"one_step": 10, "train_cant_improve_limit": 5, "max_steps": 500, "max_rows_limit": None, "max_cols_limit": None, } required_preprocessing = [ "missing_values_inputation", "convert_categorical", "target_preprocessing", ] lgbm_multi_params = copy.deepcopy(lgbm_bin_params) lgbm_multi_params["objective"] = ["multiclass"] lgbm_multi_params["metric"] = ["multi_logloss", "multi_error"] """ AlgorithmsRegistry.add( BINARY_CLASSIFICATION, LightgbmAlgorithm, lgbm_bin_params, required_preprocessing, additional, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, LightgbmAlgorithm, lgbm_multi_params, required_preprocessing, additional, )
"early_stopping_rounds": 50, "max_rows_limit": None, "max_cols_limit": None, } required_preprocessing = [ "missing_values_inputation", "convert_categorical", "datetime_transform", "text_transform", "target_as_integer", ] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, RandomForestAlgorithm, rf_params, required_preprocessing, additional, classification_default_params, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, RandomForestAlgorithm, rf_params, required_preprocessing, additional, classification_default_params, ) # # REGRESSION
"trees_in_step": 100, "max_steps": 50, "early_stopping_rounds": 50, "max_rows_limit": None, "max_cols_limit": None, } required_preprocessing = [ "missing_values_inputation", "convert_categorical", "target_as_integer", ] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, ExtraTreesAlgorithm, et_params, required_preprocessing, additional, classification_default_params, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, ExtraTreesAlgorithm, et_params, required_preprocessing, additional, classification_default_params, ) # # REGRESSION
"max_rounds": 10000, "early_stopping_rounds": 50, "max_rows_limit": None, "max_cols_limit": None, } required_preprocessing = [ "missing_values_inputation", "datetime_transform", "text_transform", "target_as_integer", ] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, CatBoostAlgorithm, classification_params, required_preprocessing, additional, classification_default_params, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, CatBoostAlgorithm, classification_params, required_preprocessing, additional, classification_default_params, ) regression_params = copy.deepcopy(classification_params)
classification_multi_default_params = { "objective": "multiclass", "num_leaves": 63, "learning_rate": 0.05, "feature_fraction": 0.9, "bagging_fraction": 0.9, "min_data_in_leaf": 10, } lgbr_params = copy.deepcopy(lgbm_bin_params) lgbr_params["objective"] = ["regression"] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, LightgbmAlgorithm, lgbm_bin_params, required_preprocessing, additional, classification_bin_default_params, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, LightgbmAlgorithm, lgbm_multi_params, required_preprocessing, additional, classification_multi_default_params, ) regression_required_preprocessing = [ "missing_values_inputation",
"max_steps": 1, "max_rows_limit": None, "max_cols_limit": None, } required_preprocessing = [ "missing_values_inputation", "convert_categorical", "datetime_transform", "text_transform", "target_as_integer", ] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, DecisionTreeAlgorithm, dt_params, required_preprocessing, additional, classification_default_params, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, DecisionTreeAlgorithm, dt_params, required_preprocessing, additional, classification_default_params, ) dt_regression_params = { "criterion": [
"train_cant_improve_limit": 5, "min_steps": 5, "max_steps": 500, "max_rows_limit": None, "max_cols_limit": None, } required_preprocessing = [ "missing_values_inputation", "convert_categorical", "target_as_integer", ] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, XgbAlgorithm, xgb_bin_class_params, required_preprocessing, additional, classification_bin_default_params, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, XgbAlgorithm, xgb_multi_class_params, required_preprocessing, additional, classification_multi_default_params, ) regression_required_preprocessing = [ "missing_values_inputation",
def file_extension(self): return "baseline" def is_fitted(self): return (hasattr(self.model, "n_outputs_") and self.model.n_outputs_ is not None and self.model.n_outputs_ > 0) additional = {"max_steps": 1, "max_rows_limit": None, "max_cols_limit": None} required_preprocessing = ["target_as_integer"] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, BaselineClassifierAlgorithm, {}, required_preprocessing, additional, {}, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, BaselineClassifierAlgorithm, {}, required_preprocessing, additional, {}, ) AlgorithmsRegistry.add(REGRESSION, BaselineRegressorAlgorithm, {}, {}, additional, {})
"max_cols_limit": None, } required_preprocessing = [ "missing_values_inputation", "convert_categorical", "datetime_transform", "text_transform", "scale", "target_as_integer", ] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, NeuralNetworkAlgorithm, nn_params, required_preprocessing, additional, default_nn_params, ) required_preprocessing = [ "missing_values_inputation", "convert_categorical", "datetime_transform", "text_transform", "scale", "target_as_one_hot", ] AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, NeuralNetworkAlgorithm,
) df.to_csv( os.path.join(model_file_path, f"{learner_name}_coefs.csv"), index=False ) additional = {"max_steps": 1, "max_rows_limit": None, "max_cols_limit": None} required_preprocessing = [ "missing_values_inputation", "convert_categorical", "scale", "target_as_integer", ] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, LinearAlgorithm, {}, required_preprocessing, additional, {} ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, LinearAlgorithm, {}, required_preprocessing, additional, {}, ) regression_required_preprocessing = [ "missing_values_inputation", "convert_categorical", "scale", "target_scale",
default_params = {"n_neighbors": 5, "weights": "uniform"} additional = {"max_rows_limit": 100000, "max_cols_limit": 100} required_preprocessing = [ "missing_values_inputation", "convert_categorical", "scale", "target_as_integer", ] AlgorithmsRegistry.add( BINARY_CLASSIFICATION, KNeighborsAlgorithm, knn_params, required_preprocessing, additional, default_params, ) AlgorithmsRegistry.add( MULTICLASS_CLASSIFICATION, KNeighborsAlgorithm, knn_params, required_preprocessing, additional, default_params, ) AlgorithmsRegistry.add( REGRESSION, KNeighborsRegressorAlgorithm,