Exemple #1
0
def fetch_predict_estimator(task_type,
                            config,
                            X_train,
                            y_train,
                            weight_balance=0,
                            data_balance=0,
                            combined=False):
    # Build the ML estimator.
    from solnml.components.utils.balancing import get_weights, smote
    _fit_params = {}
    config_dict = config.get_dictionary().copy()
    if weight_balance == 1:
        _init_params, _fit_params = get_weights(y_train, config['estimator'],
                                                None, {}, {})
        for key, val in _init_params.items():
            config_dict[key] = val
    if data_balance == 1:
        X_train, y_train = smote(X_train, y_train)
    if task_type in CLS_TASKS:
        if combined:
            from solnml.utils.combined_evaluator import get_estimator
        else:
            from solnml.components.evaluators.cls_evaluator import get_estimator
    else:
        from solnml.components.evaluators.reg_evaluator import get_estimator
    _, estimator = get_estimator(config_dict)

    estimator.fit(X_train, y_train, **_fit_params)
    return estimator
Exemple #2
0
def fetch_predict_estimator(task_type,
                            estimator_id,
                            config,
                            X_train,
                            y_train,
                            weight_balance=0,
                            data_balance=0):
    # Build the ML estimator.
    from solnml.components.utils.balancing import get_weights, smote
    _fit_params = {}
    config_dict = config.copy()
    if weight_balance == 1:
        _init_params, _fit_params = get_weights(y_train, estimator_id, None,
                                                {}, {})
        for key, val in _init_params.items():
            config_dict[key] = val
    if data_balance == 1:
        X_train, y_train = smote(X_train, y_train)
    if task_type in CLS_TASKS:
        from solnml.components.evaluators.cls_evaluator import get_estimator
    elif task_type in RGS_TASKS:
        from solnml.components.evaluators.rgs_evaluator import get_estimator
    _, estimator = get_estimator(config_dict, estimator_id)

    estimator.fit(X_train, y_train, **_fit_params)
    return estimator
Exemple #3
0
 def get_fit_params(self, y, estimator):
     from solnml.components.utils.balancing import get_weights
     _init_params, _fit_params = get_weights(y, estimator, None, {}, {})
     return _init_params, _fit_params