Create app.py

app.py

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+"""
+=======================================================================================
+Gradio demo to plot the decision surface of decision trees trained on the iris dataset
+=======================================================================================
+
+Plot the decision surface of a decision tree trained on pairs
+of features of the iris dataset.
+
+For each pair of iris features, the decision tree learns decision
+boundaries made of combinations of simple thresholding rules inferred from
+the training samples.
+
+We also show the tree structure of a model built on all of the features.
+
+Gradio demo created by Syed Affan <saffand03@gmail.com>
+"""
+from sklearn.datasets import load_iris
+from sklearn.tree import plot_tree
+import numpy as np
+import matplotlib.pyplot as plt
+import gradio as gr
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.inspection import DecisionBoundaryDisplay
+
+
+iris = load_iris()
+
+def make_plot(criterion,max_depth,ccp_alpha):
+# Parameters
+    n_classes = 3
+    plot_colors = "ryb"
+    plot_step = 0.02 
+
+    fig_1 = plt.figure()
+
+    for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):
+        # We only take the two corresponding features
+        X = iris.data[:, pair]
+        y = iris.target
+
+        # Train
+        clf = DecisionTreeClassifier(criterion=criterion,max_depth=max_depth,ccp_alpha=ccp_alpha)
+        clf.fit(X, y)
+
+        # Plot the decision boundary
+        ax = plt.subplot(2, 3, pairidx + 1)
+        plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
+        DecisionBoundaryDisplay.from_estimator(
+            clf,
+            X,
+            cmap=plt.cm.RdYlBu,
+            response_method="predict",
+            ax=ax,
+            xlabel=iris.feature_names[pair[0]],
+            ylabel=iris.feature_names[pair[1]],
+        )
+
+        # Plot the training points
+        for i, color in zip(range(n_classes), plot_colors):
+            idx = np.where(y == i)
+            plt.scatter(
+                X[idx, 0],
+                X[idx, 1],
+                c=color,
+                label=iris.target_names[i],
+                cmap=plt.cm.RdYlBu,
+                edgecolor="black",
+                s=15,
+            )
+
+    plt.suptitle("Decision surface of decision trees trained on pairs of features")
+    plt.legend(loc="lower right", borderpad=0, handletextpad=0)
+    _ = plt.axis("tight")
+
+# %%
+# Display the structure of a single decision tree trained on all the features
+# together.
+
+    fig_2 = plt.figure()
+    clf = DecisionTreeClassifier(criterion=criterion,max_depth=max_depth,ccp_alpha=ccp_alpha).fit(iris.data, iris.target)
+    plot_tree(clf, filled=True)
+    plt.title("Decision tree trained on all the iris features")
+    return fig_1,fig_2
+
+title = 'Plot the decision surface of decision trees trained on the iris dataset'
+
+model_card = f"""
+## Description:
+Plot the decision surface of a decision tree trained on pairs of features of the iris dataset.
+For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples.
+We also show the tree structure of a model built on all of the features.
+## Dataset
+Iris Dataset
+"""
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'>⚒ Plot the decision surface of decision trees trained on the iris dataset 🛠</h1>
+            </div>
+        ''')
+    gr.Markdown(model_card)
+    gr.Markdown("Author: <a href=\"https://huggingface.co/sulpha\">sulpha</a>")
+    with gr.Column():
+        d0 = gr.Radio(['gini', 'entropy', 'log_loss'],value='gini',label='Criterion')
+        d1 = gr.Slider(1,10,step=1,value=5,label = 'max_depth')
+        d2 = gr.Slider(0.0,1,step=0.001,value=0.0,label = 'ccp_alpha')
+
+        with gr.Row():
+            p_1 = gr.Plot()
+            p_2 = gr.Plot()
+
+    btn = gr.Button(value= 'Submit')
+    btn.click(make_plot,inputs=[d0,d1,d2],outputs=[p_1,p_2])
+
+demo.launch()