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Chronos: Update doc to illustrate deploy mode in TSDataset (#6871)

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* update doc to illustrate deploy mode

* fix

* revise howto guide
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Hu, Zhaojie 2022-12-13 09:44:30 +08:00 committed by GitHub
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docs/readthedocs/source/doc/Chronos/Overview/data_processing_feature_engineering.md
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@ -80,6 +80,8 @@ You can initialize a [`XShardsTSDataset`](../../PythonAPI/Chronos/tsdataset.html
If you are building a prototype for your forecasting/anomaly detection task and you need to split you TSDataset to train/valid/test set, you can use `with_split` parameter.[`TSDataset`](../../PythonAPI/Chronos/tsdataset.html) or [`XShardsTSDataset`](../../PythonAPI/Chronos/tsdataset.html#xshardstsdataset) supports split with ratio by `val_ratio` and `test_ratio`. If you are building a prototype for your forecasting/anomaly detection task and you need to split you TSDataset to train/valid/test set, you can use `with_split` parameter.[`TSDataset`](../../PythonAPI/Chronos/tsdataset.html) or [`XShardsTSDataset`](../../PythonAPI/Chronos/tsdataset.html#xshardstsdataset) supports split with ratio by `val_ratio` and `test_ratio`.
If you are deploying your model in production environment, you can use `deploy_mode` parameter and specify it to `True` when calling `TSDataset.from_pandas`, `TSDataset.from_parquet` or `TSDataset.from_prometheus`, which will reduce data processing latency and set necessary parameters for data processing and feature engineering.
## 3. Time series dataset preprocessing ## 3. Time series dataset preprocessing
[`TSDataset`](../../PythonAPI/Chronos/tsdataset.html) supports [`impute`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.impute), [`deduplicate`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.deduplicate) and [`resample`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.resample). You may fill the missing point by [`impute`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.impute) in different modes. You may remove the records that are totally the same by [`deduplicate`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.deduplicate). You may change the sample frequency by [`resample`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.resample). [`XShardsTSDataset`](../../PythonAPI/Chronos/tsdataset.html#xshardstsdataset) only supports [`impute`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.experimental.xshards_tsdataset.XShardsTSDataset.impute) for now. [`TSDataset`](../../PythonAPI/Chronos/tsdataset.html) supports [`impute`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.impute), [`deduplicate`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.deduplicate) and [`resample`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.resample). You may fill the missing point by [`impute`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.impute) in different modes. You may remove the records that are totally the same by [`deduplicate`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.deduplicate). You may change the sample frequency by [`resample`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.tsdataset.TSDataset.resample). [`XShardsTSDataset`](../../PythonAPI/Chronos/tsdataset.html#xshardstsdataset) only supports [`impute`](../../PythonAPI/Chronos/tsdataset.html#bigdl.chronos.data.experimental.xshards_tsdataset.XShardsTSDataset.impute) for now.