) | class ExecutorConfig(schema_utils.BaseMarshmallowConfig): function ExecutorDataclassField (line 78) | def ExecutorDataclassField(description: str, default: dict = {}): FILE: ludwig/schema/hyperopt/parameter.py function quantization_number_field (line 9) | def quantization_number_field(dtype: type[float] | type[int] = float, de... function log_base_field (line 22) | def log_base_field(default: float = 10) -> FieldInfo: class ChoiceParameterConfig (line 29) | class ChoiceParameterConfig(schema_utils.BaseMarshmallowConfig): class GridSearchParameterConfig (line 59) | class GridSearchParameterConfig(schema_utils.BaseMarshmallowConfig): class UniformParameterConfig (line 82) | class UniformParameterConfig(schema_utils.BaseMarshmallowConfig): class QUniformParameterConfig (line 95) | class QUniformParameterConfig(UniformParameterConfig): class LogUniformParameterConfig (line 106) | class LogUniformParameterConfig(UniformParameterConfig): class QLogUniformParameterConfig (line 117) | class QLogUniformParameterConfig(UniformParameterConfig): class RandnParameterConfig (line 130) | class RandnParameterConfig(schema_utils.BaseMarshmallowConfig): class QRandnParameterConfig (line 143) | class QRandnParameterConfig(RandnParameterConfig): class RandintParameterConfig (line 154) | class RandintParameterConfig(schema_utils.BaseMarshmallowConfig): class QRandintParameterConfig (line 167) | class QRandintParameterConfig(RandintParameterConfig): class LogRandintParameterConfig (line 178) | class LogRandintParameterConfig(RandintParameterConfig): class QLogRandintParameterConfig (line 189) | class QLogRandintParameterConfig(RandintParameterConfig): FILE: ludwig/schema/hyperopt/scheduler.py function metric_alias (line 34) | def metric_alias(default=None): function time_attr_alias (line 47) | def time_attr_alias(default=TRAINING_ITERATION): function max_t_alias (line 61) | def max_t_alias(default=100): class BaseSchedulerConfig (line 73) | class BaseSchedulerConfig(schema_utils.BaseMarshmallowConfig, ABC): method dependencies_installed (line 99) | def dependencies_installed(self): class BaseHyperbandSchedulerConfig (line 123) | class BaseHyperbandSchedulerConfig(BaseSchedulerConfig): class AsyncHyperbandSchedulerConfig (line 132) | class AsyncHyperbandSchedulerConfig(BaseHyperbandSchedulerConfig): class HyperbandSchedulerConfig (line 165) | class HyperbandSchedulerConfig(BaseHyperbandSchedulerConfig): class MedianStoppingRuleSchedulerConfig (line 185) | class MedianStoppingRuleSchedulerConfig(BaseSchedulerConfig): class PopulationBasedTrainingSchedulerConfig (line 225) | class PopulationBasedTrainingSchedulerConfig(BaseSchedulerConfig): class PopulationBasedTrainingReplaySchedulerConfig (line 332) | class PopulationBasedTrainingReplaySchedulerConfig(BaseSchedulerConfig): class PopulationBasedBanditsSchedulerConfig (line 351) | class PopulationBasedBanditsSchedulerConfig(BaseSchedulerConfig): class BOHBSchedulerConfig (line 416) | class BOHBSchedulerConfig(BaseHyperbandSchedulerConfig): class FIFOSchedulerConfig (line 436) | class FIFOSchedulerConfig(BaseSchedulerConfig): class ResourceChangingSchedulerConfig (line 446) | class ResourceChangingSchedulerConfig(BaseSchedulerConfig): function get_scheduler_conds (line 473) | def get_scheduler_conds(): function SchedulerDataclassField (line 490) | def SchedulerDataclassField(default={"type": "fifo"}, description="Hyper... FILE: ludwig/schema/hyperopt/search_algorithm.py function points_to_evaluate_field (line 12) | def points_to_evaluate_field(description: str | None = None): function evaluated_rewards_field (line 23) | def evaluated_rewards_field(description: str | None = None): class BaseSearchAlgorithmConfig (line 36) | class BaseSearchAlgorithmConfig(schema_utils.BaseMarshmallowConfig): method set_random_state (line 41) | def set_random_state(self, ludwig_random_state: int) -> None: method dependencies_installed (line 52) | def dependencies_installed(self) -> bool: function SearchAlgorithmDataclassField (line 73) | def SearchAlgorithmDataclassField(description: str = "", default: dict =... class BasicVariantSAConfig (line 125) | class BasicVariantSAConfig(BaseSearchAlgorithmConfig): class AxSAConfig (line 165) | class AxSAConfig(BaseSearchAlgorithmConfig): class BayesOptSAConfig (line 193) | class BayesOptSAConfig(BaseSearchAlgorithmConfig): class BlendsearchSAConfig (line 242) | class BlendsearchSAConfig(BaseSearchAlgorithmConfig): class BOHBSAConfig (line 251) | class BOHBSAConfig(BaseSearchAlgorithmConfig): class CFOSAConfig (line 287) | class CFOSAConfig(BaseSearchAlgorithmConfig): class DragonflySAConfig (line 296) | class DragonflySAConfig(BaseSearchAlgorithmConfig): class HEBOSAConfig (line 346) | class HEBOSAConfig(BaseSearchAlgorithmConfig): class HyperoptSAConfig (line 380) | class HyperoptSAConfig(BaseSearchAlgorithmConfig): class NevergradSAConfig (line 422) | class NevergradSAConfig(BaseSearchAlgorithmConfig): class OptunaSAConfig (line 446) | class OptunaSAConfig(BaseSearchAlgorithmConfig): class SkoptSAConfig (line 479) | class SkoptSAConfig(BaseSearchAlgorithmConfig): class ZooptSAConfig (line 513) | class ZooptSAConfig(BaseSearchAlgorithmConfig): FILE: ludwig/schema/hyperopt/utils.py function get_parameter_cls (line 15) | def get_parameter_cls(name: str) -> type["BaseParameterConfig"]: # noqa... function get_scheduler_cls (line 28) | def get_scheduler_cls(name: str) -> type["BaseSchedulerConfig"]: # noqa... function get_scheduler_dependencies (line 41) | def get_scheduler_dependencies(name: str) -> list[str]: function get_search_algorithm_cls (line 54) | def get_search_algorithm_cls(name: str) -> type["BaseSearchAlgorithmConf... function get_search_algorithm_dependencies (line 67) | def get_search_algorithm_dependencies(name: str) -> list[str]: function get_search_algorithm_random_state_field (line 80) | def get_search_algorithm_random_state_field(name: str): function register_parameter_config (line 93) | def register_parameter_config(name: str) -> Callable: function register_scheduler_config (line 119) | def register_scheduler_config(name: str, dependencies: list[tuple[str]] ... function register_search_algorithm_config (line 149) | def register_search_algorithm_config( FILE: ludwig/schema/jsonschema.py function marshmallow_schema_to_jsonschema_dict (line 7) | def marshmallow_schema_to_jsonschema_dict(schema_instance): FILE: ludwig/schema/llms/base_model.py function BaseModelDataclassField (line 67) | def BaseModelDataclassField(): FILE: ludwig/schema/llms/generation.py class LLMGenerationConfig (line 8) | class LLMGenerationConfig(schema_utils.BaseMarshmallowConfig): class LLMGenerationConfigField (line 330) | class LLMGenerationConfigField(schema_utils.DictMarshmallowField): method __init__ (line 331) | def __init__(self): method _jsonschema_type_mapping (line 334) | def _jsonschema_type_mapping(self): FILE: ludwig/schema/llms/model_parameters.py class RoPEScalingConfig (line 9) | class RoPEScalingConfig(schema_utils.BaseMarshmallowConfig): method __post_init__ (line 18) | def __post_init__(self): class RoPEScalingConfigField (line 47) | class RoPEScalingConfigField(schema_utils.DictMarshmallowField): method __init__ (line 48) | def __init__(self): method _jsonschema_type_mapping (line 51) | def _jsonschema_type_mapping(self): class ModelParametersConfig (line 57) | class ModelParametersConfig(schema_utils.BaseMarshmallowConfig): method to_dict (line 70) | def to_dict(self): class ModelParametersConfigField (line 80) | class ModelParametersConfigField(schema_utils.DictMarshmallowField): method __init__ (line 81) | def __init__(self): method _jsonschema_type_mapping (line 84) | def _jsonschema_type_mapping(self): FILE: ludwig/schema/llms/peft.py function register_adapter (line 20) | def register_adapter(name: str): class LoraPostprocessorConfig (line 30) | class LoraPostprocessorConfig(schema_utils.BaseMarshmallowConfig): class LoraPostprocessorConfigField (line 46) | class LoraPostprocessorConfigField(schema_utils.DictMarshmallowField): method __init__ (line 47) | def __init__(self): method _jsonschema_type_mapping (line 50) | def _jsonschema_type_mapping(self): class BaseAdapterConfig (line 56) | class BaseAdapterConfig(schema_utils.BaseMarshmallowConfig, ABC): method to_config (line 66) | def to_config(self, **kwargs) -> "PeftConfig": class LoraConfig (line 73) | class LoraConfig(BaseAdapterConfig): method __post_init__ (line 74) | def __post_init__(self): method to_config (line 144) | def to_config(self, task_type: str = None, **kwargs) -> "PeftConfig": method name (line 159) | def name(cls) -> str: method description (line 163) | def description(cls) -> str: class BasePromptLearningConfig (line 169) | class BasePromptLearningConfig(BaseAdapterConfig): class AdaloraConfig (line 338) | class AdaloraConfig(LoraConfig): method to_config (line 405) | def to_config(self, **kwargs) -> "PeftConfig": method name (line 426) | def name(cls) -> str: method description (line 430) | def description(cls) -> str: class AdaptionPromptConfig (line 441) | class AdaptionPromptConfig(BaseAdapterConfig): method __post_init__ (line 444) | def __post_init__(self): method to_config (line 475) | def to_config(self, task_type: str = None, **kwargs) -> "PeftConfig": method name (line 485) | def name(cls) -> str: method description (line 489) | def description(cls) -> str: class IA3Config (line 496) | class IA3Config(BaseAdapterConfig): method to_config (line 545) | def to_config(self, task_type: str = None, **kwargs) -> "PeftConfig": method name (line 558) | def name(cls) -> str: method description (line 562) | def description(cls) -> str: function get_adapter_conds (line 567) | def get_adapter_conds(): function AdapterDataclassField (line 581) | def AdapterDataclassField(default: str | None = None): FILE: ludwig/schema/llms/prompt.py class RetrievalConfig (line 11) | class RetrievalConfig(schema_utils.BaseMarshmallowConfig): method __post_init__ (line 14) | def __post_init__(self): class RetrievalConfigField (line 65) | class RetrievalConfigField(schema_utils.DictMarshmallowField): method __init__ (line 66) | def __init__(self): method _jsonschema_type_mapping (line 69) | def _jsonschema_type_mapping(self): class PromptConfig (line 75) | class PromptConfig(schema_utils.BaseMarshmallowConfig): class PromptConfigField (line 104) | class PromptConfigField(schema_utils.DictMarshmallowField): method __init__ (line 105) | def __init__(self): method _jsonschema_type_mapping (line 108) | def _jsonschema_type_mapping(self): FILE: ludwig/schema/llms/quantization.py class QuantizationConfig (line 20) | class QuantizationConfig(schema_utils.BaseMarshmallowConfig): method to_bitsandbytes (line 74) | def to_bitsandbytes(self) -> BitsAndBytesConfig: class QuantizationConfigField (line 87) | class QuantizationConfigField(schema_utils.DictMarshmallowField): method __init__ (line 88) | def __init__(self): method _jsonschema_type_mapping (line 91) | def _jsonschema_type_mapping(self): FILE: ludwig/schema/lr_scheduler.py class LRSchedulerConfig (line 14) | class LRSchedulerConfig(schema_utils.BaseMarshmallowConfig, ABC): function LRSchedulerDataclassField (line 130) | def LRSchedulerDataclassField(description: str, default: dict = None): FILE: ludwig/schema/metadata/__init__.py function _to_metadata (line 12) | def _to_metadata(d: dict[str, Any]) -> ParameterMetadata | dict[str, Any]: function _load (line 25) | def _load(fname: str) -> dict[str, Any]: FILE: ludwig/schema/metadata/parameter_metadata.py class ExpectedImpact (line 13) | class ExpectedImpact(int, Enum): class ComputeTier (line 28) | class ComputeTier(int, Enum): class ParameterMetadata (line 48) | class ParameterMetadata: method to_json_dict (line 107) | def to_json_dict(self) -> dict[str, Any]: function convert_metadata_to_json (line 112) | def convert_metadata_to_json(pm: ParameterMetadata) -> dict[str, Any]: FILE: ludwig/schema/model_types/base.py class ModelConfig (line 49) | class ModelConfig(schema_utils.BaseMarshmallowConfig, ABC): method __post_init__ (line 63) | def __post_init__(self): method from_dict (line 84) | def from_dict(config: ModelConfigDict) -> "ModelConfig": method from_yaml (line 150) | def from_yaml(config_path: str) -> "ModelConfig": method get_feature_names (line 153) | def get_feature_names(self) -> set[str]: method get_feature_config (line 160) | def get_feature_config(self, feature_column_name: str) -> BaseInputFea... function register_model_type (line 171) | def register_model_type(name: str): function _merge_encoder_cache_params (line 179) | def _merge_encoder_cache_params(preprocessing_params: dict[str, Any], en... FILE: ludwig/schema/model_types/ecd.py class ECDModelConfig (line 23) | class ECDModelConfig(ModelConfig): FILE: ludwig/schema/model_types/llm.py class LLMModelConfig (line 27) | class LLMModelConfig(ModelConfig): FILE: ludwig/schema/model_types/utils.py function merge_with_defaults (line 48) | def merge_with_defaults(config_dict: ModelConfigDict) -> ModelConfigDict: function _merge_features_ (line 61) | def _merge_features_(features: list[dict[str, Any]], defaults: dict[str,... function _merge_dict_with_types (line 75) | def _merge_dict_with_types(dct: dict[str, Any], merge_dct: dict[str, Any... function merge_fixed_preprocessing_params (line 96) | def merge_fixed_preprocessing_params(config: "ModelConfig"): function set_validation_parameters (line 102) | def set_validation_parameters(config: "ModelConfig"): function set_derived_feature_columns_ (line 168) | def set_derived_feature_columns_(config_obj: "ModelConfig"): function sanitize_and_filter_combiner_entities_ (line 186) | def sanitize_and_filter_combiner_entities_(config: "ModelConfig"): function set_hyperopt_defaults_ (line 215) | def set_hyperopt_defaults_(config: "ModelConfig"): function set_preprocessing_parameters (line 273) | def set_preprocessing_parameters(config: "ModelConfig") -> None: # noqa... function _set_max_sequence_length (line 278) | def _set_max_sequence_length(config: "ModelConfig") -> None: # noqa: F821 function set_tagger_decoder_parameters (line 294) | def set_tagger_decoder_parameters(config: "ModelConfig") -> None: function set_llm_parameters (line 311) | def set_llm_parameters(config: "ModelConfig") -> None: function _set_llm_tokenizers (line 340) | def _set_llm_tokenizers(config: "ModelConfig") -> None: function _get_maximum_possible_sequence_length (line 375) | def _get_maximum_possible_sequence_length(config: "ModelConfig", default... function _set_generation_max_new_tokens (line 404) | def _set_generation_max_new_tokens(config: "ModelConfig") -> None: function _set_mixtral_target_modules (line 431) | def _set_mixtral_target_modules(config: "ModelConfig") -> None: function _set_phi2_target_modules (line 449) | def _set_phi2_target_modules(config: "ModelConfig") -> None: function _set_phi3_target_modules (line 471) | def _set_phi3_target_modules(config: "ModelConfig") -> None: function _set_gemma_target_modules (line 490) | def _set_gemma_target_modules(config: "ModelConfig") -> None: function contains_grid_search_parameters (line 507) | def contains_grid_search_parameters(hyperopt_config: HyperoptConfigDict)... FILE: ludwig/schema/optimizers.py function register_optimizer (line 23) | def register_optimizer(name: str): function get_optimizer_cls (line 32) | def get_optimizer_cls(name: str): class BaseOptimizerConfig (line 39) | class BaseOptimizerConfig(schema_utils.BaseMarshmallowConfig, ABC): method is_paged (line 58) | def is_paged(self) -> bool: method is_8bit (line 63) | def is_8bit(self) -> bool: class SGDOptimizerConfig (line 71) | class SGDOptimizerConfig(BaseOptimizerConfig): class SGD8BitOptimizerConfig (line 112) | class SGD8BitOptimizerConfig(SGDOptimizerConfig): method is_8bit (line 132) | def is_8bit(self) -> bool: class LBFGSOptimizerConfig (line 139) | class LBFGSOptimizerConfig(BaseOptimizerConfig): class AdamOptimizerConfig (line 191) | class AdamOptimizerConfig(BaseOptimizerConfig): class Adam8BitOptimizerConfig (line 231) | class Adam8BitOptimizerConfig(AdamOptimizerConfig): method is_8bit (line 249) | def is_8bit(self) -> bool: class PagedAdamOptimizerConfig (line 255) | class PagedAdamOptimizerConfig(Adam8BitOptimizerConfig): method is_paged (line 261) | def is_paged(self) -> bool: method is_8bit (line 265) | def is_8bit(self) -> bool: class PagedAdam8BitOptimizerConfig (line 271) | class PagedAdam8BitOptimizerConfig(PagedAdamOptimizerConfig): method is_8bit (line 277) | def is_8bit(self) -> bool: class AdamWOptimizerConfig (line 284) | class AdamWOptimizerConfig(BaseOptimizerConfig): class AdamW8BitOptimizerConfig (line 324) | class AdamW8BitOptimizerConfig(AdamWOptimizerConfig): method is_8bit (line 342) | def is_8bit(self) -> bool: class PagedAdamWOptimizerConfig (line 348) | class PagedAdamWOptimizerConfig(AdamW8BitOptimizerConfig): method is_paged (line 354) | def is_paged(self) -> bool: method is_8bit (line 358) | def is_8bit(self) -> bool: class PagedAdamW8BitOptimizerConfig (line 364) | class PagedAdamW8BitOptimizerConfig(PagedAdamWOptimizerConfig): method is_8bit (line 370) | def is_8bit(self) -> bool: class AdadeltaOptimizerConfig (line 377) | class AdadeltaOptimizerConfig(BaseOptimizerConfig): class AdagradOptimizerConfig (line 410) | class AdagradOptimizerConfig(BaseOptimizerConfig): class Adagrad8BitOptimizerConfig (line 446) | class Adagrad8BitOptimizerConfig(AdagradOptimizerConfig): method is_8bit (line 464) | def is_8bit(self) -> bool: class AdamaxOptimizerConfig (line 471) | class AdamaxOptimizerConfig(BaseOptimizerConfig): class FtrlOptimizerConfig (line 503) | class FtrlOptimizerConfig(BaseOptimizerConfig): class NadamOptimizerConfig (line 527) | class NadamOptimizerConfig(BaseOptimizerConfig): class RMSPropOptimizerConfig (line 559) | class RMSPropOptimizerConfig(BaseOptimizerConfig): class RMSProp8BitOptimizerConfig (line 603) | class RMSProp8BitOptimizerConfig(RMSPropOptimizerConfig): method is_8bit (line 621) | def is_8bit(self) -> bool: class LAMBOptimizerConfig (line 630) | class LAMBOptimizerConfig(BaseOptimizerConfig): class LAMB8BitOptimizerConfig (line 698) | class LAMB8BitOptimizerConfig(LAMBOptimizerConfig): method is_8bit (line 704) | def is_8bit(self) -> bool: class LARSOptimizerConfig (line 713) | class LARSOptimizerConfig(BaseOptimizerConfig): class LARS8BitOptimizerConfig (line 770) | class LARS8BitOptimizerConfig(LARSOptimizerConfig): method is_8bit (line 776) | def is_8bit(self) -> bool: class LIONOptimizerConfig (line 785) | class LIONOptimizerConfig(BaseOptimizerConfig): class LION8BitOptimizerConfig (line 822) | class LION8BitOptimizerConfig(LIONOptimizerConfig): method is_8bit (line 828) | def is_8bit(self) -> bool: class PagedLionOptimizerConfig (line 834) | class PagedLionOptimizerConfig(LIONOptimizerConfig): method is_paged (line 840) | def is_paged(self) -> bool: class PagedLion8BitOptimizerConfig (line 846) | class PagedLion8BitOptimizerConfig(PagedLionOptimizerConfig): method is_8bit (line 852) | def is_8bit(self) -> bool: function get_optimizer_conds (line 857) | def get_optimizer_conds(): function OptimizerDataclassField (line 874) | def OptimizerDataclassField(default="adam", description="", parameter_me... class GradientClippingConfig (line 924) | class GradientClippingConfig(schema_utils.BaseMarshmallowConfig): function GradientClippingDataclassField (line 951) | def GradientClippingDataclassField(description: str, default: dict = {}): FILE: ludwig/schema/preprocessing.py class PreprocessingConfig (line 11) | class PreprocessingConfig(schema_utils.BaseMarshmallowConfig): class PreprocessingField (line 60) | class PreprocessingField(schema_utils.DictMarshmallowField): method __init__ (line 61) | def __init__(self): FILE: ludwig/schema/profiler.py class ProfilerConfig (line 11) | class ProfilerConfig(schema_utils.BaseMarshmallowConfig): function ProfilerDataclassField (line 54) | def ProfilerDataclassField(description: str, default: dict = {}): FILE: ludwig/schema/split.py function get_split_cls (line 15) | def get_split_cls(name: str): class BaseSplitConfig (line 21) | class BaseSplitConfig(schema_utils.BaseMarshmallowConfig): class RandomSplitConfig (line 31) | class RandomSplitConfig(BaseSplitConfig): class FixedSplitConfig (line 50) | class FixedSplitConfig(BaseSplitConfig): class StratifySplitConfig (line 69) | class StratifySplitConfig(BaseSplitConfig): class DateTimeSplitConfig (line 95) | class DateTimeSplitConfig(BaseSplitConfig): class HashSplitConfig (line 121) | class HashSplitConfig(BaseSplitConfig): function get_split_conds (line 154) | def get_split_conds(): function SplitDataclassField (line 171) | def SplitDataclassField(default: str) -> Field: FILE: ludwig/schema/trainer.py function register_trainer_schema (line 40) | def register_trainer_schema(model_type: str): function register_llm_trainer_schema (line 49) | def register_llm_trainer_schema(trainer_type: str): function get_llm_trainer_cls (line 58) | def get_llm_trainer_cls(trainer_type: str): class BaseTrainerConfig (line 65) | class BaseTrainerConfig(schema_utils.BaseMarshmallowConfig, ABC): method can_tune_batch_size (line 116) | def can_tune_batch_size(self) -> bool: class ECDTrainerConfig (line 123) | class ECDTrainerConfig(BaseTrainerConfig): method __post_init__ (line 126) | def __post_init__(self): method update_batch_size_grad_accum (line 465) | def update_batch_size_grad_accum(self, num_workers: int): class LLMTrainerConfig (line 473) | class LLMTrainerConfig(BaseTrainerConfig): class NoneTrainerConfig (line 556) | class NoneTrainerConfig(LLMTrainerConfig): method can_tune_batch_size (line 566) | def can_tune_batch_size(self) -> bool: class FineTuneTrainerConfig (line 573) | class FineTuneTrainerConfig(ECDTrainerConfig): function get_model_type_jsonschema (line 613) | def get_model_type_jsonschema(model_type: str = MODEL_ECD): function get_trainer_jsonschema (line 629) | def get_trainer_jsonschema(model_type: str): class ECDTrainerField (line 643) | class ECDTrainerField(schema_utils.DictMarshmallowField): method __init__ (line 644) | def __init__(self): method _jsonschema_type_mapping (line 647) | def _jsonschema_type_mapping(self): function get_llm_trainer_conds (line 652) | def get_llm_trainer_conds(): function LLMTrainerDataclassField (line 668) | def LLMTrainerDataclassField(default="none", description=""): FILE: ludwig/schema/utils.py class LudwigSchemaField (line 37) | class LudwigSchemaField: method __init__ (line 44) | def __init__(self, **kwargs): method get_default_field (line 49) | def get_default_field(self) -> FieldInfo: method _jsonschema_type_mapping (line 56) | def _jsonschema_type_mapping(self): method _deserialize (line 63) | def _deserialize(self, value, attr, data, **kwargs): function ludwig_dataclass (line 76) | def ludwig_dataclass(cls): class _SchemaAdapter (line 88) | class _SchemaAdapter: method __init__ (line 95) | def __init__(self, cls): method __call__ (line 98) | def __call__(self): method load (line 102) | def load(self, data): method dump (line 106) | def dump(self, data): method fields (line 119) | def fields(self): class _TypeSelectionMarker (line 125) | class _TypeSelectionMarker: method __init__ (line 128) | def __init__(self, type_selection): class _NestedConfigMarker (line 132) | class _NestedConfigMarker: method __init__ (line 135) | def __init__(self, cls, allow_none=True): function _convert_dataclass_field_to_pydantic (line 143) | def _convert_dataclass_field_to_pydantic(dc_field) -> FieldInfo: class _MarshmallowFieldMarker (line 191) | class _MarshmallowFieldMarker: method __init__ (line 194) | def __init__(self, marshmallow_field): class _LudwigModelMeta (line 198) | class _LudwigModelMeta(type(BaseModel)): method __new__ (line 206) | def __new__(mcs, name, bases, namespace, **kwargs): method __getattr__ (line 322) | def __getattr__(cls, name: str) -> Any: class BaseMarshmallowConfig (line 337) | class BaseMarshmallowConfig(BaseModel, metaclass=_LudwigModelMeta): method _pre_validate (line 355) | def _pre_validate(cls, data: Any) -> Any: method _validate_field_constraints (line 417) | def _validate_field_constraints(self): method __setattr__ (line 465) | def __setattr__(self, name: str, value: Any) -> None: method model_post_init (line 477) | def model_post_init(self, __context: Any) -> None: method to_dict (line 485) | def to_dict(self) -> dict[str, Any]: method from_dict (line 493) | def from_dict(cls, d: dict[str, Any]) -> "BaseMarshmallowConfig": method get_valid_field_names (line 499) | def get_valid_field_names(cls) -> set[str]: method get_class_schema (line 505) | def get_class_schema(cls): method Schema (line 513) | def Schema(cls): method __repr__ (line 517) | def __repr__(self): function get_marshmallow_field_class_name (line 522) | def get_marshmallow_field_class_name(field_info): function load_config (line 539) | def load_config(cls: type["BaseMarshmallowConfig"], **kwargs) -> "BaseMa... function load_trainer_with_kwargs (line 546) | def load_trainer_with_kwargs(model_type: str, kwargs: dict) -> tuple["Ba... function load_config_with_kwargs (line 560) | def load_config_with_kwargs( function convert_submodules (line 575) | def convert_submodules(config_dict: dict) -> dict[str, Any]: function create_cond (line 594) | def create_cond(if_pred: dict, then_pred: dict): function remove_duplicate_fields (line 603) | def remove_duplicate_fields(properties: dict, fields: list[str] | None =... class ListSerializable (line 612) | class ListSerializable(ABC): method to_list (line 614) | def to_list(self) -> list: function assert_is_a_marshmallow_class (line 619) | def assert_is_a_marshmallow_class(cls): function _default_matches_json_type (line 626) | def _default_matches_json_type(default_val, type_str) -> bool: function _field_info_to_jsonschema (line 651) | def _field_info_to_jsonschema(fname: str, finfo: FieldInfo, annotation: ... function _annotation_to_json_type (line 783) | def _annotation_to_json_type(annotation) -> str | list | None: function unload_jsonschema_from_marshmallow_class (line 836) | def unload_jsonschema_from_marshmallow_class(mclass, additional_properti... function _make_json_schema_extra (line 873) | def _make_json_schema_extra( function InitializerOptions (line 887) | def InitializerOptions(default: str = "xavier_uniform", description="", ... function ActivationOptions (line 899) | def ActivationOptions(default: str | None = "relu", description=None, pa... function ReductionOptions (line 913) | def ReductionOptions(default: None | str = None, description="", paramet... function RegularizerOptions (line 925) | def RegularizerOptions( function String (line 942) | def String( function StringOptions (line 970) | def StringOptions( function ProtectedString (line 1003) | def ProtectedString( function IntegerOptions (line 1019) | def IntegerOptions( function Boolean (line 1047) | def Boolean(default: bool, description: str = "", parameter_metadata: Pa... function Integer (line 1057) | def Integer( function PositiveInteger (line 1075) | def PositiveInteger( function NonNegativeInteger (line 1094) | def NonNegativeInteger( function IntegerRange (line 1113) | def IntegerRange( function Float (line 1146) | def Float( function NonNegativeFloat (line 1164) | def NonNegativeFloat( function FloatRange (line 1190) | def FloatRange( function Dict (line 1219) | def Dict( function List (line 1244) | def List( function DictList (line 1267) | def DictList( function Embed (line 1288) | def Embed(description: str = "", parameter_metadata: ParameterMetadata =... function InitializerOrDict (line 1300) | def InitializerOrDict( function FloatRangeTupleDataclassField (line 1317) | def FloatRangeTupleDataclassField( function OneOfOptionsField (line 1351) | def OneOfOptionsField( class TypeSelection (line 1383) | class TypeSelection(LudwigSchemaField): method __init__ (line 1389) | def __init__( method _deserialize (line 1408) | def _deserialize(self, value, attr, data, **kwargs): method resolve (line 1412) | def resolve(self, value): method str_value_to_object (line 1440) | def str_value_to_object(self, value: str) -> dict: method get_schema_from_registry (line 1444) | def get_schema_from_registry(self, key: str) -> type[BaseMarshmallowCo... method get_default_field (line 1448) | def get_default_field(self) -> FieldInfo: method _jsonschema_type_mapping (line 1471) | def _jsonschema_type_mapping(self): class DictMarshmallowField (line 1477) | class DictMarshmallowField(LudwigSchemaField): method __init__ (line 1483) | def __init__( method _deserialize (line 1496) | def _deserialize(self, value, attr, data, **kwargs): method get_default_field (line 1507) | def get_default_field(self) -> FieldInfo: method _jsonschema_type_mapping (line 1532) | def _jsonschema_type_mapping(self): FILE: ludwig/serve.py function server (line 59) | def server(model, allowed_origins=None): function _write_file (line 130) | def _write_file(v, files): function _read_image_buffer (line 140) | def _read_image_buffer(v): function convert_input (line 148) | def convert_input(form, input_features): function convert_batch_input (line 165) | def convert_batch_input(form, input_features): function run_server (line 186) | def run_server( function cli (line 211) | def cli(sys_argv): FILE: ludwig/train.py function train_cli (line 35) | def train_cli( function cli (line 204) | def cli(sys_argv): FILE: ludwig/trainers/base.py class BaseTrainer (line 10) | class BaseTrainer(ABC): method train (line 12) | def train(self, training_set, validation_set=None, test_set=None, save... method train_online (line 16) | def train_online( method tune_batch_size (line 23) | def tune_batch_size( method validation_field (line 36) | def validation_field(self): method validation_metric (line 41) | def validation_metric(self): method shutdown (line 45) | def shutdown(self): method local_rank (line 49) | def local_rank(self) -> int: method barrier (line 52) | def barrier(self): method __enter__ (line 56) | def __enter__(self): method __exit__ (line 59) | def __exit__(self, exc_type, exc_val, exc_tb): method get_schema_cls (line 64) | def get_schema_cls() -> BaseTrainerConfig: FILE: ludwig/trainers/registry.py function get_trainers_registry (line 12) | def get_trainers_registry() -> Registry: function get_ray_trainers_registry (line 17) | def get_ray_trainers_registry() -> Registry: function get_llm_trainers_registry (line 22) | def get_llm_trainers_registry() -> Registry: function get_llm_ray_trainers_registry (line 27) | def get_llm_ray_trainers_registry() -> Registry: function register_trainer (line 32) | def register_trainer(model_type: str, default=False): function register_ray_trainer (line 55) | def register_ray_trainer(model_type: str, default=False): function register_llm_trainer (line 78) | def register_llm_trainer(trainer_type: str, default=False): function register_llm_ray_trainer (line 101) | def register_llm_ray_trainer(trainer_type: str, default=False): FILE: ludwig/trainers/trainer.py class Trainer (line 102) | class Trainer(BaseTrainer): method get_schema_cls (line 106) | def get_schema_cls(): method __init__ (line 109) | def __init__( method prepare (line 241) | def prepare(self): method train_step (line 293) | def train_step( method clip_grads (line 404) | def clip_grads(self, variables): method write_eval_summary (line 414) | def write_eval_summary( method write_step_summary (line 432) | def write_step_summary( method is_cpu_training (line 549) | def is_cpu_training(self): method tune_batch_size (line 552) | def tune_batch_size( method _create_batch_size_evaluator (line 629) | def _create_batch_size_evaluator(self) -> BatchSizeEvaluator: method _create_predict_batch_size_evaluator (line 651) | def _create_predict_batch_size_evaluator(self) -> BatchSizeEvaluator: method run_evaluation (line 674) | def run_evaluation( method save_checkpoint (line 818) | def save_checkpoint(self, progress_tracker: ProgressTracker, save_path... method create_checkpoint_handle (line 829) | def create_checkpoint_handle(self): method train (line 834) | def train( method _train_loop (line 1191) | def _train_loop( method _has_nan_or_inf_weights (line 1336) | def _has_nan_or_inf_weights(self, model: torch.nn.Module) -> bool: method train_online (line 1366) | def train_online(self, dataset): method validation_field (line 1409) | def validation_field(self): method validation_metric (line 1413) | def validation_metric(self): method evaluation (line 1416) | def evaluation(self, dataset, dataset_name, metrics_log, batch_size, p... method check_progress_on_validation (line 1428) | def check_progress_on_validation( method set_steps_to_1_or_quit (line 1570) | def set_steps_to_1_or_quit(self, signum, frame): method resume_files_exist (line 1587) | def resume_files_exist( method resume_training_progress_tracker (line 1604) | def resume_training_progress_tracker(self, training_progress_tracker_p... method resume_weights_and_optimizer (line 1618) | def resume_weights_and_optimizer( method increase_batch_size (line 1625) | def increase_batch_size( method is_coordinator (line 1699) | def is_coordinator(self): method local_rank (line 1703) | def local_rank(self) -> int: method barrier (line 1706) | def