pipeline.preprocessing package

Subpackages

• pipeline.preprocessing.features package
  • Submodules
  • pipeline.preprocessing.features.abstract module
  • pipeline.preprocessing.features.class_map module
  • pipeline.preprocessing.features.emsfeatures module
  • pipeline.preprocessing.features.jimsfeatures module
  • pipeline.preprocessing.features.mentalhealthfeatures module
  • pipeline.preprocessing.features.miscfeatures module
  • pipeline.preprocessing.features.person module
  • pipeline.preprocessing.features.rsifeatures module
  • pipeline.preprocessing.features.seqfeatures module
  • Module contents

Submodules

pipeline.preprocessing.feature_processor module

class pipeline.preprocessing.feature_processor.FeatureGrabber(end_date, engine, config_db, con)

getFeature(feature_to_load)

pipeline.preprocessing.feature_processor.convert_categorical(df)

pipeline.preprocessing.feature_processor.feature_name_grabber(df)

pipeline.preprocessing.feature_processor.imputation_zero(df)

pipeline.preprocessing.feature_processor.numerical_column_clean(df)

pipeline.preprocessing.feature_table_builder module

class pipeline.preprocessing.feature_table_builder.Labeller(start_date, end_date, labels)

get_labels()

pipeline.preprocessing.feature_table_builder.chunker(seq, size)

pipeline.preprocessing.feature_table_builder.dataframe_merge(d1, d2)

pipeline.preprocessing.feature_table_builder.generate_fake_todays(fake_today, prediction_window, start_date)

Given a final prediction window start date, the length of the prediction windows, and a training start date, return the start and end dates for all prediction windows as a dictionary.

Parameters:

• fake_today (datetime) – start date for the final prediction window
• prediction_window (int) – length of the prediction windows in days
• start_date (datetime) – start date for the training period

Returns:

start and end dates for all prediction windows

Return type:

dict

pipeline.preprocessing.feature_table_builder.generate_feature_list(config)

pipeline.preprocessing.feature_table_builder.generate_feature_table(config, fake_today, prediction_window, start_date, feature_timestamp)

pipeline.preprocessing.feature_table_builder.label_feature_producer(start_date, end_date, features, labels)

pipeline.preprocessing.feature_table_builder.merge_feature_dictionaries(d1, d2)

pipeline.preprocessing.feature_table_builder.write_dataframe_to_sql(df_name, df, schema)

pipeline.preprocessing.run module

pipeline.preprocessing.run.main(config_file_name)

Module contents