Source code for evalml.pipelines.components.estimators.regressors.svm_regressor

"""Support Vector Machine Regressor."""
import numpy as np
from sklearn.svm import SVR
from skopt.space import Real

from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.problem_types import ProblemTypes


[docs]class SVMRegressor(Estimator): """Support Vector Machine Regressor. Args: C (float): The regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty. Defaults to 1.0. kernel ({"poly", "rbf", "sigmoid"}): Specifies the kernel type to be used in the algorithm. Defaults to "rbf". gamma ({"scale", "auto"} or float): Kernel coefficient for "rbf", "poly" and "sigmoid". Defaults to "auto". - If gamma='scale' is passed then it uses 1 / (n_features * X.var()) as value of gamma - If "auto" (default), uses 1 / n_features random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "SVM Regressor" hyperparameter_ranges = { "C": Real(0, 10), "kernel": ["poly", "rbf", "sigmoid"], "gamma": ["scale", "auto"], } """{ "C": Real(0, 10), "kernel": ["poly", "rbf", "sigmoid"], "gamma": ["scale", "auto"], }""" model_family = ModelFamily.SVM """ModelFamily.SVM""" supported_problem_types = [ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ] """[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION, ]""" def __init__(self, C=1.0, kernel="rbf", gamma="auto", random_seed=0, **kwargs): parameters = {"C": C, "kernel": kernel, "gamma": gamma} parameters.update(kwargs) # SVR doesn't take a random_state arg svm_regressor = SVR(**parameters) super().__init__( parameters=parameters, component_obj=svm_regressor, random_seed=random_seed ) @property def feature_importance(self): """Feature importance of fitted SVM regresor. Only works with linear kernels. If the kernel isn't linear, we return a numpy array of zeros. Returns: The feature importance of the fitted SVM regressor, or an array of zeroes if the kernel is not linear. """ if self._parameters["kernel"] != "linear": return np.zeros(self._component_obj.n_features_in_) else: return self._component_obj.coef_