[ADD] Add column transformer (automl#305)

* Match paper libraries-versions

* Update README.md

* Update README.md

* Update README.md

* [FIX] master branch README (automl#209)

* Enable github actions (automl#273)

* Update README.md

* Create CITATION.cff

* Added column transformer, changed requirements and added tests

* remove redundant lines

* Remove unwanted change made

* Fix bug in test api and dummy forward pass

* Fix silly bugs

* increase time to pass test

* remove parallel capabilities of traditional learners to resolve bug in docs building

* almost fixed

* Add documentation for tabularfeaturevalidator

* fix flake

* fix silly bug

* address comment from shuhei

* rename enc_columns to transformed_columns in the remaining places

* fix bug in test

* fix mypy

* add shuhei's suggestion

Co-authored-by: chico <[email protected]>
Co-authored-by: Marius Lindauer <[email protected]>
Co-authored-by: Frank <[email protected]>
Co-authored-by: Francisco Rivera Valverde <[email protected]>

autoPyTorch/api/base_task.py

@@ -1322,7 +1322,8 @@ def get_incumbent_results(
         if not include_traditional:
             # traditional classifiers have trainer_configuration in their additional info
             run_history_data = dict(
-                filter(lambda elem: elem[1].additional_info is not None and elem[1].
+                filter(lambda elem: elem[1].status == StatusType.SUCCESS and elem[1].
+                       additional_info is not None and elem[1].
                        additional_info['configuration_origin'] != 'traditional',
                        run_history_data.items()))
         run_history_data = dict(

autoPyTorch/data/base_feature_validator.py

@@ -35,11 +35,6 @@ class BaseFeatureValidator(BaseEstimator):
             List of the column types found by this estimator during fit.
         data_type (str):
             Class name of the data type provided during fit.
-        encoder (typing.Optional[BaseEstimator])
-            Host a encoder object if the data requires transformation (for example,
-            if provided a categorical column in a pandas DataFrame)
-        enc_columns (typing.List[str])
-            List of columns that were encoded.
     """
     def __init__(self,
                  logger: typing.Optional[typing.Union[PicklableClientLogger, logging.Logger
@@ -51,8 +46,8 @@ def __init__(self,
         self.dtypes = []  # type: typing.List[str]
         self.column_order = []  # type: typing.List[str]
 
-        self.encoder = None  # type: typing.Optional[BaseEstimator]
-        self.enc_columns = []  # type: typing.List[str]
+        self.column_transformer = None  # type: typing.Optional[BaseEstimator]
+        self.transformed_columns = []  # type: typing.List[str]
 
         self.logger: typing.Union[
             PicklableClientLogger, logging.Logger
@@ -61,6 +56,7 @@ def __init__(self,
         # Required for dataset properties
         self.num_features = None  # type: typing.Optional[int]
         self.categories = []  # type: typing.List[typing.List[int]]
+
         self.categorical_columns: typing.List[int] = []
         self.numerical_columns: typing.List[int] = []
 

autoPyTorch/data/tabular_feature_validator.py

@@ -1,5 +1,6 @@
 import functools
 import typing
+from typing import Dict, List
 
 import numpy as np
 
@@ -13,11 +14,108 @@
 from sklearn.base import BaseEstimator
 from sklearn.compose import ColumnTransformer
 from sklearn.exceptions import NotFittedError
+from sklearn.impute import SimpleImputer
+from sklearn.pipeline import make_pipeline
 
 from autoPyTorch.data.base_feature_validator import BaseFeatureValidator, SUPPORTED_FEAT_TYPES
 
