Updating README for version 0.5.0

build/lib/featurewiz/__init__.py

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+# -*- coding: utf-8 -*-
+################################################################################
+#     featurewiz - advanced feature engineering and best features selection in single line of code
+#     Python v3.6+
+#     Created by Ram Seshadri
+#     Licensed under Apache License v2
+################################################################################
+# Version
+from .__version__ import __version__
+from .featurewiz import featurewiz
+from .featurewiz import FE_split_one_field_into_many, FE_add_groupby_features_aggregated_to_dataframe
+from .featurewiz import FE_start_end_date_time_features
+from .featurewiz import classify_features
+from .featurewiz import classify_columns,FE_combine_rare_categories
+from .featurewiz import FE_count_rows_for_all_columns_by_group
+from .featurewiz import FE_add_age_by_date_col, FE_split_add_column, FE_get_latest_values_based_on_date_column
+from .featurewiz import FE_capping_outliers_beyond_IQR_Range
+from .featurewiz import EDA_classify_and_return_cols_by_type, EDA_classify_features_for_deep_learning
+from .featurewiz import FE_create_categorical_feature_crosses, EDA_find_skewed_variables
+from .featurewiz import FE_kmeans_resampler, FE_find_and_cap_outliers, EDA_find_outliers
+from .featurewiz import split_data_n_ways, FE_concatenate_multiple_columns
+from .featurewiz import FE_discretize_numeric_variables, reduce_mem_usage
+from .ml_models import simple_XGBoost_model, simple_LightGBM_model, complex_XGBoost_model
+from .ml_models import complex_LightGBM_model,data_transform, get_class_weights
+from .my_encoders import My_LabelEncoder, Groupby_Aggregator, My_LabelEncoder_Pipe, Ranking_Aggregator, DateTime_Transformer
+from .my_encoders import Rare_Class_Combiner, Rare_Class_Combiner_Pipe, FE_create_time_series_features, Binning_Transformer
+from .my_encoders import Column_Names_Transformer, FE_convert_all_object_columns_to_numeric, Numeric_Transformer
+from .my_encoders import TS_Lagging_Transformer, TS_Fourier_Transformer, TS_Trend_Seasonality_Transformer
+from .my_encoders import TS_Lagging_Transformer_Pipe, TS_Fourier_Transformer_Pipe
+from lazytransform import LazyTransformer, SuloRegressor, SuloClassifier, print_regression_metrics, print_classification_metrics
+from lazytransform import print_regression_model_stats, YTransformer, print_sulo_accuracy
+from .sulov_method import FE_remove_variables_using_SULOV_method
+from .featurewiz import FE_transform_numeric_columns_to_bins, FE_create_interaction_vars
+from .stacking_models import Stacking_Classifier, Blending_Regressor, Stacking_Regressor, stacking_models_list
+from .stacking_models import StackingClassifier_Multi, analyze_problem_type_array
+from .stacking_models import DenoisingAutoEncoder, VariationalAutoEncoder
+from .stacking_models import GAN, GANAugmenter
+from .featurewiz import EDA_binning_numeric_column_displaying_bins, FE_calculate_duration_from_timestamp
+from .featurewiz import FE_convert_mixed_datatypes_to_string, FE_drop_rows_with_infinity
+from .featurewiz import EDA_find_remove_columns_with_infinity, FE_split_list_into_columns
+from .featurewiz import EDA_remove_special_chars, FE_remove_commas_in_numerics
+from .featurewiz import EDA_randomly_select_rows_from_dataframe, remove_duplicate_cols_in_dataset
+from .featurewiz import cross_val_model_predictions, get_class_distribution
+from .featurewiz import FeatureWiz
+################################################################################
+if __name__ == "__main__":
+    module_type = 'Running'
+else:
+    module_type = 'Imported'
+version_number = __version__
+print("""%s featurewiz %s. Use the following syntax:
+    >>> wiz = FeatureWiz(feature_engg = '', nrows=None, transform_target=True, scalers="std",
+        		category_encoders="auto", add_missing=False, verbose=0. imbalanced=False,
+        		ae_options={})
+    >>> X_train_selected, y_train = wiz.fit_transform(X_train, y_train)
+    >>> X_test_selected = wiz.transform(X_test)
+    >>> selected_features = wiz.features
+    """ %(module_type, version_number))
+################################################################################

build/lib/featurewiz/__version__.py

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+# -*- coding: utf-8 -*-
+"""Specifies the version of the featurewiz package."""
+
+__title__ = "featurewiz"
+__author__ = "Ram Seshadri"
+__description__ = "Advanced Feature Engineering and Feature Selection for any data set, any size"
+__url__ = "https://github.com/Auto_ViML/featurewiz.git"
+__version__ = "0.5.0"
+__license__ = "Apache License 2.0"
+__copyright__ = "2020-23 Google"