Some changes to MLP model and some syntax fixes

spare_scores/data_prep.py

@@ -299,7 +299,9 @@ def age_sex_match(
         )[1]
     else:
         p_sex = 1
-    logging.debug(f" Original: P_age: {np.round(p_age,2)}/ P_sex: {np.round(p_sex,2)}")
+    logging.debug(
+        f" Original: P_age: {np.round(p_age, 2)}/ P_sex: {np.round(p_sex, 2)}"
+    )
 
     p_age_all, p_sex_all = np.array(p_age), np.array(p_sex)
     while np.min([p_age, p_sex]) < p_threshold:
@@ -345,7 +347,7 @@ def age_sex_match(
         df1, df2 = df2.copy(), df1.copy()
 
     logging.debug(f" {n_orig - len(df1.index) - len(df2.index)} participants excluded")
-    logging.debug(f" Final: P_age: {np.round(p_age,2)}/ P_sex {np.round(p_sex,2)}")
+    logging.debug(f" Final: P_age: {np.round(p_age, 2)}/ P_sex {np.round(p_sex, 2)}")
     logging.info("Age/Sex matched!")
     if no_df2:
         return pd.concat([df1, df2], ignore_index=True)

spare_scores/mlp.py

@@ -99,7 +99,7 @@ def __init__(
                 "mlp__alpha": [0.001, 0.01, 0.05, 0.1],
                 "mlp__learning_rate": ["constant", "adaptive"],
                 "mlp__early_stopping": [True],
-                "mlp__max_iter": [5000],
+                "mlp__max_iter": [500],
             }
 
     def set_parameters(self, **parameters: Any) -> None:
@@ -112,11 +112,11 @@ def _fit(self, df: pd.DataFrame) -> None:
         y = df[self.to_predict].astype("float64")
 
         if self.task == "Regression":
-            mlp = MLPRegressor(early_stopping=True, max_iter=5000)
+            mlp = MLPRegressor(early_stopping=True, max_iter=500)
             scoring = "neg_mean_absolute_error"
             metrics = ["MAE", "RMSE", "R2"]
         else:
-            mlp = MLPClassifier(early_stopping=True, max_iter=5000)
+            mlp = MLPClassifier(early_stopping=True, max_iter=500)
             scoring = "balanced_accuracy"
             metrics = [
                 "AUC",
@@ -136,7 +136,7 @@ def _fit(self, df: pd.DataFrame) -> None:
             pipeline_obj,
             self.param_grid,
             scoring=scoring,
-            cv=KFold(n_splits=5, shuffle=True, random_state=10086),
+            cv=KFold(n_splits=5, shuffle=True, random_state=42),
             refit=True,
         )
         grid_search.fit(X, y)
@@ -260,5 +260,5 @@ def get_stats(self, y: np.ndarray, y_hat: np.ndarray) -> None:
     def output_stats(self) -> None:
         for key, value in self.stats.items():
             logging.info(
-                f">> {key} = {np.mean(value):#.4f} \u00B1 {np.std(value):#.4f}"
+                f">> {key} = {np.mean(value): #.4f} \u00B1 {np.std(value): #.4f}"
             )

spare_scores/mlp_torch.py

@@ -488,5 +488,5 @@ def predict(self, df: pd.DataFrame) -> np.ndarray:
     def output_stats(self) -> None:
         for key, value in self.stats.items():
             logging.info(
-                f">> {key} = {np.mean(value):#.4f} \u00B1 {np.std(value):#.4f}"
+                f">> {key} = {np.mean(value): #.4f} \u00B1 {np.std(value): #.4f}"
             )

spare_scores/svm.py

@@ -272,7 +272,7 @@ def train_initialize(self, df: pd.DataFrame, to_predict: str) -> None:
     def run_CV(self, df: pd.DataFrame) -> None:
         for i, fold in enumerate(self.folds):
             if i % self.n_repeats == 0:
-                logging.info(f"  FOLD {int(i/self.n_repeats+1)}...")
+                logging.info(f"  FOLD {int(i / self.n_repeats + 1)}...")
             X_train, X_test, y_train, y_test = self.prepare_sample(
                 df, fold, self.scaler[i], classify=self.classify
             )
@@ -360,5 +360,5 @@ def correct_reg_bias(self, fold: Any, y_test: list) -> Any:
     def output_stats(self) -> None:
         for key, value in self.stats.items():
             logging.info(
-                f">> {key} = {np.mean(value):#.4f} \u00B1 {np.std(value):#.4f}"
+                f">> {key} = {np.mean(value): #.4f} \u00B1 {np.std(value): #.4f}"
             )

tests/unit/test_spare_scores.py

@@ -55,7 +55,7 @@ def test_spare_test_SVM(self):
         )
         self.assertTrue(result == ["ROI1"])
 
-    def test_spare_train_MLP(self):
+    def test_spare_train_MLP(self): 
         self.df_fixture = load_df("../fixtures/sample_data.csv")
         self.model_fixture = load_model("../fixtures/sample_model.pkl.gz")
         # Test case 1: Testing spare_train with MLP model