example/example.py
from atm import ATM
atm = ATM()
results = atm.run(train_path="/home/tqc/PycharmProjects/automl/ATM/demos/pollution_1.csv")
results.describe()
atm.worker.Worker#select_hyperpartition
调试打印的信息和论文描述的一致,超划分hyperpartition
表示条件参数树从root到leaf的一个路径
>>> pprint(hyperpartitions)
[<dt: [('criterion', 'entropy')]>,
<dt: [('criterion', 'gini')]>,
<knn: [('weights', 'uniform'), ('algorithm', 'ball_tree'), ('metric', 'minkowski')]>,
<knn: [('weights', 'uniform'), ('algorithm', 'ball_tree'), ('metric', 'euclidean')]>,...]
观察这个打印信息,会发现
>>> hyperpartitions[0].categoricals
[('criterion', 'entropy')]
>>> pprint(hyperpartitions[0].tunables)
[('max_features',
<btb.hyper_parameter.FloatHyperParameter object at 0x7fd946ae83c8>),
('max_depth',
<btb.hyper_parameter.IntHyperParameter object at 0x7fd946ae82e8>),
('min_samples_split',
<btb.hyper_parameter.IntHyperParameter object at 0x7fd946ae8e80>),
('min_samples_leaf',
<btb.hyper_parameter.IntHyperParameter object at 0x7fd946ae8f28>)]
超划分的作用就是从一个支离破碎的结构空间中取一个连续N维空间,从而使GP可以在这个空间中发挥作用。
btb.selection.uniform.Uniform#select
atm.worker.Worker#select_hyperpartition
atm.worker.Worker#run_classifier
hyperpartition = self.select_hyperpartition()
随机选择一个超划分。貌似在进行MAB
pprint(params)
{'_scale': True,
'algorithm': 'kd_tree',
'leaf_size': 38,
'metric': 'chebyshev',
'n_neighbors': 13,
'weights': 'uniform'}
atm.database.Database#start_classifier
将超参实例化为分类器对象
classifier = self.Classifier(hyperpartition_id=hyperpartition_id,
datarun_id=datarun_id,
host=host,
hyperparameter_values=hyperparameter_values,
start_time=datetime.now(),
status=ClassifierStatus.RUNNING)
又是个阴间代码
atm/database.py:382
目测是在用ORM操作数据库
model, metrics = self.test_classifier(hyperpartition.method, params)
>>> model.pipeline
Pipeline(memory=None,
steps=[('standard_scale',
StandardScaler(copy=True, with_mean=True, with_std=True)),
('knn',
KNeighborsClassifier(algorithm='ball_tree', leaf_size=20,
metric='euclidean', metric_params=None,
n_jobs=None, n_neighbors=16, p=2,
weights='distance'))],
verbose=False)
>>> metrics
{'cv': [{'accuracy': 1.0, 'cohen_kappa': 1.0, 'f1': 1.0, 'mcc': 1.0, 'roc_auc': 1.0, 'ap': 1.0}, ...
感觉总体流程也就这样
selector
和tuner
默认为uniform
的随机搜索
selector (str):
Type of selector to use. Optional. Defaults to ``'uniform'``.
tuner (str):
Type of tuner to use. Optional. Defaults to ``'uniform'``.