simpleml.models.classifiers.sklearn.svm

Wrapper module around sklearn.svm

Module Contents

Classes

SklearnLinearSVC	No different than base model. Here just to maintain the pattern
SklearnNuSVC	No different than base model. Here just to maintain the pattern
SklearnSVC	No different than base model. Here just to maintain the pattern
WrappedSklearnLinearSVC	Linear Support Vector Classification.
WrappedSklearnNuSVC	Nu-Support Vector Classification.
WrappedSklearnSVC	C-Support Vector Classification.

Attributes

LOGGER	Support Vectors
__author__

simpleml.models.classifiers.sklearn.svm.LOGGER

Support Vectors

simpleml.models.classifiers.sklearn.svm.__author__ = Elisha Yadgaran

class simpleml.models.classifiers.sklearn.svm.SklearnLinearSVC(has_external_files=True, external_model_kwargs=None, params=None, fitted=False, pipeline_id=None, **kwargs)

Bases: simpleml.models.classifiers.sklearn.base_sklearn_classifier.SklearnClassifier

No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)

Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors

Two supported patterns - full initialization in constructor or stepwise configured before fit and save

Parameters

• has_external_files (bool) –

• external_model_kwargs (Optional[Dict[str, Any]]) –

• params (Optional[Dict[str, Any]]) –

• fitted (bool) –

• pipeline_id (Optional[Union[str, uuid.uuid4]]) –

_create_external_model(self, **kwargs)

Abstract method for each subclass to implement

should return the desired model object

class simpleml.models.classifiers.sklearn.svm.SklearnNuSVC(has_external_files=True, external_model_kwargs=None, params=None, fitted=False, pipeline_id=None, **kwargs)

Bases: simpleml.models.classifiers.sklearn.base_sklearn_classifier.SklearnClassifier

No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)

Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors

Two supported patterns - full initialization in constructor or stepwise configured before fit and save

Parameters

• has_external_files (bool) –

• external_model_kwargs (Optional[Dict[str, Any]]) –

• params (Optional[Dict[str, Any]]) –

• fitted (bool) –

• pipeline_id (Optional[Union[str, uuid.uuid4]]) –

_create_external_model(self, **kwargs)

Abstract method for each subclass to implement

should return the desired model object

class simpleml.models.classifiers.sklearn.svm.SklearnSVC(has_external_files=True, external_model_kwargs=None, params=None, fitted=False, pipeline_id=None, **kwargs)

Bases: simpleml.models.classifiers.sklearn.base_sklearn_classifier.SklearnClassifier

No different than base model. Here just to maintain the pattern Generic Base -> Library Base -> Domain Base -> Individual Models (ex: [Library]Model -> SklearnModel -> SklearnClassifier -> SklearnLogisticRegression)

Need to explicitly separate passthrough kwargs to external models since most do not support arbitrary **kwargs in the constructors

Two supported patterns - full initialization in constructor or stepwise configured before fit and save

Parameters

• has_external_files (bool) –

• external_model_kwargs (Optional[Dict[str, Any]]) –

• params (Optional[Dict[str, Any]]) –

• fitted (bool) –

• pipeline_id (Optional[Union[str, uuid.uuid4]]) –

_create_external_model(self, **kwargs)

Abstract method for each subclass to implement

should return the desired model object

class simpleml.models.classifiers.sklearn.svm.WrappedSklearnLinearSVC(penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)

Bases: sklearn.svm.LinearSVC, simpleml.models.classifiers.external_models.ClassificationExternalModelMixin

Linear Support Vector Classification.

Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.

This class supports both dense and sparse input and the multiclass support is handled according to a one-vs-the-rest scheme.

Read more in the User Guide.

penalty{‘l1’, ‘l2’}, default=’l2’

Specifies the norm used in the penalization. The ‘l2’ penalty is the standard used in SVC. The ‘l1’ leads to coef_ vectors that are sparse.

loss{‘hinge’, ‘squared_hinge’}, default=’squared_hinge’

Specifies the loss function. ‘hinge’ is the standard SVM loss (used e.g. by the SVC class) while ‘squared_hinge’ is the square of the hinge loss. The combination of penalty='l1' and loss='hinge' is not supported.

dualbool, default=True

Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features.

tolfloat, default=1e-4

Tolerance for stopping criteria.

