vowpal_wabbit_classifiers ====================================================================================== .. py:module:: evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers .. autoapi-nested-parse:: Vowpal Wabbit Classifiers. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: VowpalWabbitBaseClassifier(loss_function='logistic', learning_rate=0.5, decay_learning_rate=1.0, power_t=0.5, passes=1, random_seed=0, **kwargs) Vowpal Wabbit Base Classifier. :param loss_function: Specifies the loss function to use. One of {"squared", "classic", "hinge", "logistic", "quantile"}. Defaults to "logistic". :type loss_function: str :param learning_rate: Boosting learning rate. Defaults to 0.5. :type learning_rate: float :param decay_learning_rate: Decay factor for learning_rate. Defaults to 1.0. :type decay_learning_rate: float :param power_t: Power on learning rate decay. Defaults to 0.5. :type power_t: float :param passes: Number of training passes. Defaults to 1. :type passes: int :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **hyperparameter_ranges** - None * - **model_family** - ModelFamily.VOWPAL_WABBIT * - **modifies_features** - True * - **modifies_target** - False * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.clone evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.default_parameters evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.describe evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.feature_importance evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.fit evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.load evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.name evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.needs_fitting evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.parameters evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.predict evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.predict_proba evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.save evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBaseClassifier.supported_problem_types .. py:method:: clone(self) Constructs a new component with the same parameters and random state. :returns: A new instance of this component with identical parameters and random state. .. py:method:: default_parameters(cls) Returns the default parameters for this component. Our convention is that Component.default_parameters == Component().parameters. :returns: Default parameters for this component. :rtype: dict .. py:method:: describe(self, print_name=False, return_dict=False) Describe a component and its parameters. :param print_name: whether to print name of component :type print_name: bool, optional :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters} :type return_dict: bool, optional :returns: Returns dictionary if return_dict is True, else None. :rtype: None or dict .. py:method:: feature_importance(self) :property: Feature importance for Vowpal Wabbit classifiers. This is not implemented. .. py:method:: fit(self, X, y=None) Fits estimator to data. :param X: The input training data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: The target training data of length [n_samples]. :type y: pd.Series, optional :returns: self .. py:method:: load(file_path) :staticmethod: Loads component at file path. :param file_path: Location to load file. :type file_path: str :returns: ComponentBase object .. py:method:: name(cls) :property: Returns string name of this component. .. py:method:: needs_fitting(self) Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances. This can be overridden to False for components that do not need to be fit or whose fit methods do nothing. :returns: True. .. py:method:: parameters(self) :property: Returns the parameters which were used to initialize the component. .. py:method:: predict(self, X) Make predictions using selected features. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict method or a component_obj that implements predict. .. py:method:: predict_proba(self, X) Make probability estimates for labels. :param X: Features. :type X: pd.DataFrame :returns: Probability estimates. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict_proba method or a component_obj that implements predict_proba. .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL) Saves component at file path. :param file_path: Location to save file. :type file_path: str :param pickle_protocol: The pickle data stream format. :type pickle_protocol: int .. py:method:: supported_problem_types(cls) :property: Problem types this estimator supports. .. py:class:: VowpalWabbitBinaryClassifier(loss_function='logistic', learning_rate=0.5, decay_learning_rate=1.0, power_t=0.5, passes=1, random_seed=0, **kwargs) Vowpal Wabbit Binary Classifier. :param loss_function: Specifies the loss function to use. One of {"squared", "classic", "hinge", "logistic", "quantile"}. Defaults to "logistic". :type loss_function: str :param learning_rate: Boosting learning rate. Defaults to 0.5. :type learning_rate: float :param decay_learning_rate: Decay factor for learning_rate. Defaults to 1.0. :type decay_learning_rate: float :param power_t: Power on learning rate decay. Defaults to 0.5. :type power_t: float :param passes: Number of training passes. Defaults to 1. :type passes: int :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **hyperparameter_ranges** - None * - **model_family** - ModelFamily.VOWPAL_WABBIT * - **modifies_features** - True * - **modifies_target** - False * - **name** - Vowpal Wabbit Binary Classifier * - **supported_problem_types** - [ ProblemTypes.BINARY, ProblemTypes.TIME_SERIES_BINARY,] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.clone evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.default_parameters evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.describe evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.feature_importance evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.fit evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.load evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.needs_fitting evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.parameters evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.predict evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.predict_proba evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitBinaryClassifier.save .. py:method:: clone(self) Constructs a new component with the same