Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_breast_cancer
+from sklearn.tree import DecisionTreeClassifier
+
+theme = gr.themes.Monochrome(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="slate",
+)
+
+X, y = load_breast_cancer(return_X_y=True)
+
+def get_ccp(test_size, random_state):
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state, test_size=test_size)
+    clf = DecisionTreeClassifier(random_state=random_state)
+    path = clf.cost_complexity_pruning_path(X_train, y_train)
+    ccp_alphas, impurities = path.ccp_alphas, path.impurities
+
+    fig1, ax1 = plt.subplots()
+    ax1.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
+    ax1.set_xlabel("effective alpha")
+    ax1.set_ylabel("total impurity of leaves")
+    ax1.set_title("Total Impurity vs effective alpha for training set")
+
+    clfs = []
+    for ccp_alpha in ccp_alphas:
+        clf = DecisionTreeClassifier(random_state=0, ccp_alpha=ccp_alpha)
+        clf.fit(X_train, y_train)
+        clfs.append(clf)
+    clfs = clfs[:-1]
+    ccp_alphas = ccp_alphas[:-1]
+
+    node_counts = [clf.tree_.node_count for clf in clfs]
+    depth = [clf.tree_.max_depth for clf in clfs]
+
+    fig2, ax2 = plt.subplots()
+    ax2.plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
+    ax2.set_xlabel("alpha")
+    ax2.set_ylabel("number of nodes")
+    ax2.set_title("Number of nodes vs alpha")
+
+    fig3, ax3 = plt.subplots()
+
+    ax3.plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
+    ax3.set_xlabel("alpha")
+    ax3.set_ylabel("depth of tree")
+    ax3.set_title("Depth vs alpha")
+    fig3.tight_layout()
+    
+    train_scores = [clf.score(X_train, y_train) for clf in clfs]
+    test_scores = [clf.score(X_test, y_test) for clf in clfs]
+
+    fig4, ax4 = plt.subplots()
+    ax4.set_xlabel("alpha")
+    ax4.set_ylabel("accuracy")
+    ax4.set_title("Accuracy vs alpha for training and testing sets")
+    ax4.plot(ccp_alphas, train_scores, marker="o", label="train", drawstyle="steps-post")
+    ax4.plot(ccp_alphas, test_scores, marker="o", label="test", drawstyle="steps-post")
+    ax4.legend()
+
+    score_gap = []
+    for train_score, test_score, ccp_alpha  in zip(test_scores, train_scores, ccp_alphas):
+        score_gap.append((train_score, test_score, abs(train_score - test_score), ccp_alpha))
+    score_gap.sort(key=lambda a: a[2])
+    top3_score = score_gap[:3]
+    top3_score.sort(key=lambda a: a[1], reverse=True)
+    text = f"Train accuracy: {round(top3_score[0][0], 2)}, Test accuracy: {round(top3_score[0][1], 2)}, The best value of cost complexity parameter alpha (ccp_alpha): {round(top3_score[0][2], 2)}"
+    return fig1, fig2, fig3, fig4, text
+
+
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'>⚒ Post pruning decision trees with cost complexity pruning 🛠</h1>
+            </div>
+        ''')
+    gr.Markdown("The DecisionTreeClassifier employs a pruning technique that can be configured using the cost complexity parameter, commonly referred to as ccp_alpha."
+    " By increasing the value of ccp_alpha, a greater number of nodes can be pruned. This demo demonstrate the impact of ccp_alpha on tree regularization")
+    gr.Markdown("Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>. Based on the example from <a href=\"https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py\">scikit-learn</a>")
+    test_size = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.2, label="Test size")
+    random_state = gr.Slider(minimum=0, maximum=2000, step=1, value=0, label="Random state")
+
+    with gr.Row():
+        with gr.Column():
+            plot_impurity = gr.Plot()
+        with gr.Column():
+            plot_node = gr.Plot()
+
+    with gr.Row():
+        with gr.Column():
+            plot_depth = gr.Plot()
+        with gr.Column():
+            plot_compare = gr.Plot()
+    with gr.Row():
+        result = gr.Textbox(label="Resusts")
+    test_size.change(fn=get_ccp, inputs=[test_size, random_state], outputs=[plot_impurity, plot_node, plot_depth, plot_compare, result])
+    random_state.change(fn=get_ccp, inputs=[test_size, random_state], outputs=[plot_impurity, plot_node, plot_depth, plot_compare,result])
+
+demo.launch()