Merge pull request #178 from google-research/rajat_dev
Setting median outputs as default. Also minor changes to finetuning.
This commit is contained in:
Yichen Zhou
@@ -23,7 +23,7 @@ jobs:
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# e.g. poetry version 0.1.${{ github.run_number }}
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- name: Set Version number
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run: |
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poetry version 1.2.1
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poetry version 1.2.2
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- name: Build and Publish to PyPI
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run: |
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poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }}
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+10
-11
@@ -62,15 +62,14 @@
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"outputs": [],
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"source": [
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"tfm = timesfm.TimesFm(\n",
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" context_len=512,\n",
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" horizon_len=128,\n",
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" input_patch_len=32,\n",
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" output_patch_len=128,\n",
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" num_layers=20,\n",
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" model_dims=1280,\n",
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" backend=\"gpu\",\n",
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")\n",
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"tfm.load_from_checkpoint(repo_id=\"google/timesfm-1.0-200m\")"
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" hparams=timesfm.TimesFmHparams(\n",
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" backend=\"gpu\",\n",
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" per_core_batch_size=32,\n",
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" horizon_len=128,\n",
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" ),\n",
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" checkpoint=timesfm.TimesFmCheckpoint(\n",
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" huggingface_repo_id=\"google/timesfm-1.0-200m\"),\n",
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" )"
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]
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},
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{
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@@ -209,8 +208,8 @@
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"for batch in tqdm(test_batches.as_numpy_iterator()):\n",
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" past = batch[0]\n",
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" actuals = batch[3]\n",
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" _, forecasts = tfm.forecast(list(past), [0] * past.shape[0])\n",
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" forecasts = forecasts[:, 0 : actuals.shape[1], 5]\n",
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" forecasts, _ = tfm.forecast(list(past), [0] * past.shape[0], normalize=True)\n",
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" forecasts = forecasts[:, 0 : actuals.shape[1]]\n",
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" mae_losses.append(np.abs(forecasts - actuals).mean())\n",
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"\n",
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"print(f\"MAE: {np.mean(mae_losses)}\")"
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Generated
+391
-378
@@ -247,21 +247,20 @@ lxml = ["lxml"]
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[[package]]
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name = "bleach"
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version = "6.1.0"
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version = "6.2.0"
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description = "An easy safelist-based HTML-sanitizing tool."
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optional = false
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python-versions = ">=3.8"
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python-versions = ">=3.9"
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files = [
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{file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
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{file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
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{file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
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{file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
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]
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[package.dependencies]
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six = ">=1.9.0"
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webencodings = "*"
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[package.extras]
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css = ["tinycss2 (>=1.1.0,<1.3)"]
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css = ["tinycss2 (>=1.1.0,<1.5)"]
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[[package]]
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name = "cachetools"
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@@ -1162,13 +1161,13 @@ files = [
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[[package]]
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name = "fsspec"
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version = "2024.9.0"
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version = "2024.10.0"
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description = "File-system specification"
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "fsspec-2024.9.0-py3-none-any.whl", hash = "sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b"},
