autoPipe Module

Description :

• This module is used to build Pipeline Cluster Traversal Experiments:

  • Create sequential components of Pipeline Cluster Traversal Experiments
  • Apply traversal experiments through pipeline cluster to find the best baseline model
  • Generate comparable and parameter-tracable dictionaies and reports to support autoVIZ and autoFlow modules

• Build Steps:

  • autoPP - dynaPreprocessing() Class in autoPP module
  • Datasets Splitting - pipeline_splitting_rule() Function in utilis_funs module
  • autoFS - dynaFS_clf() or dynaFS_reg() Class in autoFS module
  • autoCV - dynaClassifier() or dynaRegressor() Class in autoCV module
  • Model Evaluate - evaluate_model() Class in autoCV module

autoPipe

class dynapipe.autoPipe.autoPipe(steps)

This class is to build Pipeline Cluster Traversal Experiments.

Parameters:steps (list, default = None) – List of (name, transform) tuples (implementing fit & transform) that are chained, in the order in which they are chained, with the last object a model evaluation function.

Example

[Example]

https://dynamic-pipeline.readthedocs.io/en/latest/demos.html#build-pipeline-cluster-traveral-experiments-using-autopipe

References

None

fit(data)

Fits and transforms a chain of Dynamic Pipeline modules.

Parameters:input_data (pandas dataframe, shape = [n_samples, n_features]) – NOTE: The input_data should be the datasets after basic data cleaning & well feature deduction, the more features involve will result in more columns permutation outputs. Returns:

• DICT_PREP_INFO (dictionary) – Each key is the # of preprocessed dataset(“Dataset_xxx” format, i.e. “Dataset_10”), each value stores an info string about what transforms applied. i.e. DICT_PREPROCESSING[‘Dataset_0’] stores value “winsor_0-Scaler_None– Encoded Features:[‘diagnosis’, ‘Size_3’, ‘area_mean’]”, which means applied 1st mode of winsorization, none scaler applied, and the encoded columns names(shown the enconding approachs in the names)
• DICT_FEATURE_SELECTION_INFO (dictionary) – Each key is the # of preprocessed dataset, each value stores the name of features selected after the autoFS module.
• DICT_MODELS_EVALUATION (dictionary) – Each key is the # of preprocessed dataset, each value stores the model evaluation results with its validate dataset.
• DICT_DATA (dictionary) – Each key is the # of preprocessed dataset, and first level sub-key is the type of splitted sets(including ‘DICT_Train’,’DICT_TEST’,and’DICT_Validate’). The second level sub-key is “X” for features and “y” for label, each value stores the datasets related to the keys(Pandas Dataframe format) i.e. DICT_DATA[‘Dataset_0’][‘DICT_TEST’][“X”] is the train features of Dataset_0’s test dataset
• models_summary (Pandas Dataframe) – Model selection results ranking table among all composits of preprocessed datasets, selected features and all posible models with optimal parameters.
• NOTE - Log records will generate and save to ./logs folder automatedly.