File size: 3,112 Bytes
f114100 46378b2 f114100 3f36f69 f114100 46378b2 3f36f69 46378b2 f114100 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
import gradio as gr
from PIL import Image
def calculate_score(clf):
xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))
X_test = np.c_[xx.ravel(), yy.ravel()]
Y_test = np.logical_xor(xx.ravel() > 0, yy.ravel() > 0)
return clf.score(X_test, Y_test)
def getColorMap(kernel, gamma):
# prepare the training dataset
np.random.seed(0)
X = np.random.randn(300, 2)
Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)
# fit the model
clf = svm.NuSVC(kernel=kernel, gamma=gamma)
clf.fit(X, Y)
#create a grid for the plotting the decision function
xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500))
# plot the decision function for each datapoint on the grid
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.imshow(
Z,
interpolation="nearest",
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
aspect="auto",
origin="lower",
cmap=plt.cm.PuOr_r,
)
contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed")
plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors='k')
plt.title(f"Decision function for Non-Linear SVC with the {kernel} kernel and '{gamma}' gamma ", fontsize='14') #title
plt.xlabel("X",fontsize='13') #adds a label in the x axis
plt.ylabel("Y",fontsize='13') #adds a label in the y axis
return plt, calculate_score(clf)
with gr.Blocks() as demo:
gr.Markdown("## Learning the XOR function: An application of Binary Classification using Non-linear SVM")
gr.Markdown("### This demo is based on this [scikit-learn example](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py).")
gr.Markdown("### In this demo, we use a non-linear SVC (Support Vector Classifier) to learn the decision function of the XOR operator.")
xor_image = Image.open("xor.png")
gr.Image(xor_image, label="Table explaining the 'XOR' operator", shape = (208.5, 250))
gr.HTML("<hr>")
gr.Markdown("### Furthermore, we observe that we get different decision function plots by varying the Kernel and Gamma hyperparameters of the non-linear SVC.")
gr.Markdown("### Feel free to experiment with kernel and gamma values below to see how the quality of the decision function changes with the hyperparameters.")
inp1 = gr.Radio(['poly', 'rbf', 'sigmoid'], label="Kernel", info="Choose a kernel")
inp2 = gr.Radio(['scale', 'auto'], label="Gamma", info="Choose a gamma value")
btn = gr.Button(value="Submit")
with gr.Row():
plot = gr.Plot(label=f"Decision function plot for Non-Linear SVC with the '{inp1}' kernel and '{inp2}' gamma ")
num = gr.Textbox(label="Test Accuracy")
btn.click(getColorMap, inputs=[inp1, inp2], outputs=[plot, num])
if __name__ == "__main__":
print("hdh")
demo.launch()
print("gedhhfhf") |