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{file = "fsspec-2024.9.0.tar.gz", hash = "sha256:4b0afb90c2f21832df142f292649035d80b421f60a9e1c027802e5a0da2b04e8"},
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{file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
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{file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
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]
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[package.extras]
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@@ -1374,70 +1373,70 @@ test = ["coverage", "mock (>=4)", "pytest (>=7)", "pytest-cov", "pytest-mock (>=
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[[package]]
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name = "grpcio"
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version = "1.67.0"
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version = "1.67.1"
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description = "HTTP/2-based RPC framework"
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "grpcio-1.67.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:bd79929b3bb96b54df1296cd3bf4d2b770bd1df6c2bdf549b49bab286b925cdc"},
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{file = "grpcio-1.67.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:16724ffc956ea42967f5758c2f043faef43cb7e48a51948ab593570570d1e68b"},
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{file = "grpcio-1.67.0-cp310-cp310-manylinux_2_17_aarch64.whl", hash = "sha256:2b7183c80b602b0ad816315d66f2fb7887614ead950416d60913a9a71c12560d"},
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{file = "grpcio-1.67.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:efe32b45dd6d118f5ea2e5deaed417d8a14976325c93812dd831908522b402c9"},
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{file = "grpcio-1.67.0-cp310-cp310-win32.whl", hash = "sha256:a6b9a5c18863fd4b6624a42e2712103fb0f57799a3b29651c0e5b8119a519d65"},
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{file = "grpcio-1.67.0-cp310-cp310-win_amd64.whl", hash = "sha256:b6eb68493a05d38b426604e1dc93bfc0137c4157f7ab4fac5771fd9a104bbaa6"},
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{file = "grpcio-1.67.0-cp311-cp311-linux_armv7l.whl", hash = "sha256:e91d154689639932305b6ea6f45c6e46bb51ecc8ea77c10ef25aa77f75443ad4"},
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[package.extras]
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protobuf = ["grpcio-tools (>=1.67.0)"]
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protobuf = ["grpcio-tools (>=1.67.1)"]
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[[package]]
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name = "h11"
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@@ -1536,13 +1535,13 @@ zstd = ["zstandard (>=0.18.0)"]
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[[package]]
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name = "huggingface-hub"
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version = "0.25.2"
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version = "0.26.2"
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description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
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optional = false
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python-versions = ">=3.8.0"
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[package.dependencies]
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@@ -1556,16 +1555,16 @@ tqdm = ">=4.42.1"
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typing-extensions = ">=3.7.4.3"
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[package.extras]
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inference = ["aiohttp"]
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tensorflow = ["graphviz", "pydot", "tensorflow"]
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tensorflow-testing = ["keras (<3.0)", "tensorflow"]
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||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
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||||
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
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torch = ["safetensors[torch]", "torch"]
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@@ -1677,13 +1676,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
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||||
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[[package]]
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name = "ipython"
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version = "8.28.0"
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version = "8.29.0"
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description = "IPython: Productive Interactive Computing"
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optional = false
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python-versions = ">=3.10"
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[package.dependencies]
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||||
@@ -2713,72 +2712,72 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
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[[package]]
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optional = false
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python-versions = ">=3.9"
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files = [
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[[package]]
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[[package]]
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version = "3.4.1"
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version = "3.4.2"
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description = "Python package for creating and manipulating graphs and networks"
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[[package]]
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[[package]]
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[[package]]
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[[package]]
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[package.dependencies]
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@@ -5085,9 +5084,11 @@ httpx = ["httpx (>=0.16.0)"]
