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Setting median outputs as default. Also minor changes to finetuning. #178

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merged 4 commits into from
Nov 6, 2024
Merged

Setting median outputs as default. Also minor changes to finetuning. #178

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e2f2e68
Merge branch 'master' of https://github.com/google-research/timesfm i…
rajatsen91 Oct 17, 2024
b0cbbbf
Merge branch 'master' of https://github.com/google-research/timesfm i…
rajatsen91 Nov 5, 2024
76141bf
Setting median outputs as default. Also minor changes to finetuning n…
rajatsen91 Nov 5, 2024
be37aba
push poetry build version up
rajatsen91 Nov 5, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/main.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ jobs:
# e.g. poetry version 0.1.${{ github.run_number }}
- name: Set Version number
run: |
poetry version 1.2.1
poetry version 1.2.2
- name: Build and Publish to PyPI
run: |
poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }}
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21 changes: 10 additions & 11 deletions notebooks/finetuning.ipynb
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Expand Up @@ -62,15 +62,14 @@
"outputs": [],
"source": [
"tfm = timesfm.TimesFm(\n",
" context_len=512,\n",
" horizon_len=128,\n",
" input_patch_len=32,\n",
" output_patch_len=128,\n",
" num_layers=20,\n",
" model_dims=1280,\n",
" backend=\"gpu\",\n",
")\n",
"tfm.load_from_checkpoint(repo_id=\"google/timesfm-1.0-200m\")"
" hparams=timesfm.TimesFmHparams(\n",
" backend=\"gpu\",\n",
" per_core_batch_size=32,\n",
" horizon_len=128,\n",
" ),\n",
" checkpoint=timesfm.TimesFmCheckpoint(\n",
" huggingface_repo_id=\"google/timesfm-1.0-200m\"),\n",
" )"
]
},
{
Expand Down Expand Up @@ -209,8 +208,8 @@
"for batch in tqdm(test_batches.as_numpy_iterator()):\n",
" past = batch[0]\n",
" actuals = batch[3]\n",
" _, forecasts = tfm.forecast(list(past), [0] * past.shape[0])\n",
" forecasts = forecasts[:, 0 : actuals.shape[1], 5]\n",
" forecasts, _ = tfm.forecast(list(past), [0] * past.shape[0], normalize=True)\n",
" forecasts = forecasts[:, 0 : actuals.shape[1]]\n",
" mae_losses.append(np.abs(forecasts - actuals).mean())\n",
"\n",
"print(f\"MAE: {np.mean(mae_losses)}\")"
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