快速开始¶
本章介绍HyperGBM主要功能，假设您已经知道机器学习的基本知识（加载数据、模型训练、预测、评估等），如果您还没安装请参照安装文档来安装HyperGBM。 您可以通过Python API和命令行工具来使用HyperGBM。
通过API训练模型¶
本节将使用数据Bank Marketing 演示如何使用HyperGBM训练一个二分类模型。
使用tabular_toolbox提供的工具类来读取Bank Marketing数据集：
>>> from tabular_toolbox.datasets import dsutils
>>> df = dsutils.load_bank()
>>> df[:3]
id age job marital education default balance housing loan contact day month duration campaign pdays previous poutcome y
0 0 30 unemployed married primary no 1787 no no cellular 19 oct 79 1 -1 0 unknown no
1 1 33 services married secondary no 4789 yes yes cellular 11 may 220 1 339 4 failure no
2 2 35 management single tertiary no 1350 yes no cellular 16 apr 185 1 330 1 failure no
接着将数据拆分为训练集和测试集，分别用来训练模型和验证最终模型的效果：
>>> from sklearn.model_selection import train_test_split
>>> y = df.pop('y') # target col is "y"
>>> X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.3, random_state=9527)
HyperGBM提供了多种搜索策略，这里使用随机搜索策略在内置的搜索空间里训练：
>>> from hypernets.searchers import RandomSearcher
>>> from hypernets.core import OptimizeDirection
>>> from hypergbm.search_space import search_space_general
>>> rs = RandomSearcher(space_fn=search_space_general,
... optimize_direction=OptimizeDirection.Maximize)
>>> rs
<hypernets.searchers.random_searcher.RandomSearcher object at 0x10e5b9850>
参数space_fn用来指定搜索空间，search_space_general 是内置的搜索空间；
参数optimize_direction 用来指定优化的方向，训练模型时，对于二分类任务使用auc指标，这里设置为OptimizeDirection.Maximize表示该指标的值越大越好。
接着使用Experiment接口来训练模型：
>>> from hypergbm import HyperGBM, CompeteExperiment
>>> hk = HyperGBM(rs, reward_metric='auc', cache_dir=f'hypergbm_cache', callbacks=[])
>>> experiment = CompeteExperiment(hk, X_train, y_train, X_test=X_test)
19:19:31 I hypergbm.experiment.py 714 - create experiment with ['data_clean', 'drift_detected', 'base_search_and_train']
>>> pipeline = experiment.run(use_cache=True, max_trials=2)
...
Trial No. Reward Elapsed Space Vector
0 1 0.994731 8.490173 [2, 0, 1, 2, 0, 0, 0]
1 2 0.983054 4.980630 [1, 2, 1, 2, 215, 3, 0, 0, 4, 3]
>>> pipeline
Pipeline(steps=[('data_clean',
DataCleanStep(data_cleaner_args={}, name='data_clean',
random_state=9527)),
('drift_detected', DriftDetectStep(name='drift_detected')),
('base_search_and_train',
BaseSearchAndTrainStep(name='base_search_and_train',
scorer=make_scorer(log_loss, greater_is_better=False, needs_proba=True))),
('estimator',
<tabular_toolbox.ensemble.voting.GreedyEnsemble object at 0x1a24ca00d0>)])
训练实验结束后我们来用测试集评估一下效果：
>>> y_proba = pipeline.predict_proba(X_test)
>>> metrics.roc_auc_score(y_test, y_proba[:, 1])
0.9956872713648863
通过命令行训练模型¶
HyperGBM也提供了命令行工具来训练模型和预测数据，查看命令行帮助：
hypergm -h
usage: hypergbm [-h] --train_file TRAIN_FILE [--eval_file EVAL_FILE]
[--eval_size EVAL_SIZE] [--test_file TEST_FILE] --target
TARGET [--pos_label POS_LABEL] [--max_trials MAX_TRIALS]
[--model_output MODEL_OUTPUT]
[--prediction_output PREDICTION_OUTPUT] [--searcher SEARCHER]
...
同样以训练数据Bank Marketing为例子，我们先将数据集拆分成训练集和测试集并生成csv文件：
>>> from tabular_toolbox.datasets import dsutils
>>> from sklearn.model_selection import train_test_split
>>> df = dsutils.load_bank()
>>> df_train, df_test = train_test_split(df, test_size=0.3, random_state=9527)
>>> df_train.to_csv('bank_train.csv', index=None)
>>> df_test.to_csv('bank_test.csv', index=None)
将生成的csv文件作为训练命令参数，执行命令：
hypergbm --train_file=bank_train.csv --test_file=bank_test.csv --target=y --pos_label=yes --model_output=model.pkl prediction_output=bank_predict.csv
...
Trial No. Reward Elapsed Space Vector
0 10 1.000000 64.206514 [0, 0, 1, 3, 2, 1, 2, 1, 2, 2, 3]
1 7 0.999990 2.433192 [1, 1, 1, 2, 215, 0, 2, 3, 0, 4]
2 4 0.999950 37.057761 [0, 3, 1, 0, 2, 1, 3, 1, 3, 4, 3]
3 9 0.967292 9.977973 [1, 0, 1, 1, 485, 2, 2, 5, 3, 0]
4 1 0.965844 4.304114 [1, 2, 1, 1, 60, 2, 2, 5, 0, 1]
训练结束后模型会保持到model.pkl文件，对测试集的预测结果会保存到bank_predict.csv中。