ROC plot for milticlass classification updated

examples/tabpfn_for_multiclass_classification.py

@@ -87,7 +87,7 @@ def calc_roc_auc(y_test, y_onehot_test, y_score,n_classes):
     return fpr, tpr, roc_auc
 
 
-def plot_roc_auc(y_onehot_test, y_score,n_classes,fpr,tpr,roc_auc):    
+def plot_roc_auc(y_onehot_test, y_score,n_classes,fpr,tpr,roc_auc,target_names):    
 
 
     fig, ax = plt.subplots(figsize=(6, 6))
@@ -158,49 +158,45 @@ def run_classifier(X,y,target_names):
     #y_onehot_test.shape  # (n_samples, n_classes)
 
     fpr, tpr, roc_auc = calc_roc_auc(y_test,y_onehot_test, y_score,n_classes)
-    plot_roc_auc(y_onehot_test, y_score,n_classes,fpr,tpr,roc_auc)
-
-
-
-#iris = load_iris()
-#target_names = iris.target_names
-#X, y = iris.data, iris.target
-#y = iris.target_names[y]
-
-#run_classifier(X,y,target_names)
-
-
-df = pd.read_csv("data/exp_325/Unbalanced_325.csv")
-
-# Prepare features and target
-X = df.drop('Species', axis=1)
-y = df[['Species']]
-target_names = y['Species'].unique()
-run_classifier(X,y,target_names)
-
-'''
-class_of_interest = "virginica"
-class_id = np.flatnonzero(label_binarizer.classes_ == class_of_interest)[0]
-class_id
-
+    plot_roc_auc(y_onehot_test, y_score,n_classes,fpr,tpr,roc_auc,target_names)
 
+def run_iris():
+    print("Running classification on Iris Dataset")
+    iris = load_iris()
+    target_names = iris.target_names
+    X, y = iris.data, iris.target
+    y = iris.target_names[y]
+
+    run_classifier(X,y,target_names)
 
-display = RocCurveDisplay.from_predictions(
-    y_onehot_test[:, class_id],
-    y_score[:, class_id],
-    name=f"{class_of_interest} vs the rest",
-    color="darkorange",
-    plot_chance_level=True,
-    despine=True,
-)
-_ = display.ax_.set(
-    xlabel="False Positive Rate",
-    ylabel="True Positive Rate",
-    title="One-vs-Rest ROC curves:\nVirginica vs (Setosa & Versicolor)",
-)
-'''
+def run_exp325():
+    df = pd.read_csv("data/exp_325/Unbalanced_325.csv")
+
+    # Prepare features and target
+    X = df.drop('Species', axis=1).to_numpy()
+    y = df[['Species']].convert_dtypes()
+    #y['Species'] = y['Species'].astype("str")
+    target_names = y['Species'].unique().to_numpy().astype(str)
+    y = y.to_numpy().astype(str)
+    run_classifier(X,y,target_names)
 
 
+def run_exp310():
+    df = pd.read_csv("data/exp_310/Unbalanced_310.csv")
+
+    # Prepare features and target
+    X = df.drop('Species', axis=1).to_numpy()
+    y = df[['Species']].convert_dtypes()
+    #y['Species'] = y['Species'].astype("str")
+    target_names = y['Species'].unique().to_numpy().astype(str)
+    y = y.to_numpy().astype(str)
+    run_classifier(X,y,target_names)
 
+def main():
+
+    run_exp310()
+
 
+if __name__ == "__main__":
+    main()