SYMBOL INDEX (861 symbols across 139 files) FILE: lightwood/analysis/analyze.py function model_analyzer (line 15) | def model_analyzer( FILE: lightwood/analysis/base.py class BaseAnalysisBlock (line 7) | class BaseAnalysisBlock: method __init__ (line 9) | def __init__(self, method analyze (line 15) | def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, obje... method explain (line 30) | def explain(self, FILE: lightwood/analysis/explain.py function explain (line 14) | def explain(data: pd.DataFrame, FILE: lightwood/analysis/helpers/acc_stats.py class AccStats (line 13) | class AccStats(BaseAnalysisBlock): method __init__ (line 16) | def __init__(self, deps=('ICP',)): method analyze (line 20) | def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, obje... method fit (line 33) | def fit(self, ns: SimpleNamespace, conf=Optional[np.ndarray]): method get_accuracy_stats (line 78) | def get_accuracy_stats(self, is_classification=None, is_numerical=None): function get_value_bucket (line 143) | def get_value_bucket(value, buckets, target_dtype): function closest (line 164) | def closest(arr, value): FILE: lightwood/analysis/helpers/conf_stats.py class ConfStats (line 10) | class ConfStats(BaseAnalysisBlock): method __init__ (line 17) | def __init__(self, deps=('ICP',), ece_bins: int = 10): method analyze (line 23) | def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, obje... method _get_stats (line 48) | def _get_stats(self, confs, preds, data, target, task_type='categorica... FILE: lightwood/analysis/helpers/feature_importance.py class PermutationFeatureImportance (line 15) | class PermutationFeatureImportance(BaseAnalysisBlock): method __init__ (line 35) | def __init__(self, disable_column_importance=False, row_limit=1000, co... method analyze (line 42) | def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, obje... FILE: lightwood/analysis/helpers/pyod.py class PyOD (line 19) | class PyOD(BaseAnalysisBlock): method __init__ (line 27) | def __init__(self, contamination=0.1, deps: Optional[Tuple] = ...): method analyze (line 37) | def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, obje... method explain (line 73) | def explain(self, method _preprocess_ts_df (line 92) | def _preprocess_ts_df(self, df: pd.DataFrame, ns: SimpleNamespace) -> ... FILE: lightwood/analysis/helpers/shap.py class ShapleyValues (line 17) | class ShapleyValues(BaseAnalysisBlock): method __init__ (line 29) | def __init__(self, deps: Optional[Tuple] = ...): method analyze (line 35) | def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, obje... method explain (line 71) | def explain(self, FILE: lightwood/analysis/nc/base.py class RegressorMixin (line 11) | class RegressorMixin(object): method __init__ (line 12) | def __init__(self) -> None: method get_problem_type (line 16) | def get_problem_type(cls): class ClassifierMixin (line 20) | class ClassifierMixin(object): method __init__ (line 21) | def __init__(self) -> None: method get_problem_type (line 25) | def get_problem_type(cls) -> str: class TSMixin (line 29) | class TSMixin(object): method __init__ (line 30) | def __init__(self) -> None: method get_problem_type (line 34) | def get_problem_type(cls): class BaseModelAdapter (line 38) | class BaseModelAdapter(BaseEstimator): method __init__ (line 41) | def __init__(self, model: object, fit_params: Dict[str, object] = None... method fit (line 49) | def fit(self, x: np.array, y: np.array) -> None: method predict (line 68) | def predict(self, x: np.array) -> np.array: method _underlying_predict (line 94) | def _underlying_predict(self, x: np.array) -> np.array: class ClassifierAdapter (line 110) | class ClassifierAdapter(BaseModelAdapter): method __init__ (line 111) | def __init__(self, model: object, fit_params: Dict[str, object] = None... method _underlying_predict (line 114) | def _underlying_predict(self, x: np.array) -> np.array: class RegressorAdapter (line 118) | class RegressorAdapter(BaseModelAdapter): method __init__ (line 119) | def __init__(self, model: object, fit_params: Dict[str, object] = None... method _underlying_predict (line 122) | def _underlying_predict(self, x: np.array) -> np.array: class TSAdapter (line 126) | class TSAdapter(BaseModelAdapter): method __init__ (line 127) | def __init__(self, model: object, fit_params: Dict[str, object] = None... method _underlying_predict (line 130) | def _underlying_predict(self, x: np.array) -> np.array: class CachedRegressorAdapter (line 134) | class CachedRegressorAdapter(RegressorAdapter): method __init__ (line 135) | def __init__(self, model, fit_params=None): method fit (line 139) | def fit(self, x=None, y=None): method predict (line 144) | def predict(self, x=None): class CachedClassifierAdapter (line 150) | class CachedClassifierAdapter(ClassifierAdapter): method __init__ (line 151) | def __init__(self, model, fit_params=None): method fit (line 156) | def fit(self, x=None, y=None): method predict (line 161) | def predict(self, x=None): class CachedTSAdapter (line 170) | class CachedTSAdapter(TSAdapter): method __init__ (line 171) | def __init__(self, model, fit_params=None): method fit (line 175) | def fit(self, x=None, y=None): method predict (line 178) | def predict(self, x=None): FILE: lightwood/analysis/nc/calibrate.py class ICP (line 25) | class ICP(BaseAnalysisBlock): method __init__ (line 28) | def __init__(self, method analyze (line 38) | def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, obje... method explain (line 248) | def explain(self, row_insights: pd.DataFrame, global_insights: Dict[st... method _formatted (line 477) | def _formatted(row_insights, global_insights, ns, is_numerical): method _ts_assign_confs (line 518) | def _ts_assign_confs(result, df, confs, significances, tss) -> pd.Data... FILE: lightwood/analysis/nc/icp.py class BaseIcp (line 17) | class BaseIcp(BaseEstimator): method __init__ (line 21) | def __init__(self, nc_function: FunctionType, condition: Union[bool, F... method fit (line 45) | def fit(self, x: np.array, y: np.array) -> None: method calibrate (line 63) | def calibrate(self, x, y, increment=False): method _reduce_scores (line 108) | def _reduce_scores(self): method _update_calibration_set (line 111) | def _update_calibration_set(self, x: np.array, y: np.array, increment:... method _calibrate_hook (line 118) | def _calibrate_hook(self, x: np.array, y: np.array, increment: bool) -... class IcpClassifier (line 125) | class IcpClassifier(BaseIcp, ClassifierMixin): method __init__ (line 164) | def __init__(self, nc_function: FunctionType, condition: Union[bool, F... method _calibrate_hook (line 170) | def _calibrate_hook(self, x: np.array, y: np.array, increment: bool = ... method _update_classes (line 173) | def _update_classes(self, y: np.array, increment: bool) -> None: method predict (line 179) | def predict(self, x: np.array, significance: Optional[float] = None) -... method predict_conf (line 229) | def predict_conf(self, x): class IcpRegressor (line 259) | class IcpRegressor(BaseIcp, RegressorMixin): method __init__ (line 295) | def __init__(self, nc_function: FunctionType, condition: bool = None, ... method predict (line 298) | def predict(self, x: np.array, significance: bool = None) -> np.array: class IcpTSRegressor (line 352) | class IcpTSRegressor(BaseIcp, TSMixin): method __init__ (line 374) | def __init__(self, nc_function: FunctionType, horizon_length, conditio... method calibrate (line 378) | def calibrate(self, x, y, increment=False): method predict (line 389) | def predict(self, x: np.array, significance: bool = None) -> np.array: FILE: lightwood/analysis/nc/metrics.py function reg_n_correct (line 9) | def reg_n_correct(prediction, y, significance=None): function reg_mean_errors (line 24) | def reg_mean_errors(prediction, y, significance): function class_n_correct (line 30) | def class_n_correct(prediction, y, significance): function class_mean_errors (line 42) | def class_mean_errors(prediction, y, significance=None): function class_one_err (line 48) | def class_one_err(prediction, y, significance=None): function class_mean_errors_one_class (line 64) | def class_mean_errors_one_class(prediction, y, significance, c=0): function class_one_err_one_class (line 80) | def class_one_err_one_class(prediction, y, significance, c=0): function _reg_interval_size (line 101) | def _reg_interval_size(prediction, y, significance): function reg_min_size (line 108) | def reg_min_size(prediction, y, significance): function reg_q1_size (line 112) | def reg_q1_size(prediction, y, significance): function reg_median_size (line 116) | def reg_median_size(prediction, y, significance): function reg_q3_size (line 120) | def reg_q3_size(prediction, y, significance): function reg_max_size (line 124) | def reg_max_size(prediction, y, significance): function reg_mean_size (line 128) | def