barrier(self): method callback (line 1709) | def callback(self, fn, coordinator_only=True): method return_device (line 1715) | def return_device(self): class RemoteTrainer (line 1719) | class RemoteTrainer(Trainer): method __init__ (line 1720) | def __init__(self, gpus=None, gpu_memory_limit=None, allow_parallel_th... method return_device (line 1728) | def return_device(self): FILE: ludwig/trainers/trainer_llm.py class NoneTrainer (line 43) | class NoneTrainer(BaseTrainer): method __init__ (line 46) | def __init__( method close_writers (line 132) | def close_writers( method train (line 148) | def train( method train_online (line 229) | def train_online( method tune_batch_size (line 235) | def tune_batch_size( method validation_field (line 252) | def validation_field(self): method validation_metric (line 256) | def validation_metric(self): method shutdown (line 260) | def shutdown(self): method local_rank (line 264) | def local_rank(self) -> int: method barrier (line 267) | def barrier(self): method __enter__ (line 271) | def __enter__(self): method __exit__ (line 274) | def __exit__(self, exc_type, exc_val, exc_tb): method get_schema_cls (line 278) | def get_schema_cls() -> BaseTrainerConfig: method is_coordinator (line 281) | def is_coordinator(self) -> bool: method callback (line 284) | def callback(self, fn, coordinator_only=True): method evaluation (line 289) | def evaluation( method write_eval_summary (line 305) | def write_eval_summary( method run_evaluation (line 322) | def run_evaluation( class FineTuneTrainer (line 416) | class FineTuneTrainer(Trainer): method get_schema_cls (line 418) | def get_schema_cls(): method __init__ (line 421) | def __init__( method evaluation (line 451) | def evaluation(self, dataset, dataset_name, metrics_log, batch_size, p... method tune_batch_size (line 489) | def tune_batch_size( method _create_batch_size_evaluator (line 515) | def _create_batch_size_evaluator(self) -> BatchSizeEvaluator: method _create_predict_batch_size_evaluator (line 518) | def _create_predict_batch_size_evaluator(self) -> BatchSizeEvaluator: class RemoteLLMTrainer (line 522) | class RemoteLLMTrainer(NoneTrainer): method __init__ (line 523) | def __init__(self, gpus=None, gpu_memory_limit=None, allow_parallel_th... class RemoteLLMFineTuneTrainer (line 531) | class RemoteLLMFineTuneTrainer(FineTuneTrainer): method __init__ (line 532) | def __init__(self, gpus=None, gpu_memory_limit=None, allow_parallel_th... FILE: ludwig/upload.py function get_upload_registry (line 13) | def get_upload_registry(): function upload_cli (line 20) | def upload_cli( function cli (line 90) | def cli(sys_argv): FILE: ludwig/utils/algorithms_utils.py function topological_sort (line 19) | def topological_sort(graph_unsorted): function topological_sort_feature_dependencies (line 72) | def topological_sort_feature_dependencies(features): FILE: ludwig/utils/audio_utils.py function is_torch_audio_tuple (line 41) | def is_torch_audio_tuple(audio: Any) -> bool: function get_default_audio (line 49) | def get_default_audio(audio_lst: list[TorchAudioTuple]) -> TorchAudioTuple: function read_audio_from_path (line 71) | def read_audio_from_path(path: str) -> TorchAudioTuple | None: function read_audio_from_bytes_obj (line 90) | def read_audio_from_bytes_obj(bytes_obj: bytes) -> TorchAudioTuple | None: function _pre_emphasize_data (line 99) | def _pre_emphasize_data(data: torch.Tensor, emphasize_value: float = 0.97): function get_length_in_samp (line 113) | def get_length_in_samp(sampling_rate_in_hz: float | int, length_in_s: fl... function get_group_delay (line 118) | def get_group_delay( function get_phase_stft_magnitude (line 152) | def get_phase_stft_magnitude( function get_stft_magnitude (line 170) | def get_stft_magnitude( function get_fbank (line 191) | def get_fbank( function _get_mel_fbank_matrix (line 220) | def _get_mel_fbank_matrix( function _create_triangular_filter (line 239) | def _create_triangular_filter( function _convert_hz_to_mel (line 252) | def _convert_hz_to_mel(hz: int) -> float: function _convert_mel_to_hz (line 256) | def _convert_mel_to_hz(mel): function _get_stft (line 260) | def _get_stft( function _short_time_fourier_transform (line 285) | def _short_time_fourier_transform( function _preprocess_to_padded_matrix (line 307) | def _preprocess_to_padded_matrix( function get_num_output_padded_to_fit_input (line 326) | def get_num_output_padded_to_fit_input(num_input: int, window_length_in_... function get_window (line 332) | def get_window(window_type: str, window_length_in_samp: int, device: tor... function is_audio_score (line 355) | def is_audio_score(src_path): function _weight_data_matrix (line 360) | def _weight_data_matrix( function get_non_symmetric_length (line 371) | def get_non_symmetric_length(symmetric_length: int) -> int: function get_non_symmetric_data (line 376) | def get_non_symmetric_data(data: torch.Tensor) -> torch.Tensor: function get_max_length_stft_based (line 383) | def get_max_length_stft_based(length_in_samp, window_length_in_s, window... function calculate_incr_var (line 390) | def calculate_incr_var(var_prev, mean_prev, mean, length): function calculate_incr_mean (line 395) | def calculate_incr_mean(count, mean, length): function calculate_var (line 400) | def calculate_var(sum1, sum2, count): function calculate_mean (line 405) | def calculate_mean(sum1, count): FILE: ludwig/utils/augmentation_utils.py function get_augmentation_op_registry (line 13) | def get_augmentation_op_registry() -> Registry: function register_augmentation_op (line 18) | def register_augmentation_op(name: str, features: str | list[str]): function get_augmentation_op (line 33) | def get_augmentation_op(feature_type: str, op_name: str): class AugmentationPipelines (line 37) | class AugmentationPipelines: method __init__ (line 40) | def __init__(self, augmentation_pipelines: dict): method __getitem__ (line 43) | def __getitem__(self, key): method __contains__ (line 46) | def __contains__(self, key): method __len__ (line 49) | def __len__(self): method __iter__ (line 52) | def __iter__(self): method items (line 55) | def items(self): FILE: ludwig/utils/automl/data_source.py class DataSource (line 15) | class DataSource(ABC): method columns (line 18) | def columns(self) -> list[str]: method get_dtype (line 22) | def get_dtype(self, column: str) -> str: method get_distinct_values (line 26) | def get_distinct_values(self, column: str, max_values_to_return: int) ... method get_nonnull_values (line 30) | def get_nonnull_values(self, column: str) -> int: method get_avg_num_tokens (line 34) | def get_avg_num_tokens(self, column: str) -> int: method is_string_type (line 38) | def is_string_type(self, dtype: str) -> bool: method size_bytes (line 42) | def size_bytes(self) -> int: method __len__ (line 46) | def __len__(self) -> int: class DataframeSourceMixin (line 51) | class DataframeSourceMixin: method columns (line 55) | def columns(self) -> list[str]: method get_dtype (line 58) | def get_dtype(self, column: str) -> str: method get_distinct_values (line 61) | def get_distinct_values(self, column, max_values_to_return: int) -> tu... method get_nonnull_values (line 73) | def get_nonnull_values(self, column: str) -> int: method get_image_values (line 76) | def get_image_values(self, column: str, sample_size: int = 10) -> int: method get_audio_values (line 79) | def get_audio_values(self, column: str, sample_size: int = 10) -> int: method get_avg_num_tokens (line 82) | def get_avg_num_tokens(self, column: str) -> int: method is_string_type (line 85) | def is_string_type(self, dtype: str) -> bool: method size_bytes (line 88) | def size_bytes(self) -> int: method __len__ (line 91) | def __len__(self) -> int: class DataframeSource (line 96) | class DataframeSource(DataframeSourceMixin, DataSource): method __init__ (line 97) | def __init__(self, df): class DaskDataSource (line 102) | class DaskDataSource(DataframeSource): method get_sample (line 104) | def get_sample(self) -> pd.DataFrame: method sample (line 109) | def sample(self) -> pd.DataFrame: method get_distinct_values (line 112) | def get_distinct_values(self, column, max_values_to_return) -> tuple[i... method get_nonnull_values (line 120) | def get_nonnull_values(self, column) -> int: method get_image_values (line 123) | def get_image_values(self, column: str, sample_size: int = 10) -> int: method get_audio_values (line 126) | def get_audio_values(self, column: str, sample_size: int = 10) -> int: method get_avg_num_tokens (line 129) | def get_avg_num_tokens(self, column) -> int: function wrap_data_source (line 134) | def wrap_data_source(df: DataFrame) -> DataSource: FILE: ludwig/utils/automl/field_info.py class FieldInfo (line 11) | class FieldInfo: class FieldConfig (line 27) | class FieldConfig: class FieldMetadata (line 36) | class FieldMetadata: FILE: ludwig/utils/automl/ray_utils.py function _ray_init (line 11) | def _ray_init(): FILE: ludwig/utils/automl/type_inference.py function infer_type (line 18) | def infer_type(field: FieldInfo, missing_value_percent: float, row_count... function should_exclude (line 71) | def should_exclude( FILE: ludwig/utils/automl/utils.py function avg_num_tokens_decoder (line 30) | def avg_num_tokens_decoder(x): function avg_num_tokens (line 39) | def avg_num_tokens(field: Series) -> int: function get_model_type (line 49) | def get_model_type(config: dict) -> str: function _add_transfer_config (line 69) | def _add_transfer_config(base_config: dict, ref_configs: dict) -> dict: function _get_ratio_numeric_input_features (line 95) | def _get_ratio_numeric_input_features(input_features: dict) -> float: function _add_option_to_evaluate (line 106) | def _add_option_to_evaluate( function set_output_feature_metric (line 121) | def set_output_feature_metric(base_config): function has_imbalanced_output (line 146) | def has_imbalanced_output(base_config, features_metadata) -> bool: FILE: ludwig/utils/backward_compatibility.py function register_config_transformation (line 89) | def register_config_transformation(version: str, prefixes: str | list[st... function upgrade_config_dict_to_latest_version (line 112) | def upgrade_config_dict_to_latest_version(config: ModelConfigDict) -> Mo... function upgrade_model_progress (line 127) | def upgrade_model_progress(model_progress: dict) -> dict: function _traverse_dicts (line 208) | def _traverse_dicts(config: Any, f: Callable[[dict], None]): function _update_backend_cache_credentials (line 223) | def _update_backend_cache_credentials(backend: dict[str, Any]) -> dict[s... function update_class_weights_in_features (line 238) | def update_class_weights_in_features(feature: FeatureConfigDict) -> Feat... function _update_level_metadata (line 249) | def _update_level_metadata(config: ModelConfigDict) -> ModelConfigDict: function rename_training_to_trainer (line 298) | def rename_training_to_trainer(config: ModelConfigDict) -> ModelConfigDict: function _upgrade_use_bias_in_features (line 309) | def _upgrade_use_bias_in_features(feature: FeatureConfigDict) -> Feature... function _upgrade_feature (line 337) | def _upgrade_feature(feature: FeatureConfigDict) -> FeatureConfigDict: function upgrade_audio_preprocessing (line 355) | def upgrade_audio_preprocessing(preproc_dict: PreprocessingConfigDict) -... function _upgrade_encoder_params (line 364) | def _upgrade_encoder_params(feature: FeatureConfigDict) -> FeatureConfig... function _upgrade_decoder_params (line 369) | def _upgrade_decoder_params(feature: FeatureConfigDict) -> FeatureConfig... function _upgrade_encoder_decoder_params (line 373) | def _upgrade_encoder_decoder_params(feature: FeatureConfigDict, input_fe... function _upgrade_hyperopt (line 446) | def _upgrade_hyperopt(hyperopt: HyperoptConfigDict) -> HyperoptConfigDict: function _upgrade_trainer (line 529) | def _upgrade_trainer(trainer: TrainerConfigDict) -> TrainerConfigDict: function _upgrade_preprocessing_defaults (line 542) | def _upgrade_preprocessing_defaults(config: ModelConfigDict) -> ModelCon... function _upgrade_preprocessing_split (line 597) | def _upgrade_preprocessing_split(preprocessing: PreprocessingConfigDict)... function update_training (line 650) | def update_training(config: ModelConfigDict) -> ModelConfigDict: function upgrade_missing_value_strategy (line 661) | def upgrade_missing_value_strategy(config: ModelConfigDict) -> ModelConf... function _upgrade_max_batch_size (line 678) | def _upgrade_max_batch_size(trainer: TrainerConfigDict) -> TrainerConfig... function remove_trainer_type (line 698) | def remove_trainer_type(config: ModelConfigDict) -> ModelConfigDict: function learning_rate_scheduler (line 715) | def learning_rate_scheduler(trainer: TrainerConfigDict) -> TrainerConfig... function _upgrade_legacy_image_encoders (line 759) | def _upgrade_legacy_image_encoders(feature: FeatureConfigDict) -> Featur... function upgrade_missing_hyperopt (line 811) | def upgrade_missing_hyperopt(config: ModelConfigDict) -> ModelConfigDict: function remove_extra_type_param_in_defaults_config (line 824) | def remove_extra_type_param_in_defaults_config(defaults: FeatureTypeDefa... function upgrade_metadata (line 841) | def upgrade_metadata(metadata: TrainingSetMetadataDict) -> TrainingSetMe... function _upgrade_metadata_missing_values (line 849) | def _upgrade_metadata_missing_values(metadata: TrainingSetMetadataDict): function _update_old_missing_value_strategy (line 857) | def _update_old_missing_value_strategy(feature_config: FeatureConfigDict): function _is_old_missing_value_strategy (line 869) | def _is_old_missing_value_strategy(feature_config: FeatureConfigDict): function _is_image_feature (line 878) | def _is_image_feature(feature_config: FeatureConfigDict): function _update_old_image_preprocessing (line 883) | def _update_old_image_preprocessing(feature_config: FeatureConfigDict): FILE: ludwig/utils/batch_size_tuner.py class BatchSizeEvaluator (line 18) | class BatchSizeEvaluator(ABC): method select_best_batch_size (line 19) | def select_best_batch_size( method evaluate (line 95) | def evaluate(self, batch_size: int, total_steps: int = 5, global_max_s... method reset (line 114) | def reset(self): method step (line 117) | def step(self, batch_size: int, global_max_sequence_length: int | None... class BaseLLMBatchSizeEvaluator (line 122) | class BaseLLMBatchSizeEvaluator(BatchSizeEvaluator): method __init__ (line 125) | def __init__(self, trainer): method reset (line 144) | def reset(self): method step (line 148) | def step(self, batch_size: int, global_max_sequence_length: int | None... method perform_step (line 168) | def perform_step(self, inputs, targets): class LLMFinetuneTrainerBatchSizeEvaluator (line 172) | class LLMFinetuneTrainerBatchSizeEvaluator(BaseLLMBatchSizeEvaluator): method perform_step (line 175) | def perform_step(self, inputs, targets): class LLMFinetunePredictBatchSizeEvaluator (line 179) | class LLMFinetunePredictBatchSizeEvaluator(BaseLLMBatchSizeEvaluator): method perform_step (line 182) | def perform_step(self, inputs, targets): FILE: ludwig/utils/calibration.py function register_calibration (line 35) | def register_calibration(name: str, features: str | list[str], default=F... function get_calibration_cls (line 54) | def get_calibration_cls(feature: str, calibration_method: str) -> type["... class ECELoss (line 69) | class ECELoss(nn.Module): method __init__ (line 88) | def __init__(self, n_bins: int = 15): method forward (line 95) | def forward(self, logits: torch.Tensor, one_hot_labels: torch.Tensor) ... class CalibrationResult (line 114) | class CalibrationResult: class CalibrationModule (line 124) | class CalibrationModule(nn.Module, ABC): method train_calibration (line 126) | def train_calibration( class TemperatureScaling (line 135) | class TemperatureScaling(CalibrationModule): method __init__ (line 149) | def __init__(self, num_classes: int = 2, binary: bool = False): method train_calibration (line 156) | def train_calibration( method scale_logits (line 213) | def scale_logits(self, logits: torch.Tensor) -> torch.Tensor: method forward (line 216) | def forward(self, logits: torch.Tensor) -> torch.Tensor: class MatrixScaling (line 227) | class MatrixScaling(CalibrationModule): method __init__ (line 243) | def __init__(self, num_classes: int = 2, off_diagonal_l2: float = 0.01... method train_calibration (line 252) | def train_calibration( method regularization_terms (line 302) | def regularization_terms(self) -> torch.Tensor: method scale_logits (line 316) | def scale_logits(self, logits: torch.Tensor) -> torch.Tensor: method forward (line 319) | def forward(self, logits: torch.Tensor) -> torch.Tensor: FILE: ludwig/utils/carton_utils.py function _get_output_dicts (line 43) | def _get_output_dicts(config: ModelConfigDict) -> str: function generate_carton_torchscript (line 52) | def generate_carton_torchscript(model: LudwigModel): function _get_input_spec (line 73) | def _get_input_spec(model: LudwigModel) -> list[dict[str, Any]]: function _get_output_spec (line 87) | def _get_output_spec(model: LudwigModel) -> list[dict[str, Any]]: function export_carton (line 102) | def export_carton(model: LudwigModel, carton_path: str, carton_model_nam... FILE: ludwig/utils/checkpoint_utils.py function mkdir (line 37) | def mkdir(s): function get_files (line 44) | def get_files(d, pattern, sort=True): function get_latest_checkpoint_path (line 64) | def get_latest_checkpoint_path(directory: str) -> str: class Checkpoint (line 78) | class Checkpoint(ABC): method __init__ (line 81) | def __init__( method prepare (line 95) | def prepare(self, directory: str): method load (line 101) | def load(self, save_path: str, device: torch.device | None = None) -> ... method get_state_for_inference (line 105) | def get_state_for_inference(self, save_path: str, device: torch.device... method save (line 109) | def save(self, save_path: str, global_step: int): method _get_global_step (line 112) | def _get_global_step(self, state: dict[str, Any], save_path: str) -> int: class MultiNodeCheckpoint (line 122) | class MultiNodeCheckpoint(Checkpoint): method prepare (line 123) | def prepare(self, directory: str): method load (line 128) | def load(self, save_path: str, device: torch.device | None = None) -> ... method get_state_for_inference (line 164) | def get_state_for_inference(self, save_path: str, device: torch.device... method save (line 168) | def save(self, save_path: str, global_step: int): method get_model_state_dict (line 214) | def get_model_state_dict(self) -> dict[str, Any]: method is_local_rank_0 (line 224) | def is_local_rank_0(self) -> bool: method safe_move_file (line 227) | def safe_move_file(self, src: str, dst: str): class CheckpointManager (line 265) | class CheckpointManager: method __init__ (line 268) | def __init__(self, checkpoint: Checkpoint, directory: str, device: tor... method restore_or_initialize (line 283) | def restore_or_initialize(self) -> int: method save (line 300) | def save(self, global_step: int, tag: str = LATEST): method save_best (line 311) | def save_best(self, global_step: int): method load (line 314) | def load(self, tag: str = LATEST): method get_best_checkpoint_state_for_inference (line 323) | def get_best_checkpoint_state_for_inference(self, device: torch.device... method close (line 333) | def close(self): method load_latest_checkpoint (line 337) | def load_latest_checkpoint(checkpoint: Checkpoint, directory: str, dev... FILE: ludwig/utils/config_utils.py function get_feature_type_parameter_values_from_section (line 24) | def get_feature_type_parameter_values_from_section( function get_defaults_section_for_feature_type (line 42) | def get_defaults_section_for_feature_type( function _to_dict (line 59) | def _to_dict(obj) -> dict: function get_preprocessing_params (line 66) | def get_preprocessing_params(config_obj: ModelConfig) -> PreprocessingCo... function merge_config_preprocessing_with_feature_specific_defaults (line 84) | def merge_config_preprocessing_with_feature_specific_defaults( function has_trainable_encoder (line 96) | def has_trainable_encoder(config: ModelConfig) -> bool: function has_unstructured_input_feature (line 107) | def has_unstructured_input_feature(config: ModelConfig) -> bool: function has_pretrained_encoder (line 114) | def has_pretrained_encoder(config: ModelConfig) -> bool: function config_uses_llm (line 121) | def config_uses_llm(config: dict[str, Any] | ModelConfig) -> bool: function get_quantization (line 160) | def get_quantization(config: dict[str, Any] | ModelConfig) -> list[int |... FILE: ludwig/utils/data_utils.py function get_parquet_filename (line 119) | def get_parquet_filename(n: int): function get_split_path (line 128) | def get_split_path(dataset_fp): function get_abs_path (line 133) | def get_abs_path(src_path, file_path): function load_csv (line 143) | def load_csv(data_fp): function spread (line 151) | def spread(fn): function read_nrows_via_chunksize (line 162) | def read_nrows_via_chunksize(fp, read_fn, **kwargs): function read_xsv (line 174) | def read_xsv(data_fp, df_lib=PANDAS_DF, separator=",", header=0, nrows=N... function read_json (line 218) | def read_json(data_fp, df_lib, normalize=False, **kwargs): function read_jsonl (line 230) | def read_jsonl(data_fp, df_lib, **kwargs): function read_excel (line 236) | def read_excel(data_fp, df_lib, **kwargs): function read_parquet (line 252) | def read_parquet(data_fp, df_lib, nrows=None, **kwargs): function read_pickle (line 272) | def read_pickle(data_fp, df_lib, **kwargs): function read_fwf (line 285) | def read_fwf(data_fp, df_lib, **kwargs): function read_feather (line 291) | def read_feather(data_fp, df_lib, **kwargs): function read_html (line 304) | def read_html(data_fp, df_lib, **kwargs): function read_orc (line 323) | def read_orc(data_fp, df_lib, **kwargs): function read_sas (line 332) | def read_sas(data_fp, df_lib, **kwargs): function read_spss (line 342) | def read_spss(data_fp, df_lib, **kwargs): function read_stata (line 355) | def read_stata(data_fp, df_lib, **kwargs): function read_hdf5 (line 365) | def read_hdf5(data_fp, **_kwargs): function read_buffer (line 371) | def read_buffer(buf, fname): function read_fname (line 388) | def read_fname(fname, data_format=None, df_lib=pd, **kwargs): function save_csv (line 399) | def save_csv(data_fp, data): function csv_contains_column (line 409) | def csv_contains_column(data_fp, column_name): function load_yaml (line 414) | def load_yaml(yaml_fp): function load_config_from_str (line 420) | def load_config_from_str(config): function load_json (line 431) | def load_json(data_fp): function save_json (line 438) | def save_json(data_fp, data, sort_keys=True, indent=4): function hash_dict (line 444) | def hash_dict(d: dict, max_length: int | None = 6) -> bytes: function to_json_dict (line 459) | def to_json_dict(d): function chunk_dict (line 465) | def chunk_dict(data, chunk_size=100): function flatten_dict (line 476) | def flatten_dict(d, parent_key="", sep="."): function save_hdf5 (line 493) | def save_hdf5(data_fp, data): function load_hdf5 (line 502) | def load_hdf5(data_fp, clean_cols: bool = False): function load_object (line 516) | def load_object(object_fp): function save_object (line 522) | def save_object(object_fp, obj): function load_array (line 528) | def load_array(data_fp, dtype=float): function load_matrix (line 537) | def load_matrix(data_fp, dtype=float): function save_array (line 546) | def save_array(data_fp, array): function load_pretrained_embeddings (line 554) | def load_pretrained_embeddings(embeddings_path: str, vocab: list[str]) -... function load_glove (line 579) | def load_glove(file_path: str, return_embedding_size: bool = False) -> d... function split_data (line 623) | def split_data(split: float, data: list) -> tuple[list, list]: function split_by_slices (line 630) | def split_by_slices(slices: list[Any], n: int, probabilities: list[float... function shuffle_unison_inplace (line 641) | def shuffle_unison_inplace(list_of_lists, random_state=None): function shuffle_dict_unison_inplace (line 653) | def shuffle_dict_unison_inplace(np_dict, random_state=None): function split_dataset_ttv (line 669) | def split_dataset_ttv(dataset, split): function split_dataset (line 688) | def split_dataset(dataset, split, value_to_split=0): function collapse_rare_labels (line 694) | def collapse_rare_labels(labels, labels_limit): function class_counts (line 701) | def class_counts(dataset, labels_field): function load_from_file (line 706) | def load_from_file(file_name, field=None, dtype=int, ground_truth_split=2): function replace_file_extension (line 731) | def replace_file_extension(file_path, extension): function file_exists_with_diff_extension (line 750) | def file_exists_with_diff_extension(file_path, extension): function add_sequence_feature_column (line 755) | def add_sequence_feature_column(df, col_name, seq_length): function override_in_memory_flag (line 783) | def override_in_memory_flag(input_features, override_value): function normalize_numpy (line 794) | def normalize_numpy(obj): class NumpyEncoder (line 808) | class NumpyEncoder(json.JSONEncoder): method default (line 832) | def default(self, o): function generate_kfold_splits (line 852) | def generate_kfold_splits(data_df, num_folds, random_state): function get_path_size (line 861) | def get_path_size(start_path, regex_accept=None, regex_reject=None): function clear_data_cache (line 882) | def clear_data_cache(): function figure_data_format_dataset (line 888) | def figure_data_format_dataset(dataset): function figure_data_format (line 950) | def figure_data_format(dataset=None, training_set=None, validation_set=N... function is_model_dir (line 978) | def is_model_dir(path: str) -> bool: function ndarray2string (line 993) | def ndarray2string(parm_array): function string2ndarray (line 1002) | def string2ndarray(parm_string): function is_ludwig_ndarray_string (line 1011) | def is_ludwig_ndarray_string(parm_string): function get_pa_dtype (line 1017) | def get_pa_dtype(obj: Any): function get_pa_schema (line 1027) | def get_pa_schema(df: DataFrame): function load_dataset (line 1073) | def load_dataset(dataset, data_format=None, df_lib=PANDAS_DF): function use_credentials (line 1091) | def use_credentials(creds): function get_sanitized_feature_name (line 1122) | def get_sanitized_feature_name(feature_name: str) -> str: function sanitize_column_names (line 1130) | def sanitize_column_names(df: DataFrame) -> DataFrame: FILE: ludwig/utils/dataframe_utils.py function is_dask_lib (line 13) | def is_dask_lib(df_lib) -> bool: function is_dask_backend (line 19) | def is_dask_backend(backend: Optional["Backend"]) -> bool: # noqa: F821 function is_dask_series_or_df (line 25) | def is_dask_series_or_df(df: DataFrame, backend: Optional["Backend"]) ->... function flatten_df (line 34) | def flatten_df(df: DataFrame, df_engine: DataFrameEngine) -> tuple[DataF... function unflatten_df (line 54) | def unflatten_df(df: DataFrame, column_shapes: dict[str, tuple], df_engi... function to_numpy_dataset (line 64) | def to_numpy_dataset(df: DataFrame, backend: Optional["Backend"] = None)... function from_numpy_dataset (line 82) | def from_numpy_dataset(dataset) -> pd.DataFrame: function set_index_name (line 99) | def set_index_name(pd_df: pd.DataFrame, name: str) -> pd.DataFrame: function to_batches (line 105) | def to_batches(df: pd.DataFrame, batch_size: int) -> list[pd.DataFrame]: function from_batches (line 111) | def from_batches(batches: list[pd.DataFrame]) -> pd.DataFrame: function to_scalar_df (line 116) | def to_scalar_df(df: pd.DataFrame) -> pd.DataFrame: FILE: ludwig/utils/dataset_utils.py function get_repeatable_train_val_test_split (line 11) | def get_repeatable_train_val_test_split( function generate_dataset_statistics (line 96) | def generate_dataset_statistics( FILE: ludwig/utils/date_utils.py function create_vector_from_datetime_obj (line 28) | def create_vector_from_datetime_obj(datetime_obj): function parse_datetime (line 48) | def parse_datetime(timestamp: float | int | str) -> datetime: function convert_number_to_datetime (line 66) | def convert_number_to_datetime(timestamp: float | int | str) -> datetime: FILE: ludwig/utils/defaults.py function render_config (line 51) | def render_config(config=None, output=None, **kwargs): function cli_render_config (line 63) | def cli_render_config(sys_argv): FILE: ludwig/utils/entmax/activations.py function _make_ix_like (line 16) | def _make_ix_like(X, dim): function _roll_last (line 24) | def _roll_last(X, dim): function _sparsemax_threshold_and_support (line 34) | def _sparsemax_threshold_and_support(X, dim=-1, k=None): function _entmax_threshold_and_support (line 85) | def _entmax_threshold_and_support(X, dim=-1, k=None): class SparsemaxFunction (line 143) | class SparsemaxFunction(Function): method forward (line 145) | def forward(cls, ctx, X, dim=-1, k=None): method backward (line 152) | def backward(cls, ctx, grad_output): function _sparsemax_forward (line 164) | def _sparsemax_forward(X, dim, k): class Entmax15Function (line 172) | class Entmax15Function(Function): method forward (line 174) | def forward(cls, ctx, X, dim=0, k=None): method backward (line 181) | def backward(cls, ctx, dY): function _entmax15_forward (line 191) | def _entmax15_forward(X, dim, k): function sparsemax (line 202) | def sparsemax(X, dim=-1, k=None, training=True): function entmax15 (line 237) | def entmax15(X, dim=-1, k=None, training=True): class Sparsemax (line 274) | class Sparsemax(nn.Module): method __init__ (line 275) | def __init__(self, dim=-1, k=None): method forward (line 298) | def forward(self, X): class Entmax15 (line 302) | class Entmax15(nn.Module): method __init__ (line 303) | def __init__(self, dim=-1, k=None): method forward (line 328) | def forward(self, X): FILE: ludwig/utils/entmax/losses.py class _GenericLoss (line 10) | class _GenericLoss(nn.Module): method __init__ (line 11) | def __init__(self, ignore_index=IGNORE_INDEX_TOKEN_ID, reduction="elem... method forward (line 17) | def forward(self, X, target): class _GenericLossFunction (line 36) | class _GenericLossFunction(Function): method forward (line 38) | def forward(cls, ctx, X, target, alpha, proj_args): method backward (line 52) | def backward(cls, ctx, grad_output): class SparsemaxLossFunction (line 61) | class SparsemaxLossFunction(_GenericLossFunction): method project (line 65) | def project(cls, X, alpha, k): method omega (line 69) | def omega(cls, p_star, alpha): method forward (line 73) | def forward(cls, ctx, X, target, k=None): class SparsemaxBisectLossFunction (line 77) | class SparsemaxBisectLossFunction(_GenericLossFunction): method project (line 81) | def project(cls, X, alpha, n_iter): method omega (line 85) | def omega(cls, p_star, alpha): method forward (line 89) | def forward(cls, ctx, X, target, n_iter=50): class Entmax15LossFunction (line 93) | class Entmax15LossFunction(_GenericLossFunction): method project (line 97) | def project(cls, X, alpha, k=None): method omega (line 101) | def omega(cls, p_star, alpha): method forward (line 105) | def forward(cls, ctx, X, target, k=None): class EntmaxBisectLossFunction (line 109) | class EntmaxBisectLossFunction(_GenericLossFunction): method project (line 113) | def project(cls, X, alpha, n_iter): method omega (line 117) | def omega(cls, p_star, alpha): method forward (line 121) | def forward(cls, ctx, X, target, alpha=1.5, n_iter=50): function sparsemax_loss (line 125) | def sparsemax_loss(X, target, k=None): function sparsemax_bisect_loss (line 153) | def sparsemax_bisect_loss(X, target, n_iter=50): function entmax15_loss (line 178) | def entmax15_loss(X, target, k=None): function entmax_bisect_loss (line 206) | def entmax_bisect_loss(X, target, alpha=1.5, n_iter=50): class SparsemaxBisectLoss (line 238) | class SparsemaxBisectLoss(_GenericLoss): method __init__ (line 239) | def __init__(self, n_iter=50, ignore_index=IGNORE_INDEX_TOKEN_ID, redu... method loss (line 243) | def loss(self, X, target): class SparsemaxLoss (line 247) | class SparsemaxLoss(_GenericLoss): method __init__ (line 248) | def __init__(self, k=None, ignore_index=IGNORE_INDEX_TOKEN_ID, reducti... method loss (line 252) | def loss(self, X, target): class EntmaxBisectLoss (line 256) | class EntmaxBisectLoss(_GenericLoss): method __init__ (line 257) | def __init__( method loss (line 268) | def loss(self, X, target): class Entmax15Loss (line 272) | class Entmax15Loss(_GenericLoss): method __init__ (line 273) | def __init__(self, k=100, ignore_index=IGNORE_INDEX_TOKEN_ID, reductio... method loss (line 277) | def loss(self, X, target): FILE: ludwig/utils/entmax/root_finding.py class EntmaxBisectFunction (line 15) | class EntmaxBisectFunction(Function): method _gp (line 17) | def _gp(cls, x, alpha): method _gp_inv (line 21) | def _gp_inv(cls, y, alpha): method _p (line 25) | def _p(cls, X, alpha): method forward (line 29) | def forward(cls, ctx, X, alpha=1.5, dim=-1, n_iter=50, ensure_sum_one=... method backward (line 38) | def backward(cls, ctx, dY): function _entmax_bisect_forward (line 69) | def _entmax_bisect_forward(X, alpha, dim, n_iter, ensure_sum_one, cls=En... class SparsemaxBisectFunction (line 107) | class