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import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDClassifier
from sklearn.datasets import make_blobs
import gradio as gr
def plot_max_margin_hyperplane():
# we create 50 separable points
X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)
# fit the model
clf = SGDClassifier(loss="hinge", alpha=0.01, max_iter=200)
clf.fit(X, Y)
# plot the line, the points, and the nearest vectors to the plane
xx = np.linspace(-1, 5, 10)
yy = np.linspace(-1, 5, 10)
X1, X2 = np.meshgrid(xx, yy)
Z = np.empty(X1.shape)
for (i, j), val in np.ndenumerate(X1):
x1 = val
x2 = X2[i, j]
p = clf.decision_function([[x1, x2]])
Z[i, j] = p[0]
levels = [-1.0, 0.0, 1.0]
linestyles = ["dashed", "solid", "dashed"]
colors = "k"
fig = plt.figure()
plt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolor="black", s=20)
plt.axis("tight")
#plt.show()
return fig
heading = '🤗🧡🤍💙 SGD: Maximum Margin Separating Hyperplane'
with gr.Blocks(title = heading, theme = 'snehilsanyal/scikit-learn') as demo:
gr.Markdown("# {}".format(heading))
gr.Markdown(
"""
### This demo visualizes the maximum margin hyperplane that seperates\
a two-class separable dataset using a linear SVM classifier trained using SGD.
"""
)
gr.Markdown('Demo is based on [this script from scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py)')
button = gr.Button(value = 'Visualize Maximum Margin Hyperplane')
button.click(plot_max_margin_hyperplane, outputs = gr.Plot())
demo.launch() |