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huey = ["huey (>=2)"]
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huggingface-hub = ["huggingface-hub (>=0.22)"]
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litestar = ["litestar (>=2.0.0)"]
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loguru = ["loguru (>=0.5)"]
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openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
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opentelemetry-experimental = ["opentelemetry-distro"]
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pure-eval = ["asttokens", "executing", "pure-eval"]
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@@ -5233,23 +5234,23 @@ test = ["pytest"]
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[[package]]
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description = "Easily download, build, install, upgrade, and uninstall Python packages"
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[package.extras]
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core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.collections", "jaraco.functools", "jaraco.text (>=3.7)", "more-itertools", "more-itertools (>=8.8)", "packaging", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
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[[package]]
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name = "shellingham"
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@@ -5841,13 +5842,13 @@ files = [
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[[package]]
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||||
name = "tinycss2"
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version = "1.3.0"
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version = "1.4.0"
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description = "A tiny CSS parser"
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optional = false
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[package.dependencies]
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@@ -5859,111 +5860,123 @@ test = ["pytest", "ruff"]
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[[package]]
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|
||||
dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1,<0.14.0)", "libcst", "librosa", "nltk (<=3.8.1)", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.20,<0.21)", "urllib3 (<2.0.0)"]
|
||||
dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "libcst", "librosa", "nltk (<=3.8.1)", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.5.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.20,<0.21)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
|
||||
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
|
||||
@@ -6168,7 +6181,7 @@ torch = ["accelerate (>=0.26.0)", "torch"]
|
||||
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
|
||||
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
|
||||
torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.20,<0.21)", "torch", "tqdm (>=4.27)"]
|
||||
video = ["av (==9.2.0)", "decord (==0.6.0)"]
|
||||
video = ["av (==9.2.0)"]
|
||||
vision = ["Pillow (>=10.0.1,<=15.0)"]
|
||||
|
||||
[[package]]
|
||||
@@ -6269,13 +6282,13 @@ zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "utilsforecast"
|
||||
version = "0.2.5"
|
||||
version = "0.2.7"
|
||||
description = "Forecasting utilities"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "utilsforecast-0.2.5-py3-none-any.whl", hash = "sha256:ad41b2e387eaade62e5505086c8a6fd2145805a757079d9796718273950eea4a"},
|
||||
{file = "utilsforecast-0.2.5.tar.gz", hash = "sha256:b69486e17454170cb180fe8a7d19340ac06032c3b03e2119d417ba4d830246d9"},
|
||||
{file = "utilsforecast-0.2.7-py3-none-any.whl", hash = "sha256:ee09f03d018e1cdccd382c91897396849c5f1330361579eb7b3cfe3c6cc84e91"},
|
||||
{file = "utilsforecast-0.2.7.tar.gz", hash = "sha256:968b430a7f966dd520dc98dbac3530d2b4a2fa3f98319ece8bc55c431378abbb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -6389,13 +6402,13 @@ test = ["websockets"]
|
||||
|
||||
[[package]]
|
||||
name = "werkzeug"
|
||||
version = "3.0.4"
|
||||
version = "3.1.2"
|
||||
description = "The comprehensive WSGI web application library."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "werkzeug-3.0.4-py3-none-any.whl", hash = "sha256:02c9eb92b7d6c06f31a782811505d2157837cea66aaede3e217c7c27c039476c"},
|
||||
{file = "werkzeug-3.0.4.tar.gz", hash = "sha256:34f2371506b250df4d4f84bfe7b0921e4762525762bbd936614909fe25cd7306"},
|
||||
{file = "werkzeug-3.1.2-py3-none-any.whl", hash = "sha256:4f7d1a5de312c810a8a2c6f0b47e9f6a7cffb7c8322def35e4d4d9841ff85597"},
|
||||
{file = "werkzeug-3.1.2.tar.gz", hash = "sha256:f471a4cd167233077e9d2a8190c3471c5bc520c636a9e3c1e9300c33bced03bc"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
||||
+87
-15
@@ -62,7 +62,7 @@ def freq_map(freq: str):
|
||||
|
||||
|
||||
# Per time series normalization: forward.
|
||||
def normalize(batch):
|
||||
def _normalize(batch):
|
||||
stats = [
|
||||
(np.mean(x), np.where((w := np.std(x)) > _TOL, w, 1.0)) for x in batch
|
||||
]
|
||||
@@ -71,7 +71,7 @@ def normalize(batch):
|
||||
|
||||
|
||||
# Per time series normalization: inverse.
|
||||
def renormalize(batch, stats):
|
||||
def _renormalize(batch, stats):
|
||||
return [x * stat[1] + stat[0] for x, stat in zip(batch, stats)]
|
||||
|
||||
|
||||
@@ -109,6 +109,8 @@ class TimesFmHparams:
|
||||
per_core_batch_size: int = 32
|
||||
backend: Literal["cpu", "gpu", "tpu"] = "cpu"
|
||||
quantiles: Sequence[float] | None = DEFAULT_QUANTILES
|
||||
# Hparams beyond the model.
|
||||
point_forecast_mode: Literal["mean", "median"] = "median"
|
||||
|
||||
|
||||
@dataclasses.dataclass(kw_only=True)
|
||||
@@ -184,8 +186,9 @@ class TimesFmBase:
|
||||
"""Loads a checkpoint and compiles the decoder."""
|
||||
raise NotImplementedError("`load_from_checkpoint` is not implemented.")
|
||||
|
||||
def _preprocess(self, inputs: Sequence[np.array],
|
||||
freq: Sequence[int]) -> tuple[np.array, np.array, int]:
|
||||
def _preprocess(
|
||||
self, inputs: Sequence[np.ndarray],
|
||||
freq: Sequence[int]) -> tuple[np.ndarray, np.ndarray, np.ndarray, int]:
|
||||
"""Formats and pads raw inputs to feed into the model.
