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, convert_dtype])

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, *, convert_dtype='deprecated', ) → 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.
convert_dtypebool, optional (default = ‘deprecated’): Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.