 
+def _create_column_transformer(
+    preprocessors: Dict[str, List[BaseEstimator]],
+    categorical_columns: List[str],
+) -> ColumnTransformer:
+    """
+    Given a dictionary of preprocessors, this function
+    creates a sklearn column transformer with appropriate
+    columns associated with their preprocessors.
+
+    Args:
+        preprocessors (Dict[str, List[BaseEstimator]]):
+            Dictionary containing list of numerical and categorical preprocessors.
+        categorical_columns (List[str]):
+            List of names of categorical columns
+
+    Returns:
+        ColumnTransformer
+    """
+
+    categorical_pipeline = make_pipeline(*preprocessors['categorical'])
+
+    return ColumnTransformer([
+        ('categorical_pipeline', categorical_pipeline, categorical_columns)],
+        remainder='passthrough'
+    )
+
+
+def get_tabular_preprocessors() -> Dict[str, List[BaseEstimator]]:
+    """
+    This function creates a Dictionary containing a list
+    of numerical and categorical preprocessors
+
+    Returns:
+        Dict[str, List[BaseEstimator]]
+    """
+    preprocessors: Dict[str, List[BaseEstimator]] = dict()
+
+    # Categorical Preprocessors
+    onehot_encoder = preprocessing.OrdinalEncoder(handle_unknown='use_encoded_value',
+                                                  unknown_value=-1)
+    categorical_imputer = SimpleImputer(strategy='constant', copy=False)
+
+    preprocessors['categorical'] = [categorical_imputer, onehot_encoder]
+
+    return preprocessors
+
+
 class TabularFeatureValidator(BaseFeatureValidator):
+    """
+    A subclass of `BaseFeatureValidator` made for tabular data.
+    It ensures that the dataset provided is of the expected format.
+    Subsequently, it preprocesses the data by fitting a column
+    transformer.
+
+    Attributes:
+        categories (List[List[str]]):
+            List for which an element at each index is a
+            list containing the categories for the respective
+            categorical column.
+        transformed_columns (List[str])
+            List of columns that were transformed.
+        column_transformer (Optional[BaseEstimator])
+            Hosts an imputer and an encoder object if the data
+            requires transformation (for example, if provided a
+            categorical column in a pandas DataFrame)
+        column_order (List[str]):
+            List of the features stored in the order that
+            was fitted.
+        numerical_columns (List[int]):
+            List of indices of numerical columns
+        categorical_columns (List[int]):
+            List of indices of categorical columns
+    """
+    @staticmethod
+    def _comparator(cmp1: str, cmp2: str) -> int:
+        """Order so that categorical columns come left and numerical columns come right
+
+        Args:
+            cmp1 (str): First variable to compare
+            cmp2 (str): Second variable to compare
+
+        Raises:
+            ValueError: if the values of the variables to compare
+            are not in 'categorical' or 'numerical'
+
+        Returns:
+            int: either [0, -1, 1]
+        """
+        choices = ['categorical', 'numerical']
+        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
+
     def _fit(
         self,
         X: SUPPORTED_FEAT_TYPES,
@@ -60,51 +158,38 @@ def _fit(
             if not X.select_dtypes(include='object').empty:
                 X = self.infer_objects(X)
 
-            self.enc_columns, self.feat_type = self._get_columns_to_encode(X)
+            self.transformed_columns, self.feat_type = self._get_columns_to_encode(X)
 
-            if len(self.enc_columns) > 0:
-                X = self.impute_nan_in_categories(X)
+            assert self.feat_type is not None
 
-                self.encoder = ColumnTransformer(
-                    [
-                        ("encoder",
-                         preprocessing.OrdinalEncoder(
-                             handle_unknown='use_encoded_value',
-                             unknown_value=-1,
-                         ), self.enc_columns)],
-                    remainder="passthrough"
+            if len(self.transformed_columns) > 0:
+
+                preprocessors = get_tabular_preprocessors()
+                self.column_transformer = _create_column_transformer(
+                    preprocessors=preprocessors,
+                    categorical_columns=self.transformed_columns,
                 )
 
                 # Mypy redefinition
-                assert self.encoder is not None
-                self.encoder.fit(X)
-
-                # The column transformer reoders the feature types - we therefore need to change
-                # it as well
-                # This means columns are shifted to the right
-                def comparator(cmp1: str, cmp2: str) -> int:
-                    if (
-                        cmp1 == 'categorical' and cmp2 == 'categorical'
-                        or cmp1 == 'numerical' and cmp2 == 'numerical'
-                    ):
-                        return 0
-                    elif cmp1 == 'categorical' and cmp2 == 'numerical':
-                        return -1
-                    elif cmp1 == 'numerical' and cmp2 == 'categorical':
-                        return 1
-                    else:
-                        raise ValueError((cmp1, cmp2))
+                assert self.column_transformer is not None
+                self.column_transformer.fit(X)
 
+                # The column transformer reorders the feature types
+                # therefore, we need to change the order of columns as well
+                # This means categorical columns are shifted to the left
                 self.feat_type = sorted(
                     self.feat_type,
-                    key=functools.cmp_to_key(comparator)
+                    key=functools.cmp_to_key(self._comparator)
                 )
 
+                encoded_categories = self.column_transformer.\
+                    named_transformers_['categorical_pipeline'].\
+                    named_steps['ordinalencoder'].categories_
                 self.categories = [
                     # We fit an ordinal encoder, where all categorical
                     # columns are shifted to the left
                     list(range(len(cat)))
-                    for cat in self.encoder.transformers_[0][1].categories_
+                    for cat in encoded_categories
                 ]
 
             for i, type_ in enumerate(self.feat_type):
@@ -158,7 +243,7 @@ def transform(
         self._check_data(X)
 
         # Pandas related transformations
-        if hasattr(X, "iloc") and self.encoder is not None:
+        if hasattr(X, "iloc") 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
@@ -167,11 +252,7 @@ def transform(
                     if X[column].isna().all():
                         X[column] = pd.to_numeric(X[column])
 
-            # We also need to fillna on the transformation
-            # in case test data is provided
-            X = self.impute_nan_in_categories(X)
-
-            X = self.encoder.transform(X)
+            X = self.column_transformer.transform(X)
 
         # Sparse related transformations
         # Not all sparse format support index sorting
@@ -245,7 +326,7 @@ def _check_data(
 
