80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
"""
|
|
=====================================================================
|
|
Decision boundary of label propagation versus SVM on the Iris dataset
|
|
=====================================================================
|
|
|
|
Comparison for decision boundary generated on iris dataset
|
|
between Label Propagation and SVM.
|
|
|
|
This demonstrates Label Propagation learning a good boundary
|
|
even with a small amount of labeled data.
|
|
|
|
"""
|
|
print(__doc__)
|
|
|
|
# Authors: Clay Woolam <clay@woolam.org>
|
|
# License: BSD
|
|
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from sklearn import datasets
|
|
from sklearn import svm
|
|
from sklearn.semi_supervised import label_propagation
|
|
|
|
rng = np.random.RandomState(0)
|
|
|
|
iris = datasets.load_iris()
|
|
|
|
X = iris.data[:, :2]
|
|
y = iris.target
|
|
|
|
# step size in the mesh
|
|
h = .02
|
|
|
|
y_30 = np.copy(y)
|
|
y_30[rng.rand(len(y)) < 0.3] = -1
|
|
y_50 = np.copy(y)
|
|
y_50[rng.rand(len(y)) < 0.5] = -1
|
|
# we create an instance of SVM and fit out data. We do not scale our
|
|
# data since we want to plot the support vectors
|
|
ls30 = (label_propagation.LabelSpreading().fit(X, y_30),
|
|
y_30)
|
|
ls50 = (label_propagation.LabelSpreading().fit(X, y_50),
|
|
y_50)
|
|
ls100 = (label_propagation.LabelSpreading().fit(X, y), y)
|
|
rbf_svc = (svm.SVC(kernel='rbf').fit(X, y), y)
|
|
|
|
# create a mesh to plot in
|
|
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))
|
|
|
|
# title for the plots
|
|
titles = ['Label Spreading 30% data',
|
|
'Label Spreading 50% data',
|
|
'Label Spreading 100% data',
|
|
'SVC with rbf kernel']
|
|
|
|
color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}
|
|
|
|
for i, (clf, y_train) in enumerate((ls30, ls50, ls100, rbf_svc)):
|
|
# Plot the decision boundary. For that, we will assign a color to each
|
|
# point in the mesh [x_min, x_max]x[y_min, y_max].
|
|
plt.subplot(2, 2, i + 1)
|
|
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
|
|
|
|
# Put the result into a color plot
|
|
Z = Z.reshape(xx.shape)
|
|
plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
|
|
plt.axis('off')
|
|
|
|
# Plot also the training points
|
|
colors = [color_map[y] for y in y_train]
|
|
plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')
|
|
|
|
plt.title(titles[i])
|
|
|
|
plt.suptitle("Unlabeled points are colored white", y=0.1)
|
|
plt.show()
|