SparsemaxBisectFunction(EntmaxBisectFunction): method _gp (line 109) | def _gp(cls, x, alpha): method _gp_inv (line 113) | def _gp_inv(cls, y, alpha): method _p (line 117) | def _p(cls, x, alpha): method forward (line 121) | def forward(cls, ctx, X, dim=-1, n_iter=50, ensure_sum_one=True): method backward (line 130) | def backward(cls, ctx, dY): function _sparsemax_bisect_forward (line 140) | def _sparsemax_bisect_forward(X, dim, n_iter, ensure_sum_one): function entmax_bisect (line 144) | def entmax_bisect(X, alpha=1.5, dim=-1, n_iter=50, ensure_sum_one=True, ... function sparsemax_bisect (line 194) | def sparsemax_bisect(X, dim=-1, n_iter=50, ensure_sum_one=True, training... class SparsemaxBisect (line 234) | class SparsemaxBisect(nn.Module): method __init__ (line 235) | def __init__(self, dim=-1, n_iter=None): method forward (line 255) | def forward(self, X): class EntmaxBisect (line 259) | class EntmaxBisect(nn.Module): method __init__ (line 260) | def __init__(self, alpha=1.5, dim=-1, n_iter=50): method forward (line 297) | def forward(self, X): FILE: ludwig/utils/eval_utils.py class ConfusionMatrix (line 26) | class ConfusionMatrix: method __init__ (line 27) | def __init__(self, conditions, predictions, labels=None, sample_weight... method label_to_idx (line 62) | def label_to_idx(self, label): method true_positives (line 65) | def true_positives(self, idx): method true_negatives (line 68) | def true_negatives(self, idx): method false_positives (line 71) | def false_positives(self, idx): method false_negatives (line 74) | def false_negatives(self, idx): method true_positive_rate (line 77) | def true_positive_rate(self, idx): method true_negative_rate (line 85) | def true_negative_rate(self, idx): method positive_predictive_value (line 93) | def positive_predictive_value(self, idx): method negative_predictive_value (line 101) | def negative_predictive_value(self, idx): method false_negative_rate (line 109) | def false_negative_rate(self, idx): method false_positive_rate (line 112) | def false_positive_rate(self, idx): method false_discovery_rate (line 115) | def false_discovery_rate(self, idx): method false_omission_rate (line 118) | def false_omission_rate(self, idx): method accuracy (line 121) | def accuracy(self, idx): method precision (line 129) | def precision(self, idx): method recall (line 132) | def recall(self, idx): method fbeta_score (line 135) | def fbeta_score(self, beta, idx): method f1_score (line 146) | def f1_score(self, idx): method sensitivity (line 149) | def sensitivity(self, idx): method specificity (line 152) | def specificity(self, idx): method hit_rate (line 155) | def hit_rate(self, idx): method miss_rate (line 158) | def miss_rate(self, idx): method fall_out (line 161) | def fall_out(self, idx): method matthews_correlation_coefficient (line 164) | def matthews_correlation_coefficient(self, idx): method informedness (line 176) | def informedness(self, idx): method markedness (line 179) | def markedness(self, idx): method token_accuracy (line 182) | def token_accuracy(self): method avg_precision (line 185) | def avg_precision(self, average="macro"): method avg_recall (line 188) | def avg_recall(self, average="macro"): method avg_f1_score (line 191) | def avg_f1_score(self, average="macro"): method avg_fbeta_score (line 194) | def avg_fbeta_score(self, beta, average="macro"): method kappa_score (line 197) | def kappa_score(self): method class_stats (line 200) | def class_stats(self, idx): method per_class_stats (line 228) | def per_class_stats(self): method stats (line 234) | def stats(self): function roc_curve (line 250) | def roc_curve(conditions, prediction_scores, pos_label=None, sample_weig... function roc_auc_score (line 254) | def roc_auc_score(conditions, prediction_scores, average="micro", sample... function precision_recall_curve (line 261) | def precision_recall_curve(conditions, prediction_scores, pos_label=None... function average_precision_score (line 267) | def average_precision_score(conditions, prediction_scores, average="micr... FILE: ludwig/utils/fs_utils.py function get_default_cache_location (line 42) | def get_default_cache_location() -> str: function get_fs_and_path (line 57) | def get_fs_and_path(url): function has_remote_protocol (line 68) | def has_remote_protocol(url): function is_http (line 74) | def is_http(urlpath): function upgrade_http (line 80) | def upgrade_http(urlpath): function get_bytes_obj_from_path (line 89) | def get_bytes_obj_from_path(path: str) -> bytes | None: function stream_http_get_request (line 106) | def stream_http_get_request(path: str) -> urllib3.response.HTTPResponse: function get_bytes_obj_from_http_path (line 117) | def get_bytes_obj_from_http_path(path: str) -> bytes: function find_non_existing_dir_by_adding_suffix (line 135) | def find_non_existing_dir_by_adding_suffix(directory_name): function abspath (line 146) | def abspath(url): function path_exists (line 155) | def path_exists(url): function listdir (line 161) | def listdir(url): function safe_move_file (line 167) | def safe_move_file(src, dst): function safe_move_directory (line 199) | def safe_move_directory(src, dst): function rename (line 223) | def rename(src, tgt): function upload_file (line 233) | def upload_file(src, tgt): function copy (line 240) | def copy(src, tgt, recursive=False): function makedirs (line 247) | def makedirs(url, exist_ok=False): function delete (line 253) | def delete(url, recursive=False): function upload (line 259) | def upload(lpath, rpath): function download (line 265) | def download(rpath, lpath): function checksum (line 271) | def checksum(url): function to_url (line 277) | def to_url(path): function upload_output_directory (line 286) | def upload_output_directory(url): function open_file (line 323) | def open_file(url, *args, **kwargs): function download_h5 (line 331) | def download_h5(url): function upload_h5 (line 342) | def upload_h5(url): function upload_output_file (line 354) | def upload_output_file(url): class file_lock (line 368) | class file_lock(contextlib.AbstractContextManager): method __init__ (line 371) | def __init__(self, path: str, ignore_remote_protocol: bool = True, loc... method __enter__ (line 381) | def __enter__(self, *args, **kwargs): method __exit__ (line 385) | def __exit__(self, *args, **kwargs): function list_file_names_in_directory (line 391) | def list_file_names_in_directory(directory_name: str) -> list[str]: FILE: ludwig/utils/h3_util.py class H3Data (line 19) | class H3Data(NamedTuple): function set_bit (line 27) | def set_bit(v, index, x): function set_bits (line 36) | def set_bits(v, start_bit, slice_length, x): function components_to_h3 (line 44) | def components_to_h3(components): function bitslice (line 56) | def bitslice(x: int, start_bit: int, slice_length: int) -> int: function h3_index_mode (line 61) | def h3_index_mode(h3_long: int) -> int: function h3_edge (line 65) | def h3_edge(h3_long: int) -> int: function h3_resolution (line 69) | def h3_resolution(h3_long: int) -> int: function h3_base_cell (line 73) | def h3_base_cell(h3_long: int) -> int: function h3_octal_components (line 77) | def h3_octal_components(h3_long): function h3_component (line 82) | def h3_component(h3_long: int, i: int) -> int: function h3_components (line 86) | def h3_components(h3_long: int) -> list[int]: function h3_to_components (line 90) | def h3_to_components(h3_value: int) -> H3Data: FILE: ludwig/utils/heuristics.py function get_auto_learning_rate (line 5) | def get_auto_learning_rate(config: ModelConfig) -> float: FILE: ludwig/utils/hf_utils.py function load_pretrained_hf_model_from_hub (line 18) | def load_pretrained_hf_model_from_hub( function load_pretrained_hf_tokenizer (line 37) | def load_pretrained_hf_tokenizer( function _load_pretrained_hf_model_from_dir (line 51) | def _load_pretrained_hf_model_from_dir( function load_pretrained_hf_model_with_hub_fallback (line 63) | def load_pretrained_hf_model_with_hub_fallback( function upload_folder_to_hfhub (line 112) | def upload_folder_to_hfhub( FILE: ludwig/utils/html_utils.py class HTMLStripper (line 25) | class HTMLStripper(HTMLParser): method __init__ (line 26) | def __init__(self): method handle_data (line 33) | def handle_data(self, data): method get_data (line 36) | def get_data(self): method error (line 39) | def error(self, message): function strip_tags (line 43) | def strip_tags(html): function clean_html (line 79) | def clean_html(html_text): FILE: ludwig/utils/image_utils.py class TVModelVariant (line 36) | class TVModelVariant: class ResizeChannels (line 53) | class ResizeChannels(torch.nn.Module): method __init__ (line 54) | def __init__(self, num_channels: int): method forward (line 58) | def forward(self, imgs: torch.Tensor): function get_gray_default_image (line 77) | def get_gray_default_image(num_channels: int, height: int, width: int) -... function get_average_image (line 82) | def get_average_image(image_lst: list[np.ndarray]) -> np.array: function is_bytes_image (line 87) | def is_bytes_image(bytes_obj) -> bool: function is_image (line 102) | def is_image(src_path: str, img_entry: bytes | str, column: str) -> bool: function is_image_score (line 122) | def is_image_score(path): function get_image_read_mode_from_num_channels (line 127) | def get_image_read_mode_from_num_channels(num_channels: int) -> ImageRea... function read_image_from_path (line 145) | def read_image_from_path( function read_image_from_bytes_obj (line 166) | def read_image_from_bytes_obj(bytes_obj: bytes | None = None, num_channe... function read_image_as_png (line 186) | def read_image_as_png(bytes_obj: bytes, mode: ImageReadMode = ImageReadM... function read_image_as_numpy (line 203) | def read_image_as_numpy(bytes_obj: bytes) -> torch.Tensor | None: function read_image_as_tif (line 215) | def read_image_as_tif(bytes_obj: bytes) -> torch.Tensor | None: function pad (line 234) | def pad( function crop (line 257) | def crop( function crop_or_pad (line 275) | def crop_or_pad(img: torch.Tensor, new_size: int | tuple[int, int]): function resize_image (line 294) | def resize_image( function grayscale (line 322) | def grayscale(img: torch.Tensor) -> torch.Tensor: function num_channels_in_image (line 328) | def num_channels_in_image(img: torch.Tensor): function get_unique_channels (line 340) | def get_unique_channels( function get_class_mask_from_image (line 398) | def get_class_mask_from_image( function get_image_from_class_mask (line 435) | def get_image_from_class_mask( function to_tuple (line 460) | def to_tuple(v: int | tuple[int, int]) -> tuple[int, int]: function to_np_tuple (line 469) | def to_np_tuple(prop: int | Iterable) -> np.ndarray: function get_img_output_shape (line 486) | def get_img_output_shape( function register_torchvision_model_variants (line 518) | def register_torchvision_model_variants(variants: list[TVModelVariant]): FILE: ludwig/utils/inference_utils.py function get_filename_from_stage (line 35) | def get_filename_from_stage(stage: str, device: TorchDevice) -> str: function to_inference_module_input_from_dataframe (line 46) | def to_inference_module_input_from_dataframe( function to_inference_model_input_from_series (line 63) | def to_inference_model_input_from_series( FILE: ludwig/utils/llm_quantization_utils.py function linear4bit_to_linear (line 15) | def linear4bit_to_linear(linear4bit_layer): function convert_quantized_linear_to_linear (line 46) | def convert_quantized_linear_to_linear(module): FILE: ludwig/utils/llm_utils.py function load_pretrained_from_config (line 38) | def load_pretrained_from_config( function to_device (line 92) | def to_device( function _load_peft_config (line 168) | def _load_peft_config(pretrained_adapter_weights: str): function initialize_adapter (line 187) | def initialize_adapter( function get_context_len (line 227) | def get_context_len(model_config: AutoConfig): function has_padding_token (line 266) | def has_padding_token(input_tensor: torch.Tensor, tokenizer: PreTrainedT... function remove_left_padding (line 295) | def remove_left_padding(input_ids_sample: torch.Tensor, tokenizer: PreTr... function add_left_padding (line 334) | def add_left_padding(input_ids, max_length, pad_value=0): function create_attention_mask (line 356) | def create_attention_mask(input_ids: torch.Tensor, tokenizer: PreTrained... function find_last_matching_index (line 386) | def find_last_matching_index(tensor_a: torch.Tensor, tensor_b: torch.Ten... function pad_target_tensor_for_fine_tuning (line 422) | def pad_target_tensor_for_fine_tuning( function generate_merged_ids (line 486) | def generate_merged_ids( function _get_decoded_targets_and_predictions (line 536) | def _get_decoded_targets_and_predictions( function get_realigned_target_and_prediction_tensors_for_inference (line 559) | def get_realigned_target_and_prediction_tensors_for_inference( function update_embedding_layer (line 621) | def update_embedding_layer(model: AutoModelForCausalLM, config_obj: LLMT... function create_text_streamer (line 666) | def create_text_streamer(tokenizer: PreTrainedTokenizer) -> TextStreamer: FILE: ludwig/utils/logging_utils.py function log_once (line 4) | def log_once(key: str) -> bool: FILE: ludwig/utils/loss_utils.py function rmspe_loss (line 4) | def rmspe_loss(targets: torch.Tensor, predictions: torch.Tensor) -> torc... function mean_confidence_penalty (line 17) | def mean_confidence_penalty(probabilities: torch.Tensor, num_classes: in... FILE: ludwig/utils/math_utils.py function softmax (line 21) | def softmax(x, temperature=1.0): function int_type (line 26) | def int_type(number): function convert_size (line 37) | def convert_size(size_bytes): function round2precision (line 47) | def round2precision(val, precision: int = 0, which: str = ""): function cumsum (line 58) | def cumsum(x: list[int]) -> list[int]: FILE: ludwig/utils/metric_utils.py function sequence_mask (line 12) | def sequence_mask(lengths: Tensor, maxlen: int | None = None, dtype=torc... function dynamic_partition (line 26) | def dynamic_partition(data: Tensor, partitions: Tensor, num_partitions: ... function masked_correct_predictions (line 42) | def masked_correct_predictions(targets: Tensor, preds: Tensor, targets_s... function get_scalar_from_ludwig_metric (line 60) | def get_scalar_from_ludwig_metric(metric: Metric) -> float: function reduce_trainer_metrics_dict (line 76) | def reduce_trainer_metrics_dict( function get_metric_names (line 93) | def get_metric_names(output_features: dict[str, "OutputFeature"]) -> dic... function get_feature_to_metric_names_map (line 103) | def get_feature_to_metric_names_map(output_features: list[FeatureConfigD... function get_feature_to_metric_names_map_from_feature_collection (line 114) | def get_feature_to_metric_names_map_from_feature_collection( FILE: ludwig/utils/metrics_printed_table.py function get_metric_value_or_empty (line 11) | def get_metric_value_or_empty(metrics_log: dict[str, list[TrainerMetric]... function print_table_for_single_output_feature (line 18) | def print_table_for_single_output_feature( function print_metrics_table (line 55) | def print_metrics_table( FILE: ludwig/utils/misc_utils.py function set_random_seed (line 40) | def set_random_seed(random_seed): function merge_dict (line 50) | def merge_dict(dct, merge_dct): function sum_dicts (line 69) | def sum_dicts(dicts, dict_type=dict): function get_from_registry (line 87) | def get_from_registry(key, registry): function set_default_value (line 97) | def set_default_value(dictionary, key, value): function set_default_values (line 103) | def set_default_values(dictionary: dict, default_value_dictionary: dict): function get_class_attributes (line 124) | def get_class_attributes(c): function get_output_directory (line 129) | def get_output_directory(output_directory, experiment_name, model_name="... function get_file_names (line 135) | def get_file_names(output_directory): function get_combined_features (line 145) | def get_combined_features(config): function get_proc_features (line 150) | def get_proc_features(config): function get_proc_features_from_lists (line 155) | def get_proc_features_from_lists(*args): function set_saved_weights_in_checkpoint_flag (line 160) | def set_saved_weights_in_checkpoint_flag(config_obj: "ModelConfig"): function remove_empty_lines (line 172) | def remove_empty_lines(str): function memoized_method (line 177) | def memoized_method(*lru_args, **lru_kwargs): function get_commit_hash (line 199) | def get_commit_hash(): function scrub_creds (line 216) | def scrub_creds(config_dict: dict[str, Any]) -> dict[str, Any]: FILE: ludwig/utils/model_utils.py function extract_tensors (line 25) | def extract_tensors(model: torch.nn.Module) -> tuple[torch.nn.Module, li... function replace_tensors (line 58) | def replace_tensors(m: torch.nn.Module, tensors: list[dict], device: tor... function find_embedding_layer_with_path (line 81) | def find_embedding_layer_with_path(module, module_names=[]): function contains_nan_or_inf_tensors (line 98) | def contains_nan_or_inf_tensors(module: torch.nn.Module) -> bool: FILE: ludwig/utils/nlp_utils.py function load_nlp_pipeline (line 131) | def load_nlp_pipeline(language="xx"): function pass_filters (line 164) | def pass_filters( function process_text (line 179) | def process_text( FILE: ludwig/utils/numerical_test_utils.py function _dict_like (line 21) | def _dict_like(x): function _enumerable (line 30) | def _enumerable(x): function assert_all_finite (line 39) | def assert_all_finite(x: Any, keypath=""): FILE: ludwig/utils/output_feature_utils.py function get_feature_concat_name (line 9) | def get_feature_concat_name(feature_name: str, tensor_name: str) -> str: function get_tensor_name_from_concat_name (line 13) | def get_tensor_name_from_concat_name(concat_name: str) -> str: function get_feature_name_from_concat_name (line 17) | def get_feature_name_from_concat_name(concat_name: str) -> str: function get_single_output_feature_tensors (line 21) | def get_single_output_feature_tensors( function get_output_feature_tensor (line 32) | def get_output_feature_tensor( function set_output_feature_tensor (line 44) | def set_output_feature_tensor( function concat_dependencies (line 51) | def concat_dependencies( FILE: ludwig/utils/package_utils.py class LazyLoader (line 5) | class LazyLoader(types.ModuleType): method __init__ (line 15) | def __init__(self, local_name, parent_module_globals, name): # pylint... method _load (line 21) | def _load(self): method __getattr__ (line 33) | def __getattr__(self, item): method __dir__ (line 37) | def __dir__(self): FILE: ludwig/utils/print_utils.py function get_logging_level_registry (line 26) | def get_logging_level_registry() -> dict[str, int]: function get_logo (line 38) | def get_logo(message, ludwig_version): function print_ludwig (line 55) | def print_ludwig(message, ludwig_version): function print_boxed (line 60) | def print_boxed(text, print_fun=logger.info): function repr_ordered_dict (line 70) | def repr_ordered_dict(d: OrderedDict): function query_yes_no (line 75) | def query_yes_no(question: str, default: str | None = "yes"): FILE: ludwig/utils/registry.py class Registry (line 26) | class Registry(UserDict, Generic[T]): method __init__ (line 32) | def __init__(self, source=None): method __getitem__ (line 43) | def __getitem__(self, key: str) -> T: method __contains__ (line 48) | def __contains__(self, key: str): method __len__ (line 51) | def __len__(self) -> int: method __iter__ (line 54) | def __iter__(self): method keys (line 57) | def keys(self): method values (line 60) | def values(self): method items (line 63) | def items(self): method _merged (line 66) | def _merged(self): method register (line 69) | def register(self, name: str, default: bool = False): FILE: ludwig/utils/server_utils.py function serialize_payload (line 14) | def serialize_payload(data_source: pd.DataFrame | pd.Series) -> tuple: function _write_file (line 78) | def _write_file(v, files): function deserialize_payload (line 88) | def deserialize_payload(json_string: str) -> pd.DataFrame: function deserialize_request (line 125) | def deserialize_request(form) -> tuple: class NumpyJSONResponse (line 159) | class NumpyJSONResponse(JSONResponse): method render (line 160) | def render(self, content: dict[str, Any]) -> str: FILE: ludwig/utils/state_dict_backward_compatibility.py function _update_transformers_to_freeze_module (line 17) | def _update_transformers_to_freeze_module(state_dict): function _update_combiner_no_input_features (line 29) | def _update_combiner_no_input_features(state_dict): function _update_combiner_no_device_tensor (line 34) | def _update_combiner_no_device_tensor(state_dict): function update_state_dict (line 39) | def update_state_dict(state_dict): FILE: ludwig/utils/strings_utils.py class SpecialSymbol (line 43) | class SpecialSymbol(Enum): function all_bool_strs (line 52) | def all_bool_strs(): function make_safe_filename (line 58) | def make_safe_filename(s): function strip_accents (line 68) | def strip_accents(s): function str2bool (line 72) | def str2bool(v: str, fallback_true_label=None) -> bool: function values_are_pandas_numbers (line 94) | def values_are_pandas_numbers(values: list[str]): function values_are_pandas_bools (line 104) | def values_are_pandas_bools(values: list[str]): function are_conventional_bools (line 110) | def are_conventional_bools(values: list[str | bool]) -> bool: function is_number (line 119) | def is_number(s: str | int | float): function is_datetime (line 130) | def is_datetime(s: str | int | float): function are_all_datetimes (line 142) | def are_all_datetimes(values: list[str | int | float]): function are_all_numbers (line 150) | def are_all_numbers(values: list[str | int | float]): function is_integer (line 158) | def is_integer(s: str | int | float): function are_sequential_integers (line 168) | def are_sequential_integers(values: list[str | int | float]): function match_replace (line 178) | def match_replace(string_to_match, list_regex): function load_vocabulary (line 197) | def load_vocabulary(vocab_file): function add_or_move_symbol (line 208) | def add_or_move_symbol(vocab_list: list[str], vocab_set: set[str], symbo... class Vocabulary (line 216) | class Vocabulary: function _get_vocab_from_dict (line 251) | def _get_vocab_from_dict(vocab: dict[str, int]) -> list[str]: function _get_vocabulary (line 266) | def _get_vocabulary( function remove_bracketed_elements (line 316) | def remove_bracketed_elements(prompt_template: str) -> str: function create_vocabulary (line 322) | def create_vocabulary( function create_vocabulary_single_token (line 463) | def create_vocabulary_single_token( function _get_sequence_vector (line 503) | def _get_sequence_vector( function build_sequence_matrix (line 528) | def build_sequence_matrix( function get_tokenizer (line 584) | def get_tokenizer(tokenizer_type: str, tokenizer_vocab_file: str, pretra... FILE: ludwig/utils/structural_warning.py function warn_structure_refactor (line 6) | def warn_structure_refactor(old_module: str, new_module: str, direct: bo... FILE: ludwig/utils/system_utils.py class Resources (line 24) | class Resources: FILE: ludwig/utils/time_utils.py class WithTimer (line 23) | class WithTimer: method __init__ (line 24) | def __init__(self, title="", quiet=False): method elapsed (line 28) | def elapsed(self): method enter (line 31) | def enter(self): method __enter__ (line 35) | def __enter__(self): method __exit__ (line 40) | def __exit__(self, *args): class Timer (line 46) | class Timer: method __init__ (line 47) | def __init__(self): method reset (line 50) | def reset(self): method elapsed (line 54) | def elapsed(self): method elapsed_str (line 57) | def elapsed_str(self): method wall (line 60) | def wall(self): method proc (line 63) | def proc(self): method tic (line 66) | def tic(self): method toc (line 70) | def toc(self): method tocproc (line 74) | def tocproc(self): function timestamp (line 79) | def timestamp(): function strdelta (line 83) | def strdelta(tdelta): FILE: ludwig/utils/tokenizers.py class BaseTokenizer (line 35) | class BaseTokenizer: method __init__ (line 37) | def __init__(self, **kwargs): method __call__ (line 41) | def __call__(self, text: str): class CharactersToListTokenizer (line 45) | class CharactersToListTokenizer(BaseTokenizer): method __call__ (line 46) | def __call__(self, text): class SpaceStringToListTokenizer (line 50) | class SpaceStringToListTokenizer(torch.nn.Module): method __init__ (line 53) | def __init__(self, **kwargs): method forward (line 56) | def forward(self, v: str | list[str] | torch.Tensor) -> Any: class SpacePunctuationStringToListTokenizer (line 79) | class SpacePunctuationStringToListTokenizer(torch.nn.Module): method __init__ (line 82) | def __init__(self, **kwargs): method is_regex_w (line 85) | def is_regex_w(self, c: str) -> bool: method forward (line 88) | def forward(self, v: str | list[str] | torch.Tensor) -> Any: class StringSplitTokenizer (line 121) | class StringSplitTokenizer(BaseTokenizer): method __init__ (line 124) | def __init__(self, separator: str = " ", **kwargs): method __call__ (line 127) | def __call__(self, text): class NgramTokenizer (line 131) | class NgramTokenizer(BaseTokenizer): method __init__ (line 134) | def __init__(self, n: int = 2, **kwargs): method __call__ (line 137) | def __call__(self, text): class UnderscoreStringToListTokenizer (line 146) | class UnderscoreStringToListTokenizer(BaseTokenizer): method __call__ (line 147) | def __call__(self, text): class CommaStringToListTokenizer (line 151) | class CommaStringToListTokenizer(BaseTokenizer): method __call__ (line 152) | def __call__(self, text): class UntokenizedStringToListTokenizer (line 156) | class UntokenizedStringToListTokenizer(BaseTokenizer): method __call__ (line 157) | def __call__(self, text): class StrippedStringToListTokenizer (line 161) | class StrippedStringToListTokenizer(BaseTokenizer): method __call__ (line 162) | def __call__(self, text): class EnglishTokenizer (line 166) | class EnglishTokenizer(BaseTokenizer): method __call__ (line 167) | def __call__(self, text): class EnglishFilterTokenizer (line 171) | class EnglishFilterTokenizer(BaseTokenizer): method __call__ (line 172) | def __call__(self, text): class EnglishRemoveStopwordsTokenizer (line 178) | class EnglishRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 179) | def __call__(self, text): class EnglishLemmatizeTokenizer (line 183) | class EnglishLemmatizeTokenizer(BaseTokenizer): method __call__ (line 184) | def __call__(self, text): class EnglishLemmatizeFilterTokenizer (line 188) | class EnglishLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 189) | def __call__(self, text): class EnglishLemmatizeRemoveStopwordsTokenizer (line 200) | class EnglishLemmatizeRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 201) | def __call__(self, text): class ItalianTokenizer (line 205) | class ItalianTokenizer(BaseTokenizer): method __call__ (line 206) | def __call__(self, text): class ItalianFilterTokenizer (line 210) | class ItalianFilterTokenizer(BaseTokenizer): method __call__ (line 211) | def __call__(self, text): class ItalianRemoveStopwordsTokenizer (line 217) | class ItalianRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 218) | def __call__(self, text): class ItalianLemmatizeTokenizer (line 222) | class ItalianLemmatizeTokenizer(BaseTokenizer): method __call__ (line 223) | def __call__(self, text): class ItalianLemmatizeFilterTokenizer (line 227) | class ItalianLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 228) | def __call__(self, text): class ItalianLemmatizeRemoveStopwordsTokenizer (line 239) | class ItalianLemmatizeRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 240) | def __call__(self, text): class SpanishTokenizer (line 244) | class SpanishTokenizer(BaseTokenizer): method __call__ (line 245) | def __call__(self, text): class SpanishFilterTokenizer (line 249) | class SpanishFilterTokenizer(BaseTokenizer): method __call__ (line 250) | def __call__(self, text): class SpanishRemoveStopwordsTokenizer (line 256) | class SpanishRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 257) | def __call__(self, text): class SpanishLemmatizeTokenizer (line 261) | class SpanishLemmatizeTokenizer(BaseTokenizer): method __call__ (line 262) | def __call__(self, text): class SpanishLemmatizeFilterTokenizer (line 266) | class SpanishLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 267) | def __call__(self, text): class SpanishLemmatizeRemoveStopwordsTokenizer (line 278) | class SpanishLemmatizeRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 279) | def __call__(self, text): class GermanTokenizer (line 283) | class GermanTokenizer(BaseTokenizer): method __call__ (line 284) | def __call__(self, text): class GermanFilterTokenizer (line 288) | class GermanFilterTokenizer(BaseTokenizer): method __call__ (line 289) | def __call__(self, text): class GermanRemoveStopwordsTokenizer (line 295) | class GermanRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 296) | def __call__(self, text): class GermanLemmatizeTokenizer (line 300) | class GermanLemmatizeTokenizer(BaseTokenizer): method __call__ (line 301) | def __call__(self, text): class GermanLemmatizeFilterTokenizer (line 305) | class GermanLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 306) | def __call__(self, text): class GermanLemmatizeRemoveStopwordsTokenizer (line 317) | class GermanLemmatizeRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 318) | def __call__(self, text): class FrenchTokenizer (line 322) | class FrenchTokenizer(BaseTokenizer): method __call__ (line 323) | def __call__(self, text): class FrenchFilterTokenizer (line 327) | class FrenchFilterTokenizer(BaseTokenizer): method __call__ (line 328) | def __call__(self, text): class FrenchRemoveStopwordsTokenizer (line 334) | class FrenchRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 335) | def __call__(self, text): class FrenchLemmatizeTokenizer (line 339) | class FrenchLemmatizeTokenizer(BaseTokenizer): method __call__ (line 340) | def __call__(self, text): class FrenchLemmatizeFilterTokenizer (line 344) | class FrenchLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 345) | def __call__(self, text): class FrenchLemmatizeRemoveStopwordsTokenizer (line 356) | class FrenchLemmatizeRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 357) | def __call__(self, text): class PortugueseTokenizer (line 361) | class PortugueseTokenizer(BaseTokenizer): method __call__ (line 362) | def __call__(self, text): class PortugueseFilterTokenizer (line 366) | class PortugueseFilterTokenizer(BaseTokenizer): method __call__ (line 367) | def __call__(self, text): class PortugueseRemoveStopwordsTokenizer (line 373) | class PortugueseRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 374) | def __call__(self, text): class PortugueseLemmatizeTokenizer (line 378) | class PortugueseLemmatizeTokenizer(BaseTokenizer): method __call__ (line 379) | def __call__(self, text): class PortugueseLemmatizeFilterTokenizer (line 383) | class PortugueseLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 384) | def __call__(self, text): class PortugueseLemmatizeRemoveStopwordsTokenizer (line 395) | class PortugueseLemmatizeRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 396) | def __call__(self, text): class DutchTokenizer (line 400) | class DutchTokenizer(BaseTokenizer): method __call__ (line 401) | def __call__(self, text): class DutchFilterTokenizer (line 405) | class DutchFilterTokenizer(BaseTokenizer): method __call__ (line 406) | def __call__(self, text): class DutchRemoveStopwordsTokenizer (line 412) | class DutchRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 413) | def __call__(self, text): class DutchLemmatizeTokenizer (line 417) | class DutchLemmatizeTokenizer(BaseTokenizer): method __call__ (line 418) | def __call__(self, text): class DutchLemmatizeFilterTokenizer (line 422) | class DutchLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 423) | def __call__(self, text): class DutchLemmatizeRemoveStopwordsTokenizer (line 434) | class DutchLemmatizeRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 435) | def __call__(self, text): class GreekTokenizer (line 439) | class GreekTokenizer(BaseTokenizer): method __call__ (line 440) | def __call__(self, text): class GreekFilterTokenizer (line 444) | class GreekFilterTokenizer(BaseTokenizer): method __call__ (line 445) | def __call__(self, text): class GreekRemoveStopwordsTokenizer (line 451) | class GreekRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 452) | def __call__(self, text): class GreekLemmatizeTokenizer (line 456) | class GreekLemmatizeTokenizer(BaseTokenizer): method __call__ (line 457) | def __call__(self, text): class GreekLemmatizeFilterTokenizer (line 461) | class GreekLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 462) | def __call__(self, text): class GreekLemmatizeRemoveStopwordsFilterTokenizer (line 473) | class GreekLemmatizeRemoveStopwordsFilterTokenizer(BaseTokenizer): method __call__ (line 474) | def __call__(self, text): class NorwegianTokenizer (line 478) | class NorwegianTokenizer(BaseTokenizer): method __call__ (line 479) | def __call__(self, text): class NorwegianFilterTokenizer (line 483) | class NorwegianFilterTokenizer(BaseTokenizer): method __call__ (line 484) | def __call__(self, text): class NorwegianRemoveStopwordsTokenizer (line 490) | class NorwegianRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 491) | def __call__(self, text): class NorwegianLemmatizeTokenizer (line 495) | class NorwegianLemmatizeTokenizer(BaseTokenizer): method __call__ (line 496) | def __call__(self, text): class NorwegianLemmatizeFilterTokenizer (line 500) | class NorwegianLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 501) | def __call__(self, text): class NorwegianLemmatizeRemoveStopwordsFilterTokenizer (line 512) | class NorwegianLemmatizeRemoveStopwordsFilterTokenizer(BaseTokenizer): method __call__ (line 513) | def __call__(self, text): class LithuanianTokenizer (line 517) | class LithuanianTokenizer(BaseTokenizer): method __call__ (line 518) | def __call__(self, text): class LithuanianFilterTokenizer (line 522) | class LithuanianFilterTokenizer(BaseTokenizer): method __call__ (line 523) | def __call__(self, text): class LithuanianRemoveStopwordsTokenizer (line 529) | class LithuanianRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 530) | def __call__(self, text): class LithuanianLemmatizeTokenizer (line 534) | class LithuanianLemmatizeTokenizer(BaseTokenizer): method __call__ (line 535) | def __call__(self, text): class LithuanianLemmatizeFilterTokenizer (line 539) | class LithuanianLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 540) | def __call__(self, text): class LithuanianLemmatizeRemoveStopwordsFilterTokenizer (line 551) | class LithuanianLemmatizeRemoveStopwordsFilterTokenizer(BaseTokenizer): method __call__ (line 552) | def __call__(self, text): class DanishTokenizer (line 556) | class DanishTokenizer(BaseTokenizer): method __call__ (line 557) | def __call__(self, text): class DanishFilterTokenizer (line 561) | class DanishFilterTokenizer(BaseTokenizer): method __call__ (line 562) | def __call__(self, text): class DanishRemoveStopwordsTokenizer (line 568) | class DanishRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 569) | def __call__(self, text): class DanishLemmatizeTokenizer (line 573) | class DanishLemmatizeTokenizer(BaseTokenizer): method __call__ (line 574) | def __call__(self, text): class DanishLemmatizeFilterTokenizer (line 578) | class DanishLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 579) | def __call__(self, text): class DanishLemmatizeRemoveStopwordsFilterTokenizer (line 590) | class DanishLemmatizeRemoveStopwordsFilterTokenizer(BaseTokenizer): method __call__ (line 591) | def __call__(self, text): class PolishTokenizer (line 595) | class PolishTokenizer(BaseTokenizer): method __call__ (line 596) | def __call__(self, text): class PolishFilterTokenizer (line 600) | class PolishFilterTokenizer(BaseTokenizer): method __call__ (line 601) | def __call__(self, text): class PolishRemoveStopwordsTokenizer (line 607) | class PolishRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 608) | def __call__(self, text): class PolishLemmatizeTokenizer (line 612) | class PolishLemmatizeTokenizer(BaseTokenizer): method __call__ (line 613) | def __call__(self, text): class PolishLemmatizeFilterTokenizer (line 617) | class PolishLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 618) | def __call__(self, text): class PolishLemmatizeRemoveStopwordsFilterTokenizer (line 629) | class PolishLemmatizeRemoveStopwordsFilterTokenizer(BaseTokenizer): method __call__ (line 630) | def __call__(self, text): class RomanianTokenizer (line 634) | class RomanianTokenizer(BaseTokenizer): method __call__ (line 635) | def __call__(self, text): class RomanianFilterTokenizer (line 639) | class RomanianFilterTokenizer(BaseTokenizer): method __call__ (line 640) | def __call__(self, text): class RomanianRemoveStopwordsTokenizer (line 646) | class RomanianRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 647) | def __call__(self, text): class RomanianLemmatizeTokenizer (line 651) | class RomanianLemmatizeTokenizer(BaseTokenizer): method __call__ (line 652) | def __call__(self, text): class RomanianLemmatizeFilterTokenizer (line 656) | class RomanianLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 657) | def __call__(self, text): class RomanianLemmatizeRemoveStopwordsFilterTokenizer (line 668) | class RomanianLemmatizeRemoveStopwordsFilterTokenizer(BaseTokenizer): method __call__ (line 669) | def __call__(self, text): class JapaneseTokenizer (line 673) | class JapaneseTokenizer(BaseTokenizer): method __call__ (line 674) | def __call__(self, text): class JapaneseFilterTokenizer (line 678) | class JapaneseFilterTokenizer(BaseTokenizer): method __call__ (line 679) | def __call__(self, text): class JapaneseRemoveStopwordsTokenizer (line 685) | class JapaneseRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 686) | def __call__(self, text): class JapaneseLemmatizeTokenizer (line 690) | class JapaneseLemmatizeTokenizer(BaseTokenizer): method __call__ (line 691) | def __call__(self, text): class JapaneseLemmatizeFilterTokenizer (line 695) | class JapaneseLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 696) | def __call__(self, text): class JapaneseLemmatizeRemoveStopwordsFilterTokenizer (line 707) | class JapaneseLemmatizeRemoveStopwordsFilterTokenizer(BaseTokenizer): method __call__ (line 708) | def __call__(self, text): class ChineseTokenizer (line 712) | class ChineseTokenizer(BaseTokenizer): method __call__ (line 713) | def __call__(self, text): class ChineseFilterTokenizer (line 717) | class ChineseFilterTokenizer(BaseTokenizer): method __call__ (line 718) | def __call__(self, text): class ChineseRemoveStopwordsTokenizer (line 724) | class ChineseRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 725) | def __call__(self, text): class ChineseLemmatizeTokenizer (line 729) | class ChineseLemmatizeTokenizer(BaseTokenizer): method __call__ (line 730) | def __call__(self, text): class ChineseLemmatizeFilterTokenizer (line 734) | class ChineseLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 735) | def __call__(self, text): class ChineseLemmatizeRemoveStopwordsFilterTokenizer (line 746) | class ChineseLemmatizeRemoveStopwordsFilterTokenizer(BaseTokenizer): method __call__ (line 747) | def __call__(self, text): class MultiTokenizer (line 751) | class MultiTokenizer(BaseTokenizer): method __call__ (line 752) | def __call__(self, text): class MultiFilterTokenizer (line 758) | class MultiFilterTokenizer(BaseTokenizer): method __call__ (line 759) | def __call__(self, text): class MultiRemoveStopwordsTokenizer (line 765) | class MultiRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 766) | def __call__(self, text): class MultiLemmatizeTokenizer (line 770) | class MultiLemmatizeTokenizer(BaseTokenizer): method __call__ (line 771) | def __call__(self, text): class MultiLemmatizeFilterTokenizer (line 775) | class MultiLemmatizeFilterTokenizer(BaseTokenizer): method __call__ (line 776) | def __call__(self, text): class MultiLemmatizeRemoveStopwordsTokenizer (line 787) | class MultiLemmatizeRemoveStopwordsTokenizer(BaseTokenizer): method __call__ (line 788) | def __call__(self, text): class HFTokenizer (line 792) | class HFTokenizer(BaseTokenizer): method __init__ (line 793) | def __init__(self, pretrained_model_name_or_path, **kwargs): method __call__ (line 806) | def __call__(self, text): method get_vocab (line 809) | def get_vocab(self): method get_pad_token (line 812) | def get_pad_token(self) -> str: method get_unk_token (line 815) | def get_unk_token(self) -> str: method convert_token_to_id (line 818) | def convert_token_to_id(self, token: str) -> int: class SentencePieceTokenizer (line 933) | class SentencePieceTokenizer(torch.nn.Module): method __init__ (line 936) | def __init__(self, pretrained_model_name_or_path: str | None = None, *... method forward (line 944) | def forward(self, v: str | list[str] | torch.Tensor): class CLIPTokenizer (line 952) | class CLIPTokenizer(torch.nn.Module): method __init__ (line 955) | def __init__(self, pretrained_model_name_or_path: str | None = None, *... method __call__ (line 963) | def __call__(self, text): method get_vocab (line 968) | def get_vocab(self): class GPT2BPETokenizer (line 972) | class GPT2BPETokenizer(torch.nn.Module): method __init__ (line 975) | def __init__(self, pretrained_model_name_or_path: str | None = None, *... method __call__ (line 983) | def __call__(self, text): method get_vocab (line 988) | def get_vocab(self): class BERTTokenizer (line 992) | class BERTTokenizer(torch.nn.Module): method __init__ (line 995) | def __init__( method __call__ (line 1018) | def __call__(self, text): method get_vocab (line 1031) | def get_vocab(self): method get_pad_token (line 1034) | def get_pad_token(self) -> str: method get_unk_token (line 1037) | def get_unk_token(self) -> str: method convert_token_to_id (line 1040) | def convert_token_to_id(self, token: str) -> int: function get_hf_tokenizer (line 1056) | def get_hf_tokenizer(pretrained_model_name_or_path, **kwargs): function get_tokenizer_from_registry (line 1082) | def get_tokenizer_from_registry(tokenizer_name: str) -> torch.nn.Module: FILE: ludwig/utils/torch_utils.py function get_torch_device (line 19) | def get_torch_device(): function place_on_device (line 45) | def place_on_device(x, device): function sequence_length_2D (line 62) | def sequence_length_2D(sequence: torch.Tensor) -> torch.Tensor: function sequence_length_3D (line 74) | def sequence_length_3D(sequence: torch.Tensor) -> torch.Tensor: function sequence_mask (line 87) | def sequence_mask(lengths: torch.Tensor, maxlen: int | None = None, dtyp... function periodic (line 106) | def periodic(inputs: torch.Tensor, period: int) -> torch.Tensor: function get_activation (line 141) | def get_activation(activation): function reg_loss (line 146) | def reg_loss(model: nn.Module, regularizer: str, l1: float = 0.01, l2: f... class LudwigModule (line 172) | class LudwigModule(Module): method __init__ (line 173) | def __init__(self): method device (line 179) | def device(self): method prepare_for_training (line 182) | def prepare_for_training(self): method losses (line 185) | def losses(self): method update_loss (line 204) | def update_loss(self, key: str, loss: torch.Tensor): method input_dtype (line 209) | def input_dtype(self): method input_shape (line 214) | def input_shape(self) -> torch.Size: method output_shape (line 219) | def output_shape(self) -> torch.Size: method _computed_output_shape (line 224) | def _computed_output_shape(self) -> torch.Size: function freeze_parameters (line 236) | def freeze_parameters(module: nn.Module): class FreezeModule (line 243) | class FreezeModule(nn.Module): method __init__ (line 244) | def __init__(self, module: nn.Module, frozen: bool): method train (line 254) | def train(self, mode: bool = True): class Dense (line 263) | class Dense(LudwigModule): method __init__ (line 264) | def __init__( method input_shape (line 282) | def input_shape(self) -> torch.Size: method forward (line 285) | def forward(self, input: torch.Tensor) -> torch.Tensor: function initialize_pytorch (line 291) | def initialize_pytorch( function _set_torch_init_params (line 341) | def _set_torch_init_params(params: tuple | None): function _get_torch_init_params (line 346) | def _get_torch_init_params() -> tuple | None: function model_size (line 351) | def model_size(model: nn.Module): FILE: ludwig/utils/trainer_utils.py function initialize_trainer_metric_dict (line 30) | def initialize_trainer_metric_dict(output_features) -> dict[str, dict[st... function get_latest_metrics_dict (line 36) | def get_latest_metrics_dict( function get_new_progress_tracker (line 51) | def get_new_progress_tracker( class ProgressTracker (line 97) | class ProgressTracker: method __init__ (line 98) | def __init__( method save (line 253) | def save(self, filepath): method load (line 259) | def load(progress_tracking_dict: dict): method log_metrics (line 265) | def log_metrics(self): method _add_checkpoint_entry_for_used_tokens (line 318) | def _add_checkpoint_entry_for_used_tokens(self, checkpoint_number: int): method increment_checkpoint (line 341) | def increment_checkpoint(self): method set_token_usage_for_this_step (line 352) | def set_token_usage_for_this_step(self, used_tokens: int): function append_metrics (line 361) | def append_metrics( function get_total_steps (line 389) | def get_total_steps(epochs: int, steps_per_epoch: int, train_steps: int): function get_final_steps_per_checkpoint (line 397) | def get_final_steps_per_checkpoint( function get_total_expected_checkpoints (line 427) | def get_total_expected_checkpoints(total_steps: int, final_steps_per_che... function get_training_report (line 432) | def get_training_report( function get_rendered_batch_size_grad_accum (line 479) | def get_rendered_batch_size_grad_accum(config: "BaseTrainerConfig", num_... function freeze_layers_regex (line 516) | def freeze_layers_regex(config: ECDTrainerConfig | FineTuneTrainerConfig... function count_parameters (line 553) | def count_parameters(model) -> None: FILE: ludwig/utils/triton_utils.py function _get_type_map (line 134) | def _get_type_map(dtype: str) -> str: function to_triton_dimension (line 169) | def to_triton_dimension(content: list[str] | list[torch.Tensor] | list[T... function to_triton_type (line 179) | def to_triton_type(content: list[str] | list[torch.Tensor] | list[TorchA... class TritonArtifact (line 190) | class TritonArtifact: class TritonConfigFeature (line 214) | class TritonConfigFeature: method __post_init__ (line 235) | def __post_init__(self): method _get_feature_ensemble_value (line 246) | def _get_feature_ensemble_value(self) -> str: method _get_wrapper_signature_type (line 261) | def _get_wrapper_signature_type(self) -> str: class TritonMaster (line 276) | class TritonMaster: method __post_init__ (line 312) | def __post_init__(self): method save_model (line 342) | def save_model(self) -> TritonArtifact: method save_config (line 378) | def save_config(self) -> TritonArtifact: class TritonEnsembleConfig (line 416) | class TritonEnsembleConfig: method __post_init__ (line 437) | def __post_init__(self): method _get_ensemble_scheduling_input_maps (line 442) | def _get_ensemble_scheduling_input_maps(self, triton_features: list[Tr... method _get_ensemble_scheduling_output_maps (line 447) | def _get_ensemble_scheduling_output_maps(self, triton_features: list[T... method _get_ensemble_scheduling_step (line 452) | def _get_ensemble_scheduling_step(self, triton_master: TritonMaster) -... method _get_ensemble_spec (line 459) | def _get_ensemble_spec(self, triton_features: list[TritonConfigFeature... method get_config (line 472) | def get_config(self) -> str: method save_ensemble_config (line 488) | def save_ensemble_config(self) -> TritonArtifact: method save_ensemble_dummy_model (line 505) | def save_ensemble_dummy_model(self) -> TritonArtifact: class TritonConfig (line 531) | class TritonConfig: method _get_triton_spec (line 558) | def _get_triton_spec(self, triton_features: list[TritonConfigFeature])... method _get_reshape_spec (line 571) | def _get_reshape_spec(self, feature) -> str: method _get_instance_spec (line 576) | def _get_instance_spec(self) -> str: method _get_dynamic_batching_spec (line 584) | def _get_dynamic_batching_spec(self) -> str: method get_model_config (line 589) | def get_model_config(self) -> str: class TritonModel (line 608) | class TritonModel: method _get_dict_type_hint (line 623) | def _get_dict_type_hint(self) -> str: method _get_input_signature (line 630) | def _get_input_signature(self, triton_features: list[TritonConfigFeatu... method _get_input_dict (line 636) | def _get_input_dict(self, triton_features: list[TritonConfigFeature]) ... method _get_output_tuple (line 640) | def _get_output_tuple(self, triton_features: list[TritonConfigFeature]... method generate_inference_module_wrapper (line 644) | def generate_inference_module_wrapper(self) -> str: method generate_scripted_module (line 653) | def generate_scripted_module(self): function get_device_types_and_counts (line 670) | def get_device_types_and_counts( function get_inference_modules (line 689) | def get_inference_modules(model: LudwigModel, predictor_device_type: str... function get_example_input (line 697) | def get_example_input( function clean_up_synthetic_data (line 715) | def clean_up_synthetic_data(): function export_triton (line 722) | def export_triton( FILE: ludwig/utils/upload_utils.py class BaseModelUpload (line 15) | class BaseModelUpload(ABC): method login (line 24) | def login(self): method upload (line 36) | def upload( method _validate_upload_parameters (line 63) | def _validate_upload_parameters( function hf_hub_login (line 112) | def hf_hub_login(): class HuggingFaceHub (line 129) | class HuggingFaceHub(BaseModelUpload): method __init__ (line 130) | def __init__(self): method login (line 134) | def login(self): method _validate_upload_parameters (line 140) | def _validate_upload_parameters( method upload (line 216) | def upload( class Predibase (line 304) | class Predibase(BaseModelUpload): method __init__ (line 305) | def __init__(self): method login (line 309) | def login(self): method _validate_upload_parameters (line 334) | def _validate_upload_parameters( method upload (line 377) | def upload( FILE: ludwig/utils/version_transformation.py class VersionTransformation (line 28) | class VersionTransformation: method __init__ (line 31) | def __init__(self, transform: Callable[[dict], dict], version: str, pr... method transform_config (line 46) | def transform_config(self, config: dict): method transform_config_with_prefix (line 57) | def transform_config_with_prefix(self, config: dict, prefix: str | Non... method max_prefix_length (line 91) | def max_prefix_length(self): method longest_prefix (line 96) | def longest_prefix(self): method __lt__ (line 104) | def __lt__(self, other): method __repr__ (line 117) | def __repr__(self): class VersionTransformationRegistry (line 121) | class VersionTransformationRegistry: method __init__ (line 124) | def __init__(self): method register (line 127) | def register(self, transformation: VersionTransformation): method get_transformations (line 131) | def get_transformations(self, from_version: str, to_version: str) -> l... method update_config (line 156) | def update_config(self, config: dict, from_version: str, to_version: s... FILE: ludwig/utils/visualization_utils.py function visualize_callbacks (line 61) | def visualize_callbacks(callbacks, fig): function learning_curves_plot (line 68) | def learning_curves_plot( function compare_classifiers_plot (line 125) | def compare_classifiers_plot( function compare_classifiers_line_plot (line 204) | def compare_classifiers_line_plot( function compare_classifiers_multiclass_multimetric_plot (line 255) | def compare_classifiers_multiclass_multimetric_plot( function radar_chart (line 298) | def radar_chart( function pie (line 390) | def pie(ax, values, **kwargs): function donut (line 403) | def donut( function confidence_filtering_plot (line 498) | def confidence_filtering_plot( function confidence_filtering_data_vs_acc_plot (line 563) | def confidence_filtering_data_vs_acc_plot( function confidence_filtering_data_vs_acc_multiline_plot (line 630) | def confidence_filtering_data_vs_acc_multiline_plot( function confidence_filtering_3d_plot (line 684) | def confidence_filtering_3d_plot( function threshold_vs_metric_plot (line 812) | def threshold_vs_metric_plot( function roc_curves (line 860) | def roc_curves( function precision_recall_curves_plot (line 909) | def precision_recall_curves_plot( function calibration_plot (line 960) | def calibration_plot( function brier_plot (line 1024) | def brier_plot( function predictions_distribution_plot (line 1088) | def predictions_distribution_plot( function confusion_matrix_plot (line 1131) | def confusion_matrix_plot( function double_axis_line_plot (line 1190) | def double_axis_line_plot( function plot_matrix (line 1239) | def plot_matrix( function plot_distributions (line 1254) | def plot_distributions( function plot_distributions_difference (line 1296) | def plot_distributions_difference( function bar_plot (line 1331) | def bar_plot( function hyperopt_report (line 1398) | def hyperopt_report(hyperparameters, hyperopt_results_df, metric, filena... function hyperopt_int_plot (line 1455) | def hyperopt_int_plot(hyperopt_results_df, hp_name, metric, title, filen... function hyperopt_float_plot (line 1474) | def hyperopt_float_plot(hyperopt_results_df, hp_name, metric, title, fil... function hyperopt_category_plot (line 1491) | def hyperopt_category_plot(hyperopt_results_df, hp_name, metric, title, ... function hyperopt_pair_plot (line 1527) | def hyperopt_pair_plot(hyperopt_results_df, metric, title, filename): function hyperopt_hiplot (line 1561) | def hyperopt_hiplot( FILE: ludwig/vector_index/__init__.py function get_faiss_index_cls (line 14) | def get_faiss_index_cls() -> type[VectorIndex]: function get_vector_index_cls (line 27) | def get_vector_index_cls(type: str) -> type[VectorIndex]: FILE: ludwig/vector_index/base.py class VectorIndex (line 6) | class VectorIndex(ABC): method search (line 8) | def search(self, query: np.ndarray, k: int) -> np.ndarray: method save (line 12) | def save(self, path: str): method from_path (line 17) | def from_path(cls, path: str) -> "VectorIndex": method from_embeddings (line 22) | def from_embeddings(cls, embeddings: np.ndarray) -> "VectorIndex": FILE: ludwig/vector_index/faiss.py class FaissIndex (line 7) | class FaissIndex(VectorIndex): method __init__ (line 8) | def __init__(self, index: faiss.Index): method search (line 11) | def search(self, query: np.ndarray, k: int) -> np.ndarray: method save (line 15) | def save(self, path: str): method from_path (line 19) | def from_path(cls, path: str) -> "VectorIndex": method from_embeddings (line 24) | def from_embeddings(cls, embeddings: np.ndarray) -> "VectorIndex": FILE: ludwig/visualize.py function _convert_ground_truth (line 63) | def _convert_ground_truth(ground_truth, feature_metadata, ground_truth_a... function _vectorize_ground_truth (line 87) | def _vectorize_ground_truth( function validate_conf_thresholds_and_probabilities_2d_3d (line 102) | def validate_conf_thresholds_and_probabilities_2d_3d(probabilities, thre... function load_data_for_viz (line 122) | def load_data_for_viz(load_type, model_file_statistics, dtype=int, groun... function _load_training_stats (line 143) | def _load_training_stats(data: dict) -> TrainingStats: function load_training_stats_for_viz (line 159) | def load_training_stats_for_viz(load_type, model_file_statistics, dtype=... function convert_to_list (line 178) | def convert_to_list(item): function _validate_output_feature_name_from_train_stats (line 187) | def _validate_output_feature_name_from_train_stats(output_feature_name, ... function _validate_output_feature_name_from_test_stats (line 208) | def _validate_output_feature_name_from_test_stats(output_feature_name, t... function _encode_categorical_feature (line 229) | def _encode_categorical_feature(raw: np.array, str2idx: dict) -> np.array: function _get_ground_truth_df (line 243) | def _get_ground_truth_df(ground_truth: str) -> DataFrame: function _extract_ground_truth_values (line 258) | def _extract_ground_truth_values( function _get_cols_from_predictions (line 313) | def _get_cols_from_predictions(predictions_paths, cols, metadata): function generate_filename_template_path (line 338) | def generate_filename_template_path(output_dir, filename_template): function compare_performance_cli (line 353) | def compare_performance_cli(test_statistics: str | list[str], **kwargs: ... function learning_curves_cli (line 367) | def learning_curves_cli(training_statistics: str | list[str], **kwargs: ... function compare_classifiers_performance_from_prob_cli (line 381) | def compare_classifiers_performance_from_prob_cli( function compare_classifiers_performance_from_pred_cli (line 436) | def compare_classifiers_performance_from_pred_cli( function compare_classifiers_performance_subset_cli (line 484) | def compare_classifiers_performance_subset_cli( function compare_classifiers_performance_changing_k_cli (line 536) | def compare_classifiers_performance_changing_k_cli( function compare_classifiers_multiclass_multimetric_cli (line 588) | def compare_classifiers_multiclass_multimetric_cli( function compare_classifiers_predictions_cli (line 606) | def compare_classifiers_predictions_cli( function compare_classifiers_predictions_distribution_cli (line 653) | def compare_classifiers_predictions_distribution_cli( function confidence_thresholding_cli (line 699) | def confidence_thresholding_cli( function confidence_thresholding_data_vs_acc_cli (line 750) | def confidence_thresholding_data_vs_acc_cli( function confidence_thresholding_data_vs_acc_subset_cli (line 801) | def confidence_thresholding_data_vs_acc_subset_cli( function confidence_thresholding_data_vs_acc_subset_per_class_cli (line 852) | def confidence_thresholding_data_vs_acc_subset_per_class_cli( function confidence_thresholding_2thresholds_2d_cli (line 902) | def confidence_thresholding_2thresholds_2d_cli( function confidence_thresholding_2thresholds_3d_cli (line 961) | def confidence_thresholding_2thresholds_3d_cli( function binary_threshold_vs_metric_cli (line 1019) | def binary_threshold_vs_metric_cli( function precision_recall_curves_cli (line 1071) | def precision_recall_curves_cli( function roc_curves_cli (line 1122) | def roc_curves_cli( function roc_curves_from_test_statistics_cli (line 1174) | def roc_curves_from_test_statistics_cli(test_statistics: str | list[str]... function precision_recall_curves_from_test_statistics_cli (line 1187) | def precision_recall_curves_from_test_statistics_cli(test_statistics: st... function calibration_1_vs_all_cli (line 1205) | def calibration_1_vs_all_cli( function calibration_multiclass_cli (line 1267) | def calibration_multiclass_cli( function confusion_matrix_cli (line 1319) | def confusion_matrix_cli(test_statistics: str | list[str], ground_truth_... function frequency_vs_f1_cli (line 1335) | def frequency_vs_f1_cli(test_statistics: str | list[str], ground_truth_m... function learning_curves (line 1351) | def learning_curves( function compare_performance (line 1429) | def compare_performance( function compare_classifiers_performance_from_prob (line 1535) | def compare_classifiers_performance_from_prob( function compare_classifiers_performance_from_pred (line 1631) | def compare_classifiers_performance_from_pred( function compare_classifiers_performance_subset (line 1718) | def compare_classifiers_performance_subset( function compare_classifiers_performance_changing_k (line 1840) | def compare_classifiers_performance_changing_k( function compare_classifiers_multiclass_multimetric (line 1926) | def compare_classifiers_multiclass_multimetric( function compare_classifiers_predictions (line 2076) | def compare_classifiers_predictions( function compare_classifiers_predictions_distribution (line 2217) | def compare_classifiers_predictions_distribution( function confidence_thresholding (line 2291) | def confidence_thresholding( function confidence_thresholding_data_vs_acc (line 2382) | def confidence_thresholding_data_vs_acc( function confidence_thresholding_data_vs_acc_subset (line 2479) | def confidence_thresholding_data_vs_acc_subset( function confidence_thresholding_data_vs_acc_subset_per_class (line 2615) | def confidence_thresholding_data_vs_acc_subset_per_class( function confidence_thresholding_2thresholds_2d (line 2767) | def confidence_thresholding_2thresholds_2d( function confidence_thresholding_2thresholds_3d (line 2958) | def confidence_thresholding_2thresholds_3d( function binary_threshold_vs_metric (line 3081) | def binary_threshold_vs_metric( function precision_recall_curves (line 3195) | def precision_recall_curves( function precision_recall_curves_from_test_statistics (line 3267) | def precision_recall_curves_from_test_statistics( function roc_curves (line 3314) | def roc_curves( function roc_curves_from_test_statistics (line 3384) | def roc_curves_from_test_statistics( function calibration_1_vs_all (line 3429) | def calibration_1_vs_all( function calibration_multiclass (line 3574) | def calibration_multiclass( function confusion_matrix (line 3673) | def confusion_matrix( function frequency_vs_f1 (line 3789) | def frequency_vs_f1( function hyperopt_report_cli (line 3924) | def hyperopt_report_cli(hyperopt_stats_path, output_directory=None, file... function hyperopt_report (line 3938) | def hyperopt_report(hyperopt_stats_path: str, output_directory: str = No... function hyperopt_hiplot_cli (line 3972) | def hyperopt_hiplot_cli(hyperopt_stats_path, output_directory=None, **kw... function hyperopt_hiplot (line 3985) | def hyperopt_hiplot(hyperopt_stats_path, output_directory=None, **kwargs): function _convert_space_to_dtype (line 4014) | def _convert_space_to_dtype(space: str) -> str: function hyperopt_results_to_dataframe (line 4024) | def hyperopt_results_to_dataframe(hyperopt_results, hyperopt_parameters,... function get_visualizations_registry (line 4033) | def get_visualizations_registry() -> dict[str, Callable]: function cli (line 4065) | def cli(sys_argv): FILE: tests/conftest.py function pytest_sessionstart (line 50) | def pytest_sessionstart(session): function pytest_collection_modifyitems (line 54) | def pytest_collection_modifyitems(config, items): function check_session_time (line 61) | def check_session_time(request): function setup_tests (line 68) | def setup_tests(request): function csv_filename (line 82) | def csv_filename(): function yaml_filename (line 90) | def yaml_filename(): function hyperopt_results_single_parameter (line 98) | def hyperopt_results_single_parameter(ray_cluster_4cpu): function hyperopt_results_multiple_parameters (line 126) | def hyperopt_results_multiple_parameters(ray_cluster_4cpu): function ray_cluster_2cpu (line 157) | def ray_cluster_2cpu(request): function ray_cluster_4cpu (line 163) | def ray_cluster_4cpu(request): function ray_cluster_5cpu (line 169) | def ray_cluster_5cpu(request): function ray_cluster_7cpu (line 175) | def ray_cluster_7cpu(request): function _ray_start (line 181) | def _ray_start(request, **kwargs): function _get_default_ray_kwargs (line 215) | def _get_default_ray_kwargs(): function _get_sample_config (line 227) | def _get_sample_config(): FILE: tests/integration_tests/parameter_update_utils.py class ParameterUpdateError (line 12) | class ParameterUpdateError(Exception): function check_module_parameters_updated (line 16) | def check_module_parameters_updated( FILE: tests/integration_tests/scripts/run_train_aim.py function run (line 18) | def run(csv_filename): FILE: tests/integration_tests/scripts/run_train_comet.py function run (line 37) | def run(csv_filename): FILE: tests/integration_tests/scripts/run_train_wandb.py function run (line 27) | def run(csv_filename): FILE: tests/integration_tests/synthetic_test_data.py function get_feature_configs (line 15) | def get_feature_configs(): function get_generated_data (line 34) | def get_generated_data(): function get_generated_data_for_optimizer (line 53) | def get_generated_data_for_optimizer(): FILE: tests/integration_tests/test_api.py function run_api_experiment_separated_datasets (line 42) | def run_api_experiment_separated_datasets(input_features, output_feature... function test_api_intent_classification (line 152) | def test_api_intent_classification(csv_filename): function test_api_intent_classification_separated (line 165) | def test_api_intent_classification_separated(csv_filename): function test_api_train_online (line 178) | def test_api_train_online(csv_filename): function test_api_training_set (line 195) | def test_api_training_set(tmpdir): function test_api_training_determinism (line 216) | def test_api_training_determinism(tmpdir): function run_api_commands (line 264) | def run_api_commands( function test_api_skip_parameters_train (line 350) | def test_api_skip_parameters_train( function test_api_skip_parameters_predict (line 382) | def test_api_skip_parameters_predict( function test_api_skip_parameters_evaluate (line 414) | def test_api_skip_parameters_evaluate( function test_api_callbacks (line 451) | def test_api_callbacks(tmpdir, csv_filename, epochs, batch_size, num_exa... function test_api_callbacks_checkpoints_per_epoch (line 509) | def test_api_callbacks_checkpoints_per_epoch( function test_api_callbacks_default_train_steps (line 559) | def test_api_callbacks_default_train_steps(tmpdir, csv_filename): function test_api_callbacks_fixed_train_steps (line 586) | def test_api_callbacks_fixed_train_steps(tmpdir, csv_filename): function test_api_callbacks_fixed_train_steps_partial_epochs (line 612) | def test_api_callbacks_fixed_train_steps_partial_epochs(tmpdir, csv_file... function test_api_callbacks_batch_size_1 (line 639) | def test_api_callbacks_batch_size_1(tmpdir, csv_filename): function test_api_callbacks_fixed_train_steps_less_than_one_epoch (line 667) | def test_api_callbacks_fixed_train_steps_less_than_one_epoch(tmpdir, csv... function