reg_mean_size(prediction, y, significance): function class_avg_c (line 135) | def class_avg_c(prediction, y, significance): function class_mean_p_val (line 143) | def class_mean_p_val(prediction, y, significance): function class_one_c (line 150) | def class_one_c(prediction, y, significance): function class_empty (line 159) | def class_empty(prediction, y, significance): function n_test (line 168) | def n_test(prediction, y, significance): FILE: lightwood/analysis/nc/nc.py class ClassificationErrFunc (line 17) | class ClassificationErrFunc(object): method __init__ (line 23) | def __init__(self): method apply (line 27) | def apply(self, prediction, y): class RegressionErrFunc (line 46) | class RegressionErrFunc(object): method __init__ (line 52) | def __init__(self): method apply (line 56) | def apply(self, prediction, y): # , norm=None, beta=0): method apply_inverse (line 75) | def apply_inverse(self, nc, significance): # , norm=None, beta=0): class TSErrFunc (line 95) | class TSErrFunc(object): method __init__ (line 101) | def __init__(self): method apply (line 105) | def apply(self, prediction, y): method apply_inverse (line 124) | def apply_inverse(self, nc, significance): # , norm=None, beta=0): class InverseProbabilityErrFunc (line 144) | class InverseProbabilityErrFunc(ClassificationErrFunc): method __init__ (line 153) | def __init__(self): method apply (line 156) | def apply(self, prediction, y): class MarginErrFunc (line 166) | class MarginErrFunc(ClassificationErrFunc): method __init__ (line 176) | def __init__(self): method apply (line 179) | def apply(self, prediction, y): class AbsErrorErrFunc (line 191) | class AbsErrorErrFunc(RegressionErrFunc): method __init__ (line 200) | def __init__(self): method apply (line 203) | def apply(self, prediction, y): method apply_inverse (line 206) | def apply_inverse(self, nc, significance): class BoostedAbsErrorErrFunc (line 214) | class BoostedAbsErrorErrFunc(RegressionErrFunc): method __init__ (line 219) | def __init__(self): method apply (line 222) | def apply(self, prediction, y): method apply_inverse (line 225) | def apply_inverse(self, nc, significance): class SignErrorErrFunc (line 236) | class SignErrorErrFunc(RegressionErrFunc): method __init__ (line 251) | def __init__(self): method apply (line 254) | def apply(self, prediction, y): method apply_inverse (line 257) | def apply_inverse(self, nc, significance): class TSAbsErrorErrFunc (line 267) | class TSAbsErrorErrFunc(TSErrFunc): method __init__ (line 279) | def __init__(self, horizon_length): method apply (line 283) | def apply(self, prediction, y): method apply_inverse (line 287) | def apply_inverse(self, nc, significance): class BaseScorer (line 298) | class BaseScorer(sklearn.base.BaseEstimator): method __init__ (line 301) | def __init__(self): method fit (line 305) | def fit(self, x, y): method score (line 309) | def score(self, x, y=None): class RegressorNormalizer (line 313) | class RegressorNormalizer(BaseScorer): method __init__ (line 314) | def __init__(self, base_model, normalizer_model, err_func): method fit (line 320) | def fit(self, x, y): method score (line 327) | def score(self, x, y=None): class BaseModelNc (line 332) | class BaseModelNc(BaseScorer): method __init__ (line 353) | def __init__(self, model, err_func, normalizer=None, beta=0): method fit (line 371) | def fit(self, x, y): method score (line 391) | def score(self, x, y=None): method __deepcopy__ (line 419) | def __deepcopy__(self, memo={}): class ClassifierNc (line 434) | class ClassifierNc(BaseModelNc): method __init__ (line 464) | def __init__(self, class RegressorNc (line 478) | class RegressorNc(BaseModelNc): method __init__ (line 506) | def __init__(self, method predict (line 516) | def predict(self, x, nc, significance=None): class TSNc (line 582) | class TSNc(BaseModelNc): method __init__ (line 610) | def __init__(self, method predict (line 620) | def predict(self, x, nc, significance=None): FILE: lightwood/analysis/nc/norm.py class Normalizer (line 15) | class Normalizer(BaseMixer): method __init__ (line 29) | def __init__(self, fit_params: dict): method fit (line 45) | def fit(self, data: EncodedDs) -> None: method __call__ (line 57) | def __call__(self, ds: Union[ConcatedEncodedDs, EncodedDs, np.ndarray]... method score (line 71) | def score(self, data) -> np.ndarray: method get_labels (line 81) | def get_labels(self, preds: pd.DataFrame, truths: np.ndarray, target_e... method compute_numerical_labels (line 119) | def compute_numerical_labels(preds: np.ndarray, truths: np.ndarray, bo... method compute_categorical_labels (line 126) | def compute_categorical_labels(preds: np.ndarray, truths: np.ndarray) ... FILE: lightwood/analysis/nc/util.py function t_softmax (line 11) | def t_softmax(x, t=1.0, axis=1): function clean_df (line 16) | def clean_df(df, namespace, label_encoders): function set_conf_range (line 40) | def set_conf_range( function get_numeric_conf_range (line 106) | def get_numeric_conf_range( function get_ts_conf_range (line 164) | def get_ts_conf_range( function get_categorical_conf (line 189) | def get_categorical_conf(raw_confs: np.ndarray): function get_anomalies (line 207) | def get_anomalies(insights: pd.DataFrame, observed_series: Union[pd.Seri... FILE: lightwood/analysis/nn_conf/temp_scale.py class TempScaler (line 14) | class TempScaler(BaseAnalysisBlock): method __init__ (line 19) | def __init__(self, deps=tuple()): method temperature_scale (line 26) | def temperature_scale(self, logits): method softmax (line 30) | def softmax(self, logits): method analyze (line 33) | def analyze(self, info: Dict[str, object], **kwargs) -> Dict[str, obje... method explain (line 85) | def explain(self, FILE: lightwood/api/high_level.py function load_custom_module (line 18) | def load_custom_module(file_path: str): function predictor_from_problem (line 30) | def predictor_from_problem(df: pd.DataFrame, problem_definition: Union[P... function json_ai_from_problem (line 50) | def json_ai_from_problem(df: pd.DataFrame, problem_definition: Union[Pro... function code_from_json_ai (line 88) | def code_from_json_ai(json_ai: JsonAI) -> str: function predictor_from_code (line 99) | def predictor_from_code(code: str) -> PredictorInterface: function code_from_problem (line 108) | def code_from_problem(df: pd.DataFrame, problem_definition: Union[Proble... function predictor_from_state (line 127) | def predictor_from_state(state_file: str, code: str = None) -> Predictor... function predictor_from_json_ai (line 157) | def predictor_from_json_ai(json_ai: JsonAI) -> PredictorInterface: FILE: lightwood/api/json_ai.py function lookup_encoder (line 18) | def lookup_encoder( function generate_json_ai (line 143) | def generate_json_ai( function _merge_implicit_values (line 397) | def _merge_implicit_values(field: dict, implicit_value: dict) -> dict: function _populate_implicit_field (line 426) | def _populate_implicit_field( function add_implicit_values (line 470) | def add_implicit_values(json_ai: JsonAI) -> JsonAI: function validate_json_ai (line 725) | def validate_json_ai(json_ai: JsonAI) -> bool: FILE: lightwood/api/predictor.py class PredictorInterface (line 9) | class PredictorInterface: method __init__ (line 39) | def __init__(self): method analyze_data (line 42) | def analyze_data(self, data: pd.DataFrame) -> None: method preprocess (line 50) | def preprocess(self, data: pd.DataFrame) -> pd.DataFrame: method split (line 59) | def split(self, data: pd.DataFrame) -> Dict[str, pd.DataFrame]: method prepare (line 68) | def prepare(self, data: Dict[str, pd.DataFrame]) -> None: method featurize (line 77) | def featurize(self, split_data: Dict[str, pd.DataFrame]): method fit (line 87) | def fit(self, enc_data: Dict[str, pd.DataFrame]) -> None: method analyze_ensemble (line 95) | def analyze_ensemble(self, enc_data: Dict[str, pd.DataFrame]) -> None: method learn (line 103) | def learn(self, data: pd.DataFrame) -> None: method adjust (line 115) | def adjust(self, new_data: pd.DataFrame, old_data: Optional[pd.DataFra... method predict (line 129) | def predict(self, data: pd.DataFrame, args: Dict[str, object] = {}) ->... method test (line 140) | def test( method save (line 155) | def save(self, file_path: str) -> None: method export (line 164) | def export(self, file_path: str, json_ai_code: str) -> None: FILE: lightwood/api/types.py class Module (line 22) | class Module(TypedDict): class TimeseriesSettings (line 34) | class TimeseriesSettings: method from_dict (line 74) | def from_dict(obj: Dict): method from_json (line 113) | def from_json(data: str): method to_dict (line 123) | def to_dict(self, encode_json=False) -> Dict[str, Json]: method to_json (line 131) | def to_json(self) -> Dict[str, Json]: class ProblemDefinition (line 140) | class ProblemDefinition: method from_dict (line 196) | def from_dict(obj: Dict): method from_json (line 251) | def from_json(data: str): method to_dict (line 261) | def to_dict(self, encode_json=False) -> Dict[str, Json]: method to_json (line 269) | def to_json(self) -> Dict[str, Json]: class JsonAI (line 279) | class JsonAI: method from_dict (line 319) | def from_dict(obj: Dict): method from_json (line 360) | def from_json(data: str): method to_dict (line 364) | def to_dict(self, encode_json=False) -> Dict[str, Json]: method to_json (line 381) | def to_json(self) -> Dict[str, Json]: class SubmodelData (line 392) | class SubmodelData: class ModelAnalysis (line 400) | class ModelAnalysis: class PredictionArguments (line 430) | class PredictionArguments: method from_dict (line 462) | def from_dict(obj: Dict): method to_dict (line 498) | def to_dict(self, encode_json=False) -> Dict[str, Json]: FILE: lightwood/data/encoded_ds.py class EncodedDs (line 10) | class EncodedDs(Dataset): method __init__ (line 11) | def __init__(self, encoders: Dict[str, BaseEncoder], data_frame: pd.Da... method __len__ (line 41) | def __len__(self): method __getitem__ (line 49) | def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: method _encode_idxs (line 71) | def _encode_idxs(self, idxs: list): method get_column_original_data (line 114) | def get_column_original_data(self, column_name: str) -> pd.Series: method get_encoded_column_data (line 123) | def get_encoded_column_data(self, column_name: str) -> torch.Tensor: method get_encoded_data (line 158) | def get_encoded_data(self, include_target: bool = True) -> torch.Tensor: method build_cache (line 172) | def build_cache(self): method clear_cache (line 183) | def clear_cache(self): class ConcatedEncodedDs (line 190) | class ConcatedEncodedDs(EncodedDs): method __init__ (line 196) | def __init__(self, encoded_ds_arr: List[EncodedDs]) -> None: method __len__ (line 205) | def __len__(self): method __getitem__ (line 212) | def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]: method get_column_original_data (line 223) | def get_column_original_data(self, column_name: str) -> pd.Series: method get_encoded_column_data (line 230) | def get_encoded_column_data(self, column_name: str) -> torch.Tensor: method clear_cache (line 237) | def clear_cache(self): FILE: lightwood/data/timeseries_analyzer.py function timeseries_analyzer (line 12) | def timeseries_analyzer(data: Dict[str, pd.DataFrame], dtype_dict: Dict[... function get_naive_residuals (line 57) | def get_naive_residuals(target_data: pd.DataFrame, m: int = 1) -> Tuple[... function get_grouped_naive_residuals (line 79) | def get_grouped_naive_residuals( function get_differencers (line 98) | def get_differencers(data: pd.DataFrame, target: str, group_cols: List): FILE: lightwood/data/timeseries_transform.py function transform_timeseries (line 14) | def transform_timeseries( function _ts_infer_next_row (line 194) | def _ts_infer_next_row(df: pd.DataFrame, ob: str) -> pd.DataFrame: function _ts_to_obj (line 221) | def _ts_to_obj(df: pd.DataFrame, historical_columns: list) -> pd.DataFrame: function _ts_add_previous_rows (line 235) | def _ts_add_previous_rows(df: pd.DataFrame, order_cols: list, window: in... function _ts_add_previous_target (line 257) | def _ts_add_previous_target(df: pd.DataFrame, target: str, window: int) ... function _ts_add_future_target (line 284) | def _ts_add_future_target(df, target, horizon, data_dtype, mode): FILE: lightwood/encoder/array/array.py class ArrayEncoder (line 11) | class ArrayEncoder(BaseEncoder): method __init__ (line 24) | def __init__(self, stop_after: float, window: int = None, is_target: b... method _pad_and_strip (line 40) | def _pad_and_strip(self, array: List[object]): method prepare (line 47) | def prepare(self, train_priming_data: Iterable[Iterable], dev_priming_... method encode (line 79) | def encode(self, column_data: Iterable[Iterable]) -> torch.Tensor: method decode (line 103) | def decode(self, data: torch.Tensor) -> List[Iterable]: class CatArrayEncoder (line 114) | class CatArrayEncoder(ArrayEncoder): method __init__ (line 115) | def __init__(self, stop_after: float, window: int = None, is_target: b... method prepare (line 118) | def prepare(self, train_priming_data: Iterable[Iterable], dev_priming_... method decode (line 122) | def decode(self, data: torch.Tensor) -> List[Iterable]: class NumArrayEncoder (line 128) | class NumArrayEncoder(ArrayEncoder): method __init__ (line 129) | def __init__(self, stop_after: float, window: int = None, is_target: b... FILE: lightwood/encoder/array/ts_cat_array.py class TsCatArrayEncoder (line 10) | class TsCatArrayEncoder(BaseEncoder): method __init__ (line 11) | def __init__(self, timesteps: int, is_target: bool = False, grouped_by... method prepare (line 25) | def prepare(self, priming_data): method encode (line 37) | def encode(self, data: Iterable[Iterable], dependency_data: Optional[D... method encode_one (line 55) | def encode_one(self, data: Iterable) -> torch.Tensor: method decode (line 77) | def decode(self, encoded_values, dependency_data=None) -> List[List]: method decode_one (line 99) | def decode_one(self, encoded_value) -> List: FILE: lightwood/encoder/array/ts_num_array.py class TsArrayNumericEncoder (line 9) | class TsArrayNumericEncoder(BaseEncoder): method __init__ (line 10) | def __init__(self, timesteps: int, is_target: bool = False, positive_d... method prepare (line 29) | def prepare(self, priming_data): method encode (line 39) | def encode(self, data: Iterable[Iterable], dependency_data: Optional[D... method decode (line 59) | def decode(self, encoded_values, dependency_data=None) -> List[List]: method decode_one (line 81) | def decode_one(self, encoded_value, dependency_data={}) -> List: FILE: lightwood/encoder/audio/mfcc.py class MFCCEncoder (line 10) | class MFCCEncoder(BaseEncoder): method __init__ (line 13) | def __init__(self, is_target: bool = False): method prepare (line 29) | def prepare(self, priming_data: Iterable[str]): method encode (line 40) | def encode(self, column_data: Iterable[str]) -> torch.Tensor: method decode (line 79) | def decode(self, _): FILE: lightwood/encoder/base.py class BaseEncoder (line 5) | class BaseEncoder: method __init__ (line 35) | def __init__(self, is_target=False) -> None: method prepare (line 42) | def prepare(self, priming_data: Iterable[object]) -> None: method encode (line 50) | def encode(self, column_data: Iterable[object]) -> torch.Tensor: method decode (line 62) | def decode(self, encoded_data: torch.Tensor) -> List[object]: method to (line 73) | def to(self, device, available_devices): FILE: lightwood/encoder/categorical/autoencoder.py class CategoricalAutoEncoder (line 17) | class CategoricalAutoEncoder(BaseEncoder): method __init__ (line 27) | def __init__( method prepare (line 64) | def prepare(self, train_priming_data: pd.Series, dev_priming_data: pd.... method encode (line 115) | def encode(self, column_data: Iterable[str]) -> torch.Tensor: method decode (line 132) | def decode(self, encoded_data: torch.Tensor) -> List[str]: method _prepare_AE_input (line 148) | def _prepare_AE_input( method _prepare_catae (line 183) | def _prepare_catae(self, train_loader: DataLoader, dev_loader: DataLoa... method _encoder_targets (line 236) | def _encoder_targets(self, data): method _label_targets (line 245) | def _label_targets(self, data): FILE: lightwood/encoder/categorical/binary.py class BinaryEncoder (line 13) | class BinaryEncoder(BaseEncoder): method __init__ (line 34) | def __init__( method prepare (line 59) | def prepare(self, priming_data: Iterable[str]): method encode (line 100) | def encode(self, column_data: Iterable[str]) -> torch.Tensor: method decode (line 124) | def decode(self, encoded_data: torch.Tensor): method decode_probabilities (line 150) | def decode_probabilities(self, encoded_data: torch.Tensor) -> Tuple[Li... method _norm_vec (line 173) | def _norm_vec(vec: List[float]): FILE: lightwood/encoder/categorical/gym.py class Gym (line 10) | class Gym: method __init__ (line 12) | def __init__(self, model, optimizer, scheduler, loss_criterion, device, method fit (line 28) | def fit(self, train_data_loader, test_data_loader, desired_error, max_... FILE: lightwood/encoder/categorical/multihot.py class MultiHotEncoder (line 7) | class MultiHotEncoder(BaseEncoder): method __init__ (line 8) | def __init__(self, is_target: bool = False): method _clean_col_data (line 15) | def _clean_col_data(column_data): method prepare (line 20) | def prepare(self, priming_data, max_dimensions=100): method encode (line 29) | def encode(self, column_data): method decode (line 34) | def decode(self, vectors): FILE: lightwood/encoder/categorical/onehot.py class OneHotEncoder (line 13) | class OneHotEncoder(BaseEncoder): method __init__ (line 36) | def __init__( method prepare (line 59) | def prepare(self, priming_data: Iterable[str]): method encode (line 108) | def