【发布时间】:2021-02-05 04:18:39
【问题描述】:
neuraxle documentation 中有一个示例,使用存储库在管道中延迟加载数据,请参见以下代码:
from neuraxle.pipeline import Pipeline, MiniBatchSequentialPipeline
from neuraxle.base import ExecutionContext
from neuraxle.steps.column_transformer import ColumnTransformer
from neuraxle.steps.flow import TrainOnlyWrapper
training_data_ids = training_data_repository.get_all_ids()
context = ExecutionContext('caching_folder').set_service_locator({
BaseRepository: training_data_repository
})
pipeline = Pipeline([
ConvertIDsToLoadedData().assert_has_services(BaseRepository),
ColumnTransformer([
(range(0, 2), DateToCosineEncoder()),
(3, CategoricalEnum(categeories_count=5, starts_at_zero=True)),
]),
Normalizer(),
TrainOnlyWrapper(DataShuffler()),
MiniBatchSequentialPipeline([
Model()
], batch_size=128)
]).with_context(context)
但是,没有显示如何实现BaseRepository 和ConvertIDsToLoadedData 类。实现这些类的最佳方法是什么?谁能举个例子?
【问题讨论】:
标签: python machine-learning neuraxle