Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_classification
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.pipeline import make_pipeline
+from sklearn.svm import LinearSVC
+from sklearn.metrics import DetCurveDisplay, RocCurveDisplay
+
+def generate_synthetic_data(n_samples, n_features, n_redundant, n_informative, random_state, n_clusters_per_class):
+    X, y = make_classification(
+        n_samples=n_samples,
+        n_features=n_features,
+        n_redundant=n_redundant,
+        n_informative=n_informative,
+        random_state=random_state,
+        n_clusters_per_class=n_clusters_per_class,
+    )
+    return X, y
+
+def plot_roc_det_curves(classifier_names, svm_c, rf_max_depth, rf_n_estimators, rf_max_features,
+                        n_samples, n_features, n_redundant, n_informative, random_state, n_clusters_per_class):
+    X, y = generate_synthetic_data(n_samples, n_features, n_redundant, n_informative, random_state, n_clusters_per_class)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
+
+    classifiers = {
+        "Linear SVM": make_pipeline(StandardScaler(), LinearSVC(C=svm_c)),
+        "Random Forest": RandomForestClassifier(
+            max_depth=rf_max_depth, n_estimators=rf_n_estimators, max_features=rf_max_features
+        ),
+    }
+
+    fig, [ax_roc, ax_det] = plt.subplots(1, 2, figsize=(11, 5))
+
+    for classifier_name in classifier_names:
+        clf = classifiers[classifier_name]
+        clf.fit(X_train, y_train)
+        RocCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_roc, name=classifier_name)
+        DetCurveDisplay.from_estimator(clf, X_test, y_test, ax=ax_det, name=classifier_name)
+
+    ax_roc.set_title("Receiver Operating Characteristic (ROC) curves")
+    ax_det.set_title("Detection Error Tradeoff (DET) curves")
+
+    ax_roc.grid(linestyle="--")
+    ax_det.grid(linestyle="--")
+
+    plt.legend()
+    plt.tight_layout()
+
+    return plt
+
+parameters = [
+    gr.inputs.CheckboxGroup(["Linear SVM", "Random Forest"], label="Classifiers"),
+    gr.inputs.Slider(0.001, 0.1, step=0.001, default=0.025, label="Linear SVM C"),
+    gr.inputs.Slider(1, 10, step=1, default=5, label="Random Forest Max Depth"),
+    gr.inputs.Slider(1, 20, step=1, default=10, label="Random Forest n_estimators"),
+    gr.inputs.Slider(1, 10, step=1, default=1, label="Random Forest max_features"),
+    gr.inputs.Slider(100, 2000, step=100, default=1000, label="Number of Samples"),
+    gr.inputs.Slider(1, 10, step=1, default=2, label="Number of Features"),
+    gr.inputs.Slider(0, 10, step=1, default=0, label="Number of Redundant Features"),
+    gr.inputs.Slider(1, 10, step=1, default=2, label="Number of Informative Features"),
+    gr.inputs.Slider(0, 100, step=1, default=1, label="Random State"),
+    gr.inputs.Slider(1, 10, step=1, default=1, label="Number of Clusters per Class"),
+]
+
+examples = [
+    [
+        ["Linear SVM"],
+        0.025,
+        5,
+        10,
+        1,
+        1000,
+        2,
+        0,
+        2,
+        1,
+        1,
+    ],
+     [
+        ["Random Forest"],
+        0.025,
+        5,
+        10,
+        1,
+        1000,
+        2,
+        0,
+        2,
+        1,
+        1,
+    ],
+     [
+        ["Linear SVM", "Random Forest"],
+        0.025,
+        5,
+        10,
+        1,
+        1000,
+        2,
+        0,
+        2,
+        1,
+        1,
+    ]
+]
+
+iface = gr.Interface(title = "Detection error tradeoff (DET) curve", fn=plot_roc_det_curves, inputs=parameters, outputs="plot", description="In this example, we compare two binary classification multi-threshold metrics: the Receiver Operating Characteristic (ROC) and the Detection Error Tradeoff (DET). For such purpose, we evaluate two different classifiers for the same classification task. See the original scikit-learn example here: https://scikit-learn.org/stable/auto_examples/model_selection/plot_det.html")
+iface.launch()