"""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_