[Issue-571] make docs errors/warnings fixes (deepchecks#725)

* fixes

* added suppress_warnings option to the conf.py

deepchecks/base/check.py

@@ -308,10 +308,11 @@ def priority(self) -> int:
 
         This value is primarly used to determine suite output order.
         The logic is next:
-            - if at least one condition did not pass and is of category 'FAIL', return 1;
-            - if at least one condition did not pass and is of category 'WARN', return 2;
-            - if check result do not have assigned conditions, return 3
-            - if all conditions passed, return 4;
+
+        * if at least one condition did not pass and is of category 'FAIL', return 1;
+        * if at least one condition did not pass and is of category 'WARN', return 2;
+        * if check result do not have assigned conditions, return 3;
+        * if all conditions passed, return 4;
 
         Returns:
             int: priority of the cehck result.

deepchecks/base/dataset.py

@@ -43,13 +43,13 @@ class Dataset:
     Args:
         df (pandas.DataFrame):
             A pandas DataFrame containing data relevant for the training or validating of a ML models.
-        label (t.Union[Hashable, pd.Series, pd.DataFrame, np.ndarray])
+        label (Union[Hashable, pd.Series, pd.DataFrame, np.ndarray]):
             label column provided either as a string with the name of an existing column in the DataFrame or a label
             object including the label data (pandas Series/DataFrame or a numpy array) that will be concatenated to the
             data in the DataFrame. in case of label data the following logic is applied to set the label name:
-                - Series: takes the series name or 'target' if name is empty
-                - DataFrame: expect single column in the dataframe and use its name
-                - numpy: use 'target'
+            * Series: takes the series name or 'target' if name is empty
+            * DataFrame: expect single column in the dataframe and use its name
+            * numpy: use 'target'
         features (Optional[Sequence[Hashable]]):
             List of names for the feature columns in the DataFrame.
         cat_features (Optional[Sequence[Hashable]]):

deepchecks/checks/__init__.py

@@ -34,7 +34,7 @@
     DateTrainTestLeakageDuplicates,
     DateTrainTestLeakageOverlap,
     IdentifierLeakage,
-    ModelInferenceTimeCheck,
+    ModelInferenceTime,
     DatasetsSizeComparison
 )
 
@@ -89,7 +89,7 @@
     'DateTrainTestLeakageDuplicates',
     'DateTrainTestLeakageOverlap',
     'IdentifierLeakage',
-    'ModelInferenceTimeCheck',
+    'ModelInferenceTime',
     'DatasetsSizeComparison',
 
     # overview checks

deepchecks/checks/methodology/__init__.py

@@ -18,7 +18,7 @@
 from .date_train_test_leakage_duplicates import DateTrainTestLeakageDuplicates
 from .date_train_test_leakage_overlap import DateTrainTestLeakageOverlap
 from .identifier_leakage import IdentifierLeakage
-from .model_inference_time import ModelInferenceTimeCheck
+from .model_inference_time import ModelInferenceTime
 from .datasets_size_comparison import DatasetsSizeComparison
 
 
@@ -32,6 +32,6 @@
     'DateTrainTestLeakageDuplicates',
     'DateTrainTestLeakageOverlap',
     'IdentifierLeakage',
-    'ModelInferenceTimeCheck',
+    'ModelInferenceTime',
     'DatasetsSizeComparison',
 ]

deepchecks/checks/methodology/model_inference_time.py

@@ -20,13 +20,13 @@
 from deepchecks.errors import DeepchecksValueError
 
 
-__all__ = ['ModelInferenceTimeCheck']
+__all__ = ['ModelInferenceTime']
 
 
-MI = t.TypeVar('MI', bound='ModelInferenceTimeCheck')
+MI = t.TypeVar('MI', bound='ModelInferenceTime')
 
 
-class ModelInferenceTimeCheck(SingleDatasetBaseCheck):
+class ModelInferenceTime(SingleDatasetBaseCheck):
     """Measure model average inference time (in seconds) per sample.
 
     Args:

deepchecks/checks/performance/model_error_analysis.py

@@ -187,7 +187,7 @@ def scoring_func(dataset: Dataset):
         ok_color = colors['Test']
 
         for feature in error_fi.keys()[:self.max_features_to_show]:
-            if error_fi[feature] < self.min_feature_contribution:
+            if error_fi[feature] < self.min_feature_contribution:  # pylint: disable=unsubscriptable-object
                 break
 
             data = pd.concat([test_dataset.data[feature], display_error], axis=1)

deepchecks/datasets/classification/breast_cancer.py

@@ -80,120 +80,119 @@ def load_data(data_format: str = 'Dataset', as_train_test: bool = True) -> \
         #. fractal dimension ("coastline approximation" - 1)
 
     Dataset Shape:
-        .. list-table:: Dataset Shape
-           :widths: 50 50
-           :header-rows: 1
-
-           * - Property
-             - Value
-           * - Samples Total
-             - 569
-           * - Dimensionality
-             - 30
-           * - Features
-             - real
-           * - Targets
-             - boolean
-
+      .. list-table:: Dataset Shape
+        :widths: 50 50
+        :header-rows: 1
+
+        * - Property
+          - Value
+        * - Samples Total
+          - 569
+        * - Dimensionality
+          - 30
+        * - Features
+          - real
+        * - Targets
+          - boolean
 
