单机并行使用

Auto-sklearn 使用 dask.distributed <https://distributed.dask.org.cn/en/latest/index.html>_ 进行并行优化。

本示例展示了如何在单机上启动 Auto-sklearn 以使用多核。在这种模式下，Auto-sklearn 启动一个 dask 集群，管理工作进程并在计算完成后负责关闭集群。要在多台机器上运行 Auto-sklearn，请查看示例 并行使用：从命令行生成工作进程。

import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics

import autosklearn.classification

数据加载

X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
    X, y, random_state=1
)

构建和拟合分类器

为了使用 n_jobs_，我们必须保护代码

if __name__ == "__main__":

    automl = autosklearn.classification.AutoSklearnClassifier(
        time_left_for_this_task=120,
        per_run_time_limit=30,
        tmp_folder="/tmp/autosklearn_parallel_1_example_tmp",
        n_jobs=4,
        # Each one of the 4 jobs is allocated 3GB
        memory_limit=3072,
        seed=5,
    )
    automl.fit(X_train, y_train, dataset_name="breast_cancer")

    # Print statistics about the auto-sklearn run such as number of
    # iterations, number of models failed with a time out.
    print(automl.sprint_statistics())

auto-sklearn results:
  Dataset name: breast_cancer
  Metric: accuracy
  Best validation score: 0.985816
  Number of target algorithm runs: 43
  Number of successful target algorithm runs: 43
  Number of crashed target algorithm runs: 0
  Number of target algorithms that exceeded the time limit: 0
  Number of target algorithms that exceeded the memory limit: 0