LinearSVR#

class cuml.svm.LinearSVR( *, epsilon=0.0, penalty='l1', loss='epsilon_insensitive', C=1.0, fit_intercept=True, penalized_intercept=False, tol=0.0001, max_iter=1000, linesearch_max_iter=100, lbfgs_memory=5, verbose=False, output_type=None, )[source]#

Linear Support Vector Regression.

Similar to SVR with parameter kernel=’linear’, but implemented using a linear solver. This enables flexibility in penalties and loss functions, and can scale better for larger problems.

Parameters:

epsilonfloat, default=0.0: Epsilon parameter in the epsilon-insensitive loss function.
penalty{‘l1’, ‘l2’}, default = ‘l1’: The norm used in the penalization.
loss{‘epsilon_insensitive’, ‘squared_epsilon_insensitive’}, default=’epsilon_insensitive’: The loss function.
Cfloat, default=1.0: Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.
fit_interceptbool, default=True: Whether to fit the bias term. Set to False if you expect that the data is already centered.
penalized_interceptbool, default=False: When true, the bias term is treated the same way as other features; i.e. it’s penalized by the regularization term of the target function. Enabling this feature forces an extra copying the input data X.
tolfloat, default=1e-4: Tolerance for the stopping criterion.
max_iterint, default=1000: Maximum number of iterations for the underlying solver.
linesearch_max_iterint, default=100: Maximum number of linesearch (inner loop) iterations for the underlying (QN) solver.
lbfgs_memoryint, default=5: Number of vectors approximating the hessian for the underlying QN solver (l-bfgs).
verboseint or boolean, default=False: Sets logging level. It must be one of cuml.common.logger.level_*. See Verbosity Levels for more info.
output_type{None, ‘input’, ‘cupy’, ‘numpy’, ‘cudf’, ‘pandas’}, default=None: Return results and set estimator attributes to the indicated output type. If None, the output type set at the module level (cuml.global_settings.output_type) will be used. See Output Data Type Configuration for more info.

Attributes:

coef_array, shape (1, n_features): Weights assigned to the features (coefficients in the primal problem).
intercept_array or float, shape (1,): The constant factor in the decision function. If fit_intercept=False this is instead a float with value 0.0.
n_iter_int: The number of iterations run during the fit.

Methods

fit(X, y[, sample_weight])

Fit the model according to the given training data.

Notes

The model uses the quasi-newton (QN) solver to find the solution in the primal space. Thus, in contrast to generic SVC model, it does not compute the support coefficients/vectors.

Check the solver’s documentation for more details Quasi-Newton (L-BFGS/OWL-QN).

For additional docs, see scikitlearn’s LinearSVR.

Examples

>>> import cupy as cp
>>> from cuml.svm import LinearSVR
>>> X = cp.array([[1], [2], [3], [4], [5]], dtype=cp.float32)
>>> y = cp.array([1.1, 4, 5, 3.9, 8.], dtype=cp.float32)
>>> reg = LinearSVR(epsilon=0.1, C=10).fit(X, y)
>>> print("Predicted values:", reg.predict(X))
Predicted values: [1.8993504 3.3995128 4.899675  6.399837  7.899999]

fit( X, y, sample_weight=None, ) → LinearSVR[source]#

Fit the model according to the given training data.

Parameters:

Xarray-like (device or host) shape = (n_samples, n_features): Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.
yarray-like (device or host) shape = (n_samples, 1): Dense matrix with dtype float32 or float64. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.
sample_weightarray-like (device or host) shape = (n_samples,), default=None: The weights for each observation in X. If None, all observations are assigned equal weight. Acceptable formats: CUDA array interface compliant objects like CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas DataFrame/Series.