     Description:
-        .. list-table:: Dataset Description
-           :widths: 50 50 50
-           :header-rows: 1
-
-            * - mean radius
-              - Feature
-              - mean radius
-            * - mean texture
-              - Feature
-              - mean texture
-            * - mean perimeter
-              - Feature
-              - mean perimeter
-            * - mean area
-              - Feature
-              - mean area
-            * - mean smoothness
-              - Feature
-              - mean smoothness
-            * - mean compactness
-              - Feature
-              - mean compactness
-            * - mean concavity
-              - Feature
-              - mean concavity
-            * - mean concave points
-              - Feature
-              - mean concave points
-            * - mean symmetry
-              - Feature
-              - mean symmetry
-            * - mean fractal dimension
-              - Feature
-              - mean fractal dimension
-            * - radius error
-              - Feature
-              - radius error
-            * - texture error
-              - Feature
-              - texture error
-            * - perimeter error
-              - Feature
-              - perimeter error
-            * - area error
-              - Feature
-              - area error
-            * - smoothness error
-              - Feature
-              - smoothness error
-            * - compactness error
-              - Feature
-              - compactness error
-            * - concavity error
-              - Feature
-              - concavity error
-            * - concave points error
-              - Feature
-              - concave points error
-            * - symmetry error
-              - Feature
-              - symmetry error
-            * - fractal dimension error
-              - Feature
-              - fractal dimension error
-            * - worst radius
-              - Feature
-              - worst radius
-            * - worst texture
-              - Feature
-              - worst texture
-            * - worst perimeter
-              - Feature
-              - worst perimeter
-            * - worst area
-              - Feature
-              - worst area
-            * - worst smoothness
-              - Feature
-              - worst smoothness
-            * - worst compactness
-              - Feature
-              - worst compactness
-            * - worst concavity
-              - Feature
-              - worst concavity
-            * - worst concave points
-              - Feature
-              - worst concave points
-            * - worst symmetry
-              - Feature
-              - worst symmetry
-            * - worst fractal dimension
-              - Feature
-              - worst fractal dimension
-            * - target
-              - Label
-              - The class (Benign, Malignant)
+      .. list-table:: Dataset Description
+        :widths: 50 50 50
+        :header-rows: 1
+
+        * - mean radius
+          - Feature
+          - mean radius
+        * - mean texture
+          - Feature
+          - mean texture
+        * - mean perimeter
+          - Feature
+          - mean perimeter
+        * - mean area
+          - Feature
+          - mean area
+        * - mean smoothness
+          - Feature
+          - mean smoothness
+        * - mean compactness
+          - Feature
+          - mean compactness
+        * - mean concavity
+          - Feature
+          - mean concavity
+        * - mean concave points
+          - Feature
+          - mean concave points
+        * - mean symmetry
+          - Feature
+          - mean symmetry
+        * - mean fractal dimension
+          - Feature
+          - mean fractal dimension
+        * - radius error
+          - Feature
+          - radius error
+        * - texture error
+          - Feature
+          - texture error
+        * - perimeter error
+          - Feature
+          - perimeter error
+        * - area error
+          - Feature
+          - area error
+        * - smoothness error
+          - Feature
+          - smoothness error
+        * - compactness error
+          - Feature
+          - compactness error
+        * - concavity error
+          - Feature
+          - concavity error
+        * - concave points error
+          - Feature
+          - concave points error
+        * - symmetry error
+          - Feature
+          - symmetry error
+        * - fractal dimension error
+          - Feature
+          - fractal dimension error
+        * - worst radius
+          - Feature
+          - worst radius
+        * - worst texture
+          - Feature
+          - worst texture
+        * - worst perimeter
+          - Feature
+          - worst perimeter
+        * - worst area
+          - Feature
+          - worst area
+        * - worst smoothness
+          - Feature
+          - worst smoothness
+        * - worst compactness
+          - Feature
+          - worst compactness
+        * - worst concavity
+          - Feature
+          - worst concavity
+        * - worst concave points
+          - Feature
+          - worst concave points
+        * - worst symmetry
+          - Feature
+          - worst symmetry
+        * - worst fractal dimension
+          - Feature
+          - worst fractal dimension
+        * - target
+          - Label
+          - The class (Benign, Malignant)
 
     Args:
         data_format (str, default 'Dataset'):

deepchecks/suites/default_suites.py

@@ -17,7 +17,7 @@
     ConfusionMatrixReport, RocReport, CalibrationScore, TrustScoreComparison,
     RegressionErrorDistribution, RegressionSystematicError, PerformanceReport, SimpleModelComparison, BoostingOverfit,
     ModelInfo, ColumnsInfo, DataDuplicates, IsSingleValue, LabelAmbiguity,
-    DatasetsSizeComparison, UnusedFeatures, ModelInferenceTimeCheck, ModelErrorAnalysis, TrainTestLabelDrift
+    DatasetsSizeComparison, UnusedFeatures, ModelInferenceTime, ModelErrorAnalysis, TrainTestLabelDrift
 )
 from deepchecks import Suite
 
@@ -98,7 +98,7 @@ def model_evaluation() -> Suite:
         RegressionErrorDistribution().add_condition_kurtosis_not_less_than(),
         BoostingOverfit().add_condition_test_score_percent_decline_not_greater_than(),
         UnusedFeatures().add_condition_number_of_high_variance_unused_features_not_greater_than(),
-        ModelInferenceTimeCheck().add_condition_inference_time_is_not_greater_than()
+        ModelInferenceTime().add_condition_inference_time_is_not_greater_than()
     )
 
 

deepchecks/utils/features.py

@@ -146,9 +146,10 @@ def calculate_feature_importance(
             kwargs for permutation importance calculation
 
     Returns:
-        pandas.Series: feature importance normalized to 0-1 indexed by feature names
-        str: type of feature importance calculation
-             (types: `permutation_importance`, `feature_importances_`, `coef_`)
+        Tuple[Series, str]:
+            first item - feature importance normalized to 0-1 indexed by feature names,
+            second item - type of feature importance calculation (types: `permutation_importance`,
+            `feature_importances_`, `coef_`)
 
     Raises:
         NotFittedError: