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Tpottemplates

TPOTClassifier

Bases: TPOTEstimator

Source code in tpot2/tpot_estimator/templates/tpottemplates.py 
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class TPOTClassifier(TPOTEstimator):
    def __init__(       self,
                        scorers=['roc_auc_ovr'], 
                        scorers_weights=[1],
                        other_objective_functions=[], #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
                        other_objective_functions_weights = [],
                        objective_function_names = None,
                        bigger_is_better = True,
                        max_size = np.inf, 
                        linear_pipeline = False,
                        root_config_dict= 'Auto',
                        inner_config_dict=["selectors", "transformers"],
                        leaf_config_dict= None,                        
                        cross_val_predict_cv = 0,
                        categorical_features = None,
                        subsets = None,
                        memory = None,
                        preprocessing = False,
                        max_time_seconds=3600, 
                        max_eval_time_seconds=60*10, 
                        n_jobs = 1,
                        validation_strategy = "none",
                        validation_fraction = .2, 
                        early_stop = None,
                        warm_start = False,
                        periodic_checkpoint_folder = None, 
                        verbose = 0,
                        memory_limit = "4GB",
                        client = None

        ):
        """
        See TPOTEstimator for documentation
        """
        super(TPOTClassifier,self).__init__(
                        scorers=scorers, 
                        scorers_weights=scorers_weights,
                        cv = 5,
                        other_objective_functions=other_objective_functions, #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
                        other_objective_functions_weights = other_objective_functions_weights,
                        objective_function_names = objective_function_names,
                        bigger_is_better = bigger_is_better,
                        max_size = max_size, 
                        linear_pipeline = linear_pipeline,
                        root_config_dict = root_config_dict,
                        inner_config_dict=inner_config_dict,
                        leaf_config_dict= leaf_config_dict,                        
                        cross_val_predict_cv = cross_val_predict_cv,
                        categorical_features = categorical_features,
                        subsets = subsets,
                        memory = memory,
                        preprocessing = preprocessing,
                        max_time_seconds=max_time_seconds, 
                        max_eval_time_seconds=max_eval_time_seconds, 
                        n_jobs=n_jobs,
                        validation_strategy = validation_strategy,
                        validation_fraction = validation_fraction, 
                        early_stop = early_stop,
                        warm_start = warm_start,
                        periodic_checkpoint_folder = periodic_checkpoint_folder, 
                        verbose = verbose,
                        classification=True,
                        memory_limit = memory_limit,
                        client = client
        )


    def predict(self, X, **predict_params):
        check_is_fitted(self)
        #X=check_array(X)
        return self.fitted_pipeline_.predict(X,**predict_params)

__init__(scorers=['roc_auc_ovr'], scorers_weights=[1], other_objective_functions=[], other_objective_functions_weights=[], objective_function_names=None, bigger_is_better=True, max_size=np.inf, linear_pipeline=False, root_config_dict='Auto', inner_config_dict=['selectors', 'transformers'], leaf_config_dict=None, cross_val_predict_cv=0, categorical_features=None, subsets=None, memory=None, preprocessing=False, max_time_seconds=3600, max_eval_time_seconds=60 * 10, n_jobs=1, validation_strategy='none', validation_fraction=0.2, early_stop=None, warm_start=False, periodic_checkpoint_folder=None, verbose=0, memory_limit='4GB', client=None)

See TPOTEstimator for documentation

Source code in tpot2/tpot_estimator/templates/tpottemplates.py 
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def __init__(       self,
                    scorers=['roc_auc_ovr'], 
                    scorers_weights=[1],
                    other_objective_functions=[], #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
                    other_objective_functions_weights = [],
                    objective_function_names = None,
                    bigger_is_better = True,
                    max_size = np.inf, 
                    linear_pipeline = False,
                    root_config_dict= 'Auto',
                    inner_config_dict=["selectors", "transformers"],
                    leaf_config_dict= None,                        
                    cross_val_predict_cv = 0,
                    categorical_features = None,
                    subsets = None,
                    memory = None,
                    preprocessing = False,
                    max_time_seconds=3600, 
                    max_eval_time_seconds=60*10, 
                    n_jobs = 1,
                    validation_strategy = "none",
                    validation_fraction = .2, 
                    early_stop = None,
                    warm_start = False,
                    periodic_checkpoint_folder = None, 
                    verbose = 0,
                    memory_limit = "4GB",
                    client = None

    ):
    """
    See TPOTEstimator for documentation
    """
    super(TPOTClassifier,self).__init__(
                    scorers=scorers, 
                    scorers_weights=scorers_weights,
                    cv = 5,
                    other_objective_functions=other_objective_functions, #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
                    other_objective_functions_weights = other_objective_functions_weights,
                    objective_function_names = objective_function_names,
                    bigger_is_better = bigger_is_better,
                    max_size = max_size, 
                    linear_pipeline = linear_pipeline,
                    root_config_dict = root_config_dict,
                    inner_config_dict=inner_config_dict,
                    leaf_config_dict= leaf_config_dict,                        
                    cross_val_predict_cv = cross_val_predict_cv,
                    categorical_features = categorical_features,
                    subsets = subsets,
                    memory = memory,
                    preprocessing = preprocessing,
                    max_time_seconds=max_time_seconds, 
                    max_eval_time_seconds=max_eval_time_seconds, 
                    n_jobs=n_jobs,
                    validation_strategy = validation_strategy,
                    validation_fraction = validation_fraction, 
                    early_stop = early_stop,
                    warm_start = warm_start,
                    periodic_checkpoint_folder = periodic_checkpoint_folder, 
                    verbose = verbose,
                    classification=True,
                    memory_limit = memory_limit,
                    client = client
    )