encode(self, column_data: Iterable[str]) -> torch.Tensor: method decode (line 133) | def decode(self, encoded_data: torch.Tensor): method decode_probabilities (line 153) | def decode_probabilities(self, encoded_data: torch.Tensor) -> Tuple[Li... method _norm_vec (line 176) | def _norm_vec(vec: List[float]): FILE: lightwood/encoder/categorical/simple_label.py class SimpleLabelEncoder (line 11) | class SimpleLabelEncoder(BaseEncoder): method __init__ (line 20) | def __init__(self, is_target=False, normalize=True) -> None: method prepare (line 28) | def prepare(self, priming_data: Union[list, pd.Series]) -> None: method encode (line 40) | def encode(self, data: Union[tuple, np.ndarray, pd.Series], normalize=... method decode (line 59) | def decode(self, encoded_values: torch.Tensor, normalize=True) -> List... FILE: lightwood/encoder/datetime/datetime.py class DatetimeEncoder (line 11) | class DatetimeEncoder(BaseEncoder): method __init__ (line 17) | def __init__(self, is_target: bool = False): method prepare (line 29) | def prepare(self, priming_data): method encode (line 32) | def encode(self, data: Union[np.ndarray, pd.Series]) -> torch.Tensor: method decode (line 55) | def decode(self, encoded_data: torch.Tensor, return_as_datetime=False)... FILE: lightwood/encoder/datetime/datetime_sin_normalizer.py class DatetimeNormalizerEncoder (line 11) | class DatetimeNormalizerEncoder(BaseEncoder): method __init__ (line 12) | def __init__(self, is_target: bool = False, sinusoidal: bool = False): method prepare (line 23) | def prepare(self, priming_data): method encode (line 29) | def encode(self, data): method encode_one (line 46) | def encode_one(self, data): method decode (line 75) | def decode(self, encoded_data, return_as_datetime=False): method decode_one (line 85) | def decode_one(self, vector, return_as_datetime=False): FILE: lightwood/encoder/helpers.py class MinMaxNormalizer (line 6) | class MinMaxNormalizer: method __init__ (line 7) | def __init__(self, combination=()): method prepare (line 13) | def prepare(self, x: np.ndarray) -> None: method encode (line 27) | def encode(self, y: np.ndarray) -> torch.Tensor: method decode (line 40) | def decode(self, y): class CatNormalizer (line 44) | class CatNormalizer: method __init__ (line 45) | def __init__(self, encoder_class='one_hot'): method prepare (line 54) | def prepare(self, x): method encode (line 62) | def encode(self, Y): method decode (line 75) | def decode(self, y): FILE: lightwood/encoder/identity/identity.py class IdentityEncoder (line 7) | class IdentityEncoder(BaseEncoder): method __init__ (line 23) | def __init__(self, is_target: bool = False, handle_nan: bool = True) -... method prepare (line 32) | def prepare(self, priming_data: Iterable[Union[float, int]]) -> None: method encode (line 36) | def encode(self, column_data: Iterable[Union[float, int]]) -> torch.Te... method decode (line 53) | def decode(self, encoded_data: torch.Tensor) -> List[object]: FILE: lightwood/encoder/image/helpers/img_to_vec.py class ChannelPoolAdaptiveAvg1d (line 14) | class ChannelPoolAdaptiveAvg1d(torch.nn.AdaptiveAvgPool1d): method forward (line 18) | def forward(self, input): class Img2Vec (line 28) | class Img2Vec(nn.Module): method __init__ (line 36) | def __init__(self, device=''): method to (line 46) | def to(self, device, available_devices=1): method forward (line 51) | def forward(self, image, batch=True): FILE: lightwood/encoder/image/img_2_vec.py class Img2VecEncoder (line 14) | class Img2VecEncoder(BaseEncoder): method __init__ (line 27) | def __init__( method prepare (line 67) | def prepare(self, train_priming_data: Iterable[str], dev_priming_data:... method to (line 81) | def to(self, device, available_devices=1): method encode (line 93) | def encode(self, images: List[str]) -> torch.Tensor: method decode (line 115) | def decode(self, encoded_values_tensor: torch.Tensor): FILE: lightwood/encoder/numeric/numeric.py class NumericEncoder (line 14) | class NumericEncoder(BaseEncoder): method __init__ (line 26) | def __init__(self, data_type: dtype = None, method prepare (line 49) | def prepare(self, priming_data: pd.Series): method encode (line 61) | def encode(self, data: Union[np.ndarray, pd.Series]): method _sign_fn (line 93) | def _sign_fn(x: float) -> float: method _log_fn (line 97) | def _log_fn(x: float) -> float: method _norm_fn (line 100) | def _norm_fn(self, x: float) -> float: method _none_fn (line 104) | def _none_fn(x: float) -> float: method decode (line 107) | def decode(self, encoded_values: torch.Tensor, decode_log: bool = None... method get_weights (line 162) | def get_weights(self, label_data): FILE: lightwood/encoder/numeric/ts_numeric.py class TsNumericEncoder (line 10) | class TsNumericEncoder(NumericEncoder): method __init__ (line 16) | def __init__(self, is_target: bool = False, positive_domain: bool = Fa... method encode (line 24) | def encode(self, data: Union[np.ndarray, pd.Series], dependency_data: ... method decode (line 73) | def decode(self, encoded_values: torch.Tensor, decode_log: bool = None... FILE: lightwood/encoder/text/helpers/pretrained_helpers.py class TextEmbed (line 9) | class TextEmbed(torch.utils.data.Dataset): method __init__ (line 17) | def __init__(self, encodings, labels): method __getitem__ (line 21) | def __getitem__(self, idx): method __len__ (line 26) | def __len__(self): FILE: lightwood/encoder/text/pretrained.py class PretrainedLangEncoder (line 28) | class PretrainedLangEncoder(BaseEncoder): method __init__ (line 38) | def __init__( method prepare (line 95) | def prepare( method _tune_model (line 229) | def _tune_model(self, train_dataset, val_dataset, optim, scheduler, n_... method _call (line 315) | def _call(self, batch): method _train_callback (line 323) | def _train_callback(self, epoch, loss): method encode (line 326) | def encode(self, column_data: Iterable[str]) -> torch.Tensor: method decode (line 375) | def decode(self, encoded_values_tensor, max_length=100): method to (line 381) | def to(self, device, available_devices): FILE: lightwood/encoder/text/short.py class ShortTextEncoder (line 10) | class ShortTextEncoder(BaseEncoder): method __init__ (line 13) | def __init__(self, is_target=False, mode=None, device=''): method _unexpected_mode (line 45) | def _unexpected_mode(self): method _combine_concat (line 49) | def _combine_concat(self, vecs): method _combine_mean (line 52) | def _combine_mean(self, vecs): method prepare (line 55) | def prepare(self, priming_data): method encode (line 79) | def encode(self, column_data: List[str]) -> torch.Tensor: method decode (line 90) | def decode(self, vectors): FILE: lightwood/encoder/text/tfidf.py class TfidfEncoder (line 8) | class TfidfEncoder(BaseEncoder): method __init__ (line 9) | def __init__(self, is_target: bool = False): method prepare (line 14) | def prepare(self, priming_data, training_data=None): method encode (line 18) | def encode(self, column_data): method decode (line 23) | def decode(self, encoded_values_tensor): FILE: lightwood/encoder/text/vocab.py class VocabularyEncoder (line 7) | class VocabularyEncoder(BaseEncoder): method __init__ (line 8) | def __init__(self, is_target: bool = False): method prepare (line 16) | def prepare(self, priming_data): method encode (line 22) | def encode(self, column_data): method decode (line 30) | def decode(self, encoded_values_tensor): FILE: lightwood/encoder/time_series/helpers/common.py function generate_target_group_normalizers (line 9) | def generate_target_group_normalizers( FILE: lightwood/encoder/time_series/helpers/rnn_helpers.py class DecoderRNNNumerical (line 7) | class DecoderRNNNumerical(nn.Module): method __init__ (line 8) | def __init__(self, hidden_size, output_size): method forward (line 16) | def forward(self, input, hidden): method init_hidden (line 24) | def init_hidden(self, device, batch_size=1): method decode (line 27) | def decode(self, data, initial_tensor, criterion, device, hidden_state... class EncoderRNNNumerical (line 57) | class EncoderRNNNumerical(nn.Module): method __init__ (line 58) | def __init__(self, input_size, hidden_size): method forward (line 65) | def forward(self, input, hidden): method init_hidden (line 72) | def init_hidden(self, device, batch_size=1): method bptt (line 75) | def bptt(self, data, criterion, device): FILE: lightwood/encoder/time_series/helpers/transformer_helpers.py function len_to_mask (line 7) | def len_to_mask(lengths, zeros): function get_chunk (line 22) | def get_chunk(source, source_lengths, start, step): class PositionalEncoding (line 36) | class PositionalEncoding(nn.Module): method __init__ (line 37) | def __init__(self, d_model, dropout=0.2, max_len=5000): method forward (line 54) | def forward(self, x): class TransformerEncoder (line 60) | class TransformerEncoder(nn.Module): method __init__ (line 61) | def __init__(self, ninp, nhead, nhid, nlayers, dropout=0.2): method _generate_square_subsequent_mask (line 71) | def _generate_square_subsequent_mask(self, sz): method init_weights (line 81) | def init_weights(self): method forward (line 86) | def forward(self, src, lengths, device): method bptt (line 103) | def bptt(self, batch, criterion, device): FILE: lightwood/encoder/time_series/rnn.py class TimeSeriesEncoder (line 23) | class TimeSeriesEncoder(BaseEncoder): method __init__ (line 32) | def __init__(self, method setup_nn (line 68) | def setup_nn(self, ts_analysis, dependencies=None): method to (line 135) | def to(self, device, available_devices): method _prepare_raw_data (line 141) | def _prepare_raw_data(self, data): method _get_batch (line 156) | def _get_batch(self, source, start, end): method prepare (line 160) | def prepare(self, train_priming_data: pd.Series, dev_priming_data: pd.... method _encode_one (line 288) | def _encode_one(self, data, previous=None, initial_hidden=None, return... method encode (line 346) | def encode(self, column_data, dependency_data=None, get_next_count=None): method _decode_one (line 446) | def _decode_one(self, hidden, steps): method decode (line 463) | def decode(self, encoded_data, steps=None): method _masked_criterion (line 489) | def _masked_criterion(self, output, targets, lengths): FILE: lightwood/encoder/time_series/ts.py class TimeSeriesEncoder (line 9) | class TimeSeriesEncoder(ArrayEncoder): method __init__ (line 13) | def __init__(self, stop_after: float, window: int = None, is_target: b... method encode (line 24) | def encode(self, column_data: Iterable[Iterable]) -> torch.Tensor: method decode (line 46) | def decode(self, data: torch.Tensor) -> List[Iterable]: FILE: lightwood/ensemble/base.py class BaseEnsemble (line 10) | class BaseEnsemble: method __init__ (line 33) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, f... method __call__ (line 40) | def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.Dat... FILE: lightwood/ensemble/best_of.py class BestOf (line 15) | class BestOf(BaseEnsemble): method __init__ (line 22) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, a... method __call__ (line 58) | def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.Dat... FILE: lightwood/ensemble/embed.py class Embedder (line 10) | class Embedder(BaseEnsemble): method __init__ (line 15) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs) -... method __call__ (line 20) | def __call__(self, ds: EncodedDs, args: PredictionArguments = None) ->... FILE: lightwood/ensemble/identity.py class IdentityEnsemble (line 10) | class IdentityEnsemble(BaseEnsemble): method __init__ (line 17) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, a... method __call__ (line 24) | def __call__(self, ds: EncodedDs, args: PredictionArguments = None) ->... method active_mixer (line 30) | def active_mixer(self): method active_mixer (line 34) | def active_mixer(self, idx): FILE: lightwood/ensemble/mean_ensemble.py class MeanEnsemble (line 12) | class MeanEnsemble(BaseEnsemble): method __init__ (line 18) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, d... method __call__ (line 25) | def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.Dat... FILE: lightwood/ensemble/mode_ensemble.py class ModeEnsemble (line 16) | class ModeEnsemble(BaseEnsemble): method __init__ (line 26) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, d... method _pick_mode_highest_score (line 56) | def _pick_mode_highest_score(self, prediction: pd.Series): method __call__ (line 83) | def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.Dat... FILE: lightwood/ensemble/stacked_ensemble.py class StackedEnsemble (line 17) | class StackedEnsemble(MeanEnsemble): method __init__ (line 30) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, d... method predict (line 57) | def predict(self, ds: EncodedDs, args: PredictionArguments) -> List: method __call__ (line 65) | def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.Dat... method set_weights (line 74) | def set_weights(self, weights: List): FILE: lightwood/ensemble/ts_stacked_ensemble.py class TsStackedEnsemble (line 19) | class TsStackedEnsemble(StackedEnsemble): method __init__ (line 23) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, d... method __call__ (line 59) | def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.Dat... FILE: lightwood/ensemble/weighted_mean_ensemble.py class WeightedMeanEnsemble (line 16) | class WeightedMeanEnsemble(BaseEnsemble): method __init__ (line 26) | def __init__(self, target, mixers: List[BaseMixer], data: EncodedDs, a... method __call__ (line 58) | def __call__(self, ds: EncodedDs, args: PredictionArguments) -> pd.Dat... method accuracies_to_weights (line 69) | def accuracies_to_weights(x: np.array) -> np.array: FILE: lightwood/helpers/codegen.py function code_from_json_ai (line 22) | def code_from_json_ai(json_ai: JsonAI) -> str: function _module_from_code (line 673) | def _module_from_code(code: str, module_name: str) -> ModuleType: function _predictor_from_code (line 698) | def _predictor_from_code(code: str): FILE: lightwood/helpers/device.py function is_cuda_compatible (line 7) | def is_cuda_compatible(): function get_devices (line 43) | def get_devices(): function get_device_from_name (line 80) | def get_device_from_name(device_name=''): FILE: lightwood/helpers/general.py function is_none (line 7) | def is_none(value): FILE: lightwood/helpers/io.py function read_from_path_or_url (line 6) | def read_from_path_or_url(path: str, load_from_path): FILE: lightwood/helpers/log.py function initialize_log (line 9) | def initialize_log(): function timed_predictor (line 22) | def timed_predictor(f): function timed (line 39) | def timed(f): FILE: lightwood/helpers/numeric.py function filter_nan_and_none (line 5) | def filter_nan_and_none(series: Iterable) -> list: FILE: lightwood/helpers/parallelism.py function get_nr_procs (line 11) | def get_nr_procs(df=None): function run_mut_method (line 31) | def run_mut_method(obj: object, arg: object, method: str, identifier: st... function mut_method_call (line 40) | def mut_method_call(object_dict: Dict[str, tuple]) -> Dict[str, object]: function parallel_encoding_check (line 62) | def parallel_encoding_check(df, encoders): FILE: lightwood/helpers/seed.py function seed (line 6) | def seed(seed_nr: int) -> None: FILE: lightwood/helpers/templating.py function is_allowed (line 11) | def is_allowed(v): function call (line 34) | def call(entity: dict) -> str: function inline_dict (line 55) | def inline_dict(obj: dict) -> str: function align (line 67) | def align(code: str, indent: int) -> str: function _consolidate_analysis_blocks (line 77) | def _consolidate_analysis_blocks(jsonai, key): function _add_cls_kwarg (line 151) | def _add_cls_kwarg(cls: Callable, kwargs: dict, key: str, value): FILE: lightwood/helpers/torch.py function concat_vectors_and_pad (line 7) | def concat_vectors_and_pad(vec_list, max_): function average_vectors (line 47) | def average_vectors(vec_list): class LightwoodAutocast (line 52) | class LightwoodAutocast: method __init__ (line 72) | def __init__(self, enabled=True): method __enter__ (line 97) | def __enter__(self): method __exit__ (line 106) | def __exit__(self, *args): method __call__ (line 117) | def __call__(self, func): FILE: lightwood/helpers/ts.py function get_ts_groups (line 8) | def get_ts_groups(df: pd.DataFrame, tss) -> list: function get_delta (line 17) | def get_delta(df: pd.DataFrame, tss) -> Tuple[Dict, Dict, Dict]: function get_inferred_timestamps (line 51) | def get_inferred_timestamps(df: pd.DataFrame, col: str, deltas: dict, ts... function add_tn_num_conf_bounds (line 92) | def add_tn_num_conf_bounds(data: pd.DataFrame, tss_args): function add_tn_cat_conf_bounds (line 117) | def add_tn_cat_conf_bounds(data: pd.DataFrame, tss_args): class Differencer (line 124) | class Differencer: method __init__ (line 125) | def __init__(self): method diff (line 131) | def diff(self, series: np.array) -> pd.Series: method fit (line 136) | def fit(self, series: np.array) -> None: method transform (line 143) | def transform(self, series: np.array) -> pd.Series: method inverse_transform (line 147) | def inverse_transform(self, series: pd.Series, init=None) -> pd.Series: method _flatten_series (line 154) | def _flatten_series(series: np.ndarray) -> np.ndarray: function detect_freq_period (line 162) | def detect_freq_period(deltas: pd.DataFrame, tss, n_points) -> tuple: function freq_to_pandas (line 218) | def freq_to_pandas(freq, multiplier=1): function filter_ts (line 243) | def filter_ts(df: pd.DataFrame, tss, n_rows=1): FILE: lightwood/mixer/arima.py class ARIMAMixer (line 6) | class ARIMAMixer(SkTime): method __init__ (line 7) | def __init__( FILE: lightwood/mixer/base.py class BaseMixer (line 8) | class BaseMixer: method __init__ (line 31) | def __init__(self, stop_after: float): method fit (line 39) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method __call__ (line 49) | def __call__(self, ds: EncodedDs, method partial_fit (line 60) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, adju... FILE: lightwood/mixer/ets.py class ETSMixer (line 6) | class ETSMixer(SkTime): method __init__ (line 7) | def __init__( FILE: lightwood/mixer/helpers/ar_net.py class ArNet (line 7) | class ArNet(DefaultNet): method __init__ (line 13) | def __init__(self, method to (line 40) | def to(self, device=None, available_devices=None): method forward (line 45) | def forward(self, input): FILE: lightwood/mixer/helpers/default_net.py class DefaultNet (line 9) | class DefaultNet(torch.nn.Module): method __init__ (line 16) | def __init__(self, method to (line 57) | def to(self, device: torch.device) -> torch.nn.Module: method forward (line 66) | def forward(self, input): FILE: lightwood/mixer/helpers/qclassic_net.py class QuantumCircuit (line 9) | class QuantumCircuit: method __init__ (line 15) | def __init__(self, n_qubits, backend, shots): method run (line 32) | def run(self, thetas): class HybridSingleFunction (line 51) | class HybridSingleFunction(torch.autograd.Function): method forward (line 55) | def forward(ctx, input, quantum_circuit, shift): method backward (line 67) | def backward(ctx, grad_output): class HybridSingle (line 82) | class HybridSingle(torch.nn.Module): method __init__ (line 85) | def __init__(self, backend, shots, shift): method forward (line 90) | def forward(self, input): class QClassicNet (line 94) | class QClassicNet(DefaultNet): method __init__ (line 99) | def __init__(self, method to (line 116) | def to(self, device=None, available_devices=None): method forward (line 119) | def forward(self, input): FILE: lightwood/mixer/helpers/ranger.py class Ranger (line 6) | class Ranger(Optimizer): method __init__ (line 7) | def __init__( method __setstate__ (line 46) | def __setstate__(self, state): method step (line 49) | def step(self, closure=None): FILE: lightwood/mixer/helpers/residual_net.py class ResidualModule (line 9) | class ResidualModule(nn.Module): method __init__ (line 10) | def __init__( method forward (line 23) | def forward(self, x: torch.Tensor) -> torch.Tensor: class ResidualNet (line 36) | class ResidualNet(torch.nn.Module): method __init__ (line 37) | def __init__(self, method to (line 58) | def to(self, device: torch.device, available_devices: int = 1) -> torc... method forward (line 69) | def forward(self, input): FILE: lightwood/mixer/helpers/transform_corss_entropy_loss.py class TransformCrossEntropyLoss (line 6) | class TransformCrossEntropyLoss(torch.nn.Module): method __init__ (line 7) | def __init__(self, **kwargs): method forward (line 11) | def forward(self, preds, target): FILE: lightwood/mixer/helpers/ts.py function _transform_target (line 10) | def _transform_target(ts_analysis: Dict[str, Dict], df: pd.DataFrame, fr... function _inverse_transform_target (line 25) | def _inverse_transform_target(ts_analysis: Dict[str, Dict], predictions:... FILE: lightwood/mixer/lightgbm.py function check_gpu_support (line 22) | def check_gpu_support(): class LightGBM (line 38) | class LightGBM(BaseMixer): method __init__ (line 62) | def __init__( method _to_dataset (line 105) | def _to_dataset(self, data: Dict[str, Dict], output_dtype: str): method fit (line 156) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 271) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 305) | def __call__(self, ds: EncodedDs, FILE: lightwood/mixer/lightgbm_array.py class LightGBMArray (line 15) | class LightGBMArray(BaseMixer): method __init__ (line 24) | def __init__( method _fit (line 59) | def _fit(self, train_data: EncodedDs, dev_data: EncodedDs, submodel_me... method fit (line 70) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 74) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 78) | def __call__(self, ds: Union[EncodedDs, ConcatedEncodedDs], FILE: lightwood/mixer/neural.py class Neural (line 29) | class Neural(BaseMixer): method __init__ (line 37) | def __init__( method _final_tuning (line 77) | def _final_tuning(self, data): method _select_criterion (line 101) | def _select_criterion(self) -> torch.nn.Module: method _select_optimizer (line 115) | def _select_optimizer(self, model, lr) -> Optimizer: method _find_lr (line 119) | def _find_lr(self, train_data): method _max_fit (line 186) | def _max_fit(self, train_dl, dev_dl, criterion, optimizer, scaler, sto... method _error (line 251) | def _error(self, dev_dl, criterion) -> float: method _init_net (line 262) | def _init_net(self, ds: EncodedDs): method _net_call (line 277) | def _net_call(self, x: torch.Tensor) -> torch.Tensor: method _fit (line 282) | def _fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method fit (line 317) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 321) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 340) | def __call__(self, ds: EncodedDs, FILE: lightwood/mixer/neural_ts.py class NeuralTs (line 23) | class NeuralTs(Neural): method __init__ (line 24) | def __init__( method _select_criterion (line 68) | def _select_criterion(self) -> torch.nn.Module: method _fit (line 76) | def _fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method fit (line 119) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method __call__ (line 122) | def __call__(self, ds: EncodedDs, FILE: lightwood/mixer/nhits.py class NHitsMixer (line 17) | class NHitsMixer(BaseMixer): method __init__ (line 26) | def __init__( method fit (line 101) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 149) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 155) | def __call__(self, ds: Union[EncodedDs, ConcatedEncodedDs], method _make_initial_df (line 209) | def _make_initial_df(self, df, mode='inference'): method _set_boundary (line 249) | def _set_boundary(df: pd.DataFrame, gby: list) -> Dict[str, object]: FILE: lightwood/mixer/prophet.py class ProphetMixer (line 8) | class ProphetMixer(SkTime): method __init__ (line 9) | def __init__( FILE: lightwood/mixer/qclassic.py class QClassic (line 9) | class QClassic(Neural): method __init__ (line 11) | def __init__( FILE: lightwood/mixer/random_forest.py class RandomForest (line 22) | class RandomForest(BaseMixer): method __init__ (line 30) | def __init__( method _multi_logloss (line 72) | def _multi_logloss(self, y_true: np.ndarray, y_pred: np.ndarray, eps: ... method fit (line 79) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 180) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 190) | def __call__(self, ds: EncodedDs, FILE: lightwood/mixer/regression.py class Regression (line 15) | class Regression(BaseMixer): method __init__ (line 30) | def __init__(self, stop_after: float, target_encoder: BaseEncoder, dty... method fit (line 45) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 71) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 80) | def __call__(self, ds: EncodedDs, FILE: lightwood/mixer/sktime.py class SkTime (line 21) | class SkTime(BaseMixer): method __init__ (line 29) | def __init__( method fit (line 114) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method _fit (line 133) | def _fit(self, data): method partial_fit (line 218) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 233) | def __call__(self, ds: Union[EncodedDs, ConcatedEncodedDs], method _call_groupmodel (line 279) | def _call_groupmodel(self, method _call_default (line 314) | def _call_default(self, ydf, data, idxs): method _get_best_model (line 321) | def _get_best_model(self, trial, train_data, test_data): method _transform_index_to_datetime (line 343) | def _transform_index_to_datetime(self, series, series_oby, freq): method _get_freq (line 349) | def _get_freq(self, delta): FILE: lightwood/mixer/tabtransformer.py class TabTransformerMixer (line 12) | class TabTransformerMixer(Neural): method __init__ (line 13) | def __init__( method _init_net (line 42) | def _init_net(self, ds: EncodedDs): method _net_call (line 59) | def _net_call(self, x: torch.Tensor) -> torch.Tensor: method fit (line 63) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: FILE: lightwood/mixer/unit.py class Unit (line 13) | class Unit(BaseMixer): method __init__ (line 14) | def __init__(self, stop_after: float, target_encoder: BaseEncoder): method fit (line 30) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 33) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 36) | def __call__(self, ds: EncodedDs, FILE: lightwood/mixer/xgboost.py function check_gpu_support (line 22) | def check_gpu_support(): class XGBoostMixer (line 36) | class XGBoostMixer(BaseMixer): method __init__ (line 63) | def __init__( method _to_dataset (line 104) | def _to_dataset(self, ds: EncodedDs, output_dtype: str, mode='train'): method