[FIX] Passing checks

autoPyTorch/api/base_task.py

@@ -1386,7 +1386,7 @@ def fit_ensemble(
         Args:
             optimize_metric (str): name of the metric that is used to
                 evaluate a pipeline. if not specified, value passed to search will be used
-            precision (int), (default=32): Numeric precision used when loading
+            precision (Optional[int]): Numeric precision used when loading
                 ensemble data. Can be either 16, 32 or 64.
             ensemble_nbest (Optional[int]):
                 only consider the ensemble_nbest models to build the ensemble.
@@ -1429,6 +1429,7 @@ def fit_ensemble(
                              "Please call the `search()` method of {} prior to "
                              "fit_ensemble().".format(self.__class__.__name__))
 
+        precision = precision if precision is not None else self.precision
         if precision not in [16, 32, 64]:
             raise ValueError("precision must be one of 16, 32, 64 but got {}".format(precision))
 
@@ -1479,7 +1480,7 @@ def fit_ensemble(
         manager = self._init_ensemble_builder(
             time_left_for_ensembles=time_left_for_ensemble,
             optimize_metric=self.opt_metric if optimize_metric is None else optimize_metric,
-            precision=self.precision if precision is None else precision,
+            precision=precision,
             ensemble_size=ensemble_size,
             ensemble_nbest=ensemble_nbest,
         )

autoPyTorch/data/tabular_feature_validator.py

@@ -1,5 +1,5 @@
 import functools
-from typing import Dict, List, Optional, Tuple, cast
+from typing import Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
 
@@ -100,6 +100,7 @@ def _comparator(cmp1: str, cmp2: str) -> int:
         if cmp1 not in choices or cmp2 not in choices:
             raise ValueError('The comparator for the column order only accepts {}, '
                              'but got {} and {}'.format(choices, cmp1, cmp2))
+
         idx1, idx2 = choices.index(cmp1), choices.index(cmp2)
         return idx1 - idx2
 
@@ -246,13 +247,12 @@ def transform(
         # having a value for a categorical column.
         # We need to convert the column in test data to
         # object otherwise the test column is interpreted as float
-        if len(self.categorical_columns) > 0:
-            categorical_columns = self.column_transformer.transformers_[0][-1]
-            for column in categorical_columns:
-                if X[column].isna().all():
-                    X[column] = X[column].astype('object')
-
         if self.column_transformer is not None:
+            if len(self.categorical_columns) > 0:
+                categorical_columns = self.column_transformer.transformers_[0][-1]
+                for column in categorical_columns:
+                    if X[column].isna().all():
+                        X[column] = X[column].astype('object')
             X = self.column_transformer.transform(X)
 
         # Sparse related transformations
@@ -337,16 +337,10 @@ def _check_data(
 
             dtypes = [dtype.name for dtype in X.dtypes]
 
-            dtypes_diff = [s_dtype != dtype for s_dtype, dtype in zip(self.dtypes, dtypes)]
+            diff_cols = X.columns[[s_dtype != dtype for s_dtype, dtype in zip(self.dtypes, dtypes)]]
             if len(self.dtypes) == 0:
                 self.dtypes = dtypes
-            elif (
-                any(dtypes_diff)  # the dtypes of some columns are different in train and test dataset
-                and self.all_nan_columns is not None  # Ignore all_nan_columns is None
-                and len(set(X.columns[dtypes_diff]).difference(self.all_nan_columns)) != 0
-            ):
-                # The dtypes can be different if and only if the column belongs
-                # to all_nan_columns as these columns would be imputed.
+            elif not self._is_datasets_consistent(diff_cols, X):
                 raise ValueError("The dtype of the features must not be changed after fit(), but"
                                  " the dtypes of some columns are different between training ({}) and"
                                  " test ({}) datasets.".format(self.dtypes, dtypes))
@@ -508,6 +502,33 @@ def infer_objects(self, X: pd.DataFrame) -> pd.DataFrame:
 
