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import gradio as gr
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.inspection import DecisionBoundaryDisplay
def plot_svm_classifiers():
# import some data to play with
iris = datasets.load_iris()
# Take the first two features. We could avoid this by using a two-dim dataset
X = iris.data[:, :2]
y = iris.target
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0 # SVM regularization parameter
models = (
svm.SVC(kernel="linear", C=C),
svm.LinearSVC(C=C, max_iter=10000),
svm.SVC(kernel="rbf", gamma=0.7, C=C),
svm.SVC(kernel="poly", degree=3, gamma="auto", C=C),
)
models = (clf.fit(X, y) for clf in models)
# title for the plots
titles = (
"SVC with linear kernel",
"LinearSVC (linear kernel)",
"SVC with RBF kernel",
"SVC with polynomial (degree 3) kernel",
)
# Set-up 2x2 grid for plotting.
fig, sub = plt.subplots(2, 2)
plt.subplots_adjust(wspace=0.4, hspace=0.4)
X0, X1 = X[:, 0], X[:, 1]
for clf, title, ax in zip(models, titles, sub.flatten()):
disp = DecisionBoundaryDisplay.from_estimator(
clf,
X,
response_method="predict",
cmap=plt.cm.coolwarm,
alpha=0.8,
ax=ax,
xlabel=iris.feature_names[0],
ylabel=iris.feature_names[1],
)
ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors="k")
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(title)
plt.axis('tight')
#plt.show()
return fig
heading = '🤗🧡🤍💙 Plot different SVM Classifiers on Iris Dataset'
with gr.Blocks(title = heading, theme= 'snehilsanyal/scikit-learn') as demo:
gr.Markdown("# {}".format(heading))
gr.Markdown(
"""
### This demo visualizes different SVM Classifiers on a 2D projection of the Iris dataset.
<b>The features to be considered are:</b>\
\
1. Sepal length (cm) \
2. Sepal width (cm) \
<b>The SVM Classifiers used for this demo are:</b>\
\
1. SVC with linear kernel \
2. Linear SVC \
3. SVC with RBF kernel\
4. SVC with Polynomial (degree 3) kernel
"""
)
gr.Markdown('**[Demo is based on this script from scikit-learn documentation](https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py)**')
button = gr.Button(value = 'Visualize different SVM Classifiers on Iris Dataset')
button.click(plot_svm_classifiers, outputs = gr.Plot())
demo.launch() |