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

"""Vowpal Wabbit Classifiers."""
from abc import abstractmethod

from skopt.space import Integer, Real

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


[docs]class VowpalWabbitBaseClassifier(Estimator): """Vowpal Wabbit Base Classifier. Args: loss_function (str): Specifies the loss function to use. One of {"squared", "classic", "hinge", "logistic", "quantile"}. Defaults to "logistic". learning_rate (float): Boosting learning rate. Defaults to 0.5. decay_learning_rate (float): Decay factor for learning_rate. Defaults to 1.0. power_t (float): Power on learning rate decay. Defaults to 0.5. passes (int): Number of training passes. Defaults to 1. random_seed (int): Seed for the random number generator. Defaults to 0. """ hyperparameter_ranges = { "loss_function": ["squared", "classic", "hinge", "logistic"], "learning_rate": Real(0.0000001, 10), "decay_learning_rate": Real(0.0000001, 1.0), "power_t": Real(0.01, 1.0), "passes": Integer(1, 10), } """""" model_family = ModelFamily.VOWPAL_WABBIT """ModelFamily.VOWPAL_WABBIT""" _vowpal_wabbit_component = None def __init__( self, loss_function="logistic", learning_rate=0.5, decay_learning_rate=1.0, power_t=0.5, passes=1, random_seed=0, **kwargs, ): parameters = { "loss_function": loss_function, "learning_rate": learning_rate, "decay_learning_rate": decay_learning_rate, "power_t": power_t, "passes": passes, } parameters.update(kwargs) vw_class = self._get_component_obj_class() vw_classifier = vw_class(**parameters) super().__init__( parameters=parameters, component_obj=vw_classifier, random_seed=random_seed ) @abstractmethod def _get_component_obj_class(self): """Get the appropriate Vowpal Wabbit class.""" @property def feature_importance(self): """Feature importance for Vowpal Wabbit classifiers. This is not implemented.""" raise NotImplementedError( "Feature importance is not implemented for the Vowpal Wabbit classifiers." )
[docs]class VowpalWabbitBinaryClassifier(VowpalWabbitBaseClassifier): """Vowpal Wabbit Binary Classifier. Args: loss_function (str): Specifies the loss function to use. One of {"squared", "classic", "hinge", "logistic", "quantile"}. Defaults to "logistic". learning_rate (float): Boosting learning rate. Defaults to 0.5. decay_learning_rate (float): Decay factor for learning_rate. Defaults to 1.0. power_t (float): Power on learning rate decay. Defaults to 0.5. passes (int): Number of training passes. Defaults to 1. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Vowpal Wabbit Binary Classifier" supported_problem_types = [ ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY, ] """[ ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY, ]""" def _get_component_obj_class(self): vw_error_msg = "Vowpal Wabbit is not installed. Please install using `pip install vowpalwabbit.`" vw = import_or_raise("vowpalwabbit", error_msg=vw_error_msg) vw_classifier = vw.sklearn_vw.VWClassifier return vw_classifier
[docs]class VowpalWabbitMulticlassClassifier(VowpalWabbitBaseClassifier): """Vowpal Wabbit Multiclass Classifier. Args: loss_function (str): Specifies the loss function to use. One of {"squared", "classic", "hinge", "logistic", "quantile"}. Defaults to "logistic". learning_rate (float): Boosting learning rate. Defaults to 0.5. decay_learning_rate (float): Decay factor for learning_rate. Defaults to 1.0. power_t (float): Power on learning rate decay. Defaults to 0.5. random_seed (int): Seed for the random number generator. Defaults to 0. """ name = "Vowpal Wabbit Multiclass Classifier" supported_problem_types = [ ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS, ] """[ ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS, ]""" def _get_component_obj_class(self): vw_error_msg = "Vowpal Wabbit is not installed. Please install using `pip install vowpalwabbit.`" vw = import_or_raise("vowpalwabbit.sklearn_vw", error_msg=vw_error_msg) vw_classifier = vw.VWMultiClassifier return vw_classifier