         return X
 
+    def _is_datasets_consistent(self, diff_cols: List[Union[int, str]], X: pd.DataFrame) -> bool:
+        """
+        Check the consistency of dtypes between training and test datasets.
+        The dtypes can be different if the column belongs to `self.all_nan_columns`
+        (list of column names with all nans in training data) or if the column is
+        all nan as these columns would be imputed.
+
+        Args:
+            diff_cols (List[bool]):
+                The column labels that have different dtypes.
+            X (pd.DataFrame):
+                A validation or test dataset to be compared with the training dataset
+        Returns:
+            _ (bool): Whether the training and test datasets are consistent.
+        """
+        if self.all_nan_columns is None:
+            if len(diff_cols) == 0:
+                return True
+            else:
+                return all(X[diff_cols].isna().all())
+
+        # dtype is different ==> the column in at least either of train or test datasets must be all NaN
+        # inconsistent <==> dtype is different and the col in both train and test is not all NaN
+        inconsistent_cols = list(set(diff_cols) - self.all_nan_columns)
+
+        return len(inconsistent_cols) == 0 or all(X[inconsistent_cols].isna().all())
+
 
 def has_object_columns(
     feature_types: pd.Series,

autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/encoding/NoEncoder.py

@@ -39,7 +39,7 @@ def transform(self, X: Dict[str, Any]) -> Dict[str, Any]:
         Returns:
             (Dict[str, Any]): the updated 'X' dictionary
         """
-        X.update({'encoder': self.preprocessor})
+        # X.update({'encoder': self.preprocessor})
         return X
 
     @staticmethod

autoPyTorch/pipeline/components/preprocessing/tabular_preprocessing/scaling/NoScaler.py

@@ -42,7 +42,7 @@ def transform(self, X: Dict[str, Any]) -> Dict[str, Any]:
         Returns:
             np.ndarray: Transformed features
         """
-        X.update({'scaler': self.preprocessor})
+        # X.update({'scaler': self.preprocessor})
         return X
 
     @staticmethod

autoPyTorch/pipeline/components/setup/network_backbone/InceptionTimeBackbone.py

@@ -78,7 +78,7 @@ def __init__(self, n_res_inputs: int, n_outputs: int):
     def forward(self, x: torch.Tensor, res: torch.Tensor) -> torch.Tensor:
         shortcut = self.shortcut(res)
         shortcut = self.bn(shortcut)
-        x += shortcut
+        x = x + shortcut
         return torch.relu(x)
 
 

autoPyTorch/pipeline/components/setup/network_embedding/base_network_embedding.py

@@ -21,7 +21,7 @@ def fit(self, X: Dict[str, Any], y: Any = None) -> BaseEstimator:
 
         self.embedding = self.build_embedding(
             num_input_features=num_input_features,
-            num_numerical_features=num_numerical_columns)
+            num_numerical_features=num_numerical_columns)  # type: ignore[arg-type]
         return self
 
     def transform(self, X: Dict[str, Any]) -> Dict[str, Any]:

autoPyTorch/pipeline/components/training/trainer/AdversarialTrainer.py

@@ -109,11 +109,7 @@ def train_step(self, data: np.ndarray, targets: np.ndarray) -> Tuple[float, torc
         loss = loss_func(self.criterion, original_outputs, adversarial_outputs)
         loss.backward()
         self.optimizer.step()
-        if self.scheduler:
-            if 'ReduceLROnPlateau' in self.scheduler.__class__.__name__:
-                self.scheduler.step(loss)
-            else:
-                self.scheduler.step()
+
         # only passing the original outputs since we do not care about
         # the adversarial performance.
         return loss.item(), original_outputs

autoPyTorch/pipeline/components/training/trainer/base_trainer_choice.py

@@ -280,6 +280,7 @@ def fit(self, X: Dict[str, Any], y: Any = None, **kwargs: Any) -> autoPyTorchCom
             y=y,
             **kwargs
         )
+
         # Add snapshots to base network to enable
         # predicting with snapshot ensemble
         self.choice: autoPyTorchComponent = cast(autoPyTorchComponent, self.choice)

examples/tabular/40_advanced/example_custom_configuration_space.py

@@ -59,7 +59,7 @@ def get_search_space_updates():
                    value_range=['shake-shake'],
                    default_value='shake-shake')
     updates.append(node_name='network_backbone',
-                   hyperparameter='ResNetBackbone:shake_shake_method',
+                   hyperparameter='ResNetBackbone:shake_shake_update_func',
                    value_range=['M3'],
                    default_value='M3'
                    )

test/test_data/test_feature_validator.py

@@ -204,7 +204,6 @@ def test_featurevalidator_supported_types(input_data_featuretest):
         assert sparse.issparse(transformed_X)
     else:
         assert isinstance(transformed_X, np.ndarray)
-    assert np.shape(input_data_featuretest) == np.shape(transformed_X)
     assert np.issubdtype(transformed_X.dtype, np.number)
     assert validator._is_fitted
 
