adding an spurious_correlation as new issue type

cleanlab/datalab/datalab.py

@@ -303,6 +303,19 @@
                 f"\nAudit complete. {self.data_issues.issue_summary['num_issues'].sum()} issues found in the dataset."
             )
 
+    def _spurious_correlations(self) -> pd.DataFrame: 
+        """
+        Use this after finding issues to which examples suffer from which types of issues.
+
+        NOTE: 
+
+        Returns: 
+        --------
+        A DataFrame where each row corresponds to image_property ('dark', 'grayscale')
+        and overall datascore for that image_property
+        """
+        return self.SpuriousCorrelations.spurious_correlation    
+
     def report(
         self,
         *,

cleanlab/datalab/internal/spurious_correlations.py

@@ -0,0 +1,37 @@
+import numpy as np 
+import pandas as pd
+from sklearn.model_selection import cross_val_predict, cross_val_score
+from sklearn.naive_bayes import GaussianNB
+from statistics import mode
+import warnings 
+warnings.filterwarnings('ignore')
+
+from datalab import DataLab
+
+class SpuriousCorrelations:
+
+    def __init__(self, data: DataLab) -> None:
+        self.data = data
+        self.issues = data.issues
+        self.labels = data.labels  
+
+    def spurious_correlations(self) -> pd.DataFrame:
+        baseline_accuracy = np.bincount(self.labels).argmax() / len(self.labels)
+        image_properties = ["near_duplicate_score", "blurry_score", "light_score", "low_information_score", "dark_score", "grayscale_score", "odd_aspect_ratio_score", "odd_size_score"]
+        property_scores = {}
+        for property_of_interest in image_properties:
+            S = self.calculate_spurious_correlation(property_of_interest, baseline_accuracy)
+            property_scores[f'{property_of_interest}'] = S
+        data_score = pd.DataFrame(list(property_scores.items()), columns=['image_property', 'Overall_score'])
+        return data_score
+
+    def calculate_spurious_correlation(self, property_of_interest, baseline_accuracy):
+        X = self.issues[property_of_interest].values.reshape(-1, 1)
+        y = self.labels  
+        classifier = GaussianNB()
+        cv_accuracies = cross_val_score(classifier, X, y, cv=5, scoring='accuracy')
+        mean_accuracy = np.mean(cv_accuracies)
+        eps = 1e-8
+        S = min(1, (1 - mean_accuracy) / (1 - baseline_accuracy + eps))
+        return S
+