PassiveAggressiveClassifier

class ibex.sklearn.linear_model.PassiveAggressiveClassifier(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)

Bases: sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute coef_.

Example:

>>> import numpy as np
>>> from sklearn import datasets
>>> import pandas as pd
>>>
>>> iris = datasets.load_iris()
>>> features, targets, iris = iris['feature_names'], iris['target_names'], pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=iris['feature_names']+['class'])
>>> iris['class'] = iris['class'].map(pd.Series(targets))
>>>
>>> iris.head()
                sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)             0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2

    class
0  setosa
1  setosa
2  setosa
3  setosa
4  setosa
>>>
>>> from ibex.sklearn import linear_model as pd_linear_model
>>>
>>> clf =  pd_linear_model.PassiveAggressiveClassifier().fit(iris[features], iris['class'])
>>>
>>> clf.coef_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64

Note

The documentation following is of the original class wrapped by this class. This class wraps the attribute intercept_.

Example:

>>> import numpy as np
>>> from sklearn import datasets
>>> import pandas as pd
>>>
>>> iris = datasets.load_iris()
>>> features, targets, iris = iris['feature_names'], iris['target_names'], pd.DataFrame(
...     np.c_[iris['data'], iris['target']],
...     columns=iris['feature_names']+['class'])
>>> iris['class'] = iris['class'].map(pd.Series(targets))
>>>
>>> iris.head()
                sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)             0                5.1               3.5                1.4               0.2
1                4.9               3.0                1.4               0.2
2                4.7               3.2                1.3               0.2
3                4.6               3.1                1.5               0.2
4                5.0               3.6                1.4               0.2

    class
0  setosa
1  setosa
2  setosa
3  setosa
4  setosa
>>> from ibex.sklearn import linear_model as pd_linear_model
>>>
>>> clf = pd_linear_model.PassiveAggressiveClassifier().fit(iris[features], iris['class'])
>>>
>>> clf.intercept_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64

Passive Aggressive Classifier

Read more in the User Guide.

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.

New in version 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.

New in version 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))

New in version 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.

New in version 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.

Changed in version 0.19: Deprecated

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.
>>> 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]

SGDClassifier Perceptron

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)

decision_function(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict confidence scores for samples.

The confidence score for a sample is the signed distance of that sample to the hyperplane.

X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.
fit(X, y, coef_init=None, intercept_init=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit linear model with Passive Aggressive algorithm.

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.

self : returns an instance of self.

partial_fit(X, y, classes=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit linear model with Passive Aggressive algorithm.

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.

self : returns an instance of self.

predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Predict class labels for samples in X.

X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Samples.
C : array, shape = [n_samples]
Predicted class label per sample.
score(X, y, sample_weight=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
score : float
Mean accuracy of self.predict(X) wrt. y.