71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
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"""
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==================================
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Comparing various online solvers
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==================================
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An example showing how different online solvers perform
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on the hand-written digits dataset.
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"""
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# Author: Rob Zinkov <rob at zinkov dot com>
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets
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from sklearn.linear_model import (
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LogisticRegression,
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PassiveAggressiveClassifier,
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Perceptron,
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SGDClassifier,
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)
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from sklearn.model_selection import train_test_split
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heldout = [0.95, 0.90, 0.75, 0.50, 0.01]
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# Number of rounds to fit and evaluate an estimator.
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rounds = 10
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X, y = datasets.load_digits(return_X_y=True)
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classifiers = [
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("SGD", SGDClassifier(max_iter=110)),
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("ASGD", SGDClassifier(max_iter=110, average=True)),
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("Perceptron", Perceptron(max_iter=110)),
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(
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"Passive-Aggressive I",
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PassiveAggressiveClassifier(max_iter=110, loss="hinge", C=1.0, tol=1e-4),
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),
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(
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"Passive-Aggressive II",
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PassiveAggressiveClassifier(
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max_iter=110, loss="squared_hinge", C=1.0, tol=1e-4
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),
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),
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(
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"SAG",
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LogisticRegression(max_iter=110, solver="sag", tol=1e-1, C=1.0e4 / X.shape[0]),
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),
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]
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xx = 1.0 - np.array(heldout)
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for name, clf in classifiers:
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print("training %s" % name)
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rng = np.random.RandomState(42)
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yy = []
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for i in heldout:
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yy_ = []
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for r in range(rounds):
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=i, random_state=rng
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)
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clf.fit(X_train, y_train)
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y_pred = clf.predict(X_test)
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yy_.append(1 - np.mean(y_pred == y_test))
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yy.append(np.mean(yy_))
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plt.plot(xx, yy, label=name)
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plt.legend(loc="upper right")
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plt.xlabel("Proportion train")
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plt.ylabel("Test Error Rate")
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plt.show()
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