Cfloat, default=1.0

Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.

multi_class{‘ovr’, ‘crammer_singer’}, default=’ovr’

Determines the multi-class strategy if y contains more than two classes. "ovr" trains n_classes one-vs-rest classifiers, while "crammer_singer" optimizes a joint objective over all classes. While crammer_singer is interesting from a theoretical perspective as it is consistent, it is seldom used in practice as it rarely leads to better accuracy and is more expensive to compute. If "crammer_singer" is chosen, the options loss, penalty and dual will be ignored.

fit_interceptbool, default=True

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered).

intercept_scalingfloat, default=1

When self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.

class_weightdict or ‘balanced’, default=None

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)).

verboseint, default=0

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.

random_stateint, RandomState instance or None, default=None

Controls the pseudo random number generation for shuffling the data for the dual coordinate descent (if dual=True). When dual=False the underlying implementation of LinearSVC is not random and random_state has no effect on the results. Pass an int for reproducible output across multiple function calls. See Glossary.

max_iterint, default=1000

The maximum number of iterations to be run.

coef_ndarray of shape (1, n_features) if n_classes == 2 else (n_classes, n_features)

Weights assigned to the features (coefficients in the primal problem).

coef_ is a readonly property derived from raw_coef_ that follows the internal memory layout of liblinear.

intercept_ndarray of shape (1,) if n_classes == 2 else (n_classes,)

Constants in decision function.

classesndarray of shape (n_classes,)

The unique classes labels.

n_features_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

n_iter_int

Maximum number of iterations run across all classes.

SVCImplementation of Support Vector Machine classifier using libsvm:

the kernel can be non-linear but its SMO algorithm does not scale to large number of samples as LinearSVC does.

Furthermore SVC multi-class mode is implemented using one vs one scheme while LinearSVC uses one vs the rest. It is possible to implement one vs the rest with SVC by using the OneVsRestClassifier wrapper.

Finally SVC can fit dense data without memory copy if the input is C-contiguous. Sparse data will still incur memory copy though.

sklearn.linear_model.SGDClassifierSGDClassifier can optimize the same

cost function as LinearSVC by adjusting the penalty and loss parameters. In addition it requires less memory, allows incremental (online) learning, and implements various loss functions and regularization regimes.

The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon to have slightly different results for the same input data. If that happens, try with a smaller tol parameter.

The underlying implementation, liblinear, uses a sparse internal representation for the data that will incur a memory copy.

Predict output may not match that of standalone liblinear in certain cases. See differences from liblinear in the narrative documentation.

LIBLINEAR: A Library for Large Linear Classification

>>> from sklearn.svm import LinearSVC
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_features=4, random_state=0)
>>> clf = make_pipeline(StandardScaler(),
...                     LinearSVC(random_state=0, tol=1e-5))
>>> clf.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('linearsvc', LinearSVC(random_state=0, tol=1e-05))])

>>> print(clf.named_steps['linearsvc'].coef_)
[[0.141...   0.526... 0.679... 0.493...]]

>>> print(clf.named_steps['linearsvc'].intercept_)
[0.1693...]
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]

get_feature_metadata(self, features, **kwargs)

By default nothing is implemented

class simpleml.models.classifiers.sklearn.svm.WrappedSklearnNuSVC(*, nu=0.5, kernel='rbf', degree=3, gamma='scale', 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', break_ties=False, random_state=None)

Bases: sklearn.svm.NuSVC, simpleml.models.classifiers.external_models.ClassificationExternalModelMixin

Nu-Support Vector Classification.

Similar to SVC but uses a parameter to control the number of support vectors.

The implementation is based on libsvm.

Read more in the User Guide.

nufloat, default=0.5

An upper bound on the fraction of margin errors (see User Guide) and a lower bound of the fraction of support vectors. Should be in the interval (0, 1].

kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’

Specifies the kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used. If a callable is given it is used to precompute the kernel matrix.

degreeint, default=3

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gamma{‘scale’, ‘auto’} or float, default=’scale’

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

• if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,

• if ‘auto’, uses 1 / n_features.

Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.

coef0float, default=0.0

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

shrinkingbool, default=True

Whether to use the shrinking heuristic. See the User Guide.

probabilitybool, default=False

Whether to enable probability estimates. This must be enabled prior to calling fit, will slow down that method as it internally uses 5-fold cross-validation, and predict_proba may be inconsistent with predict. Read more in the User Guide.

tolfloat, default=1e-3

Tolerance for stopping criterion.

cache_sizefloat, default=200

Specify the size of the kernel cache (in MB).

class_weight{dict, ‘balanced’}, default=None

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 as n_samples / (n_classes * np.bincount(y)).

verbosebool, 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_iterint, 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). However, one-vs-one (‘ovo’) is always used as multi-class strategy. The parameter is ignored for binary classification.

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.

break_tiesbool, default=False

If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.

New in version 0.22.

random_stateint, RandomState instance or None, default=None

Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

class_weight_ndarray of shape (n_classes,)

Multipliers of parameter C of each class. Computed based on the class_weight parameter.

classesndarray of shape (n_classes,)

The unique classes labels.

coef_ndarray of shape (n_classes * (n_classes -1) / 2, 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 readonly property derived from dual_coef_ and support_vectors_.

dual_coef_ndarray of shape (n_classes - 1, n_SV)

Dual coefficients of the support vector in the decision function (see sgd_mathematical_formulation), multiplied by their targets. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the multi-class section of the User Guide for details.

fit_status_int

0 if correctly fitted, 1 if the algorithm did not converge.

intercept_ndarray of shape (n_classes * (n_classes - 1) / 2,)

Constants in decision function.

n_features_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

support_ndarray of shape (n_SV,)

Indices of support vectors.

support_vectors_ndarray of shape (n_SV, n_features)

Support vectors.

n_support_ndarray of shape (n_classes,), dtype=int32

Number of support vectors for each class.

fit_status_int

0 if correctly fitted, 1 if the algorithm did not converge.

probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2,) probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2,)

If probability=True, it corresponds to the parameters learned in Platt scaling to produce probability estimates from decision values. If probability=False, it’s an empty array. Platt scaling uses the logistic function 1 / (1 + exp(decision_value * probA_ + probB_)) where probA_ and probB_ are learned from the dataset [2]_. For more information on the multiclass case and training procedure see section 8 of [1]_.

shape_fit_tuple of int of shape (n_dimensions_of_X,)

Array dimensions of training vector X.

