# Authors: Rob Zinkov, Mathieu Blondel
# License: BSD 3 clause
from .stochastic_gradient import BaseSGDClassifier
from .stochastic_gradient import BaseSGDRegressor
from .stochastic_gradient import DEFAULT_EPSILON
class PassiveAggressiveClassifier(BaseSGDClassifier):
"""Passive Aggressive Classifier
Read more in the :ref:`User Guide <passive_aggressive>`.
Parameters
----------
C : float
Maximum step size (regularization). Defaults to 1.0.
fit_intercept : bool, default=False
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered.
max_iter : int, optional
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
`partial_fit`.
Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.
.. versionadded:: 0.19
tol : float or None, optional
The stopping criterion. If it is not None, the iterations will stop
when (loss > previous_loss - tol). Defaults to None.
Defaults to 1e-3 from 0.21.
.. versionadded:: 0.19
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.
verbose : integer, optional
The verbosity level
loss : string, optional
The loss function to be used:
hinge: equivalent to PA-I in the reference paper.
squared_hinge: equivalent to PA-II in the reference paper.
n_jobs : integer, optional
The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation. -1 means 'all CPUs'. Defaults
to 1.
random_state : int, RandomState instance or None, optional, default=None
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
warm_start : bool, optional
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
class_weight : dict, {class_label: weight} or "balanced" or None, optional
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes
are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
.. versionadded:: 0.17
parameter *class_weight* to automatically weight samples.
average : bool or int, optional
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So average=10 will begin averaging after seeing 10 samples.
.. versionadded:: 0.19
parameter *average* to use weights averaging in SGD
n_iter : int, optional
The number of passes over the training data (aka epochs).
Defaults to None. Deprecated, will be removed in 0.21.
.. versionchanged:: 0.19
Deprecated
Attributes
----------
coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,\
n_features]
Weights assigned to the features.
intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.
n_iter_ : int
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=None, 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
--------
SGDClassifier
Perceptron
References
----------
Online Passive-Aggressive Algorithms
<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)
"""
def __init__(self, C=1.0, fit_intercept=True, max_iter=None, tol=None,
shuffle=True, verbose=0, loss="hinge", n_jobs=1,
random_state=None, warm_start=False, class_weight=None,
average=False, n_iter=None):
super(PassiveAggressiveClassifier, self).__init__(
penalty=None,
fit_intercept=fit_intercept,
max_iter=max_iter,
tol=tol,
shuffle=shuffle,
verbose=verbose,
random_state=random_state,
eta0=1.0,
warm_start=warm_start,
class_weight=class_weight,
average=average,
n_jobs=n_jobs,
n_iter=n_iter)
self.C = C
self.loss = loss
[docs] def partial_fit(self, X, y, classes=None):
"""Fit linear model with Passive Aggressive algorithm.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Subset of the training data
y : numpy array of shape [n_samples]
Subset of the target values
classes : array, shape = [n_classes]
Classes across all calls to partial_fit.
Can be obtained by via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn't need to contain all labels in `classes`.
Returns
-------
self : returns an instance of self.
"""
if self.class_weight == 'balanced':
raise ValueError("class_weight 'balanced' is not supported for "
"partial_fit. For 'balanced' weights, use "
"`sklearn.utils.compute_class_weight` with "
"`class_weight='balanced'`. In place of y you "
"can use a large enough subset of the full "
"training set target to properly estimate the "
"class frequency distributions. Pass the "
"resulting weights as the class_weight "
"parameter.")
lr = "pa1" if self.loss == "hinge" else "pa2"
return self._partial_fit(X, y, alpha=1.0, C=self.C,
loss="hinge", learning_rate=lr, max_iter=1,
classes=classes, sample_weight=None,
coef_init=None, intercept_init=None)
[docs] def fit(self, X, y, coef_init=None, intercept_init=None):
"""Fit linear model with Passive Aggressive algorithm.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : numpy array of shape [n_samples]
Target values
coef_init : array, shape = [n_classes,n_features]
The initial coefficients to warm-start the optimization.
intercept_init : array, shape = [n_classes]
The initial intercept to warm-start the optimization.
