The passive-aggressive algorithms are a family of algorithms for large-scale learning. They are similar to the Perceptron in that they do not require a learning rate. However, contrary to the Perceptron, they include a regularization parameter C.
For classification, PassiveAggressiveClassifier can be used with loss='hinge' (PA-I) or loss='squared_hinge' (PA-II).
For regression, PassiveAggressiveRegressor can be used with loss='epsilon_insensitive' (PA-I) or loss='squared_epsilon_insensitive' (PA-II).
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"""
==================================
Comparing various online solvers
==================================
An example showing how different online solvers perform
on the hand-written digits dataset.
"""
# Author: Rob Zinkov <rob at zinkov dot com>
# Updated by Mayank Johri [mayank at gmail dot .com.]
# License: BSD 3 clause
import numpy as np
import pylab as pl
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDClassifier, Perceptron
from sklearn.linear_model import PassiveAggressiveClassifier
heldout = [0.95, 0.90, 0.75, 0.50, 0.01]
rounds = 20
digits = datasets.load_digits()
classifiers = [
("SGD", SGDClassifier(max_iter=3)),
("Perceptron", Perceptron(max_iter=1000)),
("Passive-Aggressive I", PassiveAggressiveClassifier(loss='hinge',
C=1.0, max_iter=1000 )),
("Passive-Aggressive II", PassiveAggressiveClassifier(loss='squared_hinge',
C=1.0, max_iter=1000)),
]
xx = 1 - np.array(heldout)
for name, clf in classifiers:
yy = []
for i in heldout:
yy_ = []
for r in range(rounds):
X_train, X_test, y_train, y_test = train_test_split(digits.data,
digits.target,
test_size=i)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
yy_.append(1 - np.mean(y_pred == y_test))
yy.append(np.mean(yy_))
pl.plot(xx, yy, label=name)
pl.legend(loc="upper right")
pl.xlabel("Proportion train")
pl.ylabel("Test Error Rate")
pl.show()
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