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

import numpy as np
from sklearn.svm import SVC
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 SVMClassifier(Estimator): """Support Vector Machine Classifier.""" name = "SVM Classifier" hyperparameter_ranges = { "C": Real(0, 10), "kernel": ["linear", "poly", "rbf", "sigmoid", "precomputed"], "gamma": ["scale", "auto"] } model_family = ModelFamily.SVM supported_problem_types = [ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS]
[docs] def __init__(self, C=1.0, kernel="rbf", gamma="scale", 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 """ if self._parameters['kernel'] != 'linear': return np.zeros(self._component_obj.n_features_in_) else: return self._component_obj.coef_