fit (line 159) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 257) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 262) | def __call__(self, ds: EncodedDs, FILE: lightwood/mixer/xgboost_array.py class XGBoostArrayMixer (line 15) | class XGBoostArrayMixer(BaseMixer): method __init__ (line 24) | def __init__( method _fit (line 59) | def _fit(self, train_data: EncodedDs, dev_data: EncodedDs, submodel_me... method fit (line 70) | def fit(self, train_data: EncodedDs, dev_data: EncodedDs) -> None: method partial_fit (line 74) | def partial_fit(self, train_data: EncodedDs, dev_data: EncodedDs, args... method __call__ (line 78) | def __call__(self, ds: Union[EncodedDs, ConcatedEncodedDs], FILE: tests/integration/advanced/test_array.py class TestArrayTarget (line 10) | class TestArrayTarget(unittest.TestCase): method _test_array (line 11) | def _test_array(self, df): method test_0_num_array (line 23) | def test_0_num_array(self): method test_1_cat_array (line 32) | def test_1_cat_array(self): FILE: tests/integration/advanced/test_custom_modules.py function create_custom_module (line 12) | def create_custom_module(module_name, module_code): class TestBasic (line 20) | class TestBasic(unittest.TestCase): method test_0_add_throwing_cleaner (line 21) | def test_0_add_throwing_cleaner(self): method test_1_add_analyzer_block (line 57) | def test_1_add_analyzer_block(self): FILE: tests/integration/advanced/test_text_input.py class TestText (line 7) | class TestText(unittest.TestCase): method test_0_train_and_predict_bypass (line 8) | def test_0_train_and_predict_bypass(self): method test_1_train_and_predict_model (line 21) | def test_1_train_and_predict_model(self): FILE: tests/integration/advanced/test_timeseries.py class TestTimeseries (line 22) | class TestTimeseries(unittest.TestCase): method check_ts_prediction_df (line 23) | def check_ts_prediction_df(self, df: pd.DataFrame, horizon: int, order... method split_arrivals (line 39) | def split_arrivals(self, data: pd.DataFrame, grouped: bool) -> (pd.Dat... method test_0_time_series_grouped_regression (line 57) | def test_0_time_series_grouped_regression(self): method test_1_time_series_regression (line 127) | def test_1_time_series_regression(self): method test_2_time_series_classification_short_horizon_binary (line 186) | def test_2_time_series_classification_short_horizon_binary(self): method test_3_time_series_classification_long_horizon_binary (line 209) | def test_3_time_series_classification_long_horizon_binary(self): method test_4_time_series_classification_long_horizon_multiclass (line 233) | def test_4_time_series_classification_long_horizon_multiclass(self): method test_5_time_series_arima_mixer (line 261) | def test_5_time_series_arima_mixer(self): method test_6_time_series_sktime_mixer (line 353) | def test_6_time_series_sktime_mixer(self): method test_61_offset (line 387) | def test_61_offset(self): method test_7_irregular_series (line 432) | def test_7_irregular_series(self): method test_8_time_series_double_grouped_regression (line 472) | def test_8_time_series_double_grouped_regression(self): method test_9_ts_dedupe (line 505) | def test_9_ts_dedupe(self): method test_10_ts_stacked_ensemble (line 524) | def test_10_ts_stacked_ensemble(self): method test_11_output_date_format (line 553) | def test_11_output_date_format(self): FILE: tests/integration/basic/test_airline.py class TestBasic (line 8) | class TestBasic(unittest.TestCase): method test_0_predict_file_flow (line 10) | def test_0_predict_file_flow(self): FILE: tests/integration/basic/test_categorical.py class TestBasic (line 10) | class TestBasic(unittest.TestCase): method setup_predictor (line 11) | def setup_predictor(self, df, target): method test_0_binary (line 35) | def test_0_binary(self): method test_1_categorical (line 44) | def test_1_categorical(self): method test_2_binary_no_analysis (line 60) | def test_2_binary_no_analysis(self): FILE: tests/integration/basic/test_cleaner.py class TestCleaner (line 10) | class TestCleaner(unittest.TestCase): method test_0_imputers (line 11) | def test_0_imputers(self): FILE: tests/integration/basic/test_embedding.py class TestEmbeddingPredictor (line 8) | class TestEmbeddingPredictor(unittest.TestCase): method test_0_embedding_at_inference_time (line 9) | def test_0_embedding_at_inference_time(self): method test_1_embedding_only_at_creation (line 19) | def test_1_embedding_only_at_creation(self): FILE: tests/integration/basic/test_ensembles.py class TestBasic (line 8) | class TestBasic(unittest.TestCase): method test_0_mean_ensemble (line 9) | def test_0_mean_ensemble(self): method test_1_mode_ensemble (line 35) | def test_1_mode_ensemble(self): method test_2_weighted_mean_ensemble (line 63) | def test_2_weighted_mean_ensemble(self): FILE: tests/integration/basic/test_jsonai.py class TestJsonAI (line 7) | class TestJsonAI(unittest.TestCase): method test_0_hidden_args_analysis (line 8) | def test_0_hidden_args_analysis(self): method test_1_incorrect_chain (line 26) | def test_1_incorrect_chain(self): method test_2_tempscale_analysis (line 36) | def test_2_tempscale_analysis(self): FILE: tests/integration/basic/test_model_selection.py class TestMixerSelection (line 8) | class TestMixerSelection(unittest.TestCase): method get_mixers (line 9) | def get_mixers(self, df: pd.DataFrame, target: str, prob_kwargs: dict ... method test_0_regression_task (line 16) | def test_0_regression_task(self): method test_1_multiclass_task (line 23) | def test_1_multiclass_task(self): method test_2_unit_text_task (line 30) | def test_2_unit_text_task(self): method test_3_complex_text_task (line 37) | def test_3_complex_text_task(self): method test_4_timeseries_t_plus_1 (line 44) | def test_4_timeseries_t_plus_1(self): method test_5_timeseries_t_plus_n (line 60) | def test_5_timeseries_t_plus_n(self): FILE: tests/integration/basic/test_qclassic.py class TestBasic (line 10) | class TestBasic(unittest.TestCase): method test_0_predict_file_flow (line 11) | def test_0_predict_file_flow(self): FILE: tests/integration/basic/test_regression.py class TestBasic (line 10) | class TestBasic(unittest.TestCase): method test_0_predict_file_flow (line 11) | def test_0_predict_file_flow(self): method test_1_stacked_ensemble (line 66) | def test_1_stacked_ensemble(self): FILE: tests/integration/basic/test_save_and_load.py function save (line 9) | def save(predictor, path): function train (line 13) | def train(predictor, df): function execute_first_bit (line 17) | def execute_first_bit(code, df, path): function execute_second_bit (line 22) | def execute_second_bit(code, df, path): function execute_third_bit (line 30) | def execute_third_bit(code, df, path): class TestBasic (line 39) | class TestBasic(unittest.TestCase): method test_0_predict_file_flow (line 40) | def test_0_predict_file_flow(self): FILE: tests/integration/basic/test_weird_target_dist.py class TestBasic (line 7) | class TestBasic(unittest.TestCase): method test_0_unknown_categories_in_test (line 8) | def test_0_unknown_categories_in_test(self): FILE: tests/unit_tests/analysis/test_nc_norm.py class TestNcNormalizer (line 6) | class TestNcNormalizer(unittest.TestCase): method test_compute_numerical_labels (line 7) | def test_compute_numerical_labels(self): method test_compute_categorical_labels (line 18) | def test_compute_categorical_labels(self): FILE: tests/unit_tests/analysis/test_pyod.py class TestPyOD (line 8) | class TestPyOD(unittest.TestCase): method test_0_pyod_analysis (line 9) | def test_0_pyod_analysis(self): FILE: tests/unit_tests/analysis/test_shap.py class TestSHAP (line 8) | class TestSHAP(unittest.TestCase): method test_0_shap_analysis (line 9) | def test_0_shap_analysis(self): FILE: tests/unit_tests/data/test_transform_ts.py class TestTransformTS (line 9) | class TestTransformTS(unittest.TestCase): method test_get_residuals (line 10) | def test_get_residuals(self): FILE: tests/unit_tests/encoder/audio/test_mfcc.py class TestMFCCEncoder (line 9) | class TestMFCCEncoder(unittest.TestCase): method test_encode (line 10) | def test_encode(self): FILE: tests/unit_tests/encoder/categorical/test_autoencoder.py class TestAutoencoder (line 16) | class TestAutoencoder(unittest.TestCase): method create_test_data (line 18) | def create_test_data(self, method test_autoencoder_ohe (line 44) | def test_autoencoder_ohe(self): method test_autoencoder_label (line 65) | def test_autoencoder_label(self): method check_encoder_on_device (line 84) | def check_encoder_on_device(self, device): method test_encoder_on_cpu (line 94) | def test_encoder_on_cpu(self): method test_encoder_on_cuda (line 98) | def test_encoder_on_cuda(self): FILE: tests/unit_tests/encoder/categorical/test_binary.py class TestBinary (line 12) | class TestBinary(unittest.TestCase): method test_encode_decode_with_binary (line 15) | def