lint fix

Author: Nabil Fayak

checkmates/data_checks/__init__.py

@@ -24,18 +24,31 @@
 )
 from checkmates.data_checks.checks.id_columns_data_check import IDColumnsDataCheck
 from checkmates.data_checks.checks.null_data_check import NullDataCheck
-from checkmates.data_checks.checks.class_imbalance_data_check import ClassImbalanceDataCheck
+from checkmates.data_checks.checks.class_imbalance_data_check import (
+    ClassImbalanceDataCheck,
+)
 from checkmates.data_checks.checks.no_variance_data_check import NoVarianceDataCheck
 from checkmates.data_checks.checks.outliers_data_check import OutliersDataCheck
 from checkmates.data_checks.checks.uniqueness_data_check import UniquenessDataCheck
-from checkmates.data_checks.checks.ts_splitting_data_check import TimeSeriesSplittingDataCheck
-from checkmates.data_checks.checks.ts_parameters_data_check import TimeSeriesParametersDataCheck
-from checkmates.data_checks.checks.target_leakage_data_check import TargetLeakageDataCheck
-from checkmates.data_checks.checks.target_distribution_data_check import TargetDistributionDataCheck
+from checkmates.data_checks.checks.ts_splitting_data_check import (
+    TimeSeriesSplittingDataCheck,
+)
+from checkmates.data_checks.checks.ts_parameters_data_check import (
+    TimeSeriesParametersDataCheck,
+)
+from checkmates.data_checks.checks.target_leakage_data_check import (
+    TargetLeakageDataCheck,
+)
+from checkmates.data_checks.checks.target_distribution_data_check import (
+    TargetDistributionDataCheck,
+)
 from checkmates.data_checks.checks.sparsity_data_check import SparsityDataCheck
-from checkmates.data_checks.checks.datetime_format_data_check import DateTimeFormatDataCheck
-from checkmates.data_checks.checks.multicollinearity_data_check import MulticollinearityDataCheck
-
+from checkmates.data_checks.checks.datetime_format_data_check import (
+    DateTimeFormatDataCheck,
+)
+from checkmates.data_checks.checks.multicollinearity_data_check import (
+    MulticollinearityDataCheck,
+)
 
 
 from checkmates.data_checks.datacheck_meta.utils import handle_data_check_action_code

checkmates/data_checks/checks/class_imbalance_data_check.py

@@ -233,4 +233,4 @@ def validate(self, X, y):
                     details={"target_values": severe_imbalance},
                 ).to_dict(),
             )
-        return messages
+        return messages

checkmates/data_checks/checks/datetime_format_data_check.py

@@ -484,4 +484,4 @@ def validate(self, X, y):
                 ).to_dict(),
             )
 
-        return messages
+        return messages

checkmates/data_checks/checks/multicollinearity_data_check.py

@@ -72,4 +72,4 @@ def validate(self, X, y=None):
                     details={"columns": correlated_cols},
                 ).to_dict(),
             )
-        return messages
+        return messages

checkmates/data_checks/checks/no_variance_data_check.py

@@ -271,4 +271,4 @@ def validate(self, X, y=None):
                 ).to_dict(),
             )
 
-        return messages
+        return messages

checkmates/data_checks/checks/null_data_check.py

@@ -370,4 +370,4 @@ def get_null_row_information(X, pct_null_row_threshold=0.0):
         highly_null_rows = percent_null_rows[
             percent_null_rows >= pct_null_row_threshold
         ]
-        return highly_null_rows
+        return highly_null_rows

checkmates/data_checks/checks/outliers_data_check.py

@@ -234,4 +234,4 @@ def _no_outlier_prob(num_records: int, pct_outliers: float) -> float:
         # calculate and return the probability of no true outliers for a gamma
         # cumulative density function
         prob_val = 1.0 - gamma.cdf(pct_outliers, shape_param, scale=scale_param)
-        return prob_val
+        return prob_val

checkmates/data_checks/checks/sparsity_data_check.py

@@ -140,4 +140,4 @@ def sparsity_score(col, count_threshold=10):
         counts = col.value_counts()
         score = sum(counts > count_threshold) / counts.size
 
-        return score
+        return score

checkmates/data_checks/checks/target_distribution_data_check.py

@@ -162,4 +162,4 @@ def _detect_log_distribution_helper(y):
     # with outliers dropped, then it would imply that the log transformed target has more of a normal distribution
     if norm_test_log.pvalue >= norm_test_og.pvalue:
         return True, normalization_test_string, norm_test_og
-    return False, normalization_test_string, norm_test_og
+    return False, normalization_test_string, norm_test_og

checkmates/data_checks/checks/target_leakage_data_check.py

@@ -169,4 +169,4 @@ def validate(self, X, y):
                     ],
                 ).to_dict(),
             )
-        return messages
+        return messages