# SVC¶

class ibex.sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None)

Bases: sklearn.svm.classes.SVC, 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
>>>
>>> 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))
>>>
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 svm as pd_svm
>>>
>>> clf =  pd_svm.SVC(kernel='linear').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
>>>
>>> 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))
>>>
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 svm as pd_svm
>>>
>>> clf = pd_svm.SVC(kernel='linear').fit(iris[features], iris['class'])
>>>
>>> clf.intercept_
sepal length (cm)   ...
sepal width (cm)    ...
petal length (cm)   ...
petal width (cm)    ...
dtype: float64


C-Support Vector Classification.

The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than a couple of 10000 samples.

The multiclass support is handled according to a one-vs-one scheme.

For details on the precise mathematical formulation of the provided kernel functions and how gamma, coef0 and degree affect each other, see the corresponding section in the narrative documentation: Kernel functions.

Read more in the User Guide.

C : float, optional (default=1.0)
Penalty parameter C of the error term.
kernel : string, optional (default=’rbf’)
Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape (n_samples, n_samples).
degree : int, optional (default=3)
Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
gamma : float, optional (default=’auto’)
Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
coef0 : float, optional (default=0.0)
Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
probability : boolean, optional (default=False)
Whether to enable probability estimates. This must be enabled prior to calling fit, and will slow down that method.
shrinking : boolean, optional (default=True)
Whether to use the shrinking heuristic.
tol : float, optional (default=1e-3)
Tolerance for stopping criterion.
cache_size : float, optional
Specify the size of the kernel cache (in MB).
class_weight : {dict, ‘balanced’}, optional
Set the parameter C of class i to class_weight[i]*C for SVC. 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))
verbose : bool, default: False
Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
max_iter : int, optional (default=-1)
Hard limit on iterations within solver, or -1 for no limit.
decision_function_shape : ‘ovo’, ‘ovr’, default=’ovr’

Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2).

Changed in version 0.19: decision_function_shape is ‘ovr’ by default.

New in version 0.17: decision_function_shape=’ovr’ is recommended.

Changed in version 0.17: Deprecated decision_function_shape=’ovo’ and None.

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.
support_ : array-like, shape = [n_SV]
Indices of support vectors.
support_vectors_ : array-like, shape = [n_SV, n_features]
Support vectors.
n_support_ : array-like, dtype=int32, shape = [n_class]
Number of support vectors for each class.
dual_coef_ : array, shape = [n_class-1, n_SV]
Coefficients of the support vector in the decision function. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the section about multi-class classification in the SVM section of the User Guide for details.
coef_ : array, shape = [n_class-1, n_features]

Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.

coef_ is a readonly property derived from dual_coef_ and support_vectors_.

intercept_ : array, shape = [n_class * (n_class-1) / 2]
Constants in decision function.
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import SVC
>>> clf = SVC()
>>> clf.fit(X, y)
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
>>> print(clf.predict([[-0.8, -1]]))
[1]

SVR
Support Vector Machine for Regression implemented using libsvm.
LinearSVC
Scalable Linear Support Vector Machine for classification implemented using liblinear. Check the See also section of LinearSVC for more comparison element.
decision_function(X)

Note

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

Distance of the samples X to the separating hyperplane.

X : array-like, shape (n_samples, n_features)

X : array-like, shape (n_samples, n_classes * (n_classes-1) / 2)
Returns the decision function of the sample for each class in the model. If decision_function_shape=’ovr’, the shape is (n_samples, n_classes)
fit(X, y, sample_weight=None)

Note

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

Fit the SVM model according to the given training data.

X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).
y : array-like, shape (n_samples,)
Target values (class labels in classification, real numbers in regression)
sample_weight : array-like, shape (n_samples,)
Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.
self : object
Returns self.

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.

predict(X)

Note

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

Perform classification on samples in X.

For an one-class model, +1 or -1 is returned.

X : {array-like, sparse matrix}, shape (n_samples, n_features)
For kernel=”precomputed”, the expected shape of X is [n_samples_test, n_samples_train]
y_pred : array, shape (n_samples,)
Class labels for samples in X.
predict_log_proba()

Note

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

Compute log probabilities of possible outcomes for samples in X.

The model need to have probability information computed at training time: fit with attribute probability set to True.

X : array-like, shape (n_samples, n_features)
For kernel=”precomputed”, the expected shape of X is [n_samples_test, n_samples_train]
T : array-like, shape (n_samples, n_classes)
Returns the log-probabilities of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.

predict_proba()

Note

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

Compute probabilities of possible outcomes for samples in X.

The model need to have probability information computed at training time: fit with attribute probability set to True.

X : array-like, shape (n_samples, n_features)
For kernel=”precomputed”, the expected shape of X is [n_samples_test, n_samples_train]
T : array-like, shape (n_samples, n_classes)
Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.

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.