fix: black formatting

mlederbauer · May 5, 2024 · 1ad5626 · 1ad5626
1 parent 824c7e6
commit 1ad5626
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Showing 5 changed files with 952 additions and 560 deletions.
diff --git a/nmrcraft/models/model_configs.py b/nmrcraft/models/model_configs.py
@@ -4,30 +4,30 @@
     "random_forest": {
         "model_params": {"random_state": 42},
         "hyperparameters": {
-            "n_estimators": hp.choice("n_estimators", range(10, 1000, 10)), 
+            "n_estimators": hp.choice("n_estimators", range(10, 1000, 10)),
             "criterion": hp.choice("criterion", ["gini", "entropy"]),
             # "max_depth": hp.choice("max_depth", range(10, 1200, 10)),
             "min_samples_split": hp.uniform("min_samples_split", 0.01, 1.0),
             "min_samples_leaf": hp.uniform("min_samples_leaf", 0.01, 0.5),
             "max_features": hp.choice("max_features", ["sqrt", "log2", None]),
         },
     },
-
     "gradient_boosting": {
         "model_params": {"random_state": 42},
         "hyperparameters": {
             "loss": hp.choice("loss", ["log_loss", "exponential"]),
             "learning_rate": hp.uniform("learning_rate", 0.01, 0.5),
             "n_estimators": hp.choice("n_estimators", range(10, 1000, 10)),
-            # "subsample": hp.uniform("subsample", 0.01, 1.0), 
-            "criterion": hp.choice("criterion", ["friedman_mse", "squared_error"]),
+            # "subsample": hp.uniform("subsample", 0.01, 1.0),
+            "criterion": hp.choice(
+                "criterion", ["friedman_mse", "squared_error"]
+            ),
             # "max_depth": hp.choice("max_depth", range(10, 1200, 10)),
             "min_samples_split": hp.uniform("min_samples_split", 0.01, 1.0),
             "min_samples_leaf": hp.uniform("min_samples_leaf", 0.01, 0.5),
             "max_features": hp.choice("max_features", ["sqrt", "log2", None]),
         },
     },
-
     "logistic_regression": {
         "model_params": {"random_state": 42},
         "hyperparameters": {
@@ -44,19 +44,19 @@
             "l1_ratio": hp.uniform("l1_ratio", 0.01, 1.0),
         },
     },
-
     "svc": {
         "model_params": {"random_state": 42},
         "hyperparameters": {
             "C": hp.uniform("C", 0.01, 10.0),
-            "kernel": hp.choice("kernel", ["linear", "poly", "rbf", "sigmoid"]),
+            "kernel": hp.choice(
+                "kernel", ["linear", "poly", "rbf", "sigmoid"]
+            ),
             "degree": hp.choice("degree", range(1, 10)),
             "gamma": hp.choice("gamma", ["scale", "auto"]),
             "coef0": hp.uniform("coef0", 0.0, 1.0),
             "shrinking": hp.choice("shrinking", [True, False]),
             "probability": hp.choice("probability", [True, False]),
             # "max_iter": hp.choice("max_iter", range(100, 1000, 100)),
-
         },
     },
 }
diff --git a/nmrcraft/training/hyperparameter_tune.py b/nmrcraft/training/hyperparameter_tune.py
@@ -1,5 +1,6 @@
 import numpy as np
 from hyperopt import STATUS_OK, Trials, fmin, space_eval, tpe
+
 # from sklearn.metrics import accuracy_score
 from sklearn.model_selection import cross_val_score
 
@@ -51,7 +52,7 @@ def _objective(
         model.fit(X_train, y_train)
         # y_pred = model.predict(X_test)
         # score = accuracy_score(y_test, y_pred)
-        score = cross_val_score(model, X_train, y_train, cv = 5).mean()
+        score = cross_val_score(model, X_train, y_train, cv=5).mean()
         return {"loss": -score, "status": STATUS_OK}
 
     def tune(self, X_train, y_train, X_test, y_test) -> tuple: