dasf.ml.svm

Init module for SVM ML algorithms.

Submodules

• dasf.ml.svm.svm

Classes

SVC	C-Support Vector Classification.
SVR	Epsilon-Support Vector Regression.
LinearSVC	Linear Support Vector Classification.
LinearSVR	Linear Support Vector Regression.

Package Contents

class dasf.ml.svm.SVC(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, nochange_steps=1000, random_state=None)

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

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 or other kernel_approximation.

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.

To learn how to tune SVC’s hyperparameters, see the following example: sphx_glr_auto_examples_model_selection_plot_nested_cross_validation_iris.py

Parameters

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. For an intuitive visualization of the effects of scaling the regularization parameter C, see sphx_glr_auto_examples_svm_plot_svm_scale_c.py.

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). For an intuitive visualization of different kernel types see sphx_glr_auto_examples_svm_plot_svm_kernels.py.

degreeint, default=3

Degree of the polynomial kernel function (‘poly’). Must be non-negative. 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

• if float, must be non-negative.

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, note that internally, one-vs-one (‘ovo’) is always used as a multi-class strategy to train models; an ovr matrix is only constructed from the ovo matrix. The parameter is ignored for binary classification.

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.

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.

Attributes

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.

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.

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

Number of iterations run by the optimization routine to fit the model. The shape of this attribute depends on the number of models optimized which in turn depends on the number of classes.

support_ndarray of shape (n_SV)

Indices of support vectors.

support_vectors_ndarray of shape (n_SV, n_features)

Support vectors. An empty array if kernel is precomputed.

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.

__svc_cpu

_fit_cpu(X, y, sample_weight=None)

Respective immediate fit mocked function for local CPU(s).

_fit_gpu(X, y, sample_weight=None)

Respective immediate fit mocked function for local GPU(s).

_predict_cpu(X)

Respective immediate predict mocked function for local CPU(s).

_predict_gpu(X)

Respective immediate predict mocked function for local GPU(s).

_get_params_cpu(deep=True)

Respective immediate get_params mocked function for local CPU(s).

_set_params_cpu(**params)

Respective immediate set_params mocked function for local CPU(s).

class dasf.ml.svm.SVR(kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1, nochange_steps=1000)

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict

Epsilon-Support Vector Regression.

The free parameters in the model are C and epsilon.

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 datasets with more than a couple of 10000 samples. For large datasets consider using LinearSVR or SGDRegressor instead, possibly after a Nystroem transformer or other kernel_approximation.

Read more in the User Guide.

Parameters

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’). Must be non-negative. 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

• if float, must be non-negative.

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’.

tolfloat, default=1e-3

Tolerance for stopping criterion.

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. For an intuitive visualization of the effects of scaling the regularization parameter C, see sphx_glr_auto_examples_svm_plot_svm_scale_c.py.

epsilonfloat, default=0.1

Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value. Must be non-negative.

shrinkingbool, default=True

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

cache_sizefloat, default=200

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

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.

Attributes

coef_ndarray of shape (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 readonly property derived from dual_coef_ and support_vectors_.

dual_coef_ndarray of shape (1, n_SV)

Coefficients of the support vector in the decision function.

fit_status_int

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

intercept_ndarray of shape (1,)

Constants in decision function.

n_features_in_int

Number of features seen during fit.

Added 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.

Added in version 1.0.

n_iter_int

Number of iterations run by the optimization routine to fit the model.

Added in version 1.1.

n_support_ndarray of shape (1,), dtype=int32

Number of support vectors.

shape_fit_tuple of int of shape (n_dimensions_of_X,)

Array dimensions of training vector X.

support_ndarray of shape (n_SV,)

Indices of support vectors.

support_vectors_ndarray of shape (n_SV, n_features)

Support vectors.

Constructor of the class SVR.

__svr_cpu

_fit_cpu(X, y, sample_weight=None)

Respective immediate fit mocked function for local CPU(s).

_fit_gpu(X, y, sample_weight=None)

Respective immediate fit mocked function for local GPU(s).

_predict_cpu(X)

Respective immediate predict mocked function for local CPU(s).

_predict_gpu(X)

Respective immediate predict mocked function for local GPU(s).

class dasf.ml.svm.LinearSVC(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr')

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict, dasf.transforms.base.GetParams, dasf.transforms.base.SetParams

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.

The main differences between LinearSVC and SVC lie in the loss function used by default, and in the handling of intercept regularization between those two implementations.

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.

Parameters

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.

dual“auto” or bool, default=”auto”

Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features. dual=”auto” will choose the value of the parameter automatically, based on the values of n_samples, n_features, loss, multi_class and penalty. If n_samples < n_features and optimizer supports chosen loss, multi_class and penalty, then dual will be set to True, otherwise it will be set to False.

Changed in version 1.3: The “auto” option is added in version 1.3 and will be the default in version 1.5.

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. For an intuitive visualization of the effects of scaling the regularization parameter C, see sphx_glr_auto_examples_svm_plot_svm_scale_c.py.

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 or not to fit an intercept. If set to True, the feature vector is extended to include an intercept term: [x_1, …, x_n, 1], where 1 corresponds to the intercept. If set to False, no intercept will be used in calculations (i.e. data is expected to be already centered).

intercept_scalingfloat, default=1.0

When fit_intercept is True, the instance vector x becomes [x_1, ..., x_n, intercept_scaling], i.e. a “synthetic” feature with a constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight. Note that liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the intercept_scaling parameter can be set to a value greater than 1; the higher the value of intercept_scaling, the lower the impact of regularization on it. Then, the weights become [w_x_1, …, w_x_n, w_intercept*intercept_scaling], where w_x_1, …, w_x_n represent the feature weights and the intercept weight is scaled by intercept_scaling. This scaling allows the intercept term to have a different regularization behavior compared to the other features.

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.

Attributes

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.

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.

n_iter_int

Maximum number of iterations run across all classes.

Constructor of the class LinearSVC.

__linear_svc_cpu

_fit_cpu(X, y, sample_weight=None)

Fit the model according to the given training data using CPU only.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target vector relative to X.

sample_weightarray-like of shape (n_samples,), default=None

Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

Returns

selfobject

An instance of the estimator.

_fit_gpu(X, y, sample_weight=None)

Fit the model according to the given training data using GPU only.

Parameters

X{array-like, sparse matrix} of shape (n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target vector relative to X.

sample_weightarray-like of shape (n_samples,), default=None

Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

Returns

selfobject

An instance of the estimator.

_predict_cpu(X)

Respective immediate predict mocked function for local CPU(s).

_predict_gpu(X)

Respective immediate predict mocked function for local GPU(s).

class dasf.ml.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, max_iter=1000, handle=None, penalty='l2', penalized_intercept=False, linesearch_max_iter=100, lbfgs_memory=5, grad_tol=0.0001, change_tol=1e-05, multi_class='ovr')

Bases: dasf.transforms.base.Fit, dasf.transforms.base.Predict

Linear Support Vector Regression.

Similar to SVR 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.

The main differences between LinearSVR and SVR lie in the loss function used by default, and in the handling of intercept regularization between those two implementations.

This class supports both dense and sparse input.

Read more in the User Guide.

Parameters

epsilonfloat, default=0.0

Epsilon parameter in the epsilon-insensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set epsilon=0.

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.

loss{‘epsilon_insensitive’, ‘squared_epsilon_insensitive’}, default=’epsilon_insensitive’

Specifies the loss function. The epsilon-insensitive loss (standard SVR) is the L1 loss, while the squared epsilon-insensitive loss (‘squared_epsilon_insensitive’) is the L2 loss.

fit_interceptbool, default=True

Whether or not to fit an intercept. If set to True, the feature vector is extended to include an intercept term: [x_1, …, x_n, 1], where 1 corresponds to the intercept. If set to False, no intercept will be used in calculations (i.e. data is expected to be already centered).

intercept_scalingfloat, default=1.0

When fit_intercept is True, the instance vector x becomes [x_1, …, x_n, intercept_scaling], i.e. a “synthetic” feature with a constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight. Note that liblinear internally penalizes the intercept, treating it like any other term in the feature vector. To reduce the impact of the regularization on the intercept, the intercept_scaling parameter can be set to a value greater than 1; the higher the value of intercept_scaling, the lower the impact of regularization on it. Then, the weights become [w_x_1, …, w_x_n, w_intercept*intercept_scaling], where w_x_1, …, w_x_n represent the feature weights and the intercept weight is scaled by intercept_scaling. This scaling allows the intercept term to have a different regularization behavior compared to the other features.

dual“auto” or bool, default=”auto”

Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features. dual=”auto” will choose the value of the parameter automatically, based on the values of n_samples, n_features and loss. If n_samples < n_features and optimizer supports chosen loss, then dual will be set to True, otherwise it will be set to False.

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. Pass an int for reproducible output across multiple function calls. See Glossary.

max_iterint, default=1000

The maximum number of iterations to be run.

Attributes

coef_ndarray of shape (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.

n_features_in_int

Number of features seen during fit.

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.

n_iter_int

Maximum number of iterations run across all classes.

Constructor of the class LinearSVR.

__linear_svr_cpu

_fit_cpu(X, y, sample_weight=None)

Respective immediate fit mocked function for local CPU(s).

_fit_gpu(X, y, sample_weight=None)

Respective immediate fit mocked function for local GPU(s).

_predict_cpu(X)

Respective immediate predict mocked function for local CPU(s).

_predict_gpu(X)

Respective immediate predict mocked function for local GPU(s).