Esempio n. 1
0
class FeatureExtractorClassifier(object):
    """
    Difference with the FeatureExtractorClassifier from ramp-workflow:
    `test_submission` wraps the y_proba in a DataFrame with the original
    index.
    """

    def __init__(self):
        self.element_names = ['feature_extractor', 'classifier']
        self.feature_extractor_workflow = FeatureExtractor(
            [self.element_names[0]])
        self.classifier_workflow = Classifier([self.element_names[1]])

    def train_submission(self, module_path, X_df, y_array, train_is=None):
        if train_is is None:
            train_is = slice(None, None, None)
        fe = self.feature_extractor_workflow.train_submission(
            module_path, X_df, y_array, train_is)
        X_train_array = self.feature_extractor_workflow.test_submission(
            fe, X_df.iloc[train_is])
        clf = self.classifier_workflow.train_submission(
            module_path, X_train_array, y_array[train_is])
        return fe, clf

    def test_submission(self, trained_model, X_df):
        fe, clf = trained_model
        X_test_array = self.feature_extractor_workflow.test_submission(
            fe, X_df)
        y_proba = self.classifier_workflow.test_submission(clf, X_test_array)

        arr = X_df.index.values.astype('datetime64[m]').astype(int)
        y = np.hstack((arr[:, np.newaxis], y_proba))
        return y
Esempio n. 2
0
 def __init__(self):
     self.element_names = ['feature_extractor', 'classifier']
     self.feature_extractor_workflow = FeatureExtractor(
         [self.element_names[0]])
     self.classifier_workflow = Classifier([self.element_names[1]])