test_encode_decode_with_binary(self): method test_check_only_binary (line 71) | def test_check_only_binary(self): method test_check_probabilities (line 78) | def test_check_probabilities(self): method test_target_distro_scaled_to_1 (line 96) | def test_target_distro_scaled_to_1(self): method test_distro_nonzeroweights (line 116) | def test_distro_nonzeroweights(self): method test_distro_zero (line 135) | def test_distro_zero(self): FILE: tests/unit_tests/encoder/categorical/test_label.py class TestLabel (line 10) | class TestLabel(unittest.TestCase): method test_encode_and_decode (line 13) | def test_encode_and_decode(self): FILE: tests/unit_tests/encoder/categorical/test_multihot.py class TestMultiHotEncoder (line 11) | class TestMultiHotEncoder(unittest.TestCase): method get_vocab (line 12) | def get_vocab(self): method test_multi_encoding (line 15) | def test_multi_encoding(self): method test_multi_encoding_empty_row (line 33) | def test_multi_encoding_empty_row(self): method test_handle_unseen_none (line 49) | def test_handle_unseen_none(self): FILE: tests/unit_tests/encoder/categorical/test_onehot.py class TestOnehot (line 12) | class TestOnehot(unittest.TestCase): method test_encode_and_decode_with_unknown_token (line 20) | def test_encode_and_decode_with_unknown_token(self): method test_encode_and_decode_with_return_zeros (line 65) | def test_encode_and_decode_with_return_zeros(self): method test_check_probs_with_unknown (line 106) | def test_check_probs_with_unknown(self): method test_target_distro_scaled_to_1 (line 120) | def test_target_distro_scaled_to_1(self): method test_target_distro_with_unk (line 140) | def test_target_distro_with_unk(self): method test_distro_nonzeroweights (line 160) | def test_distro_nonzeroweights(self): method test_distro_zero (line 186) | def test_distro_zero(self): FILE: tests/unit_tests/encoder/date/test_datetime.py class TestDatetimeEncoder (line 11) | class TestDatetimeEncoder(unittest.TestCase): method _create_timestamp (line 13) | def _create_timestamp(): method test_raise_encode_type (line 18) | def test_raise_encode_type(self): method test_encode (line 24) | def test_encode(self): method test_decode (line 33) | def test_decode(self): method test_sinusoidal_encoding (line 52) | def test_sinusoidal_encoding(self): method test_cap_invalid_dates (line 66) | def test_cap_invalid_dates(self): FILE: tests/unit_tests/encoder/identity/test_identity.py class TestIdentityEncoder (line 7) | class TestIdentityEncoder(unittest.TestCase): method test_encode_and_decode (line 8) | def test_encode_and_decode(self): FILE: tests/unit_tests/encoder/images/test_img_2_vec.py class TestImg2VecEncoder (line 9) | class TestImg2VecEncoder(unittest.TestCase): method test_encode (line 10) | def test_encode(self): method run_test_encoder_on_device (line 31) | def run_test_encoder_on_device(self, device): method test_encoder_on_cpu (line 37) | def test_encoder_on_cpu(self): method test_encoder_on_cuda (line 41) | def test_encoder_on_cuda(self): FILE: tests/unit_tests/encoder/numeric/test_numeric.py function _pollute (line 10) | def _pollute(array): class TestNumericEncoder (line 18) | class TestNumericEncoder(unittest.TestCase): method test_encode_and_decode (line 19) | def test_encode_and_decode(self): method test_positive_domain (line 50) | def test_positive_domain(self): method test_log_overflow_and_none (line 61) | def test_log_overflow_and_none(self): method test_nan_encoding (line 74) | def test_nan_encoding(self): method test_weights (line 114) | def test_weights(self): FILE: tests/unit_tests/encoder/text/test_pretrained.py function create_synthetic_data (line 15) | def create_synthetic_data(n, ptrain=0.7): class TestPretrainedLangEncoder (line 62) | class TestPretrainedLangEncoder(unittest.TestCase): method test_encode_and_decode (line 63) | def test_encode_and_decode(self): method test_embed_mode (line 93) | def test_embed_mode(self): method test_auto_embed_mode (line 120) | def test_auto_embed_mode(self): method run_test_encoder_on_device (line 154) | def run_test_encoder_on_device(self, device): method test_encoder_on_cpu (line 163) | def test_encoder_on_cpu(self): method test_encoder_on_cuda (line 167) | def test_encoder_on_cuda(self): FILE: tests/unit_tests/encoder/text/test_short.py function generate_sentences (line 73) | def generate_sentences(min_, max_, vocab_size): class TestShortTextEncoder (line 78) | class TestShortTextEncoder(unittest.TestCase): method test_smallvocab_target_auto_mode (line 79) | def test_smallvocab_target_auto_mode(self): method test_non_smallvocab_target_auto_mode (line 102) | def test_non_smallvocab_target_auto_mode(self): method test_smallvocab_non_target_auto_mode (line 130) | def test_smallvocab_non_target_auto_mode(self): method test_non_smallvocab_non_target_auto_mode (line 149) | def test_non_smallvocab_non_target_auto_mode(self): method test_smallvocab_non_target_manual_mode (line 168) | def test_smallvocab_non_target_manual_mode(self): method test_non_smallvocab_non_target_manual_mode (line 189) | def test_non_smallvocab_non_target_manual_mode(self): method check_encoder_on_device (line 210) | def check_encoder_on_device(self, device): method test_encoder_on_cpu (line 216) | def test_encoder_on_cpu(self): method test_encoder_on_cuda (line 220) | def test_encoder_on_cuda(self): FILE: tests/unit_tests/encoder/text/test_tfidf.py class TestTfidfEncoder (line 7) | class TestTfidfEncoder(unittest.TestCase): method test_encode (line 8) | def test_encode(self): FILE: tests/unit_tests/encoder/text/test_vocab.py class TestVocabularyEncoder (line 6) | class TestVocabularyEncoder(unittest.TestCase): method test_encode_decode (line 7) | def test_encode_decode(self): FILE: tests/unit_tests/encoder/time_series/test_timeseries_rnn.py class TestRnnEncoder (line 9) | class TestRnnEncoder(unittest.TestCase): method test_minmax_normalizer (line 11) | def test_minmax_normalizer(self): method test_cat_normalizer (line 22) | def test_cat_normalizer(self): method test_overfit (line 38) | def test_overfit(self): method check_encoder_on_device (line 88) | def check_encoder_on_device(self, device): method test_encoder_on_cpu (line 101) | def test_encoder_on_cpu(self): method test_encoder_on_cuda (line 105) | def test_encoder_on_cuda(self): FILE: tests/unit_tests/encoder/time_series/test_transformer.py class TestTransformerEncoder (line 10) | class TestTransformerEncoder(unittest.TestCase): method test_get_chunk (line 11) | def test_get_chunk(self): method test_mask (line 37) | def test_mask(self): method test_overfit (line 54) | def test_overfit(self): FILE: tests/unit_tests/helpers.py class TestTSDifferencer (line 9) | class TestTSDifferencer(unittest.TestCase): method test_numerical (line 10) | def test_numerical(self): FILE: tests/unit_tests/mixer/test_lgbm.py class TestBasic (line 12) | class TestBasic(unittest.TestCase): method get_submodels (line 14) | def get_submodels(self): method test_0_regression (line 30) | def test_0_regression(self): FILE: tests/unit_tests/mixer/test_nhits.py class TestBasic (line 11) | class TestBasic(unittest.TestCase): method get_submodels (line 12) | def get_submodels(self): method test_0_regression (line 28) | def test_0_regression(self): FILE: tests/unit_tests/mixer/test_random_forest.py class TestBasic (line 12) | class TestBasic(unittest.TestCase): method get_submodels (line 14) | def get_submodels(self): method test_0_regression (line 30) | def test_0_regression(self): method test_1_binary (line 44) | def test_1_binary(self): FILE: tests/unit_tests/mixer/test_tabtransformer.py class TestBasic (line 11) | class TestBasic(unittest.TestCase): method get_submodels (line 12) | def get_submodels(self): method test_0_regression (line 23) | def test_0_regression(self): method test_1_binary (line 37) | def test_1_binary(self): FILE: tests/unit_tests/mixer/test_xgboost.py class TestBasic (line 10) | class TestBasic(unittest.TestCase): method get_submodels (line 12) | def get_submodels(self): method test_0_regression (line 28) | def test_0_regression(self): FILE: tests/utils/data_generation.py function generate_timeseries (line 7) | def generate_timeseries( function rand_ascii_str (line 27) | def rand_ascii_str(length=30): function rand_int (line 32) | def rand_int(): function rand_float (line 36) | def rand_float(): function generate_value_cols (line 40) | def generate_value_cols(types, length, ts_period=48 * 3600): function generate_log_labels (line 79) | def generate_log_labels(columns, separator=','): function generate_timeseries_labels (line 95) | def generate_timeseries_labels(columns): function columns_to_file (line 121) | def columns_to_file(columns, filename, headers=None): FILE: tests/utils/timing.py function train_and_check_time_aim (line 6) | def train_and_check_time_aim(predictor: PredictorInterface, train_df: pd...