Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.datasets import make_blobs
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.covariance import OAS
+
+def generate_data(n_samples, n_features):
+    X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])
+
+    if n_features > 1:
+        X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])
+    return X, y
+
+def classify(n_train, n_test, n_averages, n_features_max, step):
+    acc_clf1, acc_clf2, acc_clf3 = [], [], []
+    n_features_range = range(1, n_features_max + 1, step)
+    
+    for n_features in n_features_range:
+        score_clf1, score_clf2, score_clf3 = 0, 0, 0
+        for _ in range(n_averages):
+            X, y = generate_data(n_train, n_features)
+
+            clf1 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage=None).fit(X, y)
+            clf2 = LinearDiscriminantAnalysis(solver="lsqr", shrinkage="auto").fit(X, y)
+            oa = OAS(store_precision=False, assume_centered=False)
+            clf3 = LinearDiscriminantAnalysis(solver="lsqr", covariance_estimator=oa).fit(X, y)
+
+            X, y = generate_data(n_test, n_features)
+            score_clf1 += clf1.score(X, y)
+            score_clf2 += clf2.score(X, y)
+            score_clf3 += clf3.score(X, y)
+
+        acc_clf1.append(score_clf1 / n_averages)
+        acc_clf2.append(score_clf2 / n_averages)
+        acc_clf3.append(score_clf3 / n_averages)
+
+    features_samples_ratio = np.array(n_features_range) / n_train
+
+    plt.plot(
+        features_samples_ratio,
+        acc_clf1,
+        linewidth=2,
+        label="LDA",
+        color="gold",
+        linestyle="solid",
+    )
+    plt.plot(
+        features_samples_ratio,
+        acc_clf2,
+        linewidth=2,
+        label="LDA with Ledoit Wolf",
+        color="navy",
+        linestyle="dashed",
+    )
+    plt.plot(
+        features_samples_ratio,
+        acc_clf3,
+        linewidth=2,
+        label="LDA with OAS",
+        color="red",
+        linestyle="dotted",
+    )
+
+    plt.xlabel("n_features / n_samples")
+    plt.ylabel("Classification accuracy")
+    plt.legend(loc="lower left")
+    plt.ylim((0.65, 1.0))
+    plt.suptitle(
+        "LDA (Linear Discriminant Analysis) vs. "
+        + "\n"
+        + "LDA with Ledoit Wolf vs. "
+        + "\n"
+        + "LDA with OAS (1 discriminative feature)"
+    )
+    
+    # Convert the plot to Gradio compatible format
+    plt.tight_layout()
+    plt.savefig("plot.png")
+    return "plot.png"
+
+# Define the input and output interfaces
+inputs = [
+    gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_train", default=20),
+    gr.inputs.Slider(minimum=1, maximum=500, step=1, label="n_test", default=200),
+    gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_averages", default=50),
+    gr.inputs.Slider(minimum=1, maximum=100, step=1, label="n_features_max", default=75),
+    gr.inputs.Slider(minimum=1, maximum=20, step=1, label="step", default=4),
+]
+output = gr.outputs.Image(type="pil")
+examples = [
+    [20, 200, 50, 75, 4],
+    [30, 250, 60, 80, 5],
+    [40, 300, 70, 90, 6],
+]
+
+# Create the Gradio app
+title = "Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification"
+description = "This example illustrates how the Ledoit-Wolf and Oracle Shrinkage Approximating (OAS) estimators of covariance can improve classification. See the original example: https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html"
+gr.Interface(classify, inputs, output, examples=examples, title=title, description=description).launch()