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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 <[email protected]>
"""
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(1,1001,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()
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