| #!/usr/bin/env python |
| # coding=utf-8 |
| |
| """TrainingPreparator engine action. |
| |
| Use this module to add the project main code. |
| """ |
| from .._compatibility import six |
| from .._logging import get_logger |
| from sklearn.model_selection import StratifiedShuffleSplit |
| |
| from marvin_python_toolbox.engine_base import EngineBaseDataHandler |
| |
| __all__ = ['TrainingPreparator'] |
| |
| |
| logger = get_logger('training_preparator') |
| |
| |
| class TrainingPreparator(EngineBaseDataHandler): |
| |
| def __init__(self, **kwargs): |
| super(TrainingPreparator, self).__init__(**kwargs) |
| |
| def execute(self, params, **kwargs): |
| |
| train_no_na = self.marvin_initial_dataset['train'][params["pred_cols"] + [params["dep_var"]]].dropna() |
| |
| print("Length: {}".format(len(train_no_na))) |
| |
| # Feature Engineering |
| data_X = train_no_na[params["pred_cols"]] |
| data_X.loc[:, 'Sex'] = data_X.loc[:, 'Sex'].map({'male': 1, 'female': 0}) |
| data_y = train_no_na[params["dep_var"]] |
| |
| # Prepare for Stratified Shuffle Split |
| sss = StratifiedShuffleSplit(n_splits=5, test_size=.6, random_state=0) |
| sss.get_n_splits(data_X, data_y) |
| |
| # Get Test Dataset |
| test_no_na = self.marvin_initial_dataset['test'][params["pred_cols"]].dropna() |
| |
| print("Length: {}".format(len(test_no_na))) |
| |
| # Feature Engineering |
| test_X = test_no_na[params["pred_cols"]] |
| test_X.loc[:, 'Sex'] = test_X.loc[:, 'Sex'].map({'male': 1, 'female': 0}) |
| |
| self.marvin_dataset = { |
| 'X_train': data_X, |
| 'y_train': data_y, |
| 'X_test': test_X, |
| 'sss': sss |
| } |
| |
| print ("Preparation is Done!!!!") |
| |