             # Define the column to be encoded here as the feature validator is fitted once
             # per estimator
-            enc_columns, _ = self._get_columns_to_encode(X)
+            self.transformed_columns, self.feat_type = self._get_columns_to_encode(X)
 
             column_order = [column for column in X.columns]
             if len(self.column_order) > 0:
@@ -282,13 +363,17 @@ def _get_columns_to_encode(
                 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
         Returns:
-            enc_columns (List[str]):
+            transformed_columns (List[str]):
                 Columns to encode, if any
             feat_type:
                 Type of each column numerical/categorical
         """
+
+        if len(self.transformed_columns) > 0 and self.feat_type is not None:
+            return self.transformed_columns, self.feat_type
+
         # Register if a column needs encoding
-        enc_columns = []
+        transformed_columns = []
 
         # Also, register the feature types for the estimator
         feat_type = []
@@ -297,7 +382,7 @@ def _get_columns_to_encode(
         for i, column in enumerate(X.columns):
             if X[column].dtype.name in ['category', 'bool']:
 
-                enc_columns.append(column)
+                transformed_columns.append(column)
                 feat_type.append('categorical')
             # Move away from np.issubdtype as it causes
             # TypeError: data type not understood in certain pandas types
@@ -339,7 +424,7 @@ def _get_columns_to_encode(
                     )
             else:
                 feat_type.append('numerical')
-        return enc_columns, feat_type
+        return transformed_columns, feat_type
 
     def list_to_dataframe(
         self,
@@ -429,59 +514,3 @@ def infer_objects(self, X: pd.DataFrame) -> pd.DataFrame:
             self.object_dtype_mapping = {column: X[column].dtype for column in X.columns}
         self.logger.debug(f"Infer Objects: {self.object_dtype_mapping}")
         return X
-
-    def impute_nan_in_categories(self, X: pd.DataFrame) -> pd.DataFrame:
-        """
-        impute missing values before encoding,
-        remove once sklearn natively supports
-        it in ordinal encoding. Sklearn issue:
-        "https://github.com/scikit-learn/scikit-learn/issues/17123)"
-
-        Arguments:
-            X (pd.DataFrame):
-                data to be interpreted.
-
-        Returns:
-            pd.DataFrame
-        """
-
-        # To be on the safe side, map always to the same missing
-        # value per column
-        if not hasattr(self, 'dict_nancol_to_missing'):
-            self.dict_missing_value_per_col: typing.Dict[str, typing.Any] = {}
-
-        # First make sure that we do not alter the type of the column which cause:
-        # TypeError: '<' not supported between instances of 'int' and 'str'
-        # in the encoding
-        for column in self.enc_columns:
-            if X[column].isna().any():
-                if column not in self.dict_missing_value_per_col:
-                    try:
-                        float(X[column].dropna().values[0])
-                        can_cast_as_number = True
-                    except Exception:
-                        can_cast_as_number = False
-                    if can_cast_as_number:
-                        # In this case, we expect to have a number as category
-                        # it might be string, but its value represent a number
-                        missing_value: typing.Union[str, int] = '-1' if isinstance(X[column].dropna().values[0],
-                                                                                   str) else -1
-                    else:
-                        missing_value = 'Missing!'
-
-                    # Make sure this missing value is not seen before
-                    # Do this check for categorical columns
-                    # else modify the value
-                    if hasattr(X[column], 'cat'):
-                        while missing_value in X[column].cat.categories:
-                            if isinstance(missing_value, str):
-                                missing_value += '0'
-                            else:
-                                missing_value += missing_value
-                    self.dict_missing_value_per_col[column] = missing_value
-
-                # Convert the frame in place
-                X[column].cat.add_categories([self.dict_missing_value_per_col[column]],
-                                             inplace=True)
-                X.fillna({column: self.dict_missing_value_per_col[column]}, inplace=True)
-        return X

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

@@ -79,7 +79,7 @@ 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/traditional_ml/estimator_configs/extra_trees.json

@@ -1,4 +1,3 @@
 {
-	"n_estimators" : 300,
-	"n_jobs" : -1
+	"n_estimators" : 300
 }

autoPyTorch/pipeline/components/setup/traditional_ml/estimator_configs/knn.json

@@ -1,4 +1,3 @@
 {
-	"weights" : "uniform",
-	"n_jobs" : -1
+	"weights" : "uniform"
 }

autoPyTorch/pipeline/components/setup/traditional_ml/estimator_configs/lgb.json

@@ -5,6 +5,5 @@
 	"min_data_in_leaf" : 3,
 	"feature_fraction" : 0.9,
 	"boosting_type" : "gbdt",
-	"learning_rate" : 0.03,
-	"num_threads" : -1
+	"learning_rate" : 0.03
 }

autoPyTorch/pipeline/components/setup/traditional_ml/estimator_configs/random_forest.json

@@ -1,4 +1,3 @@
 {
-	"n_estimators" : 300,
-	"n_jobs" : -1
+	"n_estimators" : 300
 }

autoPyTorch/pipeline/components/setup/traditional_ml/estimator_configs/rotation_forest.json

@@ -1,3 +1,2 @@
 {
-	"n_jobs" : -1
 }