app.py

"""
========================================
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.

"""
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 plot(alpha):
# import some data to play with
    iris = datasets.load_iris()

# 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
    mean = X.mean(axis=0)
    std = X.std(axis=0)
    X = (X - mean) / std

    clf = SGDClassifier(alpha=alpha, max_iter=100).fit(X, y)
    #ax = plt.gca()
    DecisionBoundaryDisplay.from_estimator(
        clf,
        X,
        cmap=plt.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()
    #plt.show()
    return plt

#pl = plot(0)
#pl.show()
with gr.Blocks() as demo:
    alpha = gr.Slider(minimum=0.0001, maximum=10, step=0.01, value=0.0001, label="Alpha Value")
    with gr.Row():
        plt = gr.Plot()

    alpha.change(fn=plot, inputs=[alpha],outputs=[plt])

demo.launch()