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Comparing Various Online Solvers in Scikit-learn

An example showing how different online solvers perform on the hand-written digits dataset.

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Version

In [1]:
import sklearn
sklearn.__version__
Out[1]:
'0.18.1'

Imports

In [2]:
import plotly.plotly as py
import plotly.graph_objs as go

import numpy as np
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
from sklearn.linear_model import LogisticRegression

Calculations

In [3]:
heldout = [0.95, 0.90, 0.75, 0.50, 0.01]
rounds = 20
digits = datasets.load_digits()
X, y = digits.data, digits.target

classifiers = [
    ("SGD", SGDClassifier()),
    ("ASGD", SGDClassifier(average=True)),
    ("Perceptron", Perceptron()),
    ("Passive-Aggressive I", PassiveAggressiveClassifier(loss='hinge',
                                                         C=1.0)),
    ("Passive-Aggressive II", PassiveAggressiveClassifier(loss='squared_hinge',
                                                          C=1.0)),
    ("SAG", LogisticRegression(solver='sag', tol=1e-1, C=1.e4 / X.shape[0]))
]

xx = 1. - np.array(heldout)

Plot Results

In [4]:
data = []

for name, clf in classifiers:
    print("training %s" % name)
    rng = np.random.RandomState(42)
    yy = []
    for i in heldout:
        yy_ = []
        for r in range(rounds):
            X_train, X_test, y_train, y_test = \
                train_test_split(X, y, test_size=i, random_state=rng)
            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_))
    trace = go.Scatter(x=xx, y=yy, 
                       mode='lines',
                       name=name)
    data.append(trace)
    
layout = go.Layout(xaxis=dict(title="Proportion train"),
                   yaxis=dict(title="Test Error Rate")
                  )
fig = go.Figure(data=data, layout=layout)
training SGD
training ASGD
training Perceptron
training Passive-Aggressive I
training Passive-Aggressive II
training SAG
In [5]:
py.iplot(fig)
Out[5]:

License

Author:

    Rob Zinkov <rob@zinkov.com>

License:

    BSD 3 clause
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