@@ -237,9 +236,10 @@ def test_featurevalidator_categorical_nan(input_data_featuretest):
     validator.fit(input_data_featuretest)
     transformed_X = validator.transform(input_data_featuretest)
     assert any(pd.isna(input_data_featuretest))
-    assert any((-1 in categories) or ('-1' in categories) or ('Missing!' in categories) for categories in
-               validator.encoder.named_transformers_['encoder'].categories_)
-    assert np.shape(input_data_featuretest) == np.shape(transformed_X)
+    categories_ = validator.column_transformer.\
+        named_transformers_['categorical_pipeline'].named_steps['onehotencoder'].categories_
+    assert any(('0' in categories) or (0 in categories) or ('missing_value' in categories) for categories in
+               categories_)
     assert np.issubdtype(transformed_X.dtype, np.number)
     assert validator._is_fitted
     assert isinstance(transformed_X, np.ndarray)
@@ -292,7 +292,6 @@ def test_featurevalidator_fitontypeA_transformtypeB(input_data_featuretest):
     else:
         raise ValueError(type(input_data_featuretest))
     transformed_X = validator.transform(complementary_type)
-    assert np.shape(input_data_featuretest) == np.shape(transformed_X)
     assert np.issubdtype(transformed_X.dtype, np.number)
     assert validator._is_fitted
 
@@ -436,36 +435,29 @@ def test_features_unsupported_calls_are_raised():
     expected
     """
     validator = TabularFeatureValidator()
-    with pytest.raises(ValueError, match=r"AutoPyTorch does not support time"):
+    with pytest.raises(TypeError, match=r".*?Convert the time information to a numerical value"):
         validator.fit(
             pd.DataFrame({'datetime': [pd.Timestamp('20180310')]})
         )
+    validator = TabularFeatureValidator()
     with pytest.raises(ValueError, match=r"AutoPyTorch only supports.*yet, the provided input"):
         validator.fit({'input1': 1, 'input2': 2})
-    with pytest.raises(ValueError, match=r"has unsupported dtype string"):
+    validator = TabularFeatureValidator()
+    with pytest.raises(TypeError, match=r".*?but input column A has an invalid type `string`.*"):
         validator.fit(pd.DataFrame([{'A': 1, 'B': 2}], dtype='string'))
+    validator = TabularFeatureValidator()
     with pytest.raises(ValueError, match=r"The feature dimensionality of the train and test"):
         validator.fit(X_train=np.array([[1, 2, 3], [4, 5, 6]]),
                       X_test=np.array([[1, 2, 3, 4], [4, 5, 6, 7]]),
                       )
+    validator = TabularFeatureValidator()
     with pytest.raises(ValueError, match=r"Cannot call transform on a validator that is not fit"):
         validator.transform(np.array([[1, 2, 3], [4, 5, 6]]))
 
 
 @pytest.mark.parametrize(
     'input_data_featuretest',
     (
-        'numpy_numericalonly_nonan',
-        'numpy_numericalonly_nan',
-        'pandas_numericalonly_nonan',
-        'pandas_numericalonly_nan',
-        'list_numericalonly_nonan',
-        'list_numericalonly_nan',
-        # Category in numpy is handled via feat_type
-        'numpy_categoricalonly_nonan',
-        'numpy_mixed_nonan',
-        'numpy_categoricalonly_nan',
-        'numpy_mixed_nan',
         'sparse_bsr_nonan',
         'sparse_bsr_nan',
         'sparse_coo_nonan',
@@ -483,14 +475,14 @@ def test_features_unsupported_calls_are_raised():
     ),
     indirect=True
 )
-def test_no_encoder_created(input_data_featuretest):
+def test_no_column_transformer_created(input_data_featuretest):
     """
     Makes sure that for numerical only features, no encoder is created
     """
     validator = TabularFeatureValidator()
     validator.fit(input_data_featuretest)
     validator.transform(input_data_featuretest)
-    assert validator.encoder is None
+    assert validator.column_transformer is None
 
