Fixing issues with imbalanced datasets (#197)

* adding missing method from base_feature_validator

* First try at a fix, removing redundant code

* Fix bug

* Updating unit test typo, fixing bug where the data type was not checked because X was a numpy array at the time of checking

* Fixing flake 8 failing

* Bug fix, implementation update for imbalanced datasets and unit tests to check the implementation

* flake8 fix

* Bug fix

* Making the conversion to dataframe in the unit tests consistent with what happens at the validator, so the types do not change

* flake8 fix

* Addressing Ravin's comments

autoPyTorch/data/base_feature_validator.py

@@ -111,6 +111,20 @@ def _fit(
         """
         raise NotImplementedError()
 
+    def _check_data(
+        self,
+        X: SUPPORTED_FEAT_TYPES,
+    ) -> None:
+        """
+        Feature dimensionality and data type checks
+
+        Arguments:
+            X (SUPPORTED_FEAT_TYPES):
+                A set of features that are going to be validated (type and dimensionality
+                checks) and a encoder fitted in the case the data needs encoding
+        """
+        raise NotImplementedError()
+
     def transform(
         self,
         X: SupportedFeatTypes,

autoPyTorch/data/tabular_feature_validator.py

@@ -156,9 +156,13 @@ def _fit(
             # with nan values.
             # Columns that are completely made of NaN values are provided to the pipeline
             # so that later stages decide how to handle them
+
+            # Clear whatever null column markers we had previously
+            self.null_columns.clear()
             if np.any(pd.isnull(X)):
                 for column in X.columns:
                     if X[column].isna().all():
+                        self.null_columns.add(column)
                         X[column] = pd.to_numeric(X[column])
                         # Also note this change in self.dtypes
                         if len(self.dtypes) != 0:
@@ -167,9 +171,8 @@ def _fit(
             if not X.select_dtypes(include='object').empty:
                 X = self.infer_objects(X)
 
-            self.transformed_columns, self.feat_type = self._get_columns_to_encode(X)
-
-            assert self.feat_type is not None
+            self._check_data(X)
+            self.enc_columns, self.feat_type = self._get_columns_to_encode(X)
 
             if len(self.transformed_columns) > 0:
 
@@ -238,30 +241,38 @@ def transform(
         if isinstance(X, np.ndarray):
             X = self.numpy_array_to_pandas(X)
 
-        if ispandas(X) and not issparse(X):
-            if np.any(pd.isnull(X)):
-                for column in X.columns:
-                    if X[column].isna().all():
-                        X[column] = pd.to_numeric(X[column])
+        if hasattr(X, "iloc") and not issparse(X):
+            X = cast(pd.DataFrame, X)
+            # If we had null columns in our fit call and we made them numeric, then:
+            # - If the columns are null even in transform, apply the same procedure.
+            # - Otherwise, substitute the values with np.NaN and then make the columns numeric.
+            # If the column is null here, but it was not in fit, it does not matter.
+            for column in self.null_columns:
+                # The column is not null, make it null since it was null in fit.
+                if not X[column].isna().all():
+                    X[column] = np.NaN
+                X[column] = pd.to_numeric(X[column])
+
+            # for the test set, if we have columns with only null values
+            # they will probably have a numeric type. If these columns were not
+            # with only null values in the train set, they should be converted
+            # to the type that they had during fitting.
+            for column in X.columns:
+                if X[column].isna().all():
+                    X[column] = X[column].astype(self.dtypes[list(X.columns).index(column)])
 
             # Also remove the object dtype for new data
             if not X.select_dtypes(include='object').empty:
                 X = self.infer_objects(X)
 
         # Check the data here so we catch problems on new test data
         self._check_data(X)
+        # We also need to fillna on the transformation
+        # in case test data is provided
+        X = self.impute_nan_in_categories(X)
 
-        # Pandas related transformations
-        if ispandas(X) and self.column_transformer is not None:
-            if np.any(pd.isnull(X)):
-                # After above check it means that if there is a NaN
-                # the whole column must be NaN
-                # Make sure it is numerical and let the pipeline handle it
-                for column in X.columns:
-                    if X[column].isna().all():
-                        X[column] = pd.to_numeric(X[column])
-
-            X = self.column_transformer.transform(X)
+        if self.encoder is not None:
+            X = self.encoder.transform(X)
 
