experimenting with quartile regressor functionality

src/sageworks/algorithms/dataframe/quantile_regression.py

@@ -11,7 +11,11 @@ class QuantileRegressor(BaseEstimator, TransformerMixin):
     A class for training regression models over a set of quantiles. Useful for calculating confidence intervals.
     """
 
-    def __init__(self, model: Union[RegressorMixin, XGBRegressor] = XGBRegressor, quantiles: list = [0.05, 0.25, 0.50, 0.75, 0.95]):
+    def __init__(
+        self,
+        model: Union[RegressorMixin, XGBRegressor] = XGBRegressor,
+        quantiles: list = [0.05, 0.25, 0.50, 0.75, 0.95],
+    ):
         """
         Initializes the QuantileRegressor with the specified parameters.
 
@@ -114,7 +118,7 @@ def example_confidence(q_dataframe, target="target", target_sensitivity=0.25):
     # If the interval with is greater than target_sensitivity with have 0 confidence
     # anything below that is a linear scale from 0 to 1
     confidence_interval = upper_95 - lower_05
-    q_conf = np.clip(1 - confidence_interval/(target_sensitivity * 4.0), 0, 1)
+    q_conf = np.clip(1 - confidence_interval / (target_sensitivity * 4.0), 0, 1)
 
     # Now lets look at the IQR distance for each observation
     epsilon_iqr = target_sensitivity * 0.5
@@ -206,8 +210,9 @@ def integration_test():
     confidence_df["interval"] = confidence_df["q_95"] - confidence_df["q_05"]
 
     # Compute the confidence
-    confidence_df["conf"], confidence_df["q_conf"], confidence_df["iqr_conf"] = example_confidence(confidence_df, target_column,
-                                                                                                   target_sensitivity=1.5)
+    confidence_df["conf"], confidence_df["q_conf"], confidence_df["iqr_conf"] = example_confidence(
+        confidence_df, target_column, target_sensitivity=1.5
+    )
 
     # Columns of Interest
     q_columns = [c for c in confidence_df.columns if c.startswith("q_")]

src/sageworks/core/artifacts/endpoint_core.py

@@ -914,4 +914,3 @@ def delete_endpoint_models(self):
     # Run Inference and metrics for a Classification Endpoint
     class_endpoint = EndpointCore("wine-classification-end")
     class_endpoint.auto_inference()
-

src/sageworks/utils/test_data_generator.py

@@ -1,4 +1,5 @@
 """A Test Data Generator Class"""
+
 import os
 import logging
 import pandas as pd
@@ -56,8 +57,8 @@ def confidence_data(n_samples=2000, n_features=1) -> pd.DataFrame:
         """
 
         # Generate synthetic data with even spacing from -10 to 5 and sparse spacing from 5 to 10
-        x_even = np.linspace(-10, 5, int(n_samples*7/8))  # Evenly spaced from -10 to 5
-        x_sparse = 5 + (np.linspace(0, 1, int(n_samples*1/8)) ** 2) * 5  # Increasingly sparse from 5 to 10
+        x_even = np.linspace(-10, 5, int(n_samples * 7 / 8))  # Evenly spaced from -10 to 5
+        x_sparse = 5 + (np.linspace(0, 1, int(n_samples * 1 / 8)) ** 2) * 5  # Increasingly sparse from 5 to 10
         x = np.concatenate([x_even, x_sparse])
 
         # Ensure no values are exactly zero or negative in the input to the log function
@@ -124,7 +125,7 @@ def aqsol_data(self) -> pd.DataFrame:
         """Generate a Pandas DataFrame of AQSol Data"""
 
         # Define a temporary file path
-        temp_file_path = os.path.join(tempfile.gettempdir(), 'aqsol_data.csv')
+        temp_file_path = os.path.join(tempfile.gettempdir(), "aqsol_data.csv")
 
         # First check if the data is already stored in a local temporary file
         if os.path.exists(temp_file_path):
@@ -145,9 +146,25 @@ def aqsol_data(self) -> pd.DataFrame:
 
     def aqsol_features(self) -> list:
         """Get the AQSol Feature List"""
-        return ['molwt', 'mollogp', 'molmr', 'heavyatomcount', 'numhacceptors', 'numhdonors', 'numheteroatoms',
-                'numrotatablebonds', 'numvalenceelectrons', 'numaromaticrings', 'numsaturatedrings', 'numaliphaticrings',
-                'ringcount', 'tpsa', 'labuteasa', 'balabanj', 'bertzct']
+        return [
+            "molwt",
+            "mollogp",
+            "molmr",
+            "heavyatomcount",
+            "numhacceptors",
+            "numhdonors",
+            "numheteroatoms",
+            "numrotatablebonds",
+            "numvalenceelectrons",
+            "numaromaticrings",
+            "numsaturatedrings",
+            "numaliphaticrings",
+            "ringcount",
+            "tpsa",
+            "labuteasa",
+            "balabanj",
+            "bertzct",
+        ]
 
     def aqsol_target(self) -> str:
         """Get the AQSol Target"""