lint fix

Author: Nabil Fayak

checkmates/exceptions/exceptions.py

@@ -13,16 +13,19 @@ class ObjectiveNotFoundError(Exception):
 
     pass
 
+
 class MethodPropertyNotFoundError(Exception):
     """Exception to raise when a class is does not have an expected method or property."""
 
     pass
 
+
 class ComponentNotYetFittedError(Exception):
     """An exception to be raised when predict/predict_proba/transform is called on a component without fitting first."""
 
     pass
 
+
 class ObjectiveCreationError(Exception):
     """Exception when get_objective tries to instantiate an objective and required args are not provided."""
 

checkmates/objectives/utils.py

@@ -1,14 +1,13 @@
 """Utility methods for CheckMates objectives."""
-import pandas as pd
 from typing import Optional
 
+import pandas as pd
+
 from checkmates import objectives
 from checkmates.exceptions import ObjectiveCreationError, ObjectiveNotFoundError
 from checkmates.objectives.objective_base import ObjectiveBase
-from checkmates.problem_types import handle_problem_types
+from checkmates.problem_types import ProblemTypes, handle_problem_types
 from checkmates.utils.gen_utils import _get_subclasses
-from checkmates.problem_types import ProblemTypes
-
 from checkmates.utils.logger import get_logger
 
 logger = get_logger(__file__)
@@ -97,11 +96,12 @@ def get_objective(objective, return_instance=False, **kwargs):
 
     return objective_class
 
+
 def get_problem_type(
     input_problem_type: Optional[str],
     target_data: pd.Series,
 ) -> ProblemTypes:
-    """helper function to determine if classification problem is binary or multiclass dependent on target variable values."""
+    """Helper function to determine if classification problem is binary or multiclass dependent on target variable values."""
     if not input_problem_type:
         raise ValueError("problem type is required")
     if input_problem_type.lower() == "classification":

checkmates/pipelines/__init__.py

@@ -1,3 +1,5 @@
+"""General CheckMates pipelines."""
+
 from checkmates.pipelines.component_base_meta import ComponentBaseMeta
 from checkmates.pipelines.component_base import ComponentBase
 from checkmates.pipelines.transformers import Transformer
@@ -9,5 +11,9 @@
     TimeSeriesImputer,
     TimeSeriesRegularizer,
 )
-from checkmates.pipelines.utils import _make_component_list_from_actions, split_data, drop_infinity
-from checkmates.pipelines.training_validation_split import TrainingValidationSplit
+from checkmates.pipelines.utils import (
+    _make_component_list_from_actions,
+    split_data,
+    drop_infinity,
+)
+from checkmates.pipelines.training_validation_split import TrainingValidationSplit

checkmates/pipelines/component_base.py

@@ -280,4 +280,4 @@ def _handle_nullable_types(self, X=None, y=None):
                 handle_integer_nullable=y_int_incompatible,
             )
 
-        return X, y
+        return X, y

checkmates/pipelines/component_base_meta.py

@@ -41,4 +41,4 @@ def _check_for_fit(self, X=None, y=None):
             else:
                 return method(self, X, y)
 
-        return _check_for_fit
+        return _check_for_fit

checkmates/pipelines/components.py

@@ -1,27 +1,29 @@
 """Initalizes an transformer that selects specified columns in input data."""
+import warnings
 from abc import abstractmethod
 from functools import wraps
+
 import pandas as pd
 import woodwork as ww
-import warnings
 from sklearn.impute import SimpleImputer as SkImputer
-
-from woodwork.logical_types import Datetime
+from woodwork.logical_types import (
+    BooleanNullable,
+    Datetime,
+    Double,
+)
 from woodwork.statistics_utils import infer_frequency
 
-from checkmates.pipelines.transformers import Transformer
-from checkmates.pipelines.transformers import SimpleImputer
 from checkmates.exceptions import ComponentNotYetFittedError
 from checkmates.pipelines import ComponentBaseMeta
+from checkmates.pipelines.transformers import SimpleImputer, Transformer
 from checkmates.utils import infer_feature_types
 from checkmates.utils.nullable_type_utils import (
-    _get_new_logical_types_for_imputed_data,
     _determine_fractional_type,
     _determine_non_nullable_equivalent,
+    _get_new_logical_types_for_imputed_data,
 )
 
 
-
 class ColumnSelector(Transformer):
     """Initalizes an transformer that selects specified columns in input data.
 
@@ -211,8 +213,10 @@ def transform(self, X, y=None):
         modified_cols = self._modify_columns(cols, X, y)
         return infer_feature_types(modified_cols)
 
+
 """Transformer to drop rows specified by row indices."""
 
+
 class DropRowsTransformer(Transformer):
     """Transformer to drop rows specified by row indices.
 
@@ -300,8 +304,10 @@ def transform(self, X, y=None):
             y_t = y_t.ww.drop(self.indices_to_drop)
         return X_t, y_t
 
+
 """Component that imputes missing data according to a specified imputation strategy per column."""
 
