Support Vector Machine
- class snapml.SupportVectorMachine(loss='hinge', max_iter=1000, regularizer=1.0, device_ids=[], verbose=False, use_gpu=False, class_weight=None, n_jobs=1, tol=0.001, generate_training_history=None, fit_intercept=False, intercept_scaling=1.0, normalize=False, kernel='linear', gamma=1.0, n_components=100, random_state=None)
Support Vector Machine classifier
This class implements regularized support vector machine using the IBM Snap ML solver. It supports both local and distributed(MPI) methods of the Snap ML solver. It can be used for both binary and multi-class classification problems. For multi-class classification it predicts classes or the decision function for each class in the model. It handles both dense and sparse matrix inputs. Use csr, ndarray, deviceNDArray or SnapML data partition format for both training and prediction. DeviceNDArray input data format is currently not supported for training with MPI implementation. The training uses the dual formulation. We recommend the user to normalize the input values.
- Parameters
- loss{‘hinge’, ‘squared_hinge’}, default=’hinge’
The loss function that will be used for training.
- max_iterint, default=1000
Maximum number of iterations used by the solver to converge.
- regularizerfloat, default=1.0
Regularization strength. It must be a positive float. Larger regularization values imply stronger regularization.
- use_gpubool, default=False
Flag for indicating the hardware platform used for training. If True, the training is performed using the GPU. If False, the training is performed using the CPU.
- device_idsarray-like of int, default=[]
If use_gpu is True, it indicates the IDs of the GPUs used for training. For single GPU training, set device_ids to the GPU ID to be used for training, e.g., [0]. For multi-GPU training, set device_ids to a list of GPU IDs to be used for training, e.g., [0, 1].
- class_weight{‘balanced’, None}, default=None
If set to ‘None’, all classes will have weight 1.
- verbosebool, default=False
If True, it prints the training cost, one per iteration. Warning: this will increase the training time. For performance evaluation, use verbose=False.
- n_jobsint, default=1
The number of threads used for running the training. The value of this parameter should be a multiple of 32 if the training is performed on GPU (use_gpu=True).
- tolfloat, default=0.001
The tolerance parameter. Training will finish when maximum change in model coefficients is less than tol.
- generate_training_history{‘summary’, ‘full’, None}, default=None
Determines the level of summary statistics that are generated during training. By default no information is generated (None), but this parameter can be set to “summary”, to obtain summary statistics at the end of training, or “full” to obtain a complete set of statistics for the entire training procedure. Note, enabling either option will result in slower training. generate_training_history is not supported for DeviceNDArray input format.
- fit_interceptbool, default=False
Add bias term – note, may affect speed of convergence, especially for sparse datasets.
- intercept_scalingfloat, default=1.0
Scaling of bias term. The inclusion of a bias term is implemented by appending an additional feature to the dataset. This feature has a constant value, that can be set using this parameter.
- normalizebool, default=False
Normalize rows of dataset (recommended for fast convergence).
- kernel{‘rbf’, ‘linear’}, default=’linear’
Approximate feature map of a specified kernel function.
- gammafloat, default=1.0
Parameter of RBF kernel: exp(-gamma * x^2)
- n_componentsint, default=100
Dimensionality of the feature space when approximating a kernel function.
- random_stateint, or None, default=None
If int, random_state is the seed used by the random number generator; If None, the random number generator is the RandomState instance used by np.random.
- Attributes
- coef_array-like, shape (n_features,) for binary classification or
(n_features, n_classes) for multi-class classification. Coefficients of the features in the trained model.
- intercept_: ndarray of shape (1,) if n_classes == 2 else (n_classes,)
Constants in the decision function. If fit_intercept is False, then intercept_ is 0.0. Otherwise it is an ndarray of shape (1,) if n_classes == 2 else (n_classes,)
- support_array-like, shape (n_SV)
indices of the support vectors. Currently not supported for MPI implementation.
- n_support_int
Number of support vectors. Currently not supported for MPI implementation.
- training_history_dict
Training history statistics.
- decision_function(X, n_jobs=None)
Predicts confidence scores.
The confidence score of a sample is the signed distance of that sample to the decision boundary.
- Parameters
- XDataset used for predicting distances to the decision boundary. Supports the following input data-types :
Sparse matrix (csr_matrix, csc_matrix) or dense matrix (ndarray)
SnapML data partition of type DensePartition, SparsePartition or ConstantValueSparsePartition
- n_jobsint, default=None
Number of threads used to run inference. By default the value of the class attribute is used.. This parameter is ignored for decision_function of a single observation.
- Returns
- proba: array-like, shape = (n_samples,) or (n_sample, n_classes)
Returns the distance to the decision boundary of the samples in X.
- fit(X_train, y_train=None)
Fit the model according to the given train dataset.
- Parameters
- X_trainTrain dataset. Supports the following input data-types :
Sparse matrix (csr_matrix, csc_matrix) or dense matrix (ndarray)
DeviceNDArray. Not supported for MPI execution.
SnapML data partition of type DensePartition, SparsePartition or ConstantValueSparsePartition
- y_trainThe target corresponding to X_train.
If X_train is sparse matrix or dense matrix, y_train should be array-like of shape = (n_samples,) In case of deviceNDArray, y_train should be array-like of shape = (n_samples, 1) If X_train is SnapML data partition type, then y_train is not required (i.e. None).
- Returns
- selfobject
- get_params(deep=True)
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- predict(X, n_jobs=None)
Class predictions
The returned class estimates.
- Parameters
- XDataset used for predicting estimates or class. Supports the following input data-types :
Sparse matrix (csr_matrix, csc_matrix) or dense matrix (ndarray)
SnapML data partition of type DensePartition, SparsePartition or ConstantValueSparsePartition
- n_jobsint, default=None
Number of threads used to run inference. By default the value of the class attribute is used.. This parameter is ignored for predict of a single observation.
- Returns
- pred: array-like, shape = (n_samples,)
Returns the predicted estimate/class of the sample.
- score(X, y, sample_weight=None)
Return 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.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns
- scorefloat
Mean accuracy of
self.predict(X)
wrt. y.
- set_params(**params)
Set the parameters of this model.
Valid parameter keys can be listed with
get_params()
.- Returns
- self