|
||||
|
||||
This function both pads each time series to match the context length, and
|
||||
@@ -200,6 +203,7 @@ class TimesFmBase:
|
||||
A tuple of:
|
||||
- the padded input time series to meet the model required context.
|
||||
- the padding indicator.
|
||||
- the frequency of each input time series.
|
||||
- the number of padded examples for SPMD so that each core has the same
|
||||
number (a multiple of `batch_size`) of examples.
|
||||
"""
|
||||
@@ -239,15 +243,14 @@ class TimesFmBase:
|
||||
pmap_pad,
|
||||
)
|
||||
|
||||
def forecast(
|
||||
def _forecast(
|
||||
self,
|
||||
inputs: Sequence[Any],
|
||||
freq: Sequence[int] | None = None,
|
||||
window_size: int | None = None,
|
||||
forecast_context_len: int | None = None,
|
||||
return_forecast_on_context: bool = False,
|
||||
truncate_negative: bool = False,
|
||||
) -> tuple[np.array, np.array]:
|
||||
) -> tuple[np.ndarray, np.ndarray]:
|
||||
"""Forecasts on a list of time series.
|
||||
|
||||
Args:
|
||||
@@ -261,11 +264,9 @@ class TimesFmBase:
|
||||
forecast_context_len: optional max context length.
|
||||
return_forecast_on_context: True to return the forecast on the context
|
||||
when available, i.e. after the first input patch.
|
||||
truncate_negative: truncate to only non-negative values if all the contexts
|
||||
have non-negative values.
|
||||
|
||||
Returns:
|
||||
A tuple for JTensors:
|
||||
A tuple for np.array:
|
||||
- the mean forecast of size (# inputs, # forecast horizon),
|
||||
- the full forecast (mean + quantiles) of size
|
||||
(# inputs, # forecast horizon, 1 + # quantiles).
|
||||
@@ -273,7 +274,78 @@ class TimesFmBase:
|
||||
Raises:
|
||||
ValueError: If the checkpoint is not properly loaded.
|
||||
"""
|
||||
raise NotImplementedError("`forecast` is not implemented.")
|
||||
raise NotImplementedError("`_forecast` is not implemented.")
|
||||
|
||||
def forecast(
|
||||
self,
|
||||
inputs: Sequence[Any],
|
||||
freq: Sequence[int] | None = None,
|
||||
window_size: int | None = None,
|
||||
forecast_context_len: int | None = None,
|
||||
return_forecast_on_context: bool = False,
|
||||
normalize: bool = False,
|
||||
) -> tuple[np.ndarray, np.ndarray]:
|
||||
"""Forecasts on a list of time series.
|
||||
|
||||
Args:
|
||||
inputs: list of time series forecast contexts. Each context time series
|
||||
should be in a format convertible to JTensor by `jnp.array`.
|
||||
freq: frequency of each context time series. 0 for high frequency
|
||||
(default), 1 for medium, and 2 for low. Notice this is different from
|
||||
the `freq` required by `forecast_on_df`.
|
||||
window_size: window size of trend + residual decomposition. If None then
|
||||
we do not do decomposition.
|
||||
forecast_context_len: optional max context length.
|
||||
return_forecast_on_context: True to return the forecast on the context
|
||||
when available, i.e. after the first input patch.
|
||||
normalize: If True, then we normalize the inputs before forecasting and
|
||||
the outputs are then renormalized to the original scale.
|
||||
|
||||
Returns:
|
||||
A tuple for np.array:
|
||||
- the mean forecast of size (# inputs, # forecast horizon),
|
||||
- the full forecast (mean + quantiles) of size
|
||||
(# inputs, # forecast horizon, 1 + # quantiles).
|
||||
|
||||
Raises:
|
||||
ValueError: If the checkpoint is not properly loaded.
