def test_categorical_indicators(self): self.data['categorical'] = map(str, range(10)) model_def = ModelDefinition(features=[Map('categorical', list), F('a'), Map('b', np.abs)], target='y', categorical_indicators=False) x, ff = build_featureset_safe(model_def.features, self.data) self.assertEqual(len(x.columns), len(model_def.features)) self.data['categorical'] = map(str, range(10)) model_def = ModelDefinition(features=[Map('categorical', np.abs), F('a'), Map('b', np.abs)], target='y', categorical_indicators=True) x, ff = build_featureset_safe(model_def.features, self.data) self.assertEqual(len(x.columns), len(model_def.features) + 9)
def generate_train(model_def, data, prep_index=None, train_index=None): # create training set data, prep_index, train_index = filter_data_and_indexes(model_def, data, prep_index, train_index) x_train, fitted_features = build_featureset_safe(model_def.features, data, prep_index, train_index) y_train, fitted_target = build_target_safe(model_def.target, data, prep_index, train_index) x_train = x_train.reindex(train_index) y_train = y_train.reindex(train_index) return x_train, y_train, fitted_features, fitted_target
def generate_train(model_def, data, prep_index=None, train_index=None): # create training set data, prep_index, train_index = filter_data_and_indexes( model_def, data, prep_index, train_index) x_train, fitted_features = build_featureset_safe(model_def.features, data, prep_index, train_index) y_train, fitted_target = build_target_safe(model_def.target, data, prep_index, train_index) x_train = x_train.reindex(train_index) y_train = y_train.reindex(train_index) return x_train, y_train, fitted_features, fitted_target
def fit_model(model_def, data, prep_index=None, train_index=None): # create training set x_train, fitted_features = build_featureset_safe(model_def.features, data, prep_index, train_index) y_train, fitted_target = build_target_safe(model_def.target, data, prep_index, train_index) # fit estimator model_def.estimator.fit(x_train, y_train) # unnecesary? fitted_estimator = FittedEstimator(model_def.estimator, x_train, y_train) fitted_model = FittedModel(model_def, fitted_features, fitted_target, fitted_estimator) return x_train, y_train, fitted_model
def test_categorical_indicators(self): self.data['categorical'] = map(str, range(10)) model_def = ModelDefinition( features=[Map('categorical', str), F('a'), Map('b', np.abs)], target='y', categorical_indicators=False) x, ff = build_featureset_safe(model_def.features, self.data) self.assertEqual(len(x.columns), len(model_def.features)) self.data['categorical'] = map(str, range(10)) model_def = ModelDefinition( features=[Map('categorical', str), F('a'), Map('b', np.abs)], target='y', categorical_indicators=True) print model_def.features x, ff = build_featureset_safe(model_def.features, self.data) print x for f in ff: print f.feature self.assertEqual(len(x.columns), len(model_def.features) + 9)
def generate_train(model_def, data, prep_index=None, train_index=None): # create training set x_train, fitted_features = build_featureset_safe(model_def.features, data, prep_index, train_index) y_train, fitted_target = build_target_safe(model_def.target, data, prep_index, train_index) return x_train, y_train, fitted_features, fitted_target