         # Sparse related transformations
         # Not all sparse format support index sorting
@@ -488,7 +499,7 @@ def numpy_array_to_pandas(
         Returns:
             pd.DataFrame
         """
-        return pd.DataFrame(X).infer_objects().convert_dtypes()
+        return pd.DataFrame(X).convert_dtypes()
 
     def infer_objects(self, X: pd.DataFrame) -> pd.DataFrame:
         """
@@ -506,18 +517,13 @@ def infer_objects(self, X: pd.DataFrame) -> pd.DataFrame:
         if hasattr(self, 'object_dtype_mapping'):
             # Mypy does not process the has attr. This dict is defined below
             for key, dtype in self.object_dtype_mapping.items():  # type: ignore[has-type]
-                if 'int' in dtype.name:
-                    # In the case train data was interpreted as int
-                    # and test data was interpreted as float, because of 0.0
-                    # for example, honor training data
-                    X[key] = X[key].applymap(np.int64)
-                else:
-                    try:
-                        X[key] = X[key].astype(dtype.name)
-                    except Exception as e:
-                        # Try inference if possible
-                        self.logger.warning(f"Tried to cast column {key} to {dtype} caused {e}")
-                        pass
+                # honor the training data types
+                try:
+                    X[key] = X[key].astype(dtype.name)
+                except Exception as e:
+                    # Try inference if possible
+                    self.logger.warning(f"Tried to cast column {key} to {dtype} caused {e}")
+                    pass
         else:
             X = X.infer_objects()
             for column in X.columns:

test/test_data/test_feature_validator.py

@@ -1,4 +1,4 @@
-import copy
+ import copy
 import functools
 
 import numpy as np
@@ -139,9 +139,9 @@ def test_featurevalidator_fitontypeA_transformtypeB(input_data_featuretest):
     if isinstance(input_data_featuretest, pd.DataFrame):
         pytest.skip("Column order change in pandas is not supported")
     elif isinstance(input_data_featuretest, np.ndarray):
-        complementary_type = pd.DataFrame(input_data_featuretest)
+        complementary_type = validator.numpy_array_to_pandas(input_data_featuretest)
     elif isinstance(input_data_featuretest, list):
-        complementary_type = pd.DataFrame(input_data_featuretest)
+        complementary_type, _ = validator.list_to_dataframe(input_data_featuretest)
     elif sparse.issparse(input_data_featuretest):
         complementary_type = sparse.csr_matrix(input_data_featuretest.todense())
     else:
@@ -331,8 +331,11 @@ def test_unknown_encode_value():
 )
 @pytest.mark.parametrize('train_data_type', ('numpy', 'pandas', 'list'))
 @pytest.mark.parametrize('test_data_type', ('numpy', 'pandas', 'list'))
-def test_featurevalidator_new_data_after_fit(openml_id,
-                                             train_data_type, test_data_type):
+def test_feature_validator_new_data_after_fit(
+    openml_id,
+    train_data_type,
+    test_data_type,
+):
 
