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Chronos: make roll=True the default value for to_torch_data_loader (#5605)

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binbin Deng 2022-09-06 09:28:03 +08:00 committed by GitHub
parent 2f1f3ba114
commit 01ec6107f8
2 changed files with 4 additions and 4 deletions
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4
docs/readthedocs/source/doc/Chronos/Howto/how_to_train_forecaster_on_one_node.ipynb
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@ -108,7 +108,7 @@
"train_data = tsdata_train\n", "train_data = tsdata_train\n",
"\n", "\n",
"# uncomment this line to change `train_data` as pytorch dataloader\n", "# uncomment this line to change `train_data` as pytorch dataloader\n",
"# train_data = tsdata_train.to_torch_data_loader(roll=True, lookback=48, horizon=1)\n", "# train_data = tsdata_train.to_torch_data_loader(lookback=48, horizon=1)\n",
"\n", "\n",
"# uncomment this line to change `train_data` as numpy ndarray\n", "# uncomment this line to change `train_data` as numpy ndarray\n",
"# train_data = tsdata_train.roll(lookback=48, horizon=1).to_numpy()" "# train_data = tsdata_train.roll(lookback=48, horizon=1).to_numpy()"
@ -185,7 +185,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# comment this line if you want to try other formats\n", "# comment this line if you want to try other formats\n",
"val_data = tsdata_val.to_torch_data_loader(roll=True, lookback=48, horizon=1)\n", "val_data = tsdata_val.to_torch_data_loader(lookback=48, horizon=1)\n",
"\n", "\n",
"# uncomment this line to change `val_data` as numpy ndarray\n", "# uncomment this line to change `val_data` as numpy ndarray\n",
"# val_data = tsdata_val.roll(lookback=48, horizon=1).to_numpy()" "# val_data = tsdata_val.roll(lookback=48, horizon=1).to_numpy()"

4
docs/readthedocs/source/doc/Chronos/Overview/data_processing_feature_engineering.md
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@ -189,9 +189,9 @@ Please refer to the API doc [`roll`](../../PythonAPI/Chronos/tsdataset.html#bigd
.. note:: .. note::
**Difference between `roll` and `to_torch_data_loader`**: **Difference between `roll` and `to_torch_data_loader`**:
`.roll(...)` performs the rolling before RR forecasters/auto models training while `.to_torch_data_loader(roll=True, ...)` performs rolling during the training. `.roll(...)` performs the rolling before RR forecasters/auto models training while `.to_torch_data_loader(...)` performs rolling during the training.
It is fine to use either of them when you have a relatively small dataset (less than 1G). `.to_torch_data_loader(roll=True, ...)` is recommended when you have a large dataset (larger than 1G) to save memory usage. It is fine to use either of them when you have a relatively small dataset (less than 1G). `.to_torch_data_loader(...)` is recommended when you have a large dataset (larger than 1G) to save memory usage.
``` ```
```eval_rst ```eval_rst