+
 class PerColumnImputer(Transformer):
     """Imputes missing data according to a specified imputation strategy per column.
 
@@ -396,8 +402,10 @@ def transform(self, X, y=None):
         X_t.ww.init(schema=original_schema.get_subset_schema(X_t.columns))
         return X_t
 
+
 """Component that imputes missing target data according to a specified imputation strategy."""
 
+
 class TargetImputerMeta(ComponentBaseMeta):
     """A version of the ComponentBaseMeta class which handles when input features is None."""
 
@@ -531,13 +539,9 @@ def fit_transform(self, X, y):
         """
         return self.fit(X, y).transform(X, y)
 
+
 """Component that imputes missing data according to a specified timeseries-specific imputation strategy."""
-import pandas as pd
-import woodwork as ww
-from woodwork.logical_types import (
-    BooleanNullable,
-    Double,
-)
+
 
 class TimeSeriesImputer(Transformer):
     """Imputes missing data according to a specified timeseries-specific imputation strategy.
@@ -776,8 +780,10 @@ def _handle_nullable_types(self, X=None, y=None):
 
         return X, y
 
+
 """Transformer that regularizes a dataset with an uninferrable offset frequency for time series problems."""
 
+
 class TimeSeriesRegularizer(Transformer):
     """Transformer that regularizes an inconsistently spaced datetime column.
 
@@ -1086,4 +1092,4 @@ def transform(self, X, y=None):
 
         cleaned_x.ww.init()
 
-        return cleaned_x, cleaned_y
+        return cleaned_x, cleaned_y

checkmates/pipelines/training_validation_split.py

@@ -99,4 +99,4 @@ def split(self, X, y=None):
             stratify=self.stratify,
             random_state=self.random_seed,
         )
-        return iter([(train, test)])
+        return iter([(train, test)])

checkmates/pipelines/transformers.py

@@ -1,17 +1,16 @@
 """A component that may or may not need fitting that transforms data. These components are used before an estimator."""
 from abc import abstractmethod
 
-from checkmates.exceptions import MethodPropertyNotFoundError
-from checkmates.pipelines import ComponentBase
-from checkmates.utils import infer_feature_types
 import pandas as pd
 import woodwork
 from sklearn.impute import SimpleImputer as SkImputer
 
-from checkmates.pipelines.transformers import Transformer
+from checkmates.exceptions import MethodPropertyNotFoundError
+from checkmates.pipelines import ComponentBase
 from checkmates.utils import infer_feature_types
 from checkmates.utils.nullable_type_utils import _get_new_logical_types_for_imputed_data
 
+
 class Transformer(ComponentBase):
     """A component that may or may not need fitting that transforms data. These components are used before an estimator.
 
@@ -83,8 +82,10 @@ def fit_transform(self, X, y=None):
     def _get_feature_provenance(self):
         return {}
 
+
 """Component that imputes missing data according to a specified imputation strategy."""
 
+
 class SimpleImputer(Transformer):
     """Imputes missing data according to a specified imputation strategy.  Natural language columns are ignored.
 
@@ -235,4 +236,4 @@ def fit_transform(self, X, y=None):
         Returns:
             pd.DataFrame: Transformed X
         """
-        return self.fit(X, y).transform(X, y)
+        return self.fit(X, y).transform(X, y)

checkmates/pipelines/utils.py

@@ -1,9 +1,9 @@
 """Utility methods for EvalML pipelines."""
-from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit
 from typing import Union
+
 import numpy as np
 import pandas as pd
-
+from sklearn.model_selection import ShuffleSplit, StratifiedShuffleSplit
 
 from checkmates.data_checks import DataCheckActionCode
 from checkmates.pipelines.components import (  # noqa: F401
@@ -14,11 +14,9 @@
     TimeSeriesImputer,
     TimeSeriesRegularizer,
 )
-from checkmates.utils import infer_feature_types
 from checkmates.pipelines.training_validation_split import TrainingValidationSplit
 from checkmates.problem_types import is_classification, is_regression, is_time_series
-
-
+from checkmates.utils import infer_feature_types
 
 
 def _make_component_list_from_actions(actions):
@@ -70,6 +68,7 @@ def _make_component_list_from_actions(actions):
 
     return components
 
+
 def split_data(
     X,
     y,
@@ -163,10 +162,11 @@ def split_data(
 
     return X_train, X_test, y_train, y_test
 
+
 def drop_infinity(
     data: Union[pd.DataFrame, pd.Series],
 ) -> Union[pd.DataFrame, pd.Series]:
-    """Removes infinity values"""
+    """Removes infinity values."""
     ww = data.ww._schema is not None
     replace = data.ww.replace if ww else data.replace
     return replace([np.inf, -np.inf], np.nan)

checkmates/utils/base_meta.py

@@ -43,4 +43,4 @@ def __new__(cls, name, bases, dct):
                     property_orig.__delattr__,
                     property_orig.__doc__,
                 )
-        return super().__new__(cls, name, bases, dct)
+        return super().__new__(cls, name, bases, dct)