test_api_save_torchscript (line 704) | def test_api_save_torchscript(tmpdir): function test_saved_weights_in_checkpoint (line 736) | def test_saved_weights_in_checkpoint(tmpdir): function test_constant_metadata (line 769) | def test_constant_metadata(tmpdir): function test_llm_template_too_long (line 806) | def test_llm_template_too_long(tmpdir, input_max_sequence_length, global... FILE: tests/integration_tests/test_audio_feature.py function test_audio_feature (line 17) | def test_audio_feature(enc_encoder): FILE: tests/integration_tests/test_automl.py function to_name_set (line 38) | def to_name_set(features: list[FeatureConfigDict]) -> set[str]: function merge_lists (line 43) | def merge_lists(a_features: list, b_features: list): function merge_dict_with_features (line 51) | def merge_dict_with_features(a: ModelConfigDict, b: ModelConfigDict) -> ... function check_types (line 64) | def check_types( function test_data_tabular_large (line 78) | def test_data_tabular_large(): function test_data_tabular_small (line 94) | def test_data_tabular_small(): function test_data_image (line 108) | def test_data_image(): function test_data_text (line 122) | def test_data_text(): function test_data_multimodal (line 135) | def test_data_multimodal(): function test_create_auto_config (line 192) | def test_create_auto_config(test_data, expectations, ray_cluster_2cpu, r... function _get_sample_df (line 210) | def _get_sample_df(class_probs): function test_autoconfig_preprocessing_balanced (line 228) | def test_autoconfig_preprocessing_balanced(): function test_autoconfig_preprocessing_imbalanced (line 240) | def test_autoconfig_preprocessing_imbalanced(): function test_autoconfig_preprocessing_text_image (line 254) | def test_autoconfig_preprocessing_text_image(tmpdir): function test_train_with_config (line 286) | def test_train_with_config(time_budget, test_data_tabular_large, ray_clu... function test_auto_train (line 291) | def test_auto_train(test_data_tabular_large, ray_cluster_2cpu, tmpdir): function test_train_with_config_remote (line 309) | def test_train_with_config_remote(fs_protocol, bucket, test_data_tabular... function _run_train_with_config (line 321) | def _run_train_with_config(time_budget, test_data, tmpdir, **kwargs): FILE: tests/integration_tests/test_cache_manager.py function change_test_dir (line 16) | def change_test_dir(tmpdir, monkeypatch): function test_cache_dataset (line 23) | def test_cache_dataset(use_cache_dir, use_split, use_df, tmpdir, change_... FILE: tests/integration_tests/test_cached_preprocessing.py function _onehot_encoding_config (line 18) | def _onehot_encoding_config(tmpdir): function test_onehot_encoding (line 31) | def test_onehot_encoding(tmpdir): function test_onehot_encoding_ray (line 38) | def test_onehot_encoding_ray(tmpdir, ray_cluster_2cpu): function _hf_text_embedding_config (line 43) | def _hf_text_embedding_config(tmpdir): function test_hf_text_embedding (line 62) | def test_hf_text_embedding(tmpdir): function test_hf_text_embedding_ray (line 69) | def test_hf_text_embedding_ray(tmpdir, ray_cluster_2cpu): function test_onehot_encoding_preprocessing (line 75) | def test_onehot_encoding_preprocessing(cache_encoder_embeddings, tmpdir): function test_hf_text_embedding_tied (line 122) | def test_hf_text_embedding_tied(tmpdir): FILE: tests/integration_tests/test_carton.py function test_carton_torchscript (line 37) | def test_carton_torchscript(csv_filename, tmpdir): FILE: tests/integration_tests/test_class_imbalance_feature.py function ray_start (line 24) | def ray_start(num_cpus=2, num_gpus=None): function run_test_imbalance_ray (line 41) | def run_test_imbalance_ray( function run_test_imbalance_local (line 89) | def run_test_imbalance_local( function test_imbalance_ray (line 131) | def test_imbalance_ray(balance): function test_imbalance_local (line 162) | def test_imbalance_local(balance): FILE: tests/integration_tests/test_cli.py function _run_commands (line 45) | def _run_commands(commands, **ludwig_kwargs): function _run_ludwig (line 56) | def _run_ludwig(command, **ludwig_kwargs): function _prepare_data (line 62) | def _prepare_data(csv_filename, config_filename): function _prepare_hyperopt_data (line 84) | def _prepare_hyperopt_data(csv_filename, config_filename): function test_train_cli_dataset (line 125) | def test_train_cli_dataset(tmpdir, csv_filename): function test_train_cli_gpu_memory_limit (line 132) | def test_train_cli_gpu_memory_limit(tmpdir, csv_filename): function test_train_cli_training_set (line 141) | def test_train_cli_training_set(tmpdir, csv_filename): function test_export_torchscript_cli (line 157) | def test_export_torchscript_cli(tmpdir, csv_filename): function test_export_mlflow_cli (line 169) | def test_export_mlflow_cli(tmpdir, csv_filename): function test_experiment_cli (line 181) | def test_experiment_cli(tmpdir, csv_filename): function test_predict_cli (line 188) | def test_predict_cli(tmpdir, csv_filename): function test_evaluate_cli (line 201) | def test_evaluate_cli(tmpdir, csv_filename): function test_hyperopt_cli (line 215) | def test_hyperopt_cli(tmpdir, csv_filename): function test_visualize_cli (line 222) | def test_visualize_cli(tmpdir, csv_filename): function test_collect_summary_activations_weights_cli (line 236) | def test_collect_summary_activations_weights_cli(tmpdir, csv_filename): function test_collect_summary_pretrained_model_cli (line 259) | def test_collect_summary_pretrained_model_cli(model_name): function test_synthesize_dataset_cli (line 264) | def test_synthesize_dataset_cli(tmpdir, csv_filename): function test_preprocess_cli (line 290) | def test_preprocess_cli(tmpdir, csv_filename): function test_reproducible_cli_runs (line 306) | def test_reproducible_cli_runs( function test_init_config (line 377) | def test_init_config(tmpdir): function test_render_config (line 401) | def test_render_config(tmpdir): FILE: tests/integration_tests/test_collect.py function _prepare_data (line 31) | def _prepare_data(csv_filename): function _train (line 43) | def _train(input_features, output_features, data_csv, **kwargs): function _get_layers (line 56) | def _get_layers(model_path): function _collect_activations (line 61) | def _collect_activations(model_path, layers, csv_filename, output_direct... function test_collect_weights (line 65) | def test_collect_weights(tmpdir, csv_filename): function test_collect_activations (line 92) | def test_collect_activations(tmpdir, csv_filename): function test_print_model_summary (line 108) | def test_print_model_summary(csv_filename): FILE: tests/integration_tests/test_config_global_defaults.py function _prepare_data (line 28) | def _prepare_data(csv_filename: str) -> tuple[dict, str]: function test_run_experiment_with_global_default_parameters (line 63) | def test_run_experiment_with_global_default_parameters(csv_filename): function test_global_defaults_with_encoder_dependencies (line 69) | def test_global_defaults_with_encoder_dependencies(): FILE: tests/integration_tests/test_contrib_aim.py function test_contrib_experiment (line 17) | def test_contrib_experiment(csv_filename, tmpdir): FILE: tests/integration_tests/test_contrib_comet.py function test_contrib_experiment (line 20) | def test_contrib_experiment(csv_filename): FILE: tests/integration_tests/test_contrib_wandb.py function test_contrib_experiment (line 13) | def test_contrib_experiment(csv_filename, tmpdir): FILE: tests/integration_tests/test_custom_components.py class CustomNumberEncoderConfig (line 37) | class CustomNumberEncoderConfig(BaseEncoderConfig): class CustomNumberDecoderConfig (line 45) | class CustomNumberDecoderConfig(BaseDecoderConfig): class CustomLossConfig (line 53) | class CustomLossConfig(BaseLossConfig): class CustomTestCombinerConfig (line 59) | class CustomTestCombinerConfig(BaseCombinerConfig): class CustomTestCombiner (line 66) | class CustomTestCombiner(Combiner): method __init__ (line 67) | def __init__(self, input_features: dict = None, config: CustomTestComb... method forward (line 71) | def forward(self, inputs: dict) -> dict: # encoder outputs class CustomNumberEncoder (line 84) | class CustomNumberEncoder(Encoder): method __init__ (line 85) | def __init__(self, input_size, **kwargs): method forward (line 89) | def forward(self, inputs, **kwargs): method input_shape (line 93) | def input_shape(self) -> torch.Size: method output_shape (line 97) | def output_shape(self) -> torch.Size: method get_schema_cls (line 101) | def get_schema_cls(): class CustomNumberDecoder (line 106) | class CustomNumberDecoder(Decoder): method __init__ (line 107) | def __init__(self, input_size, **kwargs): method input_shape (line 112) | def input_shape(self): method forward (line 115) | def forward(self, inputs, **kwargs): method get_schema_cls (line 119) | def get_schema_cls(): class CustomLoss (line 124) | class CustomLoss(nn.Module, LogitsInputsMixin): method __init__ (line 125) | def __init__(self, config: CustomLossConfig): method forward (line 128) | def forward(self, preds: Tensor, target: Tensor) -> Tensor: method get_schema_cls (line 132) | def get_schema_cls(): class CustomLossMetric (line 137) | class CustomLossMetric(LossMetric): method __init__ (line 138) | def __init__(self, config: CustomLossConfig, **kwargs): method get_current_value (line 142) | def get_current_value(self, preds: Tensor, target: Tensor): function test_custom_combiner (line 146) | def test_custom_combiner(): function test_custom_encoder_decoder (line 150) | def test_custom_encoder_decoder(): function test_custom_loss_metric (line 161) | def test_custom_loss_metric(): function _run_test (line 168) | def _run_test(input_features=None, output_features=None, combiner=None): FILE: tests/integration_tests/test_date_feature.py function string_date_df (line 34) | def string_date_df() -> "pd.DataFrame": function int_date_df (line 45) | def int_date_df() -> "pd.DataFrame": function float_date_df (line 56) | def float_date_df() -> "pd.DataFrame": function test_date_feature_formats (line 74) | def test_date_feature_formats(date_df, request, ray_cluster_2cpu): FILE: tests/integration_tests/test_dependencies.py function test_multiple_dependencies (line 46) | def test_multiple_dependencies(reduce_dependencies, hidden_shape, depend... function test_construct_output_features_with_dependencies (line 109) | def test_construct_output_features_with_dependencies(output_feature_defs): FILE: tests/integration_tests/test_experiment.py function test_experiment_text_feature_non_pretrained (line 74) | def test_experiment_text_feature_non_pretrained(encoder, csv_filename): function run_experiment_with_encoder (line 84) | def run_experiment_with_encoder(encoder, csv_filename): function test_experiment_seq_seq_generator (line 95) | def test_experiment_seq_seq_generator(csv_filename, encoder): function test_experiment_seq_seq_tagger (line 104) | def test_experiment_seq_seq_tagger(csv_filename, encoder): function test_experiment_seq_seq_tagger_fails_for_non_length_preserving_encoders (line 113) | def test_experiment_seq_seq_tagger_fails_for_non_length_preserving_encod... function test_experiment_seq_seq_model_def_file (line 122) | def test_experiment_seq_seq_model_def_file(csv_filename, yaml_filename): function test_experiment_seq_seq_train_test_valid (line 141) | def test_experiment_seq_seq_train_test_valid(tmpdir): function test_experiment_multi_input_intent_classification (line 161) | def test_experiment_multi_input_intent_classification(csv_filename, enco... function test_experiment_with_torch_module_dict_feature_name (line 176) | def test_experiment_with_torch_module_dict_feature_name(csv_filename): function test_experiment_multiclass_with_class_weights (line 184) | def test_experiment_multiclass_with_class_weights(csv_filename): function test_experiment_multilabel_with_class_weights (line 194) | def test_experiment_multilabel_with_class_weights(csv_filename): function test_experiment_multiple_seq_seq (line 236) | def test_experiment_multiple_seq_seq(csv_filename, output_features): function test_basic_image_feature (line 259) | def test_basic_image_feature(num_channels, image_source, in_memory, skip... function test_experiment_infer_image_metadata (line 298) | def test_experiment_infer_image_metadata(tmpdir): function test_experiment_image_inputs (line 328) | def test_experiment_image_inputs(image_params: ImageParams, tmpdir): function test_experiment_image_dataset (line 370) | def test_experiment_image_dataset(train_format, train_in_memory, test_fo... function test_experiment_dataset_formats (line 463) | def test_experiment_dataset_formats(data_format, csv_filename): function test_experiment_audio_inputs (line 500) | def test_experiment_audio_inputs(tmpdir): function test_experiment_tied_weights (line 512) | def test_experiment_tied_weights(csv_filename): function test_experiment_tied_weights_sequence_combiner (line 530) | def test_experiment_tied_weights_sequence_combiner(csv_filename): function test_sequence_tagger (line 572) | def test_sequence_tagger(enc_cell_type, attention, csv_filename): function test_sequence_tagger_text (line 588) | def test_sequence_tagger_text(csv_filename): function test_experiment_sequence_combiner_with_reduction_fails (line 625) | def test_experiment_sequence_combiner_with_reduction_fails(csv_filename): function test_experiment_sequence_combiner (line 670) | def test_experiment_sequence_combiner(sequence_encoder, csv_filename): function test_experiment_model_resume (line 711) | def test_experiment_model_resume(tmpdir): function test_experiment_model_resume_distributed (line 741) | def test_experiment_model_resume_distributed(tmpdir, dist_strategy, ray_... function test_experiment_model_resume_distributed_gpu (line 753) | def test_experiment_model_resume_distributed_gpu(tmpdir, dist_strategy, ... function _run_experiment_model_resume_distributed (line 757) | def _run_experiment_model_resume_distributed(tmpdir, dist_strategy): function test_experiment_model_resume_missing_file (line 789) | def test_experiment_model_resume_missing_file(tmpdir, missing_file): function test_experiment_model_resume_before_1st_epoch_distributed (line 825) | def test_experiment_model_resume_before_1st_epoch_distributed(tmpdir, ra... function test_tabnet_with_batch_size_1 (line 875) | def test_tabnet_with_batch_size_1(tmpdir, ray_cluster_4cpu): function test_experiment_various_feature_types (line 899) | def test_experiment_various_feature_types(csv_filename): function test_experiment_timeseries (line 908) | def test_experiment_timeseries(csv_filename): function test_visual_question_answering (line 918) | def test_visual_question_answering(tmpdir): function test_image_resizing_num_channel_handling (line 936) | def test_image_resizing_num_channel_handling(tmpdir): function test_experiment_date (line 980) | def test_experiment_date(encoder, csv_filename): function test_experiment_h3 (line 992) | def test_experiment_h3(encoder, csv_filename): function test_experiment_vector_feature (line 1003) | def test_experiment_vector_feature(csv_filename): function test_experiment_vector_feature_infer_size (line 1012) | def test_experiment_vector_feature_infer_size(csv_filename): function test_forecasting_row_major (line 1026) | def test_forecasting_row_major(csv_filename, encoder): function test_forecasting_column_major (line 1042) | def test_forecasting_column_major(csv_filename): function test_experiment_text_output_feature_with_tagger_decoder (line 1073) | def test_experiment_text_output_feature_with_tagger_decoder(csv_filename... function test_experiment_sequence_output_feature_with_tagger_decoder (line 1086) | def test_experiment_sequence_output_feature_with_tagger_decoder(csv_file... function test_experiment_category_input_feature_with_tagger_decoder (line 1098) | def test_experiment_category_input_feature_with_tagger_decoder(csv_filen... function test_experiment_category_distribution_feature (line 1116) | def test_experiment_category_distribution_feature(csv_filename): function test_experiment_ordinal_category (line 1145) | def test_experiment_ordinal_category(csv_filename): function test_experiment_feature_names_with_non_word_chars (line 1153) | def test_experiment_feature_names_with_non_word_chars(tmpdir): function test_text_output_feature_cols (line 1188) | def test_text_output_feature_cols(tmpdir, csv_filename): FILE: tests/integration_tests/test_explain.py function test_explanation_dataclass (line 36) | def test_explanation_dataclass(): function test_abstract_explainer_instantiation (line 56) | def test_abstract_explainer_instantiation(): function test_explainer_api (line 79) | def test_explainer_api(explainer_class, model_type, output_feature, addi... function test_explainer_api_ray (line 89) | def test_explainer_api_ray(output_feature, tmpdir, ray_cluster_2cpu): function test_explainer_api_ray_minimum_batch_size (line 105) | def test_explainer_api_ray_minimum_batch_size(tmpdir, ray_cluster_2cpu): function test_explainer_text_hf (line 129) | def test_explainer_text_hf(explainer_class, model_type, cache_encoder_em... function test_explainer_text_tied_weights (line 149) | def test_explainer_text_tied_weights(explainer_class, model_type, tmpdir): function run_test_explainer_api (line 156) | def run_test_explainer_api( function test_explainer_api_nonscalar_outputs (line 241) | def test_explainer_api_nonscalar_outputs(output_feature, tmpdir): function test_explainer_api_text_outputs (line 245) | def test_explainer_api_text_outputs(tmpdir): function test_explainer_sequence_feature (line 261) | def test_explainer_sequence_feature(explainer_class, model_type, encoder... FILE: tests/integration_tests/test_graph_execution.py function test_experiment_multiple_seq_seq (line 56) | def test_experiment_multiple_seq_seq(csv_filename, output_features): FILE: tests/integration_tests/test_hyperopt.py function _setup_ludwig_config (line 100) | def _setup_ludwig_config(dataset_fp: str, model_type: str = MODEL_ECD) -... function test_hyperopt_search_alg (line 130) | def test_hyperopt_search_alg( function test_hyperopt_executor_with_metric (line 190) | def test_hyperopt_executor_with_metric(model_type, csv_filename, tmpdir,... function test_hyperopt_with_split (line 203) | def test_hyperopt_with_split(split, csv_filename, tmpdir, ray_cluster_7c... function test_hyperopt_scheduler (line 216) | def test_hyperopt_scheduler( function _run_hyperopt_run_hyperopt (line 272) | def _run_hyperopt_run_hyperopt(csv_filename, search_space, tmpdir, backe... function test_hyperopt_run_hyperopt (line 359) | def test_hyperopt_run_hyperopt(csv_filename, search_space, tmpdir, ray_c... function test_hyperopt_sync_remote (line 368) | def test_hyperopt_sync_remote(csv_filename, ray_cluster_7cpu, monkeypatch): function test_hyperopt_with_feature_specific_parameters (line 395) | def test_hyperopt_with_feature_specific_parameters(csv_filename, tmpdir,... function test_hyperopt_old_config (line 449) | def test_hyperopt_old_config(csv_filename, tmpdir, ray_cluster_7cpu): function test_hyperopt_nested_parameters (line 501) | def test_hyperopt_nested_parameters(csv_filename, tmpdir, ray_cluster_7c... function test_hyperopt_without_config_defaults (line 588) | def test_hyperopt_without_config_defaults(csv_filename, tmpdir, ray_clus... function test_hyperopt_with_time_budget (line 621) | def test_hyperopt_with_time_budget(csv_filename, tmpdir, ray_cluster_7cpu): FILE: tests/integration_tests/test_hyperopt_ray.py function _get_config (line 94) | def _get_config(search_alg: dict, executor: dict, epochs: int): class HyperoptTestCallback (line 115) | class HyperoptTestCallback(TuneCallback): method __init__ (line 116) | def __init__(self, exp_name: str, model_type: str): method on_trial_start (line 124) | def on_trial_start(self, iteration: int, trials: list["Trial"], trial:... method on_trial_complete (line 128) | def on_trial_complete(self, iteration: int, trials: list["Trial"], tri... function run_hyperopt_executor (line 155) | def run_hyperopt_executor( function test_hyperopt_executor (line 210) | def test_hyperopt_executor(scenario, csv_filename, tmpdir, ray_cluster_4... function test_hyperopt_executor_with_metric (line 225) | def test_hyperopt_executor_with_metric(use_split, csv_filename, tmpdir, ... function test_hyperopt_run_hyperopt (line 246) | def test_hyperopt_run_hyperopt(csv_filename, backend, tmpdir, ray_cluste... function test_hyperopt_ray_mlflow (line 318) | def test_hyperopt_ray_mlflow(csv_filename, tmpdir, ray_cluster_4cpu): function run_hyperopt (line 347) | def run_hyperopt( FILE: tests/integration_tests/test_input_feature_tied.py function test_tied_micro_level (line 92) | def test_tied_micro_level(input_feature_options): function test_tied_macro_level (line 135) | def test_tied_macro_level(tied_use_case: TiedUseCase, csv_filename: str): FILE: tests/integration_tests/test_kfold_cv.py function test_kfold_cv_cli (line 87) | def test_kfold_cv_cli(tmpdir, features_to_use: FeaturesToUse): function test_kfold_cv_api_from_file (line 142) | def test_kfold_cv_api_from_file(tmpdir): function test_kfold_cv_api_in_memory (line 181) | def test_kfold_cv_api_in_memory(tmpdir): function test_kfold_cv_dataset_formats (line 234) | def test_kfold_cv_dataset_formats(tmpdir, data_format): FILE: tests/integration_tests/test_llm.py function get_num_non_empty_tokens (line 72) | def get_num_non_empty_tokens(iterable): function local_backend (line 78) | def local_backend(): function ray_backend (line 83) | def ray_backend(): function get_dataset (line 87) | def get_dataset(): function get_generation_config (line 104) | def get_generation_config(): function convert_preds (line 114) | def convert_preds(preds: DataFrame): function test_llm_text_to_text (line 128) | def test_llm_text_to_text(tmpdir, backend, ray_cluster_4cpu): function test_llm_zero_shot_classification (line 195) | def test_llm_zero_shot_classification(tmpdir, backend, ray_cluster_4cpu): function test_llm_few_shot_classification (line 257) | def test_llm_few_shot_classification(tmpdir, backend, csv_filename, ray_... function _prepare_finetuning_test (line 327) | def _prepare_finetuning_test( function _finetune_strategy_requires_cuda (line 374) | def _finetune_strategy_requires_cuda(finetune_strategy_name: str, quanti... function _verify_lm_lora_finetuning_layers (line 395) | def _verify_lm_lora_finetuning_layers( function test_llm_finetuning_strategies (line 613) | def test_llm_finetuning_strategies(tmpdir, csv_filename, backend, finetu... function test_llm_finetuning_strategies_quantized (line 652) | def test_llm_finetuning_strategies_quantized(tmpdir, csv_filename, finet... function test_llm_lora_finetuning_merge_and_unload_quantized_accelerate_required (line 719) | def test_llm_lora_finetuning_merge_and_unload_quantized_accelerate_requi... function test_llm_lora_finetuning_merge_and_unload_4_bit_quantization_not_supported (line 756) | def test_llm_lora_finetuning_merge_and_unload_4_bit_quantization_not_sup... function test_llm_lora_finetuning_merge_and_unload (line 864) | def test_llm_lora_finetuning_merge_and_unload( function test_llm_training_with_gradient_checkpointing (line 932) | def test_llm_training_with_gradient_checkpointing(tmpdir, csv_filename, ... function test_lora_wrap_on_init (line 961) | def test_lora_wrap_on_init(): function test_llama_rope_scaling (line 994) | def test_llama_rope_scaling(): function test_default_max_sequence_length (line 1020) | def test_default_max_sequence_length(): function test_load_pretrained_adapter_weights (line 1052) | def test_load_pretrained_adapter_weights(adapter): function _compare_models (line 1096) | def _compare_models(model_1: torch.nn.Module, model_2: torch.nn.Module) ... function test_global_max_sequence_length_for_llms (line 1132) | def test_global_max_sequence_length_for_llms(): function test_local_path_loading (line 1156) | def test_local_path_loading(tmpdir): function test_llm_finetuning_with_embedding_noise (line 1200) | def test_llm_finetuning_with_embedding_noise( function llm_encoder_config (line 1234) | def llm_encoder_config() -> dict[str, Any]: function test_llm_encoding (line 1256) | def test_llm_encoding(llm_encoder_config, adapter, quantization, tmpdir): function test_llm_batch_size_tuning (line 1292) | def test_llm_batch_size_tuning(): function test_llm_used_tokens (line 1325) | def test_llm_used_tokens(tmpdir): FILE: tests/integration_tests/test_missing_value_strategy.py function test_missing_value_prediction (line 39) | def test_missing_value_prediction(tmpdir, csv_filename): function test_missing_values_fill_with_mean (line 75) | def test_missing_values_fill_with_mean(backend, csv_filename, tmpdir, ra... function test_missing_values_drop_rows (line 98) | def test_missing_values_drop_rows(csv_filename, tmpdir): function test_outlier_strategy (line 140) | def test_outlier_strategy(outlier_strategy, outlier_threshold, backend, ... FILE: tests/integration_tests/test_mlflow.py function run_mlflow_callback_test (line 21) | def run_mlflow_callback_test(mlflow_client, config, training_data, val_d... function run_mlflow_callback_test_without_artifacts (line 85) | def run_mlflow_callback_test_without_artifacts(mlflow_client, config, tr... function test_mlflow (line 100) | def test_mlflow(tmpdir, external_run): function test_export_mlflow_local (line 147) | def test_export_mlflow_local(tmpdir): function test_mlflow_ray (line 178) | def test_mlflow_ray(tmpdir, ray_cluster_2cpu): FILE: tests/integration_tests/test_model_save_and_load.py function test_model_load_from_checkpoint (line 36) | def test_model_load_from_checkpoint(tmpdir, csv_filename, tmp_path): function test_model_save_reload_api (line 109) | def test_model_save_reload_api(tmpdir, csv_filename, tmp_path): function test_model_weights_match_training (line 215) | def test_model_weights_match_training(tmpdir, csv_filename): function test_model_save_reload_tv_model (line 263) | def test_model_save_reload_tv_model(torch_encoder, variant, tmpdir, csv_... function test_model_save_reload_hf_model (line 358) | def test_model_save_reload_hf_model(tmpdir, csv_filename, tmp_path): FILE: tests/integration_tests/test_model_training_options.py function test_early_stopping (line 41) | def test_early_stopping(early_stop, tmp_path): function test_model_progress_save (line 96) | def test_model_progress_save(skip_save_progress, skip_save_model, tmp_pa... function test_resume_training (line 142) | def test_resume_training(optimizer, tmp_path): function test_resume_training_mlflow (line 196) | def test_resume_training_mlflow(optimizer, tmp_path): function test_optimizers (line 239) | def test_optimizers(optimizer_type, tmp_path): function test_regularization (line 292) | def test_regularization(tmp_path): function test_cache_checksum (line 357) | def test_cache_checksum(csv_filename, tmp_path): function test_numeric_transformer (line 445) | def test_numeric_transformer(transformer_key, tmpdir): FILE: tests/integration_tests/test_number_feature.py function test_number_feature_zscore_normalization_error (line 8) | def test_number_feature_zscore_normalization_error(): FILE: tests/integration_tests/test_peft.py function test_text_adapter_lora (line 17) | def test_text_adapter_lora(tmpdir, backend, ray_cluster_2cpu): FILE: tests/integration_tests/test_postprocessing.py function random_binary_logits (line 38) | def random_binary_logits(*args, num_predict_samples, **kwargs): function random_set_logits (line 44) | def random_set_logits(*args, num_predict_samples, vocab_size, pct_positi... function _run_binary_predictions (line 55) | def _run_binary_predictions(tmpdir, backend, distinct_values, ray_cluste... function test_binary_predictions (line 111) | def test_binary_predictions(tmpdir, distinct_values, ray_cluster_2cpu): function test_binary_predictions_ray (line 118) | def test_binary_predictions_ray(tmpdir, distinct_values, ray_cluster_2cpu): function _run_binary_predictions_with_number_dtype (line 122) | def _run_binary_predictions_with_number_dtype(tmpdir, backend, distinct_... function test_binary_predictions_with_number_dtype (line 178) | def test_binary_predictions_with_number_dtype(tmpdir, distinct_values, r... function test_binary_predictions_with_number_dtype_ray (line 185) | def test_binary_predictions_with_number_dtype_ray(tmpdir, distinct_value... function test_set_feature_saving (line 190) | def test_set_feature_saving(tmpdir, pct_positive): function predict_with_backend (line 234) | def predict_with_backend(tmpdir, config, data_csv_path, backend, patch_a... FILE: tests/integration_tests/test_preprocessing.py function test_sample_ratio (line 68) | def test_sample_ratio(backend, tmpdir, ray_cluster_2cpu): function test_sample_ratio_deterministic (line 116) | def test_sample_ratio_deterministic(backend, tmpdir, ray_cluster_2cpu): function test_sample_size (line 173) | def test_sample_size(backend, tmpdir, ray_cluster_2cpu): function test_sample_size_deterministic (line 220) | def test_sample_size_deterministic(backend, tmpdir, ray_cluster_2cpu): function test_strip_whitespace_category (line 268) | def test_strip_whitespace_category(csv_filename, tmpdir): function test_with_split (line 298) | def test_with_split(backend, csv_filename, tmpdir, ray_cluster_2cpu): function test_dask_known_divisions (line 333) | def test_dask_known_divisions(feature_fn, csv_filename, tmpdir, ray_clus... function test_drop_empty_partitions (line 359) | def test_drop_empty_partitions(csv_filename, tmpdir, ray_cluster_2cpu): function test_read_image_from_path (line 390) | def test_read_image_from_path(tmpdir, csv_filename, generate_images_as_n... function test_read_image_from_numpy_array (line 410) | def test_read_image_from_numpy_array(tmpdir, csv_filename): function test_read_image_failure_default_image (line 444) | def test_read_image_failure_default_image(monkeypatch, tmpdir, csv_filen... function test_number_feature_wrong_dtype (line 480) | def test_number_feature_wrong_dtype(csv_filename, tmpdir): function test_seq_features_max_sequence_length (line 537) | def test_seq_features_max_sequence_length( function test_column_feature_type_mismatch_fill (line 566) | def test_column_feature_type_mismatch_fill(): function test_presplit_override (line 590) | def test_presplit_override(format, tmpdir): function test_empty_training_set_error (line 651) | def test_empty_training_set_error(backend, tmpdir, ray_cluster_2cpu): function test_in_memory_dataset_size (line 680) | def test_in_memory_dataset_size(backend, tmpdir, ray_cluster_2cpu): function test_non_conventional_bool_with_fallback (line 737) | def test_non_conventional_bool_with_fallback(binary_as_input, expected_p... function test_non_conventional_bool_without_fallback_logs_warning (line 777) | def test_non_conventional_bool_without_fallback_logs_warning(binary_as_i... function test_category_feature_vocab_size_1 (line 808) | def test_category_feature_vocab_size_1(feature_type, tmpdir) -> None: function test_vit_encoder_different_dimension_image (line 828) | def test_vit_encoder_different_dimension_image(tmpdir, csv_filename, use... function test_image_encoder_torchvision_different_num_channels (line 861) | def test_image_encoder_torchvision_different_num_channels(tmpdir, csv_fi... function test_fill_with_mode_different_df_engine (line 895) | def test_fill_with_mode_different_df_engine(tmpdir, csv_filename, df_eng... function test_prompt_template (line 981) | def test_prompt_template(input_features, expected, model_type, backend, ... function test_handle_features_with_few_shot_prompt_config (line 1055) | def test_handle_features_with_few_shot_prompt_config(backend, retrieval_... function test_handle_features_with_prompt_config_multi_col (line 1131) | def test_handle_features_with_prompt_config_multi_col(backend, ray_clust... FILE: tests/integration_tests/test_ray.py function train_gpu (line 93) | def train_gpu(config, dataset, output_directory): function predict_cpu (line 100) | def predict_cpu(model_dir, dataset): function run_api_experiment (line 105) | def run_api_experiment( function run_split_api_experiment (line 143) | def run_split_api_experiment(config, data_parquet, backend_config): function run_preprocessing (line 166) | def run_preprocessing( function check_preprocessed_df_equal (line 234) | def check_preprocessed_df_equal(df1, df2): function split (line 263) | def split(data_parquet): function run_test_with_features (line 280) | def run_test_with_features( function test_ray_read_binary_files (line 349) | def test_ray_read_binary_files(tmpdir, df_engine, ray_cluster_2cpu): function test_ray_outputs (line 400) | def test_ray_outputs(trainer_strategy, ray_cluster_2cpu): function test_ray_set_and_vector_outputs (line 436) | def test_ray_set_and_vector_outputs(dataset_type, ray_cluster_2cpu): function test_ray_tabular (line 479) | def test_ray_tabular(tmpdir, df_engine, ray_cluster_2cpu): function test_ray_tabular_save_inputs (line 504) | def test_ray_tabular_save_inputs(tmpdir, dataset_type, ray_cluster_2cpu): function test_ray_text_sequence_timeseries (line 532) | def test_ray_text_sequence_timeseries(tmpdir, dataset_type, ray_cluster_... function test_ray_vector (line 553) | def test_ray_vector(tmpdir, dataset_type, ray_cluster_2cpu): function