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_