|
||||
"""
|
||||
stats = None
|
||||
if normalize:
|
||||
inputs, stats = _normalize(inputs)
|
||||
mean_forecast, quantile_forecast = self._forecast(
|
||||
inputs,
|
||||
freq,
|
||||
window_size,
|
||||
forecast_context_len,
|
||||
return_forecast_on_context,
|
||||
)
|
||||
if stats is not None:
|
||||
stats = np.array(stats)
|
||||
mu = stats[:, 0]
|
||||
sigma = stats[:, 1]
|
||||
mean_forecast = mean_forecast * sigma[:, None] + mu[:, None]
|
||||
quantile_forecast = (quantile_forecast * sigma[:, None, None] +
|
||||
mu[:, None, None])
|
||||
if self.hparams.point_forecast_mode == "mean":
|
||||
return mean_forecast, quantile_forecast
|
||||
elif self.hparams.point_forecast_mode == "median":
|
||||
if self._median_index == -1:
|
||||
for i, quantile in enumerate(self.quantiles):
|
||||
if quantile == 0.5:
|
||||
self._median_index = i
|
||||
break
|
||||
if self._median_index == -1:
|
||||
raise ValueError("Median (0.5) is not found in the model quantiles:"
|
||||
f" {self.quantiles}. Please check the hparams.")
|
||||
return (
|
||||
quantile_forecast[:, :, 1 + self._median_index],
|
||||
quantile_forecast,
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Unsupported point forecast mode:"
|
||||
f" {self.hparams.point_forecast_mode}. Use 'mean' or 'median'.")
|
||||
|
||||
def forecast_with_covariates(
|
||||
self,
|
||||
@@ -408,7 +480,7 @@ class TimesFmBase:
|
||||
]
|
||||
per_instance_stats = None
|
||||
if normalize_xreg_target_per_input:
|
||||
targets, per_instance_stats = normalize(targets)
|
||||
targets, per_instance_stats = _normalize(targets)
|
||||
xregs = xreg_lib.BatchedInContextXRegLinear(
|
||||
targets=targets,
|
||||
train_lens=train_lens,
|
||||
@@ -431,7 +503,7 @@ class TimesFmBase:
|
||||
assert_covariate_shapes=True,
|
||||
)
|
||||
if normalize_xreg_target_per_input:
|
||||
xregs = renormalize(xregs, per_instance_stats)
|
||||
xregs = _renormalize(xregs, per_instance_stats)
|
||||
outputs = [
|
||||
(mean_output[self._horizon_start:(self._horizon_start + test_len)] +
|
||||
xreg)
|
||||
@@ -446,7 +518,7 @@ class TimesFmBase:
|
||||
]
|
||||
per_instance_stats = None
|
||||
if normalize_xreg_target_per_input:
|
||||
targets, per_instance_stats = normalize(targets)
|
||||
targets, per_instance_stats = _normalize(targets)
|
||||
xregs, xregs_on_context, _, _, _ = xreg_lib.BatchedInContextXRegLinear(
|
||||
targets=targets,
|
||||
train_lens=train_lens,
|
||||
@@ -484,7 +556,7 @@ class TimesFmBase:
|
||||
for mean_output, test_len, xreg in zip(mean_outputs, test_lens, xregs)
|
||||
]
|
||||
if normalize_xreg_target_per_input:
|
||||
outputs = renormalize(outputs, per_instance_stats)
|
||||
outputs = _renormalize(outputs, per_instance_stats)
|
||||
|
||||
return outputs, xregs
|
||||
|
||||
|
||||
@@ -234,14 +234,13 @@ class TimesFmJax(timesfm_base.TimesFmBase):
|
||||
}))
|
||||
self._logging(f"Jitted decoding in {time.time() - start_time:.2f} seconds.")
|
||||
|
||||
def forecast(
|
||||
def _forecast(
|
||||
self,
|
||||
inputs: Sequence[Any],
|
||||
freq: Sequence[int] | None = None,
|
||||
window_size: int | None = None,
|
||||
forecast_context_len: int | None = None,
|
||||
return_forecast_on_context: bool = False,
|
||||
truncate_negative: bool = False,
|
||||
) -> tuple[np.ndarray, np.ndarray]:
|
||||
"""Forecasts on a list of time series.
|
||||
|
||||
@@ -256,8 +255,6 @@ class TimesFmJax(timesfm_base.TimesFmBase):
|
||||
forecast_context_len: optional max context length.
