61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
|
"""
|
||
|
|
=====================
|
||
|
|
SGD: Weighted samples
|
||
|
|
=====================
|
||
|
|
|
||
|
|
Plot decision function of a weighted dataset, where the size of points
|
||
|
|
is proportional to its weight.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from sklearn import linear_model
|
||
|
|
|
||
|
|
# we create 20 points
|
||
|
|
np.random.seed(0)
|
||
|
|
X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
|
||
|
|
y = [1] * 10 + [-1] * 10
|
||
|
|
sample_weight = 100 * np.abs(np.random.randn(20))
|
||
|
|
# and assign a bigger weight to the last 10 samples
|
||
|
|
sample_weight[:10] *= 10
|
||
|
|
|
||
|
|
# plot the weighted data points
|
||
|
|
xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
|
||
|
|
fig, ax = plt.subplots()
|
||
|
|
ax.scatter(
|
||
|
|
X[:, 0],
|
||
|
|
X[:, 1],
|
||
|
|
c=y,
|
||
|
|
s=sample_weight,
|
||
|
|
alpha=0.9,
|
||
|
|
cmap=plt.cm.bone,
|
||
|
|
edgecolor="black",
|
||
|
|
)
|
||
|
|
|
||
|
|
# fit the unweighted model
|
||
|
|
clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)
|
||
|
|
clf.fit(X, y)
|
||
|
|
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
|
||
|
|
Z = Z.reshape(xx.shape)
|
||
|
|
no_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["solid"])
|
||
|
|
|
||
|
|
# fit the weighted model
|
||
|
|
clf = linear_model.SGDClassifier(alpha=0.01, max_iter=100)
|
||
|
|
clf.fit(X, y, sample_weight=sample_weight)
|
||
|
|
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
|
||
|
|
Z = Z.reshape(xx.shape)
|
||
|
|
samples_weights = ax.contour(xx, yy, Z, levels=[0], linestyles=["dashed"])
|
||
|
|
|
||
|
|
no_weights_handles, _ = no_weights.legend_elements()
|
||
|
|
weights_handles, _ = samples_weights.legend_elements()
|
||
|
|
ax.legend(
|
||
|
|
[no_weights_handles[0], weights_handles[0]],
|
||
|
|
["no weights", "with weights"],
|
||
|
|
loc="lower left",
|
||
|
|
)
|
||
|
|
|
||
|
|
ax.set(xticks=(), yticks=())
|
||
|
|
plt.show()
|