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X.copy() in the process method (#78)
* X.copy() in the transformer method. * update version 0.3.4
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@ -214,6 +214,7 @@ class DataTransformer:
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X[column] = X[column].fillna('__NAN__')
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cat_columns.append(column)
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else:
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# print(X[column].dtype.name)
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if X[column].nunique(dropna=True) < 2:
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X.drop(columns=column, inplace=True)
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drop = True
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@ -236,8 +237,8 @@ class DataTransformer:
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SimpleImputer(missing_values=np.nan, strategy='median'),
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X_num.columns)])
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X[num_columns] = self.transformer.fit_transform(X_num)
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self._cat_columns, self._num_columns, self._datetime_columns = \
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cat_columns, num_columns, datetime_columns
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self._cat_columns, self._num_columns, self._datetime_columns = cat_columns, \
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num_columns, datetime_columns
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self._drop = drop
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if task == 'regression':
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@ -249,13 +250,14 @@ class DataTransformer:
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return X, y
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def transform(self, X):
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X = X.copy()
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if isinstance(X, pd.DataFrame):
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cat_columns, num_columns, datetime_columns = \
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self._cat_columns, self._num_columns, self._datetime_columns
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cat_columns, num_columns, datetime_columns = self._cat_columns, \
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self._num_columns, self._datetime_columns
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X = X[cat_columns + num_columns].copy()
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if datetime_columns:
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for dt_column in datetime_columns:
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X[dt_column] = X[dt_column].map(datetime.toordinal)
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X = X[cat_columns + num_columns].copy()
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for column in cat_columns:
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# print(column, X[column].dtype.name)
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if X[column].dtype.name == 'object':
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@ -273,3 +275,4 @@ class DataTransformer:
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X_num.columns = range(X_num.shape[1])
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X[num_columns] = self.transformer.transform(X_num)
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return X
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@ -1 +1 @@
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__version__ = "0.3.3"
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__version__ = "0.3.4"
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@ -239,6 +239,8 @@ class TestAutoML(unittest.TestCase):
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y = np.array([0, 1])
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automl_experiment.fit(X_train=fake_df, X_val=fake_df, y_train=y, y_val=y, **automl_settings)
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y_pred = automl_experiment.predict(fake_df)
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def test_regression(self):
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automl_experiment = AutoML()
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