85 lines
1.9 KiB
Python
85 lines
1.9 KiB
Python
|
"""
|
||
|
|
========================================
|
||
|
|
Plot multi-class SGD on the iris dataset
|
||
|
|
========================================
|
||
|
|
|
||
|
|
Plot decision surface of multi-class SGD on iris dataset.
|
||
|
|
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
|
||
|
|
are represented by the dashed lines.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from sklearn import datasets
|
||
|
|
from sklearn.inspection import DecisionBoundaryDisplay
|
||
|
|
from sklearn.linear_model import SGDClassifier
|
||
|
|
|
||
|
|
# import some data to play with
|
||
|
|
iris = datasets.load_iris()
|
||
|
|
|
||
|
|
# we only take the first two features. We could
|
||
|
|
# avoid this ugly slicing by using a two-dim dataset
|
||
|
|
X = iris.data[:, :2]
|
||
|
|
y = iris.target
|
||
|
|
colors = "bry"
|
||
|
|
|
||
|
|
# shuffle
|
||
|
|
idx = np.arange(X.shape[0])
|
||
|
|
np.random.seed(13)
|
||
|
|
np.random.shuffle(idx)
|
||
|
|
X = X[idx]
|
||
|
|
y = y[idx]
|
||
|
|
|
||
|
|
# standardize
|
||
|
|
mean = X.mean(axis=0)
|
||
|
|
std = X.std(axis=0)
|
||
|
|
X = (X - mean) / std
|
||
|
|
|
||
|
|
clf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)
|
||
|
|
ax = plt.gca()
|
||
|
|
DecisionBoundaryDisplay.from_estimator(
|
||
|
|
clf,
|
||
|
|
X,
|
||
|
|
cmap=plt.cm.Paired,
|
||
|
|
ax=ax,
|
||
|
|
response_method="predict",
|
||
|
|
xlabel=iris.feature_names[0],
|
||
|
|
ylabel=iris.feature_names[1],
|
||
|
|
)
|
||
|
|
plt.axis("tight")
|
||
|
|
|
||
|
|
# Plot also the training points
|
||
|
|
for i, color in zip(clf.classes_, colors):
|
||
|
|
idx = np.where(y == i)
|
||
|
|
plt.scatter(
|
||
|
|
X[idx, 0],
|
||
|
|
X[idx, 1],
|
||
|
|
c=color,
|
||
|
|
label=iris.target_names[i],
|
||
|
|
edgecolor="black",
|
||
|
|
s=20,
|
||
|
|
)
|
||
|
|
plt.title("Decision surface of multi-class SGD")
|
||
|
|
plt.axis("tight")
|
||
|
|
|
||
|
|
# Plot the three one-against-all classifiers
|
||
|
|
xmin, xmax = plt.xlim()
|
||
|
|
ymin, ymax = plt.ylim()
|
||
|
|
coef = clf.coef_
|
||
|
|
intercept = clf.intercept_
|
||
|
|
|
||
|
|
|
||
|
|
def plot_hyperplane(c, color):
|
||
|
|
def line(x0):
|
||
|
|
return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
|
||
|
|
|
||
|
|
plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)
|
||
|
|
|
||
|
|
|
||
|
|
for i, color in zip(clf.classes_, colors):
|
||
|
|
plot_hyperplane(i, color)
|
||
|
|
plt.legend()
|
||
|
|
plt.show()
|