Python calculate_all_metafeatures_encoded_labelsの例

コード例 #1

ファイル: smbo.py プロジェクト: tangypnuaa/auto-sklearn

def _calculate_metafeatures_encoded(data_feat_type, basename, x_train, y_train,
                                    watcher, task, logger):
    EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \
        if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION

    task_name = 'CalculateMetafeaturesEncoded'
    watcher.start_task(task_name)
    categorical = [
        True if feat_type.lower() in ['categorical'] else False
        for feat_type in data_feat_type
    ]

    result = calculate_all_metafeatures_encoded_labels(
        x_train,
        y_train,
        categorical=categorical,
        dataset_name=basename,
        dont_calculate=EXCLUDE_META_FEATURES)
    for key in list(result.metafeature_values.keys()):
        if result.metafeature_values[key].type_ != 'METAFEATURE':
            del result.metafeature_values[key]
    watcher.stop_task(task_name)
    logger.info('Calculating Metafeatures (encoded attributes) took %5.2fsec',
                watcher.wall_elapsed(task_name))
    return result

コード例 #2

def _calculate_metafeatures_encoded(data_feat_type, basename, x_train, y_train,
                                    watcher, task, logger_):
    with warnings.catch_warnings():
        warnings.showwarning = get_send_warnings_to_logger(logger_)

        EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \
            if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION

        task_name = 'CalculateMetafeaturesEncoded'
        watcher.start_task(task_name)
        categorical = {
            col: True if feat_type.lower() == 'categorical' else False
            for col, feat_type in data_feat_type.items()
        }

        result = calculate_all_metafeatures_encoded_labels(
            x_train,
            y_train,
            categorical=categorical,
            dataset_name=basename,
            dont_calculate=EXCLUDE_META_FEATURES,
            logger=logger_)
        for key in list(result.metafeature_values.keys()):
            if result.metafeature_values[key].type_ != 'METAFEATURE':
                del result.metafeature_values[key]
        watcher.stop_task(task_name)
        logger_.info(
            'Calculating Metafeatures (encoded attributes) took %5.2fsec',
            watcher.wall_elapsed(task_name))
        return result

コード例 #3

ファイル: mismbo.py プロジェクト: Mahgoobi/auto-sklearn

def calc_meta_features_encoded(X_train, Y_train, categorical, dataset_name):
    """
    Calculate meta features with encoded labels
    :param X_train:
    :param Y_train:
    :param categorical:
    :param dataset_name:
    :return:
    """
    if np.sum(categorical) != 0:
        raise ValueError("Training matrix doesn't look OneHotEncoded!")

    return calculate_all_metafeatures_encoded_labels(
        X_train, Y_train, categorical, dataset_name + SENTINEL,
        dont_calculate=EXCLUDE_META_FUTURES)

コード例 #4

ファイル: autosklearn.py プロジェクト: zhongbinEDEN/mosaic_ml

def _calculate_metafeatures_encoded__(basename, x_train, y_train):
    EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \
        if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION

    task_name = 'CalculateMetafeaturesEncoded'
    result = calculate_all_metafeatures_encoded_labels(
        x_train,
        y_train,
        categorical=[False] * x_train.shape[1],
        dataset_name=basename,
        dont_calculate=EXCLUDE_META_FEATURES)
    for key in list(result.metafeature_values.keys()):
        if result.metafeature_values[key].type_ != 'METAFEATURE':
            del result.metafeature_values[key]

    return result

コード例 #5

ファイル: mismbo.py プロジェクト: wanjinchang/auto-sklearn

def calc_meta_features_encoded(X_train, Y_train, categorical, dataset_name):
    """
    Calculate meta features with encoded labels
    :param X_train:
    :param Y_train:
    :param categorical:
    :param dataset_name:
    :return:
    """
    if np.sum(categorical) != 0:
        raise ValueError("Training matrix doesn't look OneHotEncoded!")

    return calculate_all_metafeatures_encoded_labels(
        X_train,
        Y_train,
        categorical,
        dataset_name + SENTINEL,
        dont_calculate=EXCLUDE_META_FUTURES)

コード例 #6

ファイル: smbo.py プロジェクト: Bryan-LL/auto-sklearn

def _calculate_metafeatures_encoded(basename, x_train, y_train, watcher,
                                    task, logger):
    EXCLUDE_META_FEATURES = EXCLUDE_META_FEATURES_CLASSIFICATION \
        if task in CLASSIFICATION_TASKS else EXCLUDE_META_FEATURES_REGRESSION

    task_name = 'CalculateMetafeaturesEncoded'
    watcher.start_task(task_name)
    result = calculate_all_metafeatures_encoded_labels(
        x_train, y_train, categorical=[False] * x_train.shape[1],
        dataset_name=basename, dont_calculate=EXCLUDE_META_FEATURES)
    for key in list(result.metafeature_values.keys()):
        if result.metafeature_values[key].type_ != 'METAFEATURE':
            del result.metafeature_values[key]
    watcher.stop_task(task_name)
    logger.info(
        'Calculating Metafeatures (encoded attributes) took %5.2fsec',
        watcher.wall_elapsed(task_name))
    return result

コード例 #7

ファイル: update.py プロジェクト: dragonfly90/auto-sklearn

print(type(res))
print(len(res))

from autosklearn.metalearning.metafeatures.metafeatures import \
    calculate_all_metafeatures_with_labels, \
    calculate_all_metafeatures_encoded_labels, subsets
    
X_train, Y_train, X_test, Y_test = get_dataset(dataset_name)
print(Y_train)
categorical = [False] * X_train.shape[1]

meta_features_label = calculate_all_metafeatures_with_labels(
                    X_train, Y_train, categorical, dataset_name)
print(meta_features_label)

meta_features_encoded_label = calculate_all_metafeatures_encoded_labels(
                    X_train, Y_train, categorical, dataset_name)
print(meta_features_encoded_label)
#configuration_space = get_configuration_space(
#                {
#                 'metric': metric,
#                 'task': task,
#                 'is_sparse': False
#                },

#include_preprocessors=['no_preprocessing'])
#X_train, Y_train, X_test, Y_test = get_dataset(dataset_name)
#categorical = [False] * X_train.shape[1]
#categorical = [False] * X_train.shape[1]

#meta_features_label = calc_meta_features(X_train, y_train, categorical, 'a', REGRESSION)
#meta_features_encoded_label = calc_meta_features_encoded(X_train, y_train, categorical, 'b', task)