SVC : Support Vector Machine for classification using libsvm.

LinearSVCScalable linear Support Vector Machine for classification using

liblinear.

1

LIBSVM: A Library for Support Vector Machines

2

Platt, John (1999). “Probabilistic outputs for support vector machines and comparison to regularizedlikelihood methods.”

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.svm import NuSVC
>>> clf = make_pipeline(StandardScaler(), NuSVC())
>>> clf.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()), ('nusvc', NuSVC())])
>>> print(clf.predict([[-0.8, -1]]))
[1]

get_feature_metadata(self, features, **kwargs)

By default nothing is implemented

class simpleml.models.classifiers.sklearn.svm.WrappedSklearnSVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', 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', break_ties=False, random_state=None)

Bases: sklearn.svm.SVC, simpleml.models.classifiers.external_models.ClassificationExternalModelMixin

C-Support Vector Classification.

The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples. For large datasets consider using LinearSVC or SGDClassifier instead, possibly after a Nystroem transformer.

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: svm_kernels.

Read more in the User Guide.

Cfloat, default=1.0

Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.

kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’

Specifies the kernel type to be used in the algorithm. 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).

degreeint, default=3

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gamma{‘scale’, ‘auto’} or float, default=’scale’

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

• if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,

• if ‘auto’, uses 1 / n_features.

Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.

coef0float, default=0.0

Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

shrinkingbool, default=True

Whether to use the shrinking heuristic. See the User Guide.

probabilitybool, default=False

Whether to enable probability estimates. This must be enabled prior to calling fit, will slow down that method as it internally uses 5-fold cross-validation, and predict_proba may be inconsistent with predict. Read more in the User Guide.

tolfloat, default=1e-3

Tolerance for stopping criterion.

cache_sizefloat, default=200

Specify the size of the kernel cache (in MB).

class_weightdict or ‘balanced’, default=None

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)).

verbosebool, 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_iterint, 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). However, one-vs-one (‘ovo’) is always used as multi-class strategy. The parameter is ignored for binary classification.

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.

break_tiesbool, default=False

If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.

New in version 0.22.

random_stateint, RandomState instance or None, default=None

Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

class_weight_ndarray of shape (n_classes,)

Multipliers of parameter C for each class. Computed based on the class_weight parameter.

classesndarray of shape (n_classes,)

The classes labels.

coef_ndarray of shape (n_classes * (n_classes - 1) / 2, 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_.

dual_coef_ndarray of shape (n_classes -1, n_SV)

Dual coefficients of the support vector in the decision function (see sgd_mathematical_formulation), multiplied by their targets. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the multi-class section of the User Guide for details.

fit_status_int

0 if correctly fitted, 1 otherwise (will raise warning)

intercept_ndarray of shape (n_classes * (n_classes - 1) / 2,)

Constants in decision function.

n_features_in_int

Number of features seen during fit.

New in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

support_ndarray of shape (n_SV)

Indices of support vectors.

support_vectors_ndarray of shape (n_SV, n_features)

Support vectors.

n_support_ndarray of shape (n_classes,), dtype=int32

Number of support vectors for each class.

probA_ : ndarray of shape (n_classes * (n_classes - 1) / 2) probB_ : ndarray of shape (n_classes * (n_classes - 1) / 2)

If probability=True, it corresponds to the parameters learned in Platt scaling to produce probability estimates from decision values. If probability=False, it’s an empty array. Platt scaling uses the logistic function 1 / (1 + exp(decision_value * probA_ + probB_)) where probA_ and probB_ are learned from the dataset [2]_. For more information on the multiclass case and training procedure see section 8 of [1]_.

shape_fit_tuple of int of shape (n_dimensions_of_X,)

Array dimensions of training vector X.

SVR : Support Vector Machine for Regression implemented using libsvm.

LinearSVCScalable Linear Support Vector Machine for classification

implemented using liblinear. Check the See Also section of LinearSVC for more comparison element.

1

LIBSVM: A Library for Support Vector Machines

2

Platt, John (1999). “Probabilistic outputs for support vector machines and comparison to regularizedlikelihood methods.”

>>> import numpy as np
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import SVC
>>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
>>> clf.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('svc', SVC(gamma='auto'))])

>>> print(clf.predict([[-0.8, -1]]))
[1]

get_feature_metadata(self, features, **kwargs)

By default nothing is implemented