     # List is currently not supported as infer_objects
     # cast list objects to type objects
@@ -406,3 +409,109 @@ def test_comparator():
         key=functools.cmp_to_key(validator._comparator)
     )
     assert ans == feat_type
+
+
+# Actual checks for the features
+@pytest.mark.parametrize(
+    'input_data_featuretest',
+    (
+        'numpy_numericalonly_nonan',
+        'numpy_numericalonly_nan',
+        'numpy_mixed_nan',
+        'pandas_numericalonly_nan',
+        'sparse_bsr_nonan',
+        'sparse_bsr_nan',
+        'sparse_coo_nonan',
+        'sparse_coo_nan',
+        'sparse_csc_nonan',
+        'sparse_csc_nan',
+        'sparse_csr_nonan',
+        'sparse_csr_nan',
+        'sparse_dia_nonan',
+        'sparse_dia_nan',
+        'sparse_dok_nonan',
+        'sparse_dok_nan',
+        'openml_40981',  # Australian
+    ),
+    indirect=True
+)
+def test_featurevalidator_reduce_precision(input_data_featuretest):
+    X_train, X_test = sklearn.model_selection.train_test_split(
+        input_data_featuretest, test_size=0.1, random_state=1)
+    validator = TabularFeatureValidator(dataset_compression={'memory_allocation': 0, 'methods': ['precision']})
+    validator.fit(X_train=X_train)
+    transformed_X_train = validator.transform(X_train.copy())
+
+    assert validator._reduced_dtype is not None
+    assert megabytes(transformed_X_train) < megabytes(X_train)
+
+    transformed_X_test = validator.transform(X_test.copy())
+    assert megabytes(transformed_X_test) < megabytes(X_test)
+    if hasattr(transformed_X_train, 'iloc'):
+        assert all(transformed_X_train.dtypes == transformed_X_test.dtypes)
+        assert all(transformed_X_train.dtypes == validator._precision)
+    else:
+        assert transformed_X_train.dtype == transformed_X_test.dtype
+    assert transformed_X_test.dtype == validator._reduced_dtype
+
+
+def test_feature_validator_imbalanced_data():
+
+    # Null columns in the train split but not necessarily in the test split
+    train_features = {
+        'A': [np.NaN, np.NaN, np.NaN],
+        'B': [1, 2, 3],
+        'C': [np.NaN, np.NaN, np.NaN],
+        'D': [np.NaN, np.NaN, np.NaN],
+    }
+    test_features = {
+        'A': [3, 4, 5],
+        'B': [6, 5, 7],
+        'C': [np.NaN, np.NaN, np.NaN],
+        'D': ['Blue', np.NaN, np.NaN],
+    }
+
+    X_train = pd.DataFrame.from_dict(train_features)
+    X_test = pd.DataFrame.from_dict(test_features)
+    validator = TabularFeatureValidator()
+    validator.fit(X_train)
+
+    train_feature_types = copy.deepcopy(validator.feat_type)
+    assert train_feature_types == ['numerical', 'numerical', 'numerical', 'numerical']
+    # validator will throw an error if the column types are not the same
+    transformed_X_test = validator.transform(X_test)
+    transformed_X_test = pd.DataFrame(transformed_X_test)
+    null_columns = []
+    for column in transformed_X_test.columns:
+        if transformed_X_test[column].isna().all():
+            null_columns.append(column)
+    assert null_columns == [0, 2, 3]
+
+    # Columns with not all null values in the train split and
+    # completely null on the test split.
+    train_features = {
+        'A': [np.NaN, np.NaN, 4],
+        'B': [1, 2, 3],
+        'C': ['Blue', np.NaN, np.NaN],
+    }
+    test_features = {
+        'A': [np.NaN, np.NaN, np.NaN],
+        'B': [6, 5, 7],
+        'C': [np.NaN, np.NaN, np.NaN],
+    }
+
+    X_train = pd.DataFrame.from_dict(train_features)
+    X_test = pd.DataFrame.from_dict(test_features)
+    validator = TabularFeatureValidator()
+    validator.fit(X_train)
+    train_feature_types = copy.deepcopy(validator.feat_type)
+    assert train_feature_types == ['categorical', 'numerical', 'numerical']
+
+    transformed_X_test = validator.transform(X_test)
+    transformed_X_test = pd.DataFrame(transformed_X_test)
+    null_columns = []
+    for column in transformed_X_test.columns:
+        if transformed_X_test[column].isna().all():
+            null_columns.append(column)
+
+    assert null_columns == [1]

test/test_data/test_validation.py

@@ -32,7 +32,6 @@ def test_data_validation_for_classification(openmlid, as_frame):
         x, y, test_size=0.33, random_state=0)
 
     validator.fit(X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)
-
     X_train_t, y_train_t = validator.transform(X_train, y_train)
     assert np.shape(X_train) == np.shape(X_train_t)