SGDClassifier

Show the boundary computed by a SGDClassifier.

Traceback (most recent call last):
  File "somewhere/workspace/ensae_teaching_cs/ensae_teaching_cs_UT_37_std/_doc/examples/sklearn_ensae_course/plot_sgd_separator.py", line 12, in <module>
    from sklearn.datasets.samples_generator import make_blobs
ModuleNotFoundError: No module named 'sklearn.datasets.samples_generator'

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import SGDClassifier
from sklearn.datasets.samples_generator import make_blobs


def plot_sgd_separator():
    # we create 50 separable points
    X, Y = make_blobs(n_samples=50, centers=2,
                      random_state=0, cluster_std=0.60)

    # fit the model
    clf = SGDClassifier(loss="hinge", alpha=0.01,
                        max_iter=200, fit_intercept=True)
    clf.fit(X, Y)

    # plot the line, the points, and the nearest vectors to the plane
    xx = np.linspace(-1, 5, 10)
    yy = np.linspace(-1, 5, 10)

    X1, X2 = np.meshgrid(xx, yy)
    Z = np.empty(X1.shape)
    for (i, j), val in np.ndenumerate(X1):
        x1 = val
        x2 = X2[i, j]
        p = clf.decision_function([[x1, x2]])
        Z[i, j] = p[0]
    levels = [-1.0, 0.0, 1.0]
    linestyles = ['dashed', 'solid', 'dashed']
    colors = 'k'

    ax = plt.axes()
    ax.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
    ax.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)

    ax.axis('tight')


plot_sgd_separator()

Total running time of the script: ( 0 minutes 0.257 seconds)

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