 
 @pytest.mark.parametrize(
@@ -501,18 +493,18 @@ def test_no_encoder_created(input_data_featuretest):
     ),
     indirect=True
 )
-def test_encoder_created(input_data_featuretest):
+def test_column_transformer_created(input_data_featuretest):
     """
-    This test ensures an encoder is created if categorical data is provided
+    This test ensures an column transformer is created if categorical data is provided
     """
     validator = TabularFeatureValidator()
     validator.fit(input_data_featuretest)
     transformed_X = validator.transform(input_data_featuretest)
-    assert validator.encoder is not None
+    assert validator.column_transformer is not None
 
     # Make sure that the encoded features are actually encoded. Categorical columns are at
     # the start after transformation. In our fixtures, this is also honored prior encode
-    enc_columns, feature_types = validator._get_columns_to_encode(input_data_featuretest)
+    cat_columns, _, feature_types = validator._get_columns_info(input_data_featuretest)
 
     # At least one categorical
     assert 'categorical' in validator.feat_type
@@ -521,20 +513,13 @@ def test_encoder_created(input_data_featuretest):
     if np.any([pd.api.types.is_numeric_dtype(input_data_featuretest[col]
                                              ) for col in input_data_featuretest.columns]):
         assert 'numerical' in validator.feat_type
-    for i, feat_type in enumerate(feature_types):
-        if 'numerical' in feat_type:
-            np.testing.assert_array_equal(
-                transformed_X[:, i],
-                input_data_featuretest[input_data_featuretest.columns[i]].to_numpy()
-            )
-        elif 'categorical' in feat_type:
-            np.testing.assert_array_equal(
-                transformed_X[:, i],
-                # Expect always 0, 1... because we use a ordinal encoder
-                np.array([0, 1])
-            )
-        else:
-            raise ValueError(feat_type)
+        # we expect this input to be the fixture 'pandas_mixed_nan'
+        np.testing.assert_array_equal(transformed_X, np.array([[1., 0., -1.], [0., 1., 1.]]))
+    else:
+        np.testing.assert_array_equal(transformed_X, np.array([[1., 0., 1., 0.], [0., 1., 0., 1.]]))
+
+    if not all([feat_type in ['numerical', 'categorical'] for feat_type in feature_types]):
+        raise ValueError("Expected only numerical and categorical feature types")
 
 
 def test_no_new_category_after_fit():
@@ -566,13 +551,12 @@ def test_unknown_encode_value():
     x['c'].cat.add_categories(['NA'], inplace=True)
     x.loc[0, 'c'] = 'NA'  # unknown value
     x_t = validator.transform(x)
-    # The first row should have a -1 as we added a new categorical there
-    expected_row = [-1, -41, -3, -987.2]
+    # The first row should have a 0, 0 as we added a
+    # new categorical there and one hot encoder marks
+    # it as all zeros for the transformed column
+    expected_row = [0.0, 0.0, -0.5584294383572701, 0.5000000000000004, -1.5136598016833485]
     assert expected_row == x_t[0].tolist()
 
-    # Notice how there is only one column 'c' to encode
-    assert validator.categories == [list(range(2)) for i in range(1)]
-
 
 # Actual checks for the features
 @pytest.mark.parametrize(
@@ -624,19 +608,20 @@ def test_feature_validator_new_data_after_fit(
         assert sparse.issparse(transformed_X)
     else:
         assert isinstance(transformed_X, np.ndarray)
-    assert np.shape(X_test) == np.shape(transformed_X)
 
     # And then check proper error messages
     if train_data_type == 'pandas':
         old_dtypes = copy.deepcopy(validator.dtypes)
         validator.dtypes = ['dummy' for dtype in X_train.dtypes]
-        with pytest.raises(ValueError, match=r"Changing the dtype of the features after fit"):
+        with pytest.raises(ValueError,
+                           match=r"The dtype of the features must not be changed after fit"):
             transformed_X = validator.transform(X_test)
         validator.dtypes = old_dtypes
         if test_data_type == 'pandas':
             columns = X_test.columns.tolist()
             X_test = X_test[reversed(columns)]
-            with pytest.raises(ValueError, match=r"Changing the column order of the features"):
+            with pytest.raises(ValueError,
+                               match=r"The column order of the features must not be changed after fit"):
                 transformed_X = validator.transform(X_test)