TPOTRegressor

Bases: TPOTEstimator

Source code in tpot2/tpot_estimator/templates/tpottemplates.py 
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class TPOTRegressor(TPOTEstimator):
    def __init__(       self,
                        scorers=['neg_mean_squared_error'], 
                        scorers_weights=[1],
                        other_objective_functions=[], #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
                        other_objective_functions_weights = [],
                        objective_function_names = None,
                        bigger_is_better = True,
                        max_size = np.inf, 
                        linear_pipeline = False,
                        root_config_dict= 'Auto',
                        inner_config_dict=["selectors", "transformers"],
                        leaf_config_dict= None,                        
                        cross_val_predict_cv = 0,
                        categorical_features = None,
                        subsets = None,
                        memory = None,
                        preprocessing = False,
                        max_time_seconds=3600, 
                        max_eval_time_seconds=60*10, 
                        n_jobs = 1,
                        validation_strategy = "none",
                        validation_fraction = .2, 
                        early_stop = None,
                        warm_start = False,
                        periodic_checkpoint_folder = None, 
                        verbose = 0,
                        memory_limit = "4GB",
                        client = None
        ):
        """
        See TPOTEstimator for documentation
        """
        super(TPOTRegressor,self).__init__(
                        scorers=scorers, 
                        scorers_weights=scorers_weights,
                        cv=5,
                        other_objective_functions=other_objective_functions, #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
                        other_objective_functions_weights = other_objective_functions_weights,
                        objective_function_names = objective_function_names,
                        bigger_is_better = bigger_is_better,
                        max_size = max_size, 
                        linear_pipeline = linear_pipeline,
                        root_config_dict = root_config_dict,
                        inner_config_dict=inner_config_dict,
                        leaf_config_dict= leaf_config_dict,                        
                        cross_val_predict_cv = cross_val_predict_cv,
                        categorical_features = categorical_features,
                        subsets = subsets,
                        memory = memory,
                        preprocessing = preprocessing,
                        max_time_seconds=max_time_seconds, 
                        max_eval_time_seconds=max_eval_time_seconds, 
                        n_jobs=n_jobs,
                        validation_strategy = validation_strategy,
                        validation_fraction = validation_fraction, 
                        early_stop = early_stop,
                        warm_start = warm_start,
                        periodic_checkpoint_folder = periodic_checkpoint_folder, 
                        verbose = verbose,
                        classification=False,
                        memory_limit = memory_limit,
                        client = client
)

__init__(scorers=['neg_mean_squared_error'], scorers_weights=[1], other_objective_functions=[], other_objective_functions_weights=[], objective_function_names=None, bigger_is_better=True, max_size=np.inf, linear_pipeline=False, root_config_dict='Auto', inner_config_dict=['selectors', 'transformers'], leaf_config_dict=None, cross_val_predict_cv=0, categorical_features=None, subsets=None, memory=None, preprocessing=False, max_time_seconds=3600, max_eval_time_seconds=60 * 10, n_jobs=1, validation_strategy='none', validation_fraction=0.2, early_stop=None, warm_start=False, periodic_checkpoint_folder=None, verbose=0, memory_limit='4GB', client=None)

See TPOTEstimator for documentation

Source code in tpot2/tpot_estimator/templates/tpottemplates.py 
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    def __init__(       self,
                        scorers=['neg_mean_squared_error'], 
                        scorers_weights=[1],
                        other_objective_functions=[], #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
                        other_objective_functions_weights = [],
                        objective_function_names = None,
                        bigger_is_better = True,
                        max_size = np.inf, 
                        linear_pipeline = False,
                        root_config_dict= 'Auto',
                        inner_config_dict=["selectors", "transformers"],
                        leaf_config_dict= None,                        
                        cross_val_predict_cv = 0,
                        categorical_features = None,
                        subsets = None,
                        memory = None,
                        preprocessing = False,
                        max_time_seconds=3600, 
                        max_eval_time_seconds=60*10, 
                        n_jobs = 1,
                        validation_strategy = "none",
                        validation_fraction = .2, 
                        early_stop = None,
                        warm_start = False,
                        periodic_checkpoint_folder = None, 
                        verbose = 0,
                        memory_limit = "4GB",
                        client = None
        ):
        """
        See TPOTEstimator for documentation
        """
        super(TPOTRegressor,self).__init__(
                        scorers=scorers, 
                        scorers_weights=scorers_weights,
                        cv=5,
                        other_objective_functions=other_objective_functions, #tpot2.objectives.estimator_objective_functions.number_of_nodes_objective],
                        other_objective_functions_weights = other_objective_functions_weights,
                        objective_function_names = objective_function_names,
                        bigger_is_better = bigger_is_better,
                        max_size = max_size, 
                        linear_pipeline = linear_pipeline,
                        root_config_dict = root_config_dict,
                        inner_config_dict=inner_config_dict,
                        leaf_config_dict= leaf_config_dict,                        
                        cross_val_predict_cv = cross_val_predict_cv,
                        categorical_features = categorical_features,
                        subsets = subsets,
                        memory = memory,
                        preprocessing = preprocessing,
                        max_time_seconds=max_time_seconds, 
                        max_eval_time_seconds=max_eval_time_seconds, 
                        n_jobs=n_jobs,
                        validation_strategy = validation_strategy,
                        validation_fraction = validation_fraction, 
                        early_stop = early_stop,
                        warm_start = warm_start,
                        periodic_checkpoint_folder = periodic_checkpoint_folder, 
                        verbose = verbose,
                        classification=False,
                        memory_limit = memory_limit,
                        client = client
)