File size: 2,219 Bytes
3614731 |
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 |
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
import gradio as gr
def plot_decision_boundary(kernel_type):
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
y = np.array(iris.target, dtype=int)
h = 0.02 # step size in the mesh
if kernel_type == "isotropic":
kernel = 1.0 * RBF([1.0])
clf = GaussianProcessClassifier(kernel=kernel).fit(X, y)
elif kernel_type == "anisotropic":
kernel = 1.0 * RBF([1.0, 1.0])
clf = GaussianProcessClassifier(kernel=kernel).fit(X, y)
else:
return None
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape((xx.shape[0], xx.shape[1], 3))
plt.figure(figsize=(7, 5))
plt.imshow(Z, extent=(x_min, x_max, y_min, y_max), origin="lower")
plt.scatter(X[:, 0], X[:, 1], c=np.array(["r", "g", "b"])[y], edgecolors=(0, 0, 0))
plt.xlabel("Sepal length")
plt.ylabel("Sepal width")
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.title("%s, LML: %.3f" % (kernel_type.capitalize(), clf.log_marginal_likelihood(clf.kernel_.theta)))
plt.tight_layout()
return plt
kernel_select = gr.inputs.Radio(["isotropic", "anisotropic"], label="Kernel Type")
gr_interface = gr.Interface(fn=plot_decision_boundary, inputs=kernel_select, outputs="plot", title="Gaussian Process Classification on Iris Dataset", description="This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the iris-dataset. The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by assigning different length-scales to the two feature dimensions. See the original example at https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_iris.html")
gr_interface.launch()
|