|
||||
return_forecast_on_context: True to return the forecast on the context
|
||||
when available, i.e. after the first input patch.
|
||||
truncate_negative: truncate to only non-negative values if all the contexts
|
||||
have non-negative values.
|
||||
|
||||
Returns:
|
||||
A tuple for JTensors:
|
||||
@@ -277,7 +274,6 @@ class TimesFmJax(timesfm_base.TimesFmBase):
|
||||
else:
|
||||
fcontext_len = forecast_context_len
|
||||
inputs = [np.array(ts)[-fcontext_len:] for ts in inputs]
|
||||
inp_min = np.min([np.min(ts) for ts in inputs])
|
||||
|
||||
if window_size is not None:
|
||||
new_inputs = []
|
||||
@@ -352,7 +348,4 @@ class TimesFmJax(timesfm_base.TimesFmBase):
|
||||
if window_size is not None:
|
||||
mean_outputs = mean_outputs[0::2, ...] + mean_outputs[1::2, ...]
|
||||
full_outputs = full_outputs[0::2, ...] + full_outputs[1::2, ...]
|
||||
if inp_min >= 0 and truncate_negative:
|
||||
mean_outputs = np.maximum(mean_outputs, 0.0)
|
||||
full_outputs = np.maximum(full_outputs, 0.0)
|
||||
return mean_outputs, full_outputs
|
||||
|
||||
@@ -46,6 +46,7 @@ class TimesFmTorch(timesfm_base.TimesFmBase):
|
||||
self.global_batch_size = self.per_core_batch_size
|
||||
self._device = torch.device("cuda:0" if (
|
||||
torch.cuda.is_available() and self.backend == "gpu") else "cpu")
|
||||
self._median_index = -1
|
||||
|
||||
def load_from_checkpoint(
|
||||
self,
|
||||
@@ -67,14 +68,13 @@ class TimesFmTorch(timesfm_base.TimesFmBase):
|
||||
self._model.eval()
|
||||
# TODO: add compilation.
|
||||
|
||||
def forecast(
|
||||
def _forecast(
|
||||
self,
|
||||
inputs: Sequence[Any],
|
||||
freq: Sequence[int] | None = None,
|
||||
window_size: int | None = None,
|
||||
forecast_context_len: int | None = None,
|
||||
return_forecast_on_context: bool = False,
|
||||
truncate_negative: bool = False,
|
||||
) -> tuple[np.ndarray, np.ndarray]:
|
||||
"""Forecasts on a list of time series.
|
||||
|
||||
@@ -89,8 +89,6 @@ class TimesFmTorch(timesfm_base.TimesFmBase):
|
||||
forecast_context_len: optional max context length.
|
||||
return_forecast_on_context: True to return the forecast on the context
|
||||
when available, i.e. after the first input patch.
|
||||
truncate_negative: truncate to only non-negative values if all the contexts
|
||||
have non-negative values.
|
||||
|
||||
Returns:
|
||||
A tuple for JTensors:
|
||||
@@ -110,7 +108,6 @@ class TimesFmTorch(timesfm_base.TimesFmBase):
|
||||
else:
|
||||
fcontext_len = forecast_context_len
|
||||
inputs = [np.array(ts)[-fcontext_len:] for ts in inputs]
|
||||
inp_min = np.min([np.min(ts) for ts in inputs])
|
||||
|
||||
if window_size is not None:
|
||||
new_inputs = []
|
||||
@@ -166,7 +163,4 @@ class TimesFmTorch(timesfm_base.TimesFmBase):
|
||||
if window_size is not None:
|
||||
mean_outputs = mean_outputs[0::2, ...] + mean_outputs[1::2, ...]
|
||||
full_outputs = full_outputs[0::2, ...] + full_outputs[1::2, ...]
|
||||
if inp_min >= 0 and truncate_negative:
|
||||
mean_outputs = np.maximum(mean_outputs, 0.0)
|
||||
full_outputs = np.maximum(full_outputs, 0.0)
|
||||
return mean_outputs, full_outputs
|
||||
|
||||
Reference in New Issue
Block a user