Returns
-------
self : returns an instance of self.
"""
lr = "pa1" if self.loss == "hinge" else "pa2"
return self._fit(X, y, alpha=1.0, C=self.C,
loss="hinge", learning_rate=lr,
coef_init=coef_init, intercept_init=intercept_init)
class PassiveAggressiveRegressor(BaseSGDRegressor):
"""Passive Aggressive Regressor
Read more in the :ref:`User Guide <passive_aggressive>`.
Parameters
----------
C : float
Maximum step size (regularization). Defaults to 1.0.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
max_iter : int, optional
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
`partial_fit`.
Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.
.. versionadded:: 0.19
tol : float or None, optional
The stopping criterion. If it is not None, the iterations will stop
when (loss > previous_loss - tol). Defaults to None.
Defaults to 1e-3 from 0.21.
.. versionadded:: 0.19
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.
verbose : integer, optional
The verbosity level
loss : string, optional
The loss function to be used:
epsilon_insensitive: equivalent to PA-I in the reference paper.
squared_epsilon_insensitive: equivalent to PA-II in the reference
paper.
epsilon : float
If the difference between the current prediction and the correct label
is below this threshold, the model is not updated.
random_state : int, RandomState instance or None, optional, default=None
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
warm_start : bool, optional
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
average : bool or int, optional
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So average=10 will begin averaging after seeing 10 samples.
.. versionadded:: 0.19
parameter *average* to use weights averaging in SGD
n_iter : int, optional
The number of passes over the training data (aka epochs).
Defaults to None. Deprecated, will be removed in 0.21.
.. versionchanged:: 0.19
Deprecated
Attributes
----------
coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,\
n_features]
Weights assigned to the features.
intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.
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=None, 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
--------
SGDRegressor
References
----------
Online Passive-Aggressive Algorithms
<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006)
"""
def __init__(self, C=1.0, fit_intercept=True, max_iter=None, tol=None,
shuffle=True, verbose=0, loss="epsilon_insensitive",
epsilon=DEFAULT_EPSILON, random_state=None, warm_start=False,
average=False, n_iter=None):
super(PassiveAggressiveRegressor, self).__init__(
penalty=None,
l1_ratio=0,
epsilon=epsilon,
eta0=1.0,
fit_intercept=fit_intercept,
max_iter=max_iter,
tol=tol,
shuffle=shuffle,
verbose=verbose,
random_state=random_state,
warm_start=warm_start,
average=average,
n_iter=n_iter)
self.C = C
self.loss = loss
[docs] def partial_fit(self, X, y):
"""Fit linear model with Passive Aggressive algorithm.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Subset of training data
y : numpy array of shape [n_samples]
Subset of target values
Returns
-------
self : returns an instance of self.
"""
self._validate_params()
lr = "pa1" if self.loss == "epsilon_insensitive" else "pa2"
return self._partial_fit(X, y, alpha=1.0, C=self.C,
loss="epsilon_insensitive",
learning_rate=lr, max_iter=1,
sample_weight=None,
coef_init=None, intercept_init=None)
[docs] def fit(self, X, y, coef_init=None, intercept_init=None):
"""Fit linear model with Passive Aggressive algorithm.
Parameters
----------
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training data
y : numpy array of shape [n_samples]
Target values
coef_init : array, shape = [n_features]
The initial coefficients to warm-start the optimization.
intercept_init : array, shape = [1]
The initial intercept to warm-start the optimization.
Returns
-------
self : returns an instance of self.
"""
lr = "pa1" if self.loss == "epsilon_insensitive" else "pa2"
return self._fit(X, y, alpha=1.0, C=self.C,
loss="epsilon_insensitive",
learning_rate=lr,
coef_init=coef_init,
intercept_init=intercept_init)