test_ray_audio (line 572) | def test_ray_audio(tmp_path, dataset_type, ray_cluster_2cpu): function test_ray_image (line 601) | def test_ray_image(tmpdir, dataset_type, ray_cluster_2cpu): function test_ray_image_with_fill_strategy_edge_cases (line 629) | def test_ray_image_with_fill_strategy_edge_cases(tmpdir, settings, ray_c... function test_ray_image_modin (line 667) | def test_ray_image_modin(tmpdir, ray_cluster_2cpu): function test_ray_image_multiple_features (line 693) | def test_ray_image_multiple_features(tmpdir, ray_cluster_2cpu): function test_ray_split (line 721) | def test_ray_split(ray_cluster_2cpu): function test_ray_lazy_load_audio_error (line 736) | def test_ray_lazy_load_audio_error(tmpdir, ray_cluster_2cpu): function test_ray_lazy_load_image_works (line 754) | def test_ray_lazy_load_image_works(tmpdir, ray_cluster_2cpu): function test_balance_ray (line 795) | def test_balance_ray(method, balance, ray_cluster_2cpu): function _run_train_gpu_load_cpu (line 828) | def _run_train_gpu_load_cpu(config, data_parquet): function test_tune_batch_size_lr_cpu (line 842) | def test_tune_batch_size_lr_cpu(tmpdir, ray_cluster_2cpu, max_batch_size... function test_ray_calibration (line 883) | def test_ray_calibration(calibration, ray_cluster_2cpu): function test_ray_distributed_predict (line 899) | def test_ray_distributed_predict(use_placement_group, ray_cluster_2cpu): FILE: tests/integration_tests/test_reducers.py function test_reduction (line 8) | def test_reduction(reduce_output, csv_filename): FILE: tests/integration_tests/test_regularizers.py function test_regularizers (line 49) | def test_regularizers( FILE: tests/integration_tests/test_remote.py function test_remote_training_set (line 37) | def test_remote_training_set(csv_filename, fs_protocol, bucket, creds, b... FILE: tests/integration_tests/test_reproducibility.py function raw_dataset_fp (line 28) | def raw_dataset_fp(tmpdir: pathlib.Path) -> str: function test_preprocess (line 41) | def test_preprocess(raw_dataset_fp: str, random_seed: int, second_seed_o... function test_preprocess_ignore_torch_seed (line 75) | def test_preprocess_ignore_torch_seed(raw_dataset_fp: str, random_seed: ... function test_train (line 110) | def test_train(raw_dataset_fp: str, random_seed: int, second_seed_offset... function test_train_ignore_torch_seed (line 156) | def test_train_ignore_torch_seed(raw_dataset_fp: str, random_seed: int) ... function test_experiment (line 197) | def test_experiment(raw_dataset_fp: str, random_seed: int, second_seed_o... function test_experiment_ignore_torch_seed (line 242) | def test_experiment_ignore_torch_seed(raw_dataset_fp: str, random_seed: ... FILE: tests/integration_tests/test_sequence_decoders.py function test_sequence_decoder_predictions (line 32) | def test_sequence_decoder_predictions(tmpdir, csv_filename, ray_cluster_... FILE: tests/integration_tests/test_sequence_encoders.py function input_sequence (line 40) | def input_sequence() -> torch.Tensor: function test_sequence_encoders (line 69) | def test_sequence_encoders( function test_passthrough_encoder (line 168) | def test_passthrough_encoder(enc_reduce_output, input_sequence): function test_sequence_embed_encoder (line 184) | def test_sequence_embed_encoder(enc_embedding_size: int, input_sequence:... FILE: tests/integration_tests/test_sequence_features.py function generate_sequence_training_data (line 31) | def generate_sequence_training_data(): function setup_model_scaffolding (line 66) | def setup_model_scaffolding(raw_df, input_features, output_features): function test_sequence_decoders (line 107) | def test_sequence_decoders( function test_sequence_generator (line 158) | def test_sequence_generator(enc_encoder, enc_cell_type, dec_cell_type, c... FILE: tests/integration_tests/test_server.py function train_and_predict_model (line 52) | def train_and_predict_model(input_features, output_features, data_csv, o... function train_and_predict_model_with_stratified_split (line 79) | def train_and_predict_model_with_stratified_split(input_features, output... function output_keys_for (line 102) | def output_keys_for(output_features): function convert_to_form (line 120) | def convert_to_form(entry): function convert_to_batch_form (line 132) | def convert_to_batch_form(data_df): function test_server_integration_with_images (line 144) | def test_server_integration_with_images(tmpdir): function test_server_integration_with_stratified_split (line 207) | def test_server_integration_with_stratified_split(tmpdir): function test_server_integration_with_audio (line 264) | def test_server_integration_with_audio(single_record, tmpdir): FILE: tests/integration_tests/test_simple_features.py function test_feature (line 54) | def test_feature(input_test_feature, output_test_feature, output_loss_pa... function test_feature_multiple_outputs (line 91) | def test_feature_multiple_outputs(input_test_feature, output_test_featur... function test_category_int_dtype (line 98) | def test_category_int_dtype(tmpdir): FILE: tests/integration_tests/test_timeseries_feature.py function test_timeseries_feature (line 19) | def test_timeseries_feature(enc_encoder): function test_timeseries_preprocessing_with_nan (line 51) | def test_timeseries_preprocessing_with_nan(): FILE: tests/integration_tests/test_torchscript.py function test_torchscript (line 60) | def test_torchscript(tmpdir, csv_filename, should_load_model): function test_torchscript_e2e_tabular (line 208) | def test_torchscript_e2e_tabular(csv_filename, tmpdir): function test_torchscript_e2e_binary_only (line 265) | def test_torchscript_e2e_binary_only(csv_filename, tmpdir): function test_torchscript_e2e_tabnet_combiner (line 288) | def test_torchscript_e2e_tabnet_combiner(csv_filename, tmpdir): function test_torchscript_e2e_audio (line 323) | def test_torchscript_e2e_audio(csv_filename, tmpdir): function test_torchscript_e2e_image (line 355) | def test_torchscript_e2e_image(tmpdir, csv_filename, kwargs): function test_torchscript_e2e_text (line 376) | def test_torchscript_e2e_text(tmpdir, csv_filename): function test_torchscript_e2e_text_hf_tokenizer (line 401) | def test_torchscript_e2e_text_hf_tokenizer(tmpdir, csv_filename): function test_torchscript_e2e_text_hf_tokenizer_truncated_sequence (line 423) | def test_torchscript_e2e_text_hf_tokenizer_truncated_sequence(tmpdir, cs... function test_torchscript_e2e_sequence (line 441) | def test_torchscript_e2e_sequence(tmpdir, csv_filename): function test_torchscript_e2e_timeseries (line 461) | def test_torchscript_e2e_timeseries(tmpdir, csv_filename): function test_torchscript_e2e_h3 (line 481) | def test_torchscript_e2e_h3(tmpdir, csv_filename): function test_torchscript_e2e_date (line 501) | def test_torchscript_e2e_date(tmpdir, csv_filename): function test_torchscript_preproc_vector_alternative_type (line 522) | def test_torchscript_preproc_vector_alternative_type(tmpdir, csv_filenam... function test_torchscript_preproc_timeseries_alternative_type (line 580) | def test_torchscript_preproc_timeseries_alternative_type(tmpdir, csv_fil... function test_torchscript_preproc_with_nans (line 658) | def test_torchscript_preproc_with_nans(tmpdir, csv_filename, feature): function test_torchscript_preproc_gpu (line 723) | def test_torchscript_preproc_gpu(tmpdir, csv_filename, feature_fn): function test_torchscript_postproc_gpu (line 782) | def test_torchscript_postproc_gpu(tmpdir, csv_filename, feature_fn): function validate_torchscript_outputs (line 825) | def validate_torchscript_outputs(tmpdir, config, backend, training_data_... function initialize_torchscript_module (line 861) | def initialize_torchscript_module(tmpdir, config, backend, training_data... FILE: tests/integration_tests/test_trainer.py function test_tune_learning_rate (line 34) | def test_tune_learning_rate(tmpdir): function test_ecd_tune_batch_size_and_lr (line 65) | def test_ecd_tune_batch_size_and_lr(tmpdir, eval_batch_size, effective_b... function test_changing_parameters_on_plateau (line 149) | def test_changing_parameters_on_plateau(tmpdir): function test_mixed_precision (line 176) | def test_mixed_precision(tmpdir): function test_compile (line 207) | def test_compile(tmpdir): function test_gradient_accumulation (line 236) | def test_gradient_accumulation(gradient_accumulation_steps: int, tmpdir): function test_enable_gradient_checkpointing (line 265) | def test_enable_gradient_checkpointing(tmpdir, caplog): FILE: tests/integration_tests/test_triton.py function test_triton_torchscript (line 40) | def test_triton_torchscript(csv_filename, tmpdir): function get_test_config_filenames (line 157) | def get_test_config_filenames() -> list[str]: function test_triton_exportability (line 164) | def test_triton_exportability(config_path, tmpdir): FILE: tests/integration_tests/test_visualization.py function run_experiment_with_visualization (line 51) | def run_experiment_with_visualization(input_features, output_features, d... function get_output_feature_name (line 79) | def get_output_feature_name(experiment_dir, output_feature=0): function test_visualization_learning_curves_output_saved (line 93) | def test_visualization_learning_curves_output_saved(csv_filename): function test_visualization_confusion_matrix_output_saved (line 136) | def test_visualization_confusion_matrix_output_saved(csv_filename): function test_visualization_compare_performance_output_saved (line 179) | def test_visualization_compare_performance_output_saved(csv_filename): function test_visualization_compare_classifiers_from_prob_csv_output_saved (line 226) | def test_visualization_compare_classifiers_from_prob_csv_output_saved(cs... function test_visualization_compare_classifiers_from_prob_npy_output_saved (line 283) | def test_visualization_compare_classifiers_from_prob_npy_output_saved(cs... function test_visualization_compare_classifiers_from_pred_npy_output_saved (line 340) | def test_visualization_compare_classifiers_from_pred_npy_output_saved(cs... function test_visualization_compare_classifiers_from_pred_csv_output_saved (line 397) | def test_visualization_compare_classifiers_from_pred_csv_output_saved(cs... function test_visualization_compare_classifiers_subset_output_saved (line 454) | def test_visualization_compare_classifiers_subset_output_saved(csv_filen... function test_visualization_compare_classifiers_changing_k_output_pdf (line 511) | def test_visualization_compare_classifiers_changing_k_output_pdf(csv_fil... function test_visualization_compare_classifiers_multiclass_multimetric_output_saved (line 564) | def test_visualization_compare_classifiers_multiclass_multimetric_output... function test_visualization_compare_classifiers_predictions_npy_output_saved (line 611) | def test_visualization_compare_classifiers_predictions_npy_output_saved(... function test_visualization_compare_classifiers_predictions_csv_output_saved (line 667) | def test_visualization_compare_classifiers_predictions_csv_output_saved(... function test_visualization_cmp_classifiers_predictions_distribution_output_saved (line 723) | def test_visualization_cmp_classifiers_predictions_distribution_output_s... function test_visualization_cconfidence_thresholding_output_saved (line 778) | def test_visualization_cconfidence_thresholding_output_saved(csv_filename): function test_visualization_confidence_thresholding_data_vs_acc_output_saved (line 833) | def test_visualization_confidence_thresholding_data_vs_acc_output_saved(... function test_visualization_confidence_thresholding_data_vs_acc_subset_output_saved (line 888) | def test_visualization_confidence_thresholding_data_vs_acc_subset_output... function test_vis_confidence_thresholding_data_vs_acc_subset_per_class_output_saved (line 945) | def test_vis_confidence_thresholding_data_vs_acc_subset_per_class_output... function test_vis_confidence_thresholding_2thresholds_2d_output_saved (line 1004) | def test_vis_confidence_thresholding_2thresholds_2d_output_saved(csv_fil... function test_vis_confidence_thresholding_2thresholds_3d_output_saved (line 1070) | def test_vis_confidence_thresholding_2thresholds_3d_output_saved(csv_fil... function test_visualization_binary_threshold_vs_metric_output_saved (line 1135) | def test_visualization_binary_threshold_vs_metric_output_saved(csv_filen... function test_visualization_precision_recall_curves_output_saved (line 1209) | def test_visualization_precision_recall_curves_output_saved(csv_filename... function test_visualization_precision_recall_curves_from_test_statistics_output_saved (line 1265) | def test_visualization_precision_recall_curves_from_test_statistics_outp... function test_visualization_roc_curves_output_saved (line 1310) | def test_visualization_roc_curves_output_saved(csv_filename, binary_outp... function test_visualization_roc_curves_from_test_statistics_output_saved (line 1372) | def test_visualization_roc_curves_from_test_statistics_output_saved(csv_... function test_visualization_calibration_1_vs_all_output_saved (line 1416) | def test_visualization_calibration_1_vs_all_output_saved(csv_filename): function test_visualization_calibration_multiclass_output_saved (line 1475) | def test_visualization_calibration_multiclass_output_saved(csv_filename): function test_visualization_frequency_vs_f1_output_saved (line 1530) | def test_visualization_frequency_vs_f1_output_saved(csv_filename): function test_load_ground_truth_split_from_df (line 1580) | def test_load_ground_truth_split_from_df(csv_filename): function test_load_ground_truth_split_from_file (line 1610) | def test_load_ground_truth_split_from_file(csv_filename): FILE: tests/integration_tests/test_visualization_api.py function run_api_experiment (line 44) | def run_api_experiment(input_features, output_features): function experiment_to_use (line 63) | def experiment_to_use(): class Experiment (line 69) | class Experiment: method __init__ (line 75) | def __init__(self, csv_filename, tmpdir): method _create_model (line 107) | def _create_model(self): function obtain_df_splits (line 118) | def obtain_df_splits(data_csv): function test_learning_curves_vis_api (line 132) | def test_learning_curves_vis_api(experiment_to_use, training_only): function test_compare_performance_vis_api (line 155) | def test_compare_performance_vis_api(experiment_to_use): function test_compare_classifier_performance_from_prob_vis_api (line 179) | def test_compare_classifier_performance_from_prob_vis_api(experiment_to_... function test_compare_classifier_performance_from_pred_vis_api (line 206) | def test_compare_classifier_performance_from_pred_vis_api(experiment_to_... function test_compare_classifiers_performance_subset_vis_api (line 232) | def test_compare_classifiers_performance_subset_vis_api(experiment_to_use): function test_compare_classifiers_performance_changing_k_vis_api (line 260) | def test_compare_classifiers_performance_changing_k_vis_api(experiment_t... function test_compare_classifiers_multiclass_multimetric_vis_api (line 287) | def test_compare_classifiers_multiclass_multimetric_vis_api(experiment_t... function test_compare_classifiers_predictions_vis_api (line 313) | def test_compare_classifiers_predictions_vis_api(experiment_to_use): function test_compare_classifiers_predictions_distribution_vis_api (line 339) | def test_compare_classifiers_predictions_distribution_vis_api(experiment... function test_confidence_thresholding_vis_api (line 365) | def test_confidence_thresholding_vis_api(experiment_to_use): function test_confidence_thresholding_data_vs_acc_vis_api (line 391) | def test_confidence_thresholding_data_vs_acc_vis_api(experiment_to_use): function test_confidence_thresholding_data_vs_acc_subset_vis_api (line 417) | def test_confidence_thresholding_data_vs_acc_subset_vis_api(experiment_t... function test_confidence_thresholding_data_vs_acc_subset_per_class_vis_api (line 445) | def test_confidence_thresholding_data_vs_acc_subset_per_class_vis_api(ex... function test_confidence_thresholding_2thresholds_2d_vis_api (line 475) | def test_confidence_thresholding_2thresholds_2d_vis_api(csv_filename): function test_confidence_thresholding_2thresholds_3d_vis_api (line 547) | def test_confidence_thresholding_2thresholds_3d_vis_api(csv_filename): function test_binary_threshold_vs_metric_vis_api (line 620) | def test_binary_threshold_vs_metric_vis_api(experiment_to_use): function test_precision_recall_curves_vis_api (line 649) | def test_precision_recall_curves_vis_api(experiment_to_use): function test_precision_recall_curves_from_test_statistics_vis_api (line 676) | def test_precision_recall_curves_from_test_statistics_vis_api(csv_filena... function test_roc_curves_vis_api (line 710) | def test_roc_curves_vis_api(experiment_to_use): function test_roc_curves_from_test_statistics_vis_api (line 737) | def test_roc_curves_from_test_statistics_vis_api(csv_filename): function test_calibration_1_vs_all_vis_api (line 771) | def test_calibration_1_vs_all_vis_api(experiment_to_use): function test_calibration_multiclass_vis_api (line 798) | def test_calibration_multiclass_vis_api(experiment_to_use): function test_confusion_matrix_vis_api (line 824) | def test_confusion_matrix_vis_api(experiment_to_use): function test_frequency_vs_f1_vis_api (line 851) | def test_frequency_vs_f1_vis_api(experiment_to_use): function test_hyperopt_report_vis_api (line 878) | def test_hyperopt_report_vis_api(hyperopt_results_multiple_parameters, t... function test_hyperopt_hiplot_vis_api (line 898) | def test_hyperopt_hiplot_vis_api(hyperopt_results_multiple_parameters, t... function test_hyperopt_report_vis_api_no_pairplot (line 918) | def test_hyperopt_report_vis_api_no_pairplot(hyperopt_results_single_par... FILE: tests/integration_tests/utils.py function strtobool (line 28) | def strtobool(val): class LocalTestBackend (line 115) | class LocalTestBackend(LocalBackend): method supports_multiprocessing (line 117) | def supports_multiprocessing(self): class FakeRemoteBackend (line 122) | class FakeRemoteBackend(LocalBackend): method create_trainer (line 123) | def create_trainer(self, **kwargs) -> "BaseTrainer": # noqa: F821 method supports_multiprocessing (line 127) | def supports_multiprocessing(self): class FakeRemoteTrainer (line 131) | class FakeRemoteTrainer(Trainer): method train (line 132) | def train(self, *args, save_path=MODEL_FILE_NAME, **kwargs): function parse_flag_from_env (line 137) | def parse_flag_from_env(key, default=False): function private_param (line 164) | def private_param(param): function generate_data (line 178) | def generate_data( function generate_data_as_dataframe (line 205) | def generate_data_as_dataframe( function recursive_update (line 243) | def recursive_update(dictionary, values): function random_string (line 252) | def random_string(length=5): function number_feature (line 256) | def number_feature(normalization=None, **kwargs): function category_feature (line 268) | def category_feature(output_feature=False, **kwargs): function text_feature (line 293) | def text_feature(output_feature: bool = False, name: str = None, **kwargs): function set_feature (line 329) | def set_feature(output_feature=False, **kwargs): function sequence_feature (line 354) | def sequence_feature(output_feature=False, **kwargs): function image_feature (line 392) | def image_feature(folder, **kwargs): function audio_feature (line 408) | def audio_feature(folder, **kwargs): function timeseries_feature (line 436) | def timeseries_feature(**kwargs): function binary_feature (line 462) | def binary_feature(**kwargs): function bag_feature (line 473) | def bag_feature(**kwargs): function date_feature (line 485) | def date_feature(**kwargs): function h3_feature (line 499) | def h3_feature(**kwargs): function vector_feature (line 510) | def vector_feature(**kwargs): function category_distribution_feature (line 524) | def category_distribution_feature(**kwargs): function run_experiment (line 539) | def run_experiment( function generate_output_features_with_dependencies (line 590) | def generate_output_features_with_dependencies(main_feature, dependencies): function generate_output_features_with_dependencies_complex (line 625) | def generate_output_features_with_dependencies_complex(): function _subproc_wrapper (line 644) | def _subproc_wrapper(fn, queue, *args, **kwargs): function spawn (line 654) | def spawn(fn): function get_weights (line 673) | def get_weights(model: torch.nn.Module) -> list[torch.Tensor]: function has_no_grad (line 677) | def has_no_grad( function is_all_close (line 688) | def is_all_close( function is_all_tensors_cuda (line 705) | def is_all_tensors_cuda(val: np.ndarray | torch.Tensor | str | list) -> ... function run_api_experiment (line 714) | def run_api_experiment(input_features, output_features, data_csv): function add_nans_to_df_in_place (line 764) | def add_nans_to_df_in_place(df: pd.DataFrame, nan_percent: float): function read_csv_with_nan (line 783) | def read_csv_with_nan(path, nan_percent=0.0): function create_data_set_to_use (line 790) | def create_data_set_to_use(data_format, raw_data, nan_percent=0.0): function augment_dataset_with_none (line 882) | def augment_dataset_with_none( function train_with_backend (line 907) | def train_with_backend( function assert_all_required_metrics_exist (line 1014) | def assert_all_required_metrics_exist( function assert_preprocessed_dataset_shape_and_dtype_for_feature (line 1058) | def assert_preprocessed_dataset_shape_and_dtype_for_feature( function remote_tmpdir (line 1107) | def remote_tmpdir(fs_protocol, bucket): function minio_test_creds (line 1127) | def minio_test_creds(): function clear_huggingface_cache (line 1139) | def clear_huggingface_cache(): function run_test_suite (line 1171) | def run_test_suite(config, dataset, backend): FILE: tests/ludwig/accounting/test_used_tokens.py function test_get_used_tokens_for_ecd (line 6) | def test_get_used_tokens_for_ecd(): function test_get_used_tokens_for_ecd_no_targets (line 13) | def test_get_used_tokens_for_ecd_no_targets(): function test_get_used_tokens_for_llm (line 20) | def test_get_used_tokens_for_llm(): FILE: tests/ludwig/augmentation/test_augmentation_pipeline.py function test_image (line 19) | def test_image(): function train_data_rgb (line 26) | def train_data_rgb(): function train_data_gray_scale (line 67) | def train_data_gray_scale(): function run_augmentation_training (line 107) | def run_augmentation_training( function test_image_augmentation (line 171) | def test_image_augmentation(test_image, augmentation_pipeline_ops): function test_local_model_training_with_augmentation_pipeline (line 209) | def test_local_model_training_with_augmentation_pipeline( function test_ray_model_training_with_augmentation_pipeline (line 235) | def test_ray_model_training_with_augmentation_pipeline( function test_ludwig_encoder_gray_scale_image_augmentation_pipeline (line 272) | def test_ludwig_encoder_gray_scale_image_augmentation_pipeline( function test_invalid_augmentation_parameters (line 298) | def test_invalid_augmentation_parameters( function test_load_model_with_augmentation_pipeline (line 313) | def test_load_model_with_augmentation_pipeline( FILE: tests/ludwig/augmentation/test_auto_augmentation.py function test_image (line 10) | def test_image(): function test_auto_augmentation (line 22) | def test_auto_augmentation(test_image, augmentation_type, augmentation_p... FILE: tests/ludwig/augmentation/test_image_augmentation.py function test_image (line 10) | def test_image(): function test_image_augmentation (line 27) | def test_image_augmentation(test_image, augmentation_type, augmentation_... FILE: tests/ludwig/automl/test_base_config.py function dummy_df (line 31) | def dummy_df(): function test_is_field_boolean (line 68) | def test_is_field_boolean(df_engine, dummy_df): function test_dataset_info (line 87) | def test_dataset_info(df_engine, dummy_df): function test_object_and_bool_type_inference (line 108) | def test_object_and_bool_type_inference(col, expected_dtype): function test_reference_configs (line 114) | def test_reference_configs(): function repeat (line 123) | def repeat(df, n): function test_infer_parquet_types (line 128) | def test_infer_parquet_types(tmpdir): FILE: tests/ludwig/automl/test_data_source.py function get_test_df (line 23) | def get_test_df(): function test_mixed_csv_data_source (line 33) | def test_mixed_csv_data_source(ray_cluster_2cpu): FILE: tests/ludwig/automl/test_tune_config.py function test_reduce_text_model_mem_99ptile (line 10) | def test_reduce_text_model_mem_99ptile(): function test_reduce_text_model_mem_128 (line 22) | def test_reduce_text_model_mem_128(): function test_reduce_text_model_mem_override (line 34) | def test_reduce_text_model_mem_override(): function test_reduce_text_model_mem_respect (line 49) | def test_reduce_text_model_mem_respect(): FILE: tests/ludwig/automl/test_utils.py function _features (line 10) | def _features(*in_types, out): function test_get_model_type (line 25) | def test_get_model_type(config, expected): FILE: tests/ludwig/backend/test_ray.py function test_get_trainer_kwargs (line 41) | def test_get_trainer_kwargs(trainer_config, cluster_resources, num_nodes... function test_set_max_concurrent_trials (line 111) | def test_set_max_concurrent_trials(hyperopt_config_old, hyperopt_config_... FILE: tests/ludwig/benchmarking/example_files/process_config.py function process_config (line 1) | def process_config(ludwig_config: dict, experiment_dict: dict) -> dict: FILE: tests/ludwig/benchmarking/test_benchmarking.py function get_benchamrking_configs (line 10) | def get_benchamrking_configs(validity): function test_valid_benchmarking_configs_valid (line 19) | def test_valid_benchmarking_configs_valid(benchmarking_config_fp): function test_invalid_benchmarking_configs_valid (line 27) | def test_invalid_benchmarking_configs_valid(benchmarking_config_fp): FILE: tests/ludwig/benchmarking/test_profiler.py function test_ludwig_profiler (line 23) | def test_ludwig_profiler(tmpdir): FILE: tests/ludwig/combiners/test_combiners.py class PseudoInputFeature (line 49) | class PseudoInputFeature: method __init__ (line 50) | def __init__(self, feature_name, output_shape, feature_type=None): method type (line 55) | def type(self): method output_shape (line 59) | def output_shape(self): function check_combiner_output (line 64) | def check_combiner_output(combiner, combiner_output, batch_size): function features_to_test (line 81) | def features_to_test(feature_list: list[tuple[str, list]]) -> tuple[dict... function encoder_outputs (line 103) | def encoder_outputs(): function encoder_comparator_outputs (line 139) | def encoder_comparator_outputs(): function test_concat_combiner (line 191) | def test_concat_combiner( function test_sequence_concat_combiner (line 247) | def test_sequence_concat_combiner( function test_sequence_combiner (line 288) | def test_sequence_combiner( function test_tabnet_combiner (line 356) | def test_tabnet_combiner(features_to_test: dict, size: int, output_size:... function test_comparator_combiner (line 396) | def test_comparator_combiner( function test_transformer_combiner (line 432) | def test_transformer_combiner(encoder_outputs: tuple, transformer_output... function test_project_aggregate_combiner (line 471) | def test_project_aggregate_combiner(encoder_outputs: tuple, projection_s... function test_tabtransformer_combiner_binary_and_number_without_category (line 547) | def test_tabtransformer_combiner_binary_and_number_without_category( function test_tabtransformer_combiner_number_and_binary_with_category (line 626) | def test_tabtransformer_combiner_number_and_binary_with_category( function test_tabtransformer_combiner_number_or_binary_without_category (line 700) | def test_tabtransformer_combiner_number_or_binary_without_category( function test_tabtransformer_combiner_number_or_binary_with_category (line 784) | def test_tabtransformer_combiner_number_or_binary_with_category( FILE: tests/ludwig/config_sampling/test_config_sampling.py function full_config_generator (line 11) | def full_config_generator(generator_fn, *args): function test_config_sampling (line 17) | def test_config_sampling(): FILE: tests/ludwig/config_validation/test_checks.py function test_passthrough_number_decoder (line 20) | def test_passthrough_number_decoder(): function test_sequence_combiner_with_embed_encoder (line 37) | def test_sequence_combiner_with_embed_encoder(): function test_balance_multiple_class_failure (line 54) | def test_balance_multiple_class_failure(): function test_all_features_present_in_comparator_entities (line 72) | def test_all_features_present_in_comparator_entities(): function test_balance_non_binary_failure (line 101) | def test_balance_non_binary_failure(): function test_supported_features_config (line 116) | def test_supported_features_config(): function test_comparator_fc_layer_config (line 137) | def test_comparator_fc_layer_config(num_fc_layers: int | None, fc_layers... function test_dense_binary_encoder_0_layer (line 163) | def test_dense_binary_encoder_0_layer(): function test_comparator_combiner_entities (line 193) | def test_comparator_combiner_entities(entity_1: list[str], entity_2: lis... function test_experiment_binary_fill_with_const (line 216) | def test_experiment_binary_fill_with_const(): function test_check_concat_combiner_requirements (line 230) | def test_check_concat_combiner_requirements(): function test_check_llm_input_features (line 260) | def test_check_llm_input_features(): function test_retrieval_config_none_type (line 291) | def test_retrieval_config_none_type(): function test_retrieval_config_random_type (line 318) | def test_retrieval_config_random_type(): function test_retrieval_config_semantic_type (line 340) | def test_retrieval_config_semantic_type(): function test_check_llm_quantization_backend_incompatibility (line 368) | def test_check_llm_quantization_backend_incompatibility(): function test_check_qlora (line 398) | def test_check_qlora(): function test_check_prompt_requirements (line 429) | def test_check_prompt_requirements(): function test_check_sample_ratio_and_size_compatible (line 464) | def test_check_sample_ratio_and_size_compatible(): function test_check_llm_text_encoder_is_not_used_with_ecd (line 490) | def test_check_llm_text_encoder_is_not_used_with_ecd(): FILE: tests/ludwig/config_validation/test_validate_config_combiner.py function test_combiner_schema_is_not_empty_for_ECD (line 9) | def test_combiner_schema_is_not_empty_for_ECD(): function test_config_tabnet (line 15) | def test_config_tabnet(eval_batch_size): function test_config_bad_combiner (line 54) | def test_config_bad_combiner(): function test_config_bad_combiner_types_enums (line 102) | def test_config_bad_combiner_types_enums(): FILE: tests/ludwig/config_validation/test_validate_config_encoder.py function test_default_transformer_encoder (line 16) | def test_default_transformer_encoder(feature_type): function test_input_feature_transformer_encoder (line 39) | def test_input_feature_transformer_encoder(feature_gen): FILE: tests/ludwig/config_validation/test_validate_config_features.py function test_config_input_output_features (line 8) | def test_config_input_output_features(): function test_incorrect_input_features_config (line 20) | def test_incorrect_input_features_config(): function test_incorrect_output_features_config (line 57) | def test_incorrect_output_features_config(): function test_too_few_features_config (line 70) | def test_too_few_features_config(): function test_multi_output_features_config (line 100) | def test_multi_output_features_config(): FILE: tests/ludwig/config_validation/test_validate_config_hyperopt.py function test_check_scheduler_dependencies_installed (line 36) | def test_check_scheduler_dependencies_installed(dependencies, raises_exc... function test_check_search_algorithm_dependencies_installed (line 64) | def test_check_search_algorithm_dependencies_installed(dependencies, rai... function test_parameter_type_check (line 87) | def test_parameter_type_check(space, raises_exception): function test_parameter_key_check (line 133) | def test_parameter_key_check(referenced_parameter, raises_exception): function test_nested_parameter_key_check (line 259) | def test_nested_parameter_key_check(categories, raises_exception): function test_flat_parameter_edge_cases (line 289) | def test_flat_parameter_edge_cases(config): FILE: tests/ludwig/config_validation/test_validate_config_misc.py function test_config_features (line 61) | def test_config_features(): function test_config_encoders (line 108) | def test_config_encoders(): function test_config_with_backend (line 121) | def test_config_with_backend(): function test_config_bad_feature_type (line 160) | def test_config_bad_feature_type(): function test_config_bad_encoder_name (line 171) | def test_config_bad_encoder_name(): function test_config_fill_values (line 182) | def test_config_fill_values(): function test_validate_with_preprocessing_defaults (line 207) | def test_validate_with_preprocessing_defaults(): function test_ecd_defaults_schema (line 248) | def test_ecd_defaults_schema(): function test_validate_defaults_schema (line 259) | def test_validate_defaults_schema(): function test_validate_no_trainer_type (line 303) | def test_validate_no_trainer_type(): function test_schema_no_duplicates (line 318) | def test_schema_no_duplicates(): function test_ludwig_schema_serialization (line 343) | def test_ludwig_schema_serialization(model_type): function test_encoder_descriptions (line 356) | def test_encoder_descriptions(): function test_combiner_descriptions (line 366) | def test_combiner_descriptions(): function test_decoder_descriptions (line 374) | def test_decoder_descriptions(): function test_deprecation_warning_raised_for_unknown_parameters (line 384) | def test_deprecation_warning_raised_for_unknown_parameters(): function test_text_encoder_adapter (line 419) | def test_text_encoder_adapter(encoder_config, expected_adapter): function test_default_param_metadata (line 429) | def test_default_param_metadata(): FILE: tests/ludwig/config_validation/test_validate_config_preprocessing.py function test_config_preprocessing (line 8) | def test_config_preprocessing(): function test_check_global_max_sequence_length_fits_prompt_template (line 33) | def test_check_global_max_sequence_length_fits_prompt_template(): FILE: tests/ludwig/config_validation/test_validate_config_trainer.py function test_config_trainer_empty_null_and_default (line 14) | def test_config_trainer_empty_null_and_default(): function test_config_trainer_bad_optimizer (line 36) | def test_config_trainer_bad_optimizer(): function test_optimizer_property_validation (line 70) | def test_optimizer_property_validation(): function test_clipper_property_validation (line 108) | def test_clipper_property_validation(): FILE: tests/ludwig/contrib/test_contrib.py function test_add_contrib_callback_args (line 23) | def test_add_contrib_callback_args(sys_argv: Sequence[str], expected: li... FILE: tests/ludwig/data/dataframe/test_dask.py function test_from_ray_dataset_empty (line 9) | def test_from_ray_dataset_empty(tmpdir, ray_cluster_2cpu): FILE: tests/ludwig/data/test_cache_util.py function _gen_config (line 14) | def _gen_config(input_features: list[FeatureConfigDict]) -> ModelConfigD... function test_calculate_checksum (line 54) | def test_calculate_checksum(input_features: list[FeatureConfigDict], dif... function test_proc_col_checksum_consistency (line 69) | def test_proc_col_checksum_consistency(): function test_proc_col_checksum_consistency_same_preprocessing_different_types (line 96) | def test_proc_col_checksum_consistency_same_preprocessing_different_type... function test_checksum_determinism (line 114) | def test_checksum_determinism(ray_cluster_2cpu): FILE: tests/ludwig/data/test_dataset_synthesizer.py function test_build_synthetic_dataset (line 4) | def test_build_synthetic_dataset(tmpdir): FILE: tests/ludwig/data/test_negative_sampling.py function test_negative_sample (line 6) | def test_negative_sample(): FILE: tests/ludwig/data/test_postprocessing.py function test_convert_dict_to_df (line 6) | def test_convert_dict_to_df(): FILE: tests/ludwig/data/test_preprocessing.py function test_is_input_feature (line 5) | def test_is_input_feature(): FILE: tests/ludwig/data/test_ray_data.py function test_async_reader_error (line 18) | def test_async_reader_error(): function parquet_file (line 51) | def parquet_file(ray_cluster_2cpu) -> str: function parquet_filepath (line 75) | def parquet_filepath(parquet_file: str, request: "pytest.FixtureRequest"... function test_read_remote_parquet (line 89) | def test_read_remote_parquet(parquet_filepath: str): FILE: tests/ludwig/data/test_split.py function test_make_divisions_ensure_minimum_rows (line 19) | def test_make_divisions_ensure_minimum_rows(): function test_random_split (line 47) | def test_random_split(df_engine, ray_cluster_2cpu): function test_random_split_zero_probability_for_test_produces_no_zombie (line 95) | def test_random_split_zero_probability_for_test_produces_no_zombie(df_en... function test_fixed_split (line 124) | def test_fixed_split(df_engine, ray_cluster_2cpu): function test_stratify_split (line 180) | def test_stratify_split(df_engine, nrows, atol, class_probs, ray_cluster... function test_single_occurrence_stratified_split (line 243) | def test_single_occurrence_stratified_split(df_engine, atol, ray_cluster... function test_datetime_split (line 283) | def test_datetime_split(format, df_engine, ray_cluster_2cpu): function test_hash_split (line 340) | def test_hash_split(df_engine, ray_cluster_2cpu): FILE: tests/ludwig/datasets/download_all_datasets.py function download_all_datasets (line 12) | def download_all_datasets(): FILE: tests/ludwig/datasets/mnist/test_mnist_workflow.py function test_download_mnist_dataset (line 11) | def test_download_mnist_dataset(tmpdir): FILE: tests/ludwig/datasets/model_configs/train_all_model_configs.py class TrainingResults (line 21) | class TrainingResults: function _train_dataset_process (line 38) | def _train_dataset_process(dataset_name, results_queue): function train_all_datasets (line 105) | def train_all_datasets(): FILE: tests/ludwig/datasets/test_dataset_configs.py function test_get_config_and_load (line 8) | def test_get_config_and_load(tmpdir): function test_get_config_kaggle (line 30) | def test_get_config_kaggle(tmpdir): FILE: tests/ludwig/datasets/test_dataset_links.py function test_links (line 18) | def test_links(): function _check_url (line 41) | def _check_url(dataset_name, url): FILE: tests/ludwig/datasets/test_datasets.py function test_load_csv_dataset (line 20) | def test_load_csv_dataset(tmpdir): function test_multifile_join_dataset (line 52) | def test_multifile_join_dataset(tmpdir, f_type): function test_get_datasets_info (line 117) | def test_get_datasets_info(include_competitions, include_data_modalities): function test_get_dataset_buffer (line 147) | def test_get_dataset_buffer(): function test_train_dataset_uri (line 153) | def test_train_dataset_uri(tmpdir): function test_ad_hoc_dataset_download (line 226) | def test_ad_hoc_dataset_download(tmpdir, dataset_name, size): function test_hf_dataset_loading (line 238) | def test_hf_dataset_loading(): FILE: tests/ludwig/datasets/test_model_configs.py function test_default_model_config (line 4) | def test_default_model_config(tmpdir): function test_best_model_config (line 14) | def test_best_model_config(tmpdir): function test_dataset_has_no_model_configs (line 26) | def test_dataset_has_no_model_configs(tmpdir): FILE: tests/ludwig/datasets/titanic/test_titanic_workflow.py function test_download_titanic_dataset (line 13) | def test_download_titanic_dataset(tmpdir): FILE: tests/ludwig/decoders/test_image_decoder.py function test_unet_decoder (line 15) | def test_unet_decoder(height, width, num_channels, num_classes, batch_si... FILE: tests/ludwig/decoders/test_llm_decoders.py function test_text_extractor_decoder (line 12) | def test_text_extractor_decoder(): FILE: tests/ludwig/decoders/test_sequence_decoder.py function test_rnn_decoder (line 21) | def test_rnn_decoder(cell_type, num_layers, batch_size): function test_lstm_decoder (line 38) | def test_lstm_decoder(num_layers, batch_size): function test_sequence_rnn_decoder (line 58) | def test_sequence_rnn_decoder(cell_type, num_layers, batch_size): function test_sequence_lstm_decoder (line 83) | def test_sequence_lstm_decoder(num_layers, batch_size): function test_sequence_generator_decoder (line 109) | def test_sequence_generator_decoder(cell_type, num_layers, batch_size): FILE: tests/ludwig/decoders/test_sequence_decoder_utils.py function test_get_rnn_init_state_uses_hidden (line 10) | def test_get_rnn_init_state_uses_hidden(num_layers): function test_get_rnn_init_state_prefers_encoder_output_state (line 27) | def test_get_rnn_init_state_prefers_encoder_output_state(num_layers): function test_get_lstm_init_state_uses_hidden (line 40) | def test_get_lstm_init_state_uses_hidden(num_layers): function test_get_lstm_init_state_prefers_encoder_output_state (line 60) | def test_get_lstm_init_state_prefers_encoder_output_state(num_layers): FILE: tests/ludwig/decoders/test_sequence_tagger.py function test_sequence_tagger (line 14) | def test_sequence_tagger(use_attention, use_bias): FILE: tests/ludwig/encoders/test_bag_encoders.py function test_set_encoder (line 18) | def test_set_encoder(vocab: list[str], embedding_size: int, representati... FILE: tests/ludwig/encoders/test_category_encoders.py function test_categorical_dense_encoder (line 16) | def test_categorical_dense_encoder(vocab: list[str], embedding_size: int... function test_categorical_sparse_encoder (line 47) | def test_categorical_sparse_encoder(vocab: list[str], trainable: bool): FILE: tests/ludwig/encoders/test_date_encoders.py function test_date_embed (line 17) | def test_date_embed(): function test_date_wave (line 35) | def test_date_wave(): FILE: tests/ludwig/encoders/test_generic_encoders.py function test_generic_passthrough_encoder (line 10) | def test_generic_passthrough_encoder(input_size: int, categorical: bool): function test_generic_dense_encoder (line 25) | def test_generic_dense_encoder(input_size: int, num_layers: int, output_... FILE: tests/ludwig/encoders/test_h3_encoders.py function test_h3_embed (line 17) | def test_h3_embed(): function test_h3_weighted_sum (line 39) | def test_h3_weighted_sum(): function test_h3_rnn_embed (line 61) | def test_h3_rnn_embed(): FILE: tests/ludwig/encoders/test_image_encoders.py function test_stacked2d_cnn (line 35) | def test_stacked2d_cnn(height: int, width: int, num_conv_layers: int, nu... function test_resnet_encoder (line 54) | def test_resnet_encoder(height: int, width: int, num_channels: int): function test_mlp_mixer_encoder (line 71) | def test_mlp_mixer_encoder(height: int, width: int, num_channels: int): function test_vit_encoder (line 89) | def test_vit_encoder(image_size: int, num_channels: int, use_pretrained:... function test_unet_encoder (line 111) | def test_unet_encoder(height: int, width: int, num_channels: int): function test_tv_alexnet_encoder (line 133) | def test_tv_alexnet_encoder( function test_tv_convnext_encoder (line 159) | def test_tv_convnext_encoder( function test_tv_densenet_encoder (line 185) | def test_tv_densenet_encoder( function test_tv_efficientnet_encoder (line 212) | def test_tv_efficientnet_encoder( function test_tv_googlenet_encoder (line 238) | def test_tv_googlenet_encoder( function test_tv_inceptionv3_encoder (line 264) | def test_tv_inceptionv3_encoder( function test_tv_maxvit_encoder (line 290) | def test_tv_maxvit_encoder( function test_tv_mnasnet_encoder (line 316) | def test_tv_mnasnet_encoder( function test_tv_mobilenetv2_encoder (line 342) | def test_tv_mobilenetv2_encoder( function test_tv_mobilenetv3_encoder (line 368) | def test_tv_mobilenetv3_encoder( function test_tv_regnet_encoder (line 395) | def test_tv_regnet_encoder( function test_tv_resnet_torch_encoder (line 421) | def test_tv_resnet_torch_encoder( function test_tv_resnext_encoder (line 447) | def test_tv_resnext_encoder( function test_tv_shufflenet_v2_encoder (line 473) | def test_tv_shufflenet_v2_encoder( function test_tv_squeezenet_encoder (line 499) | def test_tv_squeezenet_encoder( function test_tv_swin_transformer_encoder (line 527) | def test_tv_swin_transformer_encoder( function test_tv_vgg_encoder (line 553) | def test_tv_vgg_encoder( function test_tv_vit_encoder (line 580) | def test_tv_vit_encoder( function test_tv_wide_resnet_encoder (line 606) | def test_tv_wide_resnet_encoder( FILE: tests/ludwig/encoders/test_llm_encoders.py function encoder_config (line 22) | def encoder_config() -> LLMEncoderConfig: function model_config (line 38) | def model_config(encoder_config): class WrapperModule (line 42) | class WrapperModule(nn.Module): method __init__ (line 43) | def __init__(self, encoder: LLMEncoder): class TestLLMEncoder (line 48) | class TestLLMEncoder: method create_encoder_config_with_adapter (line 49) | def create_encoder_config_with_adapter( method adapter_param_name_prefix (line 65) | def adapter_param_name_prefix(self, adapter: str) -> str: method test_init (line 79) | def test_init(self, encoder_config: LLMEncoderConfig, model_config): method test_init_with_adapter (line 117) | def test_init_with_adapter(self, encoder_config: LLMEncoderConfig, ada... method test_prepare_for_training (line 137) | def test_prepare_for_training(self, encoder_config: LLMEncoderConfig, ... method test_save_to_state_dict (line 155) | def test_save_to_state_dict(self, encoder_config: LLMEncoderConfig, tm... method test_save_to_state_dict_adapter (line 164) | def test_save_to_state_dict_adapter(self, encoder_config: LLMEncoderCo... method test_load_from_state_dict (line 174) | def test_load_from_state_dict(self, encoder_config: LLMEncoderConfig, ... method test_load_from_state_dict_adapter (line 203) | def test_load_from_state_dict_adapter(self, encoder_config: LLMEncoder... FILE: tests/ludwig/encoders/test_sequence_encoders.py function test_sequence_passthrough_encoder (line 19) | def test_sequence_passthrough_encoder(reduce_output: str): function test_sequence_encoders (line 37) | def test_sequence_encoders(encoder_type: type, reduce_output: str, vocab... FILE: tests/ludwig/encoders/test_set_encoders.py function test_set_encoder (line 18) | def test_set_encoder( FILE: tests/ludwig/encoders/test_text_encoders.py function _load_pretrained_hf_model_no_weights (line 32) | def _load_pretrained_hf_model_no_weights( function get_mismatched_config_params (line 44) | def get_mismatched_config_params(ludwig_results_dir, ludwig_model): function test_hf_ludwig_model_e2e (line 76) | def test_hf_ludwig_model_e2e(tmpdir, csv_filename, encoder_name): function test_hf_ludwig_model_reduce_options (line 133) | def test_hf_ludwig_model_reduce_options(tmpdir, csv_filename, encoder_na... function test_hf_ludwig_model_auto_transformers (line 199) | def test_hf_ludwig_model_auto_transformers(tmpdir, csv_filename, pretrai... function test_distilbert_param_updates (line 236) | def test_distilbert_param_updates(trainable: bool): function test_encoder_names_constant_synced_with_schema (line 267) | def test_encoder_names_constant_synced_with_schema(encoder_name): function test_tfidf_encoder (line 273) | def test_tfidf_encoder(vocab_size: int): function test_hf_auto_transformer_use_pretrained (line 297) | def test_hf_auto_transformer_use_pretrained(): FILE: tests/ludwig/encoders/test_timm_encoder.py function test_timm_encoder_forward (line 24) | def test_timm_encoder_forward(encoder_cls, model_name): function test_timm_encoder_trainable (line 40) | def test_timm_encoder_trainable(trainable): function test_timm_encoder_output_shape_property (line 47) | def test_timm_encoder_output_shape_property(): FILE: tests/ludwig/evaluation/test_evaluation.py function test_eval_steps_determinism (line 9) | def test_eval_steps_determinism(): function _run_eval_steps_determinism (line 22) | def _run_eval_steps_determinism(): FILE: tests/ludwig/explain/test_captum.py function test_get_token_attributions (line 6) | def test_get_token_attributions(): FILE: tests/ludwig/explain/test_util.py function test_get_absolute_module_key_from_submodule (line 13) | def test_get_absolute_module_key_from_submodule(): function test_replace_layer_with_copy (line 51) | def test_replace_layer_with_copy(tmpdir): FILE: tests/ludwig/features/test_audio_feature.py function test_audio_input_feature (line 27) | def test_audio_input_feature(encoder: str) -> None: function test_add_feature_data (line 56) | def test_add_feature_data(feature_type, tmpdir): FILE: tests/ludwig/features/test_bag_feature.py function bag_config (line 24) | def bag_config(): function test_bag_input_feature (line 38) | def test_bag_input_feature(bag_config: dict, encoder: str) -> None: FILE: tests/ludwig/features/test_binary_feature.py function binary_config (line 16) | def binary_config(): function test_binary_input_feature (line 24) | def test_binary_input_feature(binary_config: dict, encoder: str): function test_binary_output_feature (line 38) | def test_binary_output_feature(): function test_binary_output_feature_without_positive_class_weight (line 66) | def test_binary_output_feature_without_positive_class_weight(): FILE: tests/ludwig/features/test_category_feature.py function category_config (line 18) | def category_config(): function test_category_input_feature (line 36) | def test_category_input_feature( FILE: tests/ludwig/features/test_date_feature.py function date_config (line 25) | def date_config(): function test_date_input_feature (line 29) | def test_date_input_feature(date_config: FeatureConfigDict): function test_date_to_list (line 58) | def test_date_to_list(date_str, datetime_format, expected_list): function reference_date_list (line 66) | def reference_date_list() -> list[int]: function fill_value (line 73) | def fill_value() -> str: function fill_value_list (line 78) | def fill_value_list(fill_value: str) -> list[int]: function test_date_to_list_numeric (line 106) | def test_date_to_list_numeric(timestamp: Any, datetime_format: str, expe... function test_date_to_list__DatetimeObjectFromParsedJSON (line 131) | def test_date_to_list__DatetimeObjectFromParsedJSON(): function test_date_to_list__UsesFillValueOnInvalidDate (line 147) | def test_date_to_list__UsesFillValueOnInvalidDate(): function date_obj (line 165) | def date_obj(): function date_obj_vec (line 170) | def date_obj_vec(): function test_date_object_to_list (line 174) | def test_date_object_to_list(date_obj, date_obj_vec, fill_value): FILE: tests/ludwig/features/test_feature_utils.py function test_ludwig_feature_dict (line 8) | def test_ludwig_feature_dict(): function test_ludwig_feature_dict_with_periods (line 30) | def test_ludwig_feature_dict_with_periods(): function test_compute_token_probabilities (line 43) | def test_compute_token_probabilities(sequence_type): function test_compute_sequence_probability (line 56) | def test_compute_sequence_probability(): FILE: tests/ludwig/features/test_h3_feature.py function test_h3_to_list (line 4) | def test_h3_to_list(): FILE: tests/ludwig/features/test_image_feature.py function image_config (line 28) | def image_config(): function test_image_input_feature (line 80) | def test_image_input_feature(image_config: dict, encoder: str, height: i... function test_image_output_feature (line 131) | def test_image_output_feature( function test_image_preproc_module_bad_num_channels (line 194) | def test_image_preproc_module_bad_num_channels(): function test_image_preproc_module_list_of_tensors (line 223) | def test_image_preproc_module_list_of_tensors(resize_method, num_channel... function test_image_preproc_module_tensor (line 253) | def test_image_preproc_module_tensor(resize_method, num_channels, num_ch... function test_image_preproc_module_class_map (line 282) | def test_image_preproc_module_class_map(height, width): FILE: tests/ludwig/features/test_number_feature.py function number_config (line 19) | def number_config(): function test_number_input_feature (line 23) | def test_number_input_feature( function test_outlier_replacer (line 46) | def test_outlier_replacer(): FILE: tests/ludwig/features/test_sequence_features.py function input_sequence (line 25) | def input_sequence() -> tuple[torch.Tensor, list]: function test_sequence_input_feature (line 50) | def test_sequence_input_feature(input_sequence: tuple, encoder: str, seq... function test_sequence_output_feature (line 92) | def test_sequence_output_feature(sequence_type: str): function test_sequence_preproc_module_bad_tokenizer (line 119) | def test_sequence_preproc_module_bad_tokenizer(): function test_sequence_preproc_module_space_tokenizer (line 136) | def test_sequence_preproc_module_space_tokenizer(): function test_text_preproc_module_space_punct_tokenizer (line 165) | def test_text_preproc_module_space_punct_tokenizer(): function test_sequence_preproc_module_sentencepiece_tokenizer (line 201) | def test_sequence_preproc_module_sentencepiece_tokenizer(): function test_sequence_preproc_module_clip_tokenizer (line 237) | def test_sequence_preproc_module_clip_tokenizer(): function test_sequence_preproc_module_gpt2bpe_tokenizer (line 271) | def test_sequence_preproc_module_gpt2bpe_tokenizer(): function test_sequence_preproc_module_bert_tokenizer (line 308) | def test_sequence_preproc_module_bert_tokenizer(): FILE: tests/ludwig/features/test_set_feature.py function set_config (line 18) | def set_config(): function test_set_input_feature (line 45) | def test_set_input_feature(set_config: dict) -> None: FILE: tests/ludwig/features/test_text_feature.py function test_backwards_compatibility (line 13) | def test_backwards_compatibility(): function test_get_decoded_targets_and_predictions (line 121) | def test_get_decoded_targets_and_predictions(vocab_size, targets, predic... FILE: tests/ludwig/features/test_timeseries_feature.py function timeseries_config (line 18) | def timeseries_config(): function test_timeseries_input_feature (line 35) | def test_timeseries_input_feature(timeseries_config: dict, encoder: str)... FILE: tests/ludwig/hyperopt/test_hyperopt.py function _get_config (line 13) | def _get_config(): function test_substitute_parameters (line 103) | def test_substitute_parameters(parameters, expected): function test_grid_search_more_than_one_sample (line 108) | def test_grid_search_more_than_one_sample(): function test_default_num_samples (line 157) | def test_default_num_samples(parameters, expected_num_samples): FILE: tests/ludwig/model_export/test_onnx_exporter.py class TestOnnxExporter (line 13) | class TestOnnxExporter(unittest.TestCase): method test_onnx_export (line 17) | def test_onnx_export( FILE: tests/ludwig/models/test_trainable_image_layers.py function setup_data (line 14) | def setup_data(tmp_path_factory): function test_trainable_torchvision_layers (line 40) | def test_trainable_torchvision_layers(setup_data, trainable): FILE: tests/ludwig/models/test_training_determinism.py function _assert_stats_close (line 12) | def _assert_stats_close(stats1, stats2, rtol=1e-2, atol=1e-2): function test_training_determinism_ray_backend (line 54) | def test_training_determinism_ray_backend(csv_filename, tmpdir, ray_clus... function test_training_determinism_local_backend (line 69) | def test_training_determinism_local_backend(csv_filename, tmpdir): function train_twice (line 84) | def train_twice(backend, csv_filename, tmpdir): FILE: tests/ludwig/models/test_training_success.py function generate_data_and_train (line 8) | def generate_data_and_train(config, csv_filename): function test_category_passthrough_encoder (line 27) | def test_category_passthrough_encoder(csv_filename): function test_binary_encoders (line 39) | def test_binary_encoders(csv_filename): FILE: tests/ludwig/modules/test_attention.py function test_feed_forward_attention_reducer (line 19) | def test_feed_forward_attention_reducer(input_batch_size: int, input_seq... function test_multihead_self_attention (line 47) | def test_multihead_self_attention(input_batch_size: int, input_seq_size:... function test_transformer_block (line 84) | def test_transformer_block( function test_transformer_stack (line 124) | def test_transformer_stack( FILE: tests/ludwig/modules/test_convolutional_modules.py function expected_seq_size (line 34) | def expected_seq_size( function test_conv1d_layer (line 77) | def test_conv1d_layer( function test_conv1d_stack (line 135) | def test_conv1d_stack(layers: None | list, num_layers: None | int, dropo... function test_parallel_conv1d (line 210) | def test_parallel_conv1d(layers: None | list) -> None: function test_parallel_conv1d_stack (line 277) | def test_parallel_conv1d_stack(stacked_layers: None | list, dropout: flo... function test_conv2d_layer (line 344) | def test_conv2d_layer( function test_conv2d_stack (line 389) | def test_conv2d_stack( function test_conv2d_layer_fixed_padding (line 411) | def test_conv2d_layer_fixed_padding( function test_resnet_block (line 427) | def test_resnet_block( function test_resnet_bottleneck_block (line 447) | def test_resnet_bottleneck_block( function test_resnet_block_layer (line 464) | def test_resnet_block_layer( function test_resnet (line 489) | def test_resnet( FILE: tests/ludwig/modules/test_embedding_modules.py function test_embed (line 13) | def test_embed( function test_embed_set (line 31) | def test_embed_set( function test_embed_weighted (line 49) | def test_embed_weighted( function test_embed_sequence (line 63) | def test_embed_sequence( function test_token_and_position_embedding (line 82) | def test_token_and_position_embedding( FILE: tests/ludwig/modules/test_encoder.py function create_encoder (line 41) | def create_encoder(encoder_type, **encoder_kwargs): function _generate_image (line 46) | def _generate_image(image_size): function generate_images (line 50) | def generate_images(image_size, num_images): function _generate_sentence (line 54) | def _generate_sentence(vocab_size, max_len): function generate_random_sentences (line 62) | def generate_random_sentences(num_sentences=10, max_len=10, vocab_size=10): function encoder_test (line 71) | def encoder_test( function test_image_encoders_stacked_2dcnn (line 103) | def test_image_encoders_stacked_2dcnn(): function test_image_encoders_mlpmixer (line 158) | def test_image_encoders_mlpmixer(): function test_sequence_encoder_embed (line 215) | def test_sequence_encoder_embed(): function test_sequence_encoders (line 274) | def test_sequence_encoders(encoder_type: Encoder, trainable: bool, reduc... FILE: tests/ludwig/modules/test_fully_connected_modules.py function test_fc_layer (line 19) | def test_fc_layer( function test_fc_stack (line 44) | def test_fc_stack( function test_fc_stack_input_size_mismatch_fails (line 56) | def test_fc_stack_input_size_mismatch_fails(): function test_fc_stack_no_layers_behaves_like_passthrough (line 70) | def test_fc_stack_no_layers_behaves_like_passthrough(): FILE: tests/ludwig/modules/test_initializer_modules.py function test_get_initializer (line 10) | def test_get_initializer(): FILE: tests/ludwig/modules/test_loss_modules.py function from_float (line 27) | def from_float(v: float) -> torch.Tensor: function test_mse_loss (line 34) | def test_mse_loss(preds: torch.Tensor, target: torch.Tensor, output: tor... function test_mae_loss (line 42) | def test_mae_loss(preds: torch.Tensor, target: torch.Tensor, output: tor... function test_mape_loss (line 50) | def test_mape_loss(preds: torch.Tensor, target: torch.Tensor, output: to... function test_rmse_loss (line 58) | def test_rmse_loss(preds: torch.Tensor, target: torch.Tensor, output: to... function test_rmspe_loss (line 66) | def test_rmspe_loss(preds: torch.Tensor, target: torch.Tensor, output: t... function test_rmspe_loss_zero_targets (line 74) | def test_rmspe_loss_zero_targets(preds: torch.Tensor, target: torch.Tens... function test_bwcew_loss (line 91) | def test_bwcew_loss( function test_softmax_cross_entropy_loss (line 112) | def test_softmax_cross_entropy_loss(preds: torch.Tensor, target: torch.T... function test_sigmoid_cross_entropy_loss (line 120) | def test_sigmoid_cross_entropy_loss(preds: torch.Tensor, target: torch.T... function test_huber_loss (line 136) | def test_huber_loss(preds: torch.Tensor, target: torch.Tensor, delta: fl... function test_corn_loss (line 146) | def test_corn_loss(preds: torch.Tensor, target: torch.Tensor, output: to... function test_dict_class_weights_category (line 151) | def test_dict_class_weights_category(): function test_dict_class_weights_text (line 189) | def test_dict_class_weights_text(): function test_dict_class_weights_set (line 260) | def test_dict_class_weights_set(): FILE: tests/ludwig/modules/test_lr_scheduler.py function test_lr_scheduler_warmup_decay (line 16) | def test_lr_scheduler_warmup_decay(): function test_lr_scheduler_reduce_on_plateau (line 93) | def test_lr_scheduler_reduce_on_plateau(): function test_lr_scheduler_cosine_decay_fixed_period (line 151) | def test_lr_scheduler_cosine_decay_fixed_period(): function test_lr_scheduler_cosine_decay_increasing_period (line 183) | def test_lr_scheduler_cosine_decay_increasing_period(): function test_lr_scheduler_save_load (line 220) | def test_lr_scheduler_save_load(): FILE: tests/ludwig/modules/test_metric_modules.py function test_rmse_metric (line 19) | def test_rmse_metric(preds: torch.Tensor, target: torch.Tensor, output: ... function test_roc_auc_metric (line 29) | def test_roc_auc_metric(preds: torch.Tensor, target: torch.Tensor, outpu... function test_specificity_metric (line 39) | def test_specificity_metric(preds: torch.Tensor, target: torch.Tensor, o... function test_rmspe_metric (line 49) | def test_rmspe_metric(preds: torch.Tensor, target: torch.Tensor, output:... function test_r2_score (line 63) | def test_r2_score(preds: torch.Tensor, target: torch.Tensor, num_outputs... function test_r2_score_single_sample (line 70) | def test_r2_score_single_sample(): function test_bwcewl_metric (line 80) | def test_bwcewl_metric(preds: torch.Tensor, target: torch.Tensor, output... function test_softmax_cross_entropy_metric (line 90) | def test_softmax_cross_entropy_metric(preds: torch.Tensor, target: torch... function test_sigmoid_cross_entropy_metric (line 100) | def test_sigmoid_cross_entropy_metric(preds: torch.Tensor, target: torch... function test_token_accuracy_metric (line 127) | def test_token_accuracy_metric(preds: torch.Tensor, target: torch.Tensor... function test_sequence_accuracy_metric (line 134) | def test_sequence_accuracy_metric(): function test_category_accuracy (line 217) | def test_category_accuracy(preds: torch.Tensor, target: torch.Tensor, ou... function test_category_accuracy_micro (line 230) | def test_category_accuracy_micro(preds: torch.Tensor, target: torch.Tens... function test_hits_at_k_metric (line 250) | def test_hits_at_k_metric(preds: torch.Tensor, target: torch.Tensor, out... function test_mae_metric (line 260) | def test_mae_metric(preds: torch.Tensor, target: torch.Tensor, output: t... function test_mse_metric (line 270) | def test_mse_metric(preds: torch.Tensor, target: torch.Tensor, output: t... function test_mape_metric (line 280) | def test_mape_metric(preds: torch.Tensor, target: torch.Tensor, output: ... function test_jaccard_metric (line 290) | def test_jaccard_metric(preds: torch.Tensor, target: torch.Tensor, outpu... function test_char_error_rate (line 297) | def test_char_error_rate(): FILE: tests/ludwig/modules/test_mlp_mixer_modules.py function test_mlp (line 9) | def test_mlp(in_features: int, hidden_size: int, out_features: int): function test_mixer_block (line 14) | def test_mixer_block( function test_mlp_mixer (line 26) | def test_mlp_mixer(img_height: int, img_width: int, in_channels: int): FILE: tests/ludwig/modules/test_normalization_modules.py function test_ghostbatchnormalization (line 12) | def test_ghostbatchnormalization(mode: bool, virtual_batch_size: int | N... function test_ghostbatchnormalization_chunk_size_2 (line 41) | def test_ghostbatchnormalization_chunk_size_2() -> None: FILE: tests/ludwig/modules/test_recurrent_modules.py function test_recurrent_stack (line 12) | def test_recurrent_stack(max_sequence_length, expected_output_shape): FILE: tests/ludwig/modules/test_reduction_modules.py function test_sequence_reducer (line 12) | def test_sequence_reducer(reduce_mode: str, test_input_shape: tuple[int,... FILE: tests/ludwig/modules/test_regex_freezing.py function test_tv_efficientnet_freezing (line 40) | def test_tv_efficientnet_freezing(regex): function test_llm_freezing (line 56) | def test_llm_freezing(tmpdir, csv_filename): function _run_llm_freezing (line 69) | def _run_llm_freezing(tmpdir, csv_filename): function test_frozen_tv_training (line 96) | def test_frozen_tv_training(tmpdir, csv_filename): FILE: tests/ludwig/modules/test_tabnet_modules.py function test_sparsemax (line 20) | def test_sparsemax(input_tensor: torch.Tensor) -> None: function test_feature_block (line 33) | def test_feature_block( function test_feature_transformer (line 70) | def test_feature_transformer( function test_attentive_transformer (line 107) | def test_attentive_transformer( function test_tabnet (line 150) | def test_tabnet( FILE: tests/ludwig/modules/test_utils.py function assert_output_shapes (line 6) | def assert_output_shapes(module: LudwigModule, input_shape: tuple[int]): FILE: tests/ludwig/schema/hyperopt/test_scheduler.py function dependency_check_config (line 17) | def dependency_check_config(request): function test_dependency_check (line 29) | def test_dependency_check(dependency_check_config): FILE: tests/ludwig/schema/hyperopt/test_search_algorithm.py function dependency_check_config (line 17) | def dependency_check_config(request): function test_dependency_check (line 29) | def test_dependency_check(dependency_check_config): FILE: tests/ludwig/schema/test_model_config.py function test_config_object (line 52) | def test_config_object(): function test_config_object_defaults (line 139) | def test_config_object_defaults(): function test_config_object_to_config_dict (line 189) | def test_config_object_to_config_dict(): function test_update_config_object (line 237) | def test_update_config_object(): function test_config_object_validation_parameters_defaults (line 273) | def test_config_object_validation_parameters_defaults(model_type: str): function test_config_object_validation_parameters_multiple_output_features (line 293) | def test_config_object_validation_parameters_multiple_output_features(): function test_config_object_validation_parameters_explicitly_set_validation_field (line 326) | def test_config_object_validation_parameters_explicitly_set_validation_f... function test_config_object_validation_parameters_explicitly_set_validation_metric (line 352) | def test_config_object_validation_parameters_explicitly_set_validation_m... function test_config_object_validation_parameters_invalid_metric (line 379) | def test_config_object_validation_parameters_invalid_metric(): function test_config_object_validation_parameters_metric_conflict (line 399) | def test_config_object_validation_parameters_metric_conflict(): function test_constructors_yaml (line 423) | def test_constructors_yaml(): function test_constructors_dict (line 447) | def test_constructors_dict(): function test_feature_enabling_disabling (line 466) | def test_feature_enabling_disabling(): function test_sequence_combiner (line 487) | def test_sequence_combiner(): function test_shared_state (line 509) | def test_shared_state(session): function test_convert_submodules (line 554) | def test_convert_submodules(): function test_defaults_mixins (line 570) | def test_defaults_mixins(): function test_initializer_recursion (line 584) | def test_initializer_recursion(): function test_number_feature_zscore_preprocessing_default (line 625) | def test_number_feature_zscore_preprocessing_default(): function test_augmentation_pipeline (line 659) | def test_augmentation_pipeline(augmentation, expected): function test_preprocessing_max_sequence_length (line 702) | def test_preprocessing_max_sequence_length(sequence_length, max_sequence... function test_encoder_decoder_values_as_str (line 734) | def test_encoder_decoder_values_as_str(): function test_llm_base_model_config (line 757) | def test_llm_base_model_config(base_model_config, model_name): function test_llm_base_model_config_error (line 777) | def test_llm_base_model_config_error(base_model_config): function test_llm_quantization_config (line 797) | def test_llm_quantization_config(bits: int | None, expected_qconfig: Qua... function test_llm_rope_scaling_failure_modes (line 822) | def test_llm_rope_scaling_failure_modes( function test_llm_model_parameters_no_rope_scaling (line 839) | def test_llm_model_parameters_no_rope_scaling(): function test_llm_finetuning_output_feature_config (line 853) | def test_llm_finetuning_output_feature_config(): function test_llm_quantization_backend_compatibility (line 875) | def test_llm_quantization_backend_compatibility(): class TestMaxNewTokensOverride (line 909) | class TestMaxNewTokensOverride: method test_max_new_tokens_override_no_changes_to_max_new_tokens (line 910) | def test_max_new_tokens_override_no_changes_to_max_new_tokens(self): method test_max_new_tokens_override_large_max_sequence_length (line 924) | def test_max_new_tokens_override_large_max_sequence_length(self): method test_max_new_tokens_override_large_global_max_sequence_length (line 938) | def test_max_new_tokens_override_large_global_max_sequence_length(self): method test_max_new_tokens_override_fallback_to_model_window_size (line 953) | def test_max_new_tokens_override_fallback_to_model_window_size(self): FILE: tests/ludwig/schema/test_schema_utils.py function test_remove_duplicate_fields (line 5) | def test_remove_duplicate_fields(): FILE: tests/ludwig/schema_fields/test_fields_misc.py function get_marshmallow_field_from_metadata (line 9) | def get_marshmallow_field_from_metadata(dfield): function test_StringOptions (line 24) | def test_StringOptions(): function test_Embed (line 44) | def test_Embed(): function test_InitializerOrDict (line 59) | def test_InitializerOrDict(): function test_FloatRangeTupleDataclassField (line 77) | def test_FloatRangeTupleDataclassField(): function test_OneOfOptionsField (line 105) | def test_OneOfOptionsField(): function test_OneOfOptionsField_allows_none (line 146) | def test_OneOfOptionsField_allows_none(): function test_OneOfOptionsField_multiple_fields_allow_none (line 170) | def test_OneOfOptionsField_multiple_fields_allow_none(): function test_OneOfOptionsField_allows_none_one_field_allows_none (line 186) | def test_OneOfOptionsField_allows_none_one_field_allows_none(): FILE: tests/ludwig/schema_fields/test_fields_optimization.py function test_torch_description_pull (line 11) | def test_torch_description_pull(): function test_OptimizerDataclassField (line 23) | def test_OptimizerDataclassField(): function test_ClipperDataclassField (line 65) | def test_ClipperDataclassField(): FILE: tests/ludwig/schema_fields/test_fields_preprocessing.py function get_marshmallow_from_dataclass_field (line 9) | def get_marshmallow_from_dataclass_field(dfield): function test_preprocessing_dataclass_field (line 14) | def test_preprocessing_dataclass_field(): FILE: tests/ludwig/schema_fields/test_marshmallow_misc.py class CustomTestSchema (line 14) | class CustomTestSchema(BaseMarshmallowConfig): function test_assert_is_a_marshmallow_clas (line 21) | def test_assert_is_a_marshmallow_clas(): function test_load_config_with_kwargs (line 27) | def test_load_config_with_kwargs(): FILE: tests/ludwig/utils/automl/test_type_inference.py function test_infer_type (line 50) | def test_infer_type(num_distinct_values, distinct_values, img_values, au... function test_infer_type_explicit_date (line 63) | def test_infer_type_explicit_date(): function test_should_exclude (line 87) | def test_should_exclude(idx, num_distinct_values, dtype, name, expected): function test_auto_type_inference_single_value_binary_feature (line 93) | def test_auto_type_inference_single_value_binary_feature(): function test_should_exclude_text (line 112) | def test_should_exclude_text(column_count, avg_words, expected): function test_type_inference_with_negative_positive_binary_values (line 118) | def test_type_inference_with_negative_positive_binary_values(negative_cl... FILE: tests/ludwig/utils/automl/test_utils.py function test_avg_num_tokens (line 17) | def test_avg_num_tokens(field, expected): FILE: tests/ludwig/utils/entmax/test_losses.py function test_non_neg (line 27) | def test_non_neg(Loss): function test_loss (line 37) | def test_loss(Loss, ignore_index, reduction): function test_index_ignored (line 45) | def test_index_ignored(Loss): FILE: tests/ludwig/utils/entmax/test_mask.py function test_mask (line 19) | def test_mask(func, dtype): function test_mask_alphas (line 34) | def test_mask_alphas(alpha): FILE: tests/ludwig/utils/entmax/test_root_finding.py function test_sparsemax (line 20) | def test_sparsemax(training, bisect_training): function test_entmax15 (line 29) | def test_entmax15(training, bisect_training): function test_sparsemax_grad (line 36) | def test_sparsemax_grad(): function test_entmax_grad (line 42) | def test_entmax_grad(alpha): function test_entmax_correct_multiple_alphas (line 48) | def test_entmax_correct_multiple_alphas(): function test_entmax_grad_multiple_alphas (line 60) | def test_entmax_grad_multiple_alphas(): function test_arbitrary_dimension (line 68) | def test_arbitrary_dimension(dim): function test_arbitrary_dimension_grad (line 89) | def test_arbitrary_dimension_grad(dim): FILE: tests/ludwig/utils/entmax/test_topk.py function test_mapping (line 15) | def test_mapping(dim, Map): function test_entmax_topk (line 23) | def test_entmax_topk(dim, coef): function test_sparsemax_topk (line 35) | def test_sparsemax_topk(dim, coef, k): FILE: tests/ludwig/utils/test_algorithm_utils.py function test_topological_sort (line 19) | def test_topological_sort(unsorted: list, sorted: list) -> None: FILE: tests/ludwig/utils/test_audio_utils.py function test_is_audio_score (line 22) | def test_is_audio_score(path: str, score: int): FILE: tests/ludwig/utils/test_backward_compatibility.py function test_preprocessing_backward_compatibility (line 40) | def test_preprocessing_backward_compatibility(): function test_audio_feature_backward_compatibility (line 55) | def test_audio_feature_backward_compatibility(): function test_encoder_decoder_backwards_compatibility (line 134) | def test_encoder_decoder_backwards_compatibility(): function test_deprecated_field_aliases (line 306) | def test_deprecated_field_aliases(): function test_deprecated_split_aliases (line 376) | def test_deprecated_split_aliases(stratify, force_split): function test_deprecated_hyperopt_sampler_early_stopping (line 408) | def test_deprecated_hyperopt_sampler_early_stopping(use_scheduler): function test_validate_old_model_config (line 472) | def test_validate_old_model_config(): function test_update_missing_value_strategy (line 500) | def test_update_missing_value_strategy(missing_value_strategy: str): function test_update_increase_batch_size_on_plateau_max (line 525) | def test_update_increase_batch_size_on_plateau_max(): function test_old_class_weights_default (line 544) | def test_old_class_weights_default(): function test_upgrade_model_progress (line 581) | def test_upgrade_model_progress(): function test_upgrade_model_progress_already_valid (line 669) | def test_upgrade_model_progress_already_valid(): function test_cache_credentials_backward_compatibility (line 774) | def test_cache_credentials_backward_compatibility(): function test_legacy_image_encoders (line 794) | def test_legacy_image_encoders(encoder: dict[str, Any], upgraded_type: s... function test_load_config_missing_hyperopt (line 809) | def test_load_config_missing_hyperopt(): function test_type_removed_from_defaults_config (line 828) | def test_type_removed_from_defaults_config(): FILE: tests/ludwig/utils/test_calibration.py function uncalibrated_logits_and_labels (line 8) | def uncalibrated_logits_and_labels(): function test_temperature_scaling_binary (line 32) | def test_temperature_scaling_binary(uncalibrated_logits_and_labels): function test_temperature_scaling_category (line 46) | def test_temperature_scaling_category(uncalibrated_logits_and_labels): function test_matrix_scaling_category (line 58) | def test_matrix_scaling_category(uncalibrated_logits_and_labels): FILE: tests/ludwig/utils/test_class_balancing.py function test_balance (line 22) | def test_balance(method, balance): FILE: tests/ludwig/utils/test_config_utils.py function test_set_fixed_preprocessing_params (line 31) | def test_set_fixed_preprocessing_params(pretrained_model_name_or_path: s... function test_set_fixed_preprocessing_params_cache_embeddings (line 63) | def test_set_fixed_preprocessing_params_cache_embeddings(encoder_params:... function llm_config_dict (line 78) | def llm_config_dict() -> dict[str, Any]: function ecd_config_dict_llm_encoder (line 88) | def ecd_config_dict_llm_encoder() -> dict[str, Any]: function ecd_config_dict_llm_encoder_multiple_features (line 103) | def ecd_config_dict_llm_encoder_multiple_features() -> dict[str, Any]: function ecd_config_dict_no_llm_encoder (line 119) | def ecd_config_dict_no_llm_encoder() -> dict[str, Any]: function ecd_config_dict_no_text_features (line 128) | def ecd_config_dict_no_text_features() -> dict[str, Any]: function test_is_or_uses_llm (line 152) | def test_is_or_uses_llm(config: dict[str, Any], expectation: bool, confi... function test_is_or_uses_llm_invalid_input (line 168) | def test_is_or_uses_llm_invalid_input(invalid_config): function quantization_4bit_config (line 181) | def quantization_4bit_config() -> dict[str, Any]: function quantization_8bit_config (line 186) | def quantization_8bit_config() -> dict[str, Any]: function llm_config_dict_4bit (line 191) | def llm_config_dict_4bit(llm_config_dict: dict[str, Any], quantization_4... function llm_config_dict_8bit (line 198) | def llm_config_dict_8bit(llm_config_dict: dict[str, Any], quantization_8... function ecd_config_dict_llm_encoder_4bit (line 205) | def ecd_config_dict_llm_encoder_4bit( function ecd_config_dict_llm_encoder_8bit (line 214) | def ecd_config_dict_llm_encoder_8bit( function test_get_quantization (line 236) | def test_get_quantization( function test_get_quantization_multiple_features (line 297) | def test_get_quantization_multiple_features( function test_get_quantization_invalid_input (line 327) | def test_get_quantization_invalid_input(invalid_config): FILE: tests/ludwig/utils/test_data_utils.py function test_add_sequence_feature_column (line 47) | def test_add_sequence_feature_column(): function test_get_abs_path (line 84) | def test_get_abs_path(): function test_figure_data_format_dataset_strip (line 92) | def test_figure_data_format_dataset_strip(path, expected_format): function test_figure_data_format_dataset (line 97) | def test_figure_data_format_dataset(): function test_hash_dict_numpy_types (line 124) | def test_hash_dict_numpy_types(): function test_use_credentials (line 129) | def test_use_credentials(): function test_numpy_encoder (line 150) | def test_numpy_encoder(): function test_dataset_synthesizer_output_feature_decoder (line 176) | def test_dataset_synthesizer_output_feature_decoder(): function synthetic_1k_files (line 188) | def synthetic_1k_files(tmp_path): function test_chunking (line 200) | def test_chunking(synthetic_1k_files, fmt_idx, nrows): function test_read_html (line 213) | def test_read_html(df_lib, nrows): function test_sanitize_column_names (line 234) | def test_sanitize_column_names(): FILE: tests/ludwig/utils/test_dataframe_utils.py function test_to_numpy_dataset_with_dask (line 15) | def test_to_numpy_dataset_with_dask(ray_cluster_2cpu): function test_to_numpy_dataset_with_dask_backend_mismatch (line 25) | def test_to_numpy_dataset_with_dask_backend_mismatch(): function test_to_numpy_dataset_with_pandas (line 32) | def test_to_numpy_dataset_with_pandas(): function test_to_numpy_dataset_empty_with_columns (line 40) | def test_to_numpy_dataset_empty_with_columns(): function test_to_numpy_dataset_empty (line 48) | def test_to_numpy_dataset_empty(): function test_to_numpy_dataset_with_pandas_backend_mismatch (line 57) | def test_to_numpy_dataset_with_pandas_backend_mismatch(ray_cluster_2cpu): function test_to_scalar_df (line 65) | def test_to_scalar_df(): FILE: tests/ludwig/utils/test_dataset_utils.py function test_get_repeatable_train_val_test_split (line 6) | def test_get_repeatable_train_val_test_split(): FILE: tests/ludwig/utils/test_date_utils.py function reference_datetime (line 11) | def reference_datetime() -> datetime.datetime: function test_convert_number_to_datetime (line 32) | def test_convert_number_to_datetime(reference_datetime: datetime.datetim... FILE: tests/ludwig/utils/test_defaults.py function test_merge_with_defaults_early_stop (line 82) | def test_merge_with_defaults_early_stop(use_train, use_hyperopt_scheduler): function test_missing_outputs_drop_rows (line 118) | def test_missing_outputs_drop_rows(): function test_default_model_type (line 140) | def test_default_model_type(): function test_set_default_values (line 151) | def test_set_default_values(): function test_merge_with_defaults (line 187) | def test_merge_with_defaults(): FILE: tests/ludwig/utils/test_error_handling_utils.py function test_default_retry_success (line 7) | def test_default_retry_success(): function test_default_retry_failure (line 22) | def test_default_retry_failure(): function test_default_retry_success_custom_num_tries (line 38) | def test_default_retry_success_custom_num_tries(): FILE: tests/ludwig/utils/test_errors.py function test_input_data_error_serializeable (line 6) | def test_input_data_error_serializeable(): function test_config_validation_error_serializeable (line 19) | def test_config_validation_error_serializeable(): FILE: tests/ludwig/utils/test_fs_utils.py function create_file (line 14) | def create_file(url): function test_get_fs_and_path_simple (line 25) | def test_get_fs_and_path_simple(): function test_get_fs_and_path_query_string (line 30) | def test_get_fs_and_path_query_string(): function test_get_fs_and_path_decode (line 35) | def test_get_fs_and_path_decode(): function test_get_fs_and_path_unicode (line 40) | def test_get_fs_and_path_unicode(): function test_get_fs_and_path_invalid_linux (line 46) | def test_get_fs_and_path_invalid_linux(): function test_get_fs_and_path_invalid_windows (line 59) | def test_get_fs_and_path_invalid_windows(): function test_safe_move_directory (line 79) | def test_safe_move_directory(tmpdir): function test_list_file_names_in_directory (line 98) | def test_list_file_names_in_directory(tmpdir): FILE: tests/ludwig/utils/test_heuristics.py function test_get_auto_learning_rate (line 22) | def test_get_auto_learning_rate(text_encoder: dict[str, Any] | None, exp... FILE: tests/ludwig/utils/test_hf_utils.py function test_load_pretrained_hf_model_from_hub (line 22) | def test_load_pretrained_hf_model_from_hub(model: type, name: str, tmpdi... function test_load_pretrained_hf_model_with_hub_fallback (line 31) | def test_load_pretrained_hf_model_with_hub_fallback(tmpdir): function tmp_folder_with_file (line 54) | def tmp_folder_with_file(tmpdir): function test_upload_folder_to_hfhub_folder_not_exist (line 69) | def test_upload_folder_to_hfhub_folder_not_exist(): function test_upload_folder_to_hfhub_folder_empty (line 74) | def test_upload_folder_to_hfhub_folder_empty(tmpdir): function test_upload_folder_to_hfhub_folder_is_file (line 80) | def test_upload_folder_to_hfhub_folder_is_file(tmpdir): function test_upload_folder_to_hfhub_invalid_repo_type (line 88) | def test_upload_folder_to_hfhub_invalid_repo_type(tmp_folder_with_file): FILE: tests/ludwig/utils/test_hyperopt_ray_utils.py function test_grid_strategy (line 68) | def test_grid_strategy(key): FILE: tests/ludwig/utils/test_image_utils.py function test_pad (line 101) | def test_pad(pad_fn: Callable, img: torch.Tensor, size: int, padded_img:... function test_crop (line 117) | def test_crop(crop_fn: Callable, img: torch.Tensor, size: int, cropped_i... function test_crop_or_pad (line 191) | def test_crop_or_pad(crop_or_pad_fn: Callable, img: torch.Tensor, new_si... function test_resize_image (line 212) | def test_resize_image(resize_image_fn: Callable, img: torch.Tensor, new_... function test_grayscale (line 230) | def test_grayscale(grayscale_fn: Callable, input_img: torch.Tensor, gray... function test_num_channels_in_image (line 235) | def test_num_channels_in_image(): function test_ResizeChannels_module (line 248) | def test_ResizeChannels_module(image_shape, num_channels_expected): function test_ResizeChannels_module_with_batch_dim (line 256) | def test_ResizeChannels_module_with_batch_dim(image_shape, num_channels_... function test_read_image_as_tif (line 262) | def test_read_image_as_tif(): function test_is_image_score (line 284) | def test_is_image_score(extension: str, score: int): function test_unique_channels (line 322) | def test_unique_channels( function test_class_mask_from_image (line 356) | def test_class_mask_from_image(img: torch.Tensor, channel_class_map: tor... function test_image_from_class_mask (line 387) | def test_image_from_class_mask(mask: torch.Tensor, channel_class_map: to... FILE: tests/ludwig/utils/test_llm_utils.py function tokenizer (line 29) | def tokenizer(): function input_ids (line 34) | def input_ids(): function target_ids (line 40) | def target_ids(): class TestSetContextLen (line 45) | class TestSetContextLen: method test_max_sequence_length (line 46) | def test_max_sequence_length(self): method test_max_position_embeddings (line 51) | def test_max_position_embeddings(self): method test_n_positions (line 57) | def test_n_positions(self): method test_default_value (line 62) | def test_default_value(self): function test_has_padding_token_with_padding_tokens (line 68) | def test_has_padding_token_with_padding_tokens(tokenizer): function test_has_padding_token_without_padding_tokens (line 77) | def test_has_padding_token_without_padding_tokens(tokenizer): function test_remove_left_padding (line 99) | def test_remove_left_padding(input_ids, expected, tokenizer): function test_add_left_padding (line 112) | def test_add_left_padding(input_ids, max_length, pad_value, expected): function test_create_attention_mask_last_token_padding (line 118) | def test_create_attention_mask_last_token_padding(tokenizer): function test_create_attention_mask (line 134) | def test_create_attention_mask(input_ids, expected_output, tokenizer): function test_find_last_matching_index (line 151) | def test_find_last_matching_index(tensor_a, tensor_b, expected_index): function test_generate_merged_ids_with_target (line 156) | def test_generate_merged_ids_with_target(tokenizer, input_ids, target_ids): function test_generate_merged_ids_with_max_sequence_length (line 164) | def test_generate_merged_ids_with_max_sequence_length(tokenizer, input_i... function test_generate_merged_ids_padding_removal (line 172) | def test_generate_merged_ids_padding_removal(tokenizer, input_ids, targe... function test_generate_merged_ids_returns_tensor (line 194) | def test_generate_merged_ids_returns_tensor(tokenizer, input_ids, target... function test_pad_target_tensor_for_fine_tuning (line 201) | def test_pad_target_tensor_for_fine_tuning(): function test_get_realigned_target_and_prediction_tensors_for_inference (line 233) | def test_get_realigned_target_and_prediction_tensors_for_inference(token... function _setup_models_for_neftune (line 304) | def _setup_models_for_neftune(): function _forward_pass_and_assert_neftune_hook (line 315) | def _forward_pass_and_assert_neftune_hook(module_without_hook, module_wi... function test_neftune_hook_with_noise_alpha_train_mode (line 336) | def test_neftune_hook_with_noise_alpha_train_mode(): function test_neftune_hook_with_noise_alpha_eval_mode (line 342) | def test_neftune_hook_with_noise_alpha_eval_mode(): FILE: tests/ludwig/utils/test_metric_utils.py function test_dynamic_partition (line 9) | def test_dynamic_partition(): function test_dynamic_partition_2D (line 19) | def test_dynamic_partition_2D(): function test_masked_correct_predictions (line 37) | def test_masked_correct_predictions(): function test_reduce_trainer_metrics_dict (line 49) | def test_reduce_trainer_metrics_dict(): function test_reduce_trainer_metrics_dict_ordered_dict (line 59) | def test_reduce_trainer_metrics_dict_ordered_dict(): FILE: tests/ludwig/utils/test_model_utils.py class SampleModel (line 13) | class SampleModel(torch.nn.Module): method __init__ (line 14) | def __init__(self): function test_extract_tensors (line 20) | def test_extract_tensors(): function test_replace_tensors (line 45) | def test_replace_tensors(): class SampleModule (line 69) | class SampleModule(torch.nn.Module): method __init__ (line 70) | def __init__(self): function test_find_embedding_layer_with_path_simple (line 76) | def test_find_embedding_layer_with_path_simple(): function test_find_embedding_layer_with_path_complex (line 85) | def test_find_embedding_layer_with_path_complex(): function test_no_embedding_layer (line 95) | def test_no_embedding_layer(): class TestHasNanOrInfTensors (line 103) | class TestHasNanOrInfTensors: class SampleModel (line 107) | class SampleModel(torch.nn.Module): method __init__ (line 108) | def __init__(self): method setup (line 114) | def setup(self): method test_has_nan_or_inf_tensors_without_nan_or_inf (line 119) | def test_has_nan_or_inf_tensors_without_nan_or_inf(self): method test_has_nan_or_inf_tensors_with_nan (line 122) | def test_has_nan_or_inf_tensors_with_nan(self): method test_has_nan_or_inf_tensors_without_nan (line 126) | def test_has_nan_or_inf_tensors_without_nan(self): method test_has_nan_or_inf_tensors_transformer_model (line 130) | def test_has_nan_or_inf_tensors_transformer_model(self): method test_has_nan_or_inf_tensors_transformer_model_with_nan (line 133) | def test_has_nan_or_inf_tensors_transformer_model_with_nan(self): method test_has_nan_or_inf_tensors_transformer_model_with_inf (line 137) | def test_has_nan_or_inf_tensors_transformer_model_with_inf(self): FILE: tests/ludwig/utils/test_normalization.py function number_feature (line 27) | def number_feature(): function get_test_data (line 33) | def get_test_data(backend: str) -> tuple[DataFrame, DataFrame]: function test_norm (line 52) | def test_norm(backend, ray_cluster_2cpu): function test_numeric_transformation_registry (line 114) | def test_numeric_transformation_registry(transformation, backend, ray_cl... FILE: tests/ludwig/utils/test_numerical_test_utils.py function finite_valued_dict (line 8) | def finite_valued_dict(): function test_assert_all_finite (line 15) | def test_assert_all_finite(finite_valued_dict): function test_fail_with_nan (line 19) | def test_fail_with_nan(finite_valued_dict): function test_fail_with_inf (line 25) | def test_fail_with_inf(finite_valued_dict): function test_fail_with_nan_in_list (line 31) | def test_fail_with_nan_in_list(finite_valued_dict): function test_fail_with_nan_in_ndarray (line 37) | def test_fail_with_nan_in_ndarray(finite_valued_dict): FILE: tests/ludwig/utils/test_output_feature_utils.py function test_output_feature_utils (line 7) | def test_output_feature_utils(): FILE: tests/ludwig/utils/test_server_utils.py function test_numpy_json_response (line 6) | def test_numpy_json_response(): FILE: tests/ludwig/utils/test_state_dict_backward_compatibility.py function test_update_transformer_module_keys (line 4) | def test_update_transformer_module_keys(): function test_does_not_update_freeze_module (line 22) | def test_does_not_update_freeze_module(): FILE: tests/ludwig/utils/test_strings_utils.py function test_is_number (line 11) | def test_is_number(): function test_are_sequential_integers (line 22) | def test_are_sequential_integers(): function test_str_to_bool (line 30) | def test_str_to_bool(): function test_are_conventional_bools (line 51) | def test_are_conventional_bools(): function test_create_vocabulary_chars (line 65) | def test_create_vocabulary_chars(): function test_create_vocabulary_word (line 88) | def test_create_vocabulary_word(): function test_create_vocabulary_no_special_symbols (line 112) | def test_create_vocabulary_no_special_symbols(): function test_create_vocabulary_from_hf (line 134) | def test_create_vocabulary_from_hf(): function test_create_vocabulary_single_token (line 153) | def test_create_vocabulary_single_token(): function test_create_vocabulary_single_token_small_most_frequent (line 167) | def test_create_vocabulary_single_token_small_most_frequent(): function test_build_sequence_matrix (line 178) | def test_build_sequence_matrix(): function test_get_vocabulary_hf (line 205) | def test_get_vocabulary_hf(pretrained_model_name_or_path): function test_create_vocabulary_idf (line 252) | def test_create_vocabulary_idf(compute_idf: bool): FILE: tests/ludwig/utils/test_tokenizers.py function test_ngram_tokenizer (line 4) | def test_ngram_tokenizer(): function test_string_split_tokenizer (line 22) | def test_string_split_tokenizer(): function test_english_lemmatize_filter_tokenizer (line 29) | def test_english_lemmatize_filter_tokenizer(): FILE: tests/ludwig/utils/test_torch_utils.py function test_sequence_length_2D (line 19) | def test_sequence_length_2D(input_sequence: list[list[int]], expected_ou... function test_sequence_length_3D (line 26) | def test_sequence_length_3D(input_sequence: list[list[list[int]]], expec... function clean_params (line 34) | def clean_params(): function test_initialize_pytorch_only_once (line 52) | def test_initialize_pytorch_only_once(mock_torch): function test_initialize_pytorch_with_gpu_list (line 75) | def test_initialize_pytorch_with_gpu_list(mock_torch): function test_initialize_pytorch_with_gpu_string (line 86) | def test_initialize_pytorch_with_gpu_string(mock_torch): function test_initialize_pytorch_with_gpu_int (line 95) | def test_initialize_pytorch_with_gpu_int(mock_torch): function test_initialize_pytorch_without_gpu (line 105) | def test_initialize_pytorch_without_gpu(mock_torch): FILE: tests/ludwig/utils/test_trainer_utils.py function test_get_latest_metrics_dict (line 16) | def test_get_latest_metrics_dict(): function test_get_latest_metrics_dict_empty (line 60) | def test_get_latest_metrics_dict_empty(): function test_progress_tracker_empty (line 70) | def test_progress_tracker_empty(): function test_progress_tracker (line 112) | def test_progress_tracker(): function test_full_progress_tracker (line 158) | def test_full_progress_tracker(): function test_get_final_steps_per_checkpoint (line 314) | def test_get_final_steps_per_checkpoint(): function test_get_rendered_batch_size_grad_accum (line 350) | def test_get_rendered_batch_size_grad_accum( FILE: tests/ludwig/utils/test_upload_utils.py function _build_fake_model_repo (line 15) | def _build_fake_model_repo( function output_directory_manager (line 42) | def output_directory_manager(tmpdir) -> str: function test_upload_to_hf_hub__validate_upload_parameters (line 138) | def test_upload_to_hf_hub__validate_upload_parameters( FILE: tests/ludwig/utils/test_version_transformation.py function test_version_transformation_registry (line 4) | def test_version_transformation_registry(): function test_version_transformation_order (line 39) | def test_version_transformation_order(): FILE: tests/regression_tests/automl/scripts/update_golden_types.py function write_json_files (line 10) | def write_json_files(): FILE: tests/regression_tests/automl/test_auto_type_inference.py function test_auto_type_inference_regression (line 16) | def test_auto_type_inference_regression(dataset_name): FILE: tests/regression_tests/automl/utils.py function get_dataset_golden_types_path (line 10) | def get_dataset_golden_types_path(dataset_name: str) -> str: function get_dataset_object (line 15) | def get_dataset_object(dataset_name: str) -> DatasetLoader: FILE: tests/regression_tests/benchmark/expected_metric.py class ExpectedMetric (line 8) | class ExpectedMetric: FILE: tests/regression_tests/benchmark/test_model_performance.py function update_skipped_configs_issues (line 22) | def update_skipped_configs_issues(config_filename): function get_test_config_filenames (line 27) | def get_test_config_filenames() -> list[str]: function get_dataset_from_config_path (line 33) | def get_dataset_from_config_path(config_path: str) -> str: function test_performance (line 40) | def test_performance(config_filename, tmpdir): FILE: tests/regression_tests/model/test_old_models.py function test_model_loaded_from_old_config_prediction_works (line 15) | def test_model_loaded_from_old_config_prediction_works(tmpdir): function test_predict_deprecated_model (line 51) | def test_predict_deprecated_model(model_url, tmpdir): FILE: tests/training_success/test_training_success.py function defaults_config_generator (line 23) | def defaults_config_generator( function ecd_trainer_config_generator (line 59) | def ecd_trainer_config_generator(static_schema: dict[str, Any] = None) -... function combiner_config_generator (line 94) | def combiner_config_generator( function train_and_evaluate (line 124) | def train_and_evaluate(config: ModelConfigDict, dataset: pd.DataFrame): function test_ecd_sequence_concat_combiner (line 141) | def test_ecd_sequence_concat_combiner(config, dataset): function test_ecd_sequence_combiner (line 147) | def test_ecd_sequence_combiner(config, dataset): function test_ecd_comparator_combiner (line 153) | def test_ecd_comparator_combiner(config, dataset): function test_ecd_concat_combiner (line 159) | def test_ecd_concat_combiner(config, dataset): function test_ecd_project_aggregate_combiner (line 165) | def test_ecd_project_aggregate_combiner(config, dataset): function test_ecd_tabnet_combiner (line 171) | def test_ecd_tabnet_combiner(config, dataset): function test_ecd_tabtransformer_combiner (line 177) | def test_ecd_tabtransformer_combiner(config, dataset): function test_ecd_transformer_combiner (line 183) | def test_ecd_transformer_combiner(config, dataset): function test_ecd_trainer (line 189) | def test_ecd_trainer(config, dataset): function test_number_encoder_defaults (line 195) | def test_number_encoder_defaults(config, dataset): function test_number_decoder_defaults (line 201) | def test_number_decoder_defaults(config, dataset): function test_number_encoder_loss (line 207) | def test_number_encoder_loss(config, dataset): function test_number_preprocessing_defaults (line 213) | def test_number_preprocessing_defaults(config, dataset): function test_category_encoder_defaults (line 219) | def test_category_encoder_defaults(config, dataset): function test_category_decoder_defaults (line 225) | def test_category_decoder_defaults(config, dataset): function test_category_loss_defaults (line 231) | def test_category_loss_defaults(config, dataset): function test_category_preprocessing_defaults (line 237) | def test_category_preprocessing_defaults(config, dataset): function test_binary_encoder_defaults (line 243) | def test_binary_encoder_defaults(config, dataset): function test_binary_decoder_defaults (line 249) | def test_binary_decoder_defaults(config, dataset): function test_binary_loss_defaults (line 255) | def test_binary_loss_defaults(config, dataset): function test_binary_preprocessing_defaults (line 261) | def test_binary_preprocessing_defaults(config, dataset):