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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
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:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
predict
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
-
score
(X, y, sample_weight=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
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.
- A parameter
- A parameter