Source code for evalml.pipelines.components.estimators.classifiers.svm_classifier

"""Support Vector Machine Classifier."""
import numpy as np
from sklearn.svm import SVC
from import Real

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

[docs]class SVMClassifier(Estimator): """Support Vector Machine Classifier. 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 probability (boolean): Whether to enable probability estimates. Defaults to True. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "SVM Classifier" 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.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS, ] """[ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS, ]""" def __init__( self, C=1.0, kernel="rbf", gamma="auto", probability=True, random_seed=0, **kwargs, ): parameters = { "C": C, "kernel": kernel, "gamma": gamma, "probability": probability, } parameters.update(kwargs) svm_classifier = SVC(random_state=random_seed, **parameters) super().__init__( parameters=parameters, component_obj=svm_classifier, random_seed=random_seed, ) @property def feature_importance(self): """Feature importance only works with linear kernels. If the kernel isn't linear, we return a numpy array of zeros. Returns: Feature importance of fitted SVM classifier or a numpy 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_