fix: black formatting

nmrcraft/analysis/plotting.py

@@ -18,9 +18,7 @@
 plt.rcParams["text.usetex"] = False
 
 
-def plot_predicted_vs_ground_truth(
-    y_test: np.array, y_pred: np.array, title: str
-):
+def plot_predicted_vs_ground_truth(y_test: np.array, y_pred: np.array, title: str):
     """
     Plots the predicted values against the ground truth values.
     Parameters:
@@ -33,9 +31,7 @@ def plot_predicted_vs_ground_truth(
     # Creating the plot
     plt.figure(figsize=(10, 8))
     plt.scatter(y_test, y_pred, color=first_color, edgecolor="k", alpha=0.6)
-    plt.plot(
-        [y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=2
-    )
+    plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=2)
     plt.xlabel("Actual")
     plt.ylabel("Predicted")
     plt.title(title)
@@ -68,12 +64,8 @@ def plot_predicted_vs_ground_truth_density(
 
     # Creating the plot
     plt.figure(figsize=(10, 8))
-    plt.scatter(
-        y_test, y_pred, c=scalar_map.to_rgba(kernel), edgecolor="k", alpha=0.9
-    )
-    plt.plot(
-        [y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=2
-    )
+    plt.scatter(y_test, y_pred, c=scalar_map.to_rgba(kernel), edgecolor="k", alpha=0.9)
+    plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=2)
     plt.xlabel("Actual")
     plt.ylabel("Predicted")
     plt.title(title)

nmrcraft/data/dataset.py

@@ -53,9 +53,7 @@ def load_dataset_from_hf(
     Returns:
         pandas.DataFrame: The loaded dataset as a pandas DataFrame.
     """
-    dataset = load_dataset(dataset_name, data_files=data_files)[
-        "train"
-    ].to_pandas()
+    dataset = load_dataset(dataset_name, data_files=data_files)["train"].to_pandas()
     return dataset
 
 

nmrcraft/models/model_configs.py

@@ -4,30 +4,28 @@
     "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), 
+            # "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": {
@@ -44,7 +42,6 @@
             "l1_ratio": hp.uniform("l1_ratio", 0.01, 1.0),
         },
     },
-
     "svc": {
         "model_params": {"random_state": 42},
         "hyperparameters": {
@@ -56,7 +53,6 @@
             "shrinking": hp.choice("shrinking", [True, False]),
             "probability": hp.choice("probability", [True, False]),
             # "max_iter": hp.choice("max_iter", range(100, 1000, 100)),
-
         },
     },
 }

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
 
@@ -29,9 +30,7 @@ def __init__(
         self.max_evals = max_evals
         self.algo = algo
 
-    def _objective(
-        self, params: dict, X_train, y_train, X_test, y_test
-    ) -> dict:
+    def _objective(self, params: dict, X_train, y_train, X_test, y_test) -> dict:
         """
         Objective function for hyperparameter tuning.
 
@@ -51,7 +50,7 @@ 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:
@@ -68,9 +67,7 @@ def tune(self, X_train, y_train, X_test, y_test) -> tuple:
             tuple: The best parameters and the tuning trials.
         """
         best = fmin(
-            fn=lambda params: self._objective(
-                params, X_train, y_train, X_test, y_test
-            ),
+            fn=lambda params: self._objective(params, X_train, y_train, X_test, y_test),
             space=self.model_config["hyperparameters"],
             algo=self.algo,
             max_evals=self.max_evals,