再探askl元學習

按照**的說法，landmarking方法因為過於耗時所以不算在元特徵中

exclude_meta_features_classification

out[5]
:exclude_meta_features_regression
out[6]
:

'classentropy'

,'classoccurences'
,'classprobabilitymax'
,'classprobabilitymean'
,'classprobabilitymin'
,'classprobabilitystd'
,

calculate.update(npy_metafeatures)

只有npy_metafeatures需要用datapreprocessor做transform

landmarkrandomnodelearner，

a sk

laskl

askl

計算資料集元特徵的方法其實就放在smbo.py裡面，一共有兩個函式，乙個函式用在計算x,y

x,yx,

y的時候不需要做encode，只獲取general的元特徵。另乙個需要做encode。

# metalearning helpers

def_calculate_metafeatures
(data_feat_type, data_info_task, basename,
x_train, y_train, watcher, logger)
:# == calculate metafeatures
task_name =
'calculatemetafeatures'
watcher.start_task(task_name)
categorical =
[true
if feat_type.lower()in
['categorical'
]else
false
for feat_type in data_feat_type]
exclude_meta_features = exclude_meta_features_classification \
if data_info_task in classification_tasks else exclude_meta_features_regression
if data_info_task in
[multiclass_classification, binary_classification,
multilabel_classification, regression,
multioutput_regression]
:        logger.info(
'start calculating metafeatures for %s'
, basename)
result = calculate_all_metafeatures_with_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]
else
:        result =
none
logger.info(
'metafeatures not calculated'
)    watcher.stop_task(task_name)
logger.info(
'calculating metafeatures (categorical attributes) took %5.2f'
,        watcher.wall_elapsed(task_name)
)return result
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

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