flake8/linter fixes

SuperCowPowers · Jun 19, 2024 · 6055d85 · 6055d85
1 parent 9838e84
commit 6055d85
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Showing 3 changed files with 29 additions and 15 deletions.
diff --git a/experiments/quantile_regression.py b/experiments/quantile_regression.py
@@ -10,11 +10,7 @@
 quantiles = [0.1, 0.50, 0.9]
 models = {}
 for q in quantiles:
-    params = {
-        'objective': 'reg:quantileerror',
-        'eval_metric': 'mae',
-        'quantile_alpha': q
-    }
+    params = {"objective": "reg:quantileerror", "eval_metric": "mae", "quantile_alpha": q}
     model = xgb.XGBRegressor(**params)
     model.fit(X_train, y_train)
     models[q] = model

diff --git a/src/sageworks/algorithms/dataframe/quantile_regression.py b/src/sageworks/algorithms/dataframe/quantile_regression.py
@@ -4,7 +4,6 @@
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.base import RegressorMixin
 from xgboost import XGBRegressor
-from sklearn.model_selection import train_test_split
 
 
 class QuantileRegressor(BaseEstimator, TransformerMixin):
@@ -52,9 +51,9 @@ def fit(self, X: pd.DataFrame, y: pd.Series) -> BaseEstimator:
         # Train models for each of the quantiles
         for q in self.quantiles:
             params = {
-                'objective': 'reg:quantileerror',
-                'eval_metric': 'rmse',
-                'quantile_alpha': q,
+                "objective": "reg:quantileerror",
+                "eval_metric": "rmse",
+                "quantile_alpha": q,
                 # 'n_estimators': 400,     # More trees
                 # 'max_depth': 1,         # Shallow trees
                 # 'learning_rate': 0.1,   # Lower learning rate
@@ -95,7 +94,8 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame:
 
     def fit_transform(self, X: pd.DataFrame, y: pd.Series, **fit_params) -> pd.DataFrame:
         """
-        Fits the model and transforms the input DataFrame by adding 'quantile_05', 'quantile_50', and 'quantile_95' columns.
+        Fits the model and transforms the input DataFrame by adding 'quantile_05', 'quantile_50',
+        and 'quantile_95' columns.
 
         Args:
             X (pd.DataFrame): The input features.
@@ -136,7 +136,15 @@ def unit_test():
     confidence_df["interval"] = confidence_df["quantile_95"] - confidence_df["quantile_05"]
 
     # Columns of Interest
-    dropdown_columns = ["quantile_05", "quantile_25", "quantile_50", "quantile_75", "quantile_95", "interval", target_column]
+    dropdown_columns = [
+        "quantile_05",
+        "quantile_25",
+        "quantile_50",
+        "quantile_75",
+        "quantile_95",
+        "interval",
+        target_column,
+    ]
 
     # Run the Unit Test on the Plugin
     plugin_test = PluginUnitTest(
@@ -186,7 +194,16 @@ def integration_test():
     confidence_df["confidence"] = 1.0 - (np.clip(confidence_df["interval"], 0, 4) * 0.25)
 
     # Columns of Interest
-    dropdown_columns = ["quantile_05", "quantile_25", "quantile_50", "quantile_75", "quantile_95", "interval", "confidence", target_column]
+    dropdown_columns = [
+        "quantile_05",
+        "quantile_25",
+        "quantile_50",
+        "quantile_75",
+        "quantile_95",
+        "interval",
+        "confidence",
+        target_column,
+    ]
 
     # Run the Unit Test on the Plugin
     plugin_test = PluginUnitTest(

diff --git a/src/sageworks/utils/test_data_generator.py b/src/sageworks/utils/test_data_generator.py
@@ -56,15 +56,16 @@ def regression_with_varying_noise(n_samples: int = 1000, n_features: int = 4) ->
         # Generate a simple regression dataset with one feature and one target
         # Both the feature and the target vary from 0 to 100, the target is the feature plus 10% noise
         feature = np.float32(range(0, 1000))
-        target = [x+1000.0 + x*np.random.uniform(-.1, .1) for x in feature]
+        target = [x + 1000.0 + x * np.random.uniform(-0.1, 0.1) for x in feature]
 
         # Create a DataFrame
-        df = pd.DataFrame({'feature': feature, 'target': target})
+        df = pd.DataFrame({"feature": feature, "target": target})
         return df
 
         """
         # Generate basic synthetic data
-        X, y = make_regression(n_samples=n_samples, n_features=n_features, n_informative=n_features, noise=0.0, random_state=42)
+        X, y = make_regression(n_samples=n_samples, n_features=n_features, n_informative=n_features,
+                               noise=0.0, random_state=42)
 
         # Normalize target values to the range [0, 100]
         y = (y - y.min()) / (y.max() - y.min()) * 100