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Simplify pretraining README snippet #160

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Jul 24, 2024
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Simplify pretraining README snippet #160

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23 changes: 15 additions & 8 deletions scripts/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@
- Convert your time series dataset into a GluonTS-compatible file dataset. We recommend using the arrow format. You may use the `convert_to_arrow` function from the following snippet for that. Optionally, you may use [synthetic data from KernelSynth](#generating-synthetic-time-series-kernelsynth) to follow along.
```py
from pathlib import Path
from typing import List, Optional, Union
from typing import List, Union

import numpy as np
from gluonts.dataset.arrow import ArrowWriter
Expand All @@ -35,18 +35,26 @@
def convert_to_arrow(
path: Union[str, Path],
time_series: Union[List[np.ndarray], np.ndarray],
start_times: Optional[Union[List[np.datetime64], np.ndarray]] = None,
compression: str = "lz4",
):
if start_times is None:
# Set an arbitrary start time
start_times = [np.datetime64("2000-01-01 00:00", "s")] * len(time_series)
"""
Store a given set of series into Arrow format at the specified path.

Input data can be either a list of 1D numpy arrays, or a single 2D
numpy array of shape (num_series, time_length).
"""
assert isinstance(time_series, list) or (
isinstance(time_series, np.ndarray) and
time_series.ndim == 2
)

assert len(time_series) == len(start_times)
# Set an arbitrary start time
start = np.datetime64("2000-01-01 00:00", "s")

dataset = [
{"start": start, "target": ts} for ts, start in zip(time_series, start_times)
{"start": start, "target": ts} for ts in time_series
]

ArrowWriter(compression=compression).write_to_file(
dataset,
path=path,
Expand All @@ -59,7 +67,6 @@

# Convert to GluonTS arrow format
convert_to_arrow("./noise-data.arrow", time_series=time_series)

```
- Modify the [training configs](training/configs) to use your data. Let's use the KernelSynth data as an example.
```yaml
Expand Down