Create app.py

app.py

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+"""
+==========================================================
+Gradio demo to Plot multi-class SGD on the iris dataset
+==========================================================
+
+Plot decision surface of multi-class SGD on iris dataset.
+The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
+are represented by the dashed lines.
+
+Created by Syed Affan <saffand03@gmail.com>
+
+"""
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.linear_model import SGDClassifier
+from sklearn.inspection import DecisionBoundaryDisplay
+import matplotlib.cm
+
+def make_plot(alpha,max_iter,Standardize):
+# import some data to play with
+    iris = datasets.load_iris()
+    fig = plt.figure()
+
+# we only take the first two features. We could
+# avoid this ugly slicing by using a two-dim dataset
+    X = iris.data[:, :2]
+    y = iris.target
+    colors = "bry"
+
+# shuffle
+    idx = np.arange(X.shape[0])
+    np.random.seed(13)
+    np.random.shuffle(idx)
+    X = X[idx]
+    y = y[idx]
+
+# standardize
+    if Standardize:
+        mean = X.mean(axis=0)
+        std = X.std(axis=0)
+        X = (X - mean) / std
+
+
+    clf = SGDClassifier(alpha=alpha, max_iter=max_iter).fit(X, y)
+    accuracy = clf.score(X,y)
+    acc = f'## The Accuracy on the entire dataset: {accuracy}'
+    ax = plt.gca()
+    DecisionBoundaryDisplay.from_estimator(
+        clf,
+        X,
+        cmap=matplotlib.cm.Paired,
+        ax=ax,
+        response_method="predict",
+        xlabel=iris.feature_names[0],
+        ylabel=iris.feature_names[1],
+    )
+    plt.axis("tight")
+
+# Plot also the training points
+    for i, color in zip(clf.classes_, colors):
+        idx = np.where(y == i)
+        plt.scatter(
+            X[idx, 0],
+            X[idx, 1],
+            c=color,
+            label=iris.target_names[i],
+            cmap=matplotlib.cm.Paired,
+            edgecolor="black",
+            s=20,
+        )
+    plt.title("Decision surface of multi-class SGD")
+    plt.axis("tight")
+
+# Plot the three one-against-all classifiers
+    xmin, xmax = plt.xlim()
+    ymin, ymax = plt.ylim()
+    coef = clf.coef_
+    intercept = clf.intercept_
+
+
+    def plot_hyperplane(c, color):
+        def line(x0):
+            return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
+
+        plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)
+
+
+    for i, color in zip(clf.classes_, colors):
+        plot_hyperplane(i, color)
+    plt.legend()
+    
+    return fig,acc
+
+title = "Plot multi-class SGD on the iris dataset"
+
+model_card = f"""
+## Description
+Plot decision surface of multi-class SGD on iris dataset.
+The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines.
+## Dataset
+Iris Dataset
+"""
+
+with gr.Blocks(title=title) as demo:
+    gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'>⚒ Plot multi-class SGD on iris dataset 🛠</h1>
+            </div>
+        ''')
+ 
+    gr.Markdown(model_card)
+    d0 = gr.Slider(0.001,5,step=0.001,value=0.001,label='alpha')
+    d1 = gr.Slider(1,1000,step=10,value=100,label='max_iter')
+    d2 = gr.Checkbox(value=True,label='Standardize')
+
+    btn =gr.Button(value='Submit')
+    btn.click(make_plot,inputs=[d0,d1,d2],outputs=[gr.Plot(),gr.Markdown()])
+
+demo.launch()