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[MRG + 1] Examples to doc strings of PassiveAgressiveClassifier and Regressor #9373

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Jul 16, 2017
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[MRG + 1] Examples to doc strings of PassiveAgressiveClassifier and Regressor #9373

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38 changes: 38 additions & 0 deletions sklearn/linear_model/passive_aggressive.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,25 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
The actual number of iterations to reach the stopping criterion.
For multiclass fits, it is the maximum over every binary fit.

Examples
--------
>>> from sklearn.linear_model import PassiveAggressiveClassifier
>>> from sklearn.datasets import make_classification
>>>
>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = PassiveAggressiveClassifier(random_state=0)
>>> clf.fit(X, y)
PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,
fit_intercept=True, loss='hinge', max_iter=5, n_iter=None,
n_jobs=1, random_state=0, shuffle=True, tol=None, verbose=0,
warm_start=False)
>>> print(clf.coef_)
[[ 0.49324685 1.0552176 1.49519589 1.33798314]]
>>> print(clf.intercept_)
[ 2.18438388]
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]

See also
--------

Expand Down Expand Up @@ -291,6 +310,25 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
n_iter_ : int
The actual number of iterations to reach the stopping criterion.

Examples
--------
>>> from sklearn.linear_model import PassiveAggressiveRegressor
>>> from sklearn.datasets import make_regression
>>>
>>> X, y = make_regression(n_features=4, random_state=0)
>>> regr = PassiveAggressiveRegressor(random_state=0)
>>> regr.fit(X, y)
PassiveAggressiveRegressor(C=1.0, average=False, epsilon=0.1,
fit_intercept=True, loss='epsilon_insensitive', max_iter=5,
n_iter=None, random_state=0, shuffle=True, tol=None,
verbose=0, warm_start=False)
>>> print(regr.coef_)
[ 20.48736655 34.18818427 67.59122734 87.94731329]
>>> print(regr.intercept_)
[-0.02306214]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-0.02306214]

See also
--------

Expand Down