[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/spikeinterface/curation/train_manual_curation.py

@@ -9,8 +9,11 @@
 seed = 42
 warnings.filterwarnings("ignore")
 
+
 class CurationModelTrainer:
-    def __init__(self, target_column, output_folder, imputation_strategies=None, scaling_techniques=None, metrics_list=None):
+    def __init__(
+        self, target_column, output_folder, imputation_strategies=None, scaling_techniques=None, metrics_list=None
+    ):
         from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
 
         if imputation_strategies is None:
@@ -49,12 +52,12 @@ def load_data_file(self, path):
 
     def process_test_data_for_classification(self):
         import pandas as pd
-        
+
         if self.target_column in self.testing_metrics[0].columns:
             self.y = self.testing_metrics[0][self.target_column]
 
             self.X = self.testing_metrics[0].dropna(subset=[self.target_column])
-            self.X.drop(columns = self.target_column, inplace=True)
+            self.X.drop(columns=self.target_column, inplace=True)
 
             self.metrics_list = self.X.columns
 
@@ -63,7 +66,9 @@ def process_test_data_for_classification(self):
                 self.X = self.X[self.metrics_list]
             except KeyError:
                 # Work out and print which metrics are missing
-                missing_metrics = [metric for metric in self.metrics_list if metric not in self.testing_metrics[0].columns]
+                missing_metrics = [
+                    metric for metric in self.metrics_list if metric not in self.testing_metrics[0].columns
+                ]
                 raise ValueError(f"Metrics list contains uncomputed metrics: {missing_metrics}")
         else:
             raise ValueError(f"Target column {self.target_column} not found in testing metrics file")
@@ -153,7 +158,7 @@ def get_classifier_search_space(self, classifier):
                 "solver": ["adam"],
                 "alpha": [1e-7, 1e-1],
                 "learning_rate": ["constant", "adaptive"],
-                "n_iter_no_change": [32]
+                "n_iter_no_change": [32],
             }
             model = MLPClassifier(random_state=seed)
         else:
@@ -162,33 +167,16 @@ def get_classifier_search_space(self, classifier):
 
     # TODO: sort out function naming so things are actually clear
     # E.g. evaluate_model_config, _evaluate, _train_and_evaluate - what do they all actually do?
-    def evaluate_model_config(
-        self, imputation_strategies, scaling_techniques, classifiers):
+    def evaluate_model_config(self, imputation_strategies, scaling_techniques, classifiers):
 
         from sklearn.model_selection import train_test_split
-
-        X_train, X_test, y_train, y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=seed, stratify=self.y)
-        self._evaluate(
-            imputation_strategies,
-            scaling_techniques,
-            classifiers,
-            X_train,
-            X_test,
-            y_train,
-            y_test
+
+        X_train, X_test, y_train, y_test = train_test_split(
+            self.X, self.y, test_size=0.2, random_state=seed, stratify=self.y
         )
+        self._evaluate(imputation_strategies, scaling_techniques, classifiers, X_train, X_test, y_train, y_test)
 
-
-    def _evaluate(
-        self,
-        imputation_strategies,
-        scaling_techniques,
-        classifiers,
-        X_train,
-        X_test,
-        y_train,
-        y_test
-    ):
+    def _evaluate(self, imputation_strategies, scaling_techniques, classifiers, X_train, X_test, y_train, y_test):
 
         from joblib import Parallel, delayed
         from sklearn.pipeline import Pipeline
@@ -291,18 +279,15 @@ def train_model(metrics_path, output_folder, target_label):
         MLPClassifier,
     ]
 
-    trainer = CurationModelTrainer(target_label, 
-                                output_folder,
-                                imputation_strategies=imputation_strategies,
-                                scaling_techniques=scaling_techniques,
-                                metrics_list=None
-                                )
-
+    trainer = CurationModelTrainer(
+        target_label,
+        output_folder,
+        imputation_strategies=imputation_strategies,
+        scaling_techniques=scaling_techniques,
+        metrics_list=None,
+    )
+
     trainer.load_and_preprocess_full(metrics_path)
 
-    trainer.evaluate_model_config(
-        imputation_strategies,
-        scaling_techniques,
-        classifiers
-    )
+    trainer.evaluate_model_config(imputation_strategies, scaling_techniques, classifiers)
     return trainer