parameters and random state. :returns: A new instance of this component with identical parameters and random state. .. py:method:: default_parameters(cls) Returns the default parameters for this component. Our convention is that Component.default_parameters == Component().parameters. :returns: Default parameters for this component. :rtype: dict .. py:method:: describe(self, print_name=False, return_dict=False) Describe a component and its parameters. :param print_name: whether to print name of component :type print_name: bool, optional :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters} :type return_dict: bool, optional :returns: Returns dictionary if return_dict is True, else None. :rtype: None or dict .. py:method:: feature_importance(self) :property: Feature importance for Vowpal Wabbit classifiers. This is not implemented. .. py:method:: fit(self, X, y=None) Fits estimator to data. :param X: The input training data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: The target training data of length [n_samples]. :type y: pd.Series, optional :returns: self .. py:method:: load(file_path) :staticmethod: Loads component at file path. :param file_path: Location to load file. :type file_path: str :returns: ComponentBase object .. py:method:: needs_fitting(self) Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances. This can be overridden to False for components that do not need to be fit or whose fit methods do nothing. :returns: True. .. py:method:: parameters(self) :property: Returns the parameters which were used to initialize the component. .. py:method:: predict(self, X) Make predictions using selected features. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict method or a component_obj that implements predict. .. py:method:: predict_proba(self, X) Make probability estimates for labels. :param X: Features. :type X: pd.DataFrame :returns: Probability estimates. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict_proba method or a component_obj that implements predict_proba. .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL) Saves component at file path. :param file_path: Location to save file. :type file_path: str :param pickle_protocol: The pickle data stream format. :type pickle_protocol: int .. py:class:: VowpalWabbitMulticlassClassifier(loss_function='logistic', learning_rate=0.5, decay_learning_rate=1.0, power_t=0.5, passes=1, random_seed=0, **kwargs) Vowpal Wabbit Multiclass Classifier. :param loss_function: Specifies the loss function to use. One of {"squared", "classic", "hinge", "logistic", "quantile"}. Defaults to "logistic". :type loss_function: str :param learning_rate: Boosting learning rate. Defaults to 0.5. :type learning_rate: float :param decay_learning_rate: Decay factor for learning_rate. Defaults to 1.0. :type decay_learning_rate: float :param power_t: Power on learning rate decay. Defaults to 0.5. :type power_t: float :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **hyperparameter_ranges** - None * - **model_family** - ModelFamily.VOWPAL_WABBIT * - **modifies_features** - True * - **modifies_target** - False * - **name** - Vowpal Wabbit Multiclass Classifier * - **supported_problem_types** - [ ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS,] * - **training_only** - False **Methods** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.clone evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.default_parameters evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.describe evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.feature_importance evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.fit evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.load evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.needs_fitting evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.parameters evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.predict evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.predict_proba evalml.pipelines.components.estimators.classifiers.vowpal_wabbit_classifiers.VowpalWabbitMulticlassClassifier.save .. py:method:: clone(self) Constructs a new component with the same parameters and random state. :returns: A new instance of this component with identical parameters and random state. .. py:method:: default_parameters(cls) Returns the default parameters for this component. Our convention is that Component.default_parameters == Component().parameters. :returns: Default parameters for this component. :rtype: dict .. py:method:: describe(self, print_name=False, return_dict=False) Describe a component and its parameters. :param print_name: whether to print name of component :type print_name: bool, optional :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters} :type return_dict: bool, optional :returns: Returns dictionary if return_dict is True, else None. :rtype: None or dict .. py:method:: feature_importance(self) :property: Feature importance for Vowpal Wabbit classifiers. This is not implemented. .. py:method:: fit(self, X, y=None) Fits estimator to data. :param X: The input training data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: The target training data of length [n_samples]. :type y: pd.Series, optional :returns: self .. py:method:: load(file_path) :staticmethod: Loads component at file path. :param file_path: Location to load file. :type file_path: str :returns: ComponentBase object .. py:method:: needs_fitting(self) Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances. This can be overridden to False for components that do not need to be fit or whose fit methods do nothing. :returns: True. .. py:method:: parameters(self) :property: Returns the parameters which were used to initialize the component. .. py:method:: predict(self, X) Make predictions using selected features. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict method or a component_obj that implements predict. .. py:method:: predict_proba(self, X) Make probability estimates for labels. :param X: Features. :type X: pd.DataFrame :returns: Probability estimates. :rtype: pd.Series :raises MethodPropertyNotFoundError: If estimator does not have a predict_proba method or a component_obj that implements predict_proba. .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL) Saves component at file path. :param file_path: Location to save file. :type file_path: str :param pickle_protocol: The pickle data stream format. :type pickle_protocol: int