From a721c8ba0dcacf17c1f6761a7ea2e1f85f9029b7 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Mon, 14 Sep 2026 12:59:48 -0600 Subject: [PATCH 1/2] feat(tabpfn): add TabPFN-3 support and prepare v0.0.33 release Integrate foundationforecast 0.1.6 TabPFN-3 via model_path, update mock conftest, add LOCAL integration tests, TabPFN family notebook, CI token/cache, and release checklist. Uses a temporary git source until 0.1.6 is on PyPI. Co-authored-by: Cursor --- .github/workflows/ci.yaml | 7 + docs/RELEASE.md | 34 + docs/changelogs/index.md | 1 + docs/changelogs/v0.0.33.md | 38 ++ docs/examples/index.md | 1 + docs/examples/tabpfn-family.ipynb | 858 ++++++++++++++++++++++++ docs/model-hub.md | 2 +- mkdocs.yml | 2 + pyproject.toml | 9 +- tests/models/conftest.py | 22 +- tests/models/foundation/test_tabpfn.py | 144 ++++ timecopilot/models/foundation/tabpfn.py | 3 +- uv.lock | 91 ++- 13 files changed, 1190 insertions(+), 22 deletions(-) create mode 100644 docs/RELEASE.md create mode 100644 docs/changelogs/v0.0.33.md create mode 100644 docs/examples/tabpfn-family.ipynb create mode 100644 tests/models/foundation/test_tabpfn.py diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 5b5cd69..07f0a16 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -48,10 +48,17 @@ jobs: path: ~/.cache/huggingface/hub/ key: ${{ Runner.os }}-huggingface-${{ hashFiles('**/tests/') }} + - name: Cache TabPFN checkpoints + uses: actions/cache@v4 + with: + path: ~/.cache/tabpfn + key: ${{ runner.os }}-tabpfn-v1 + - name: Run tests run: uv run pytest env: HF_TOKEN: ${{ secrets.HF_TOKEN }} + TABPFN_TOKEN: ${{ secrets.TABPFN_TOKEN }} - name: Test import class run: uv run -- python -c "from timecopilot import TimeCopilot, TimeCopilotForecaster" diff --git a/docs/RELEASE.md b/docs/RELEASE.md new file mode 100644 index 0000000..d64dcea --- /dev/null +++ b/docs/RELEASE.md @@ -0,0 +1,34 @@ +# Release checklist (v0.0.33) + +Depends on **foundationforecast v0.1.6** (TabPFN-3). See the [foundationforecast release checklist](https://github.com/TimeCopilot/foundationforecast/blob/main/docs/RELEASE.md). + +## Before tagging + +1. Merge PR for TabPFN-3 support into `main`. +2. Confirm `foundationforecast` **0.1.6** is on PyPI. +3. Remove the temporary git source from `pyproject.toml`: + + ```toml + [tool.uv.sources] + foundationforecast = { git = "...", branch = "feat/tabpfn-ts-3" } + ``` + +4. Refresh the lock file: + + ```bash + uv lock --upgrade-package foundationforecast + uv sync + uv run pytest tests/models/foundation/test_tabpfn.py -m models + ``` + +5. Verify version `0.0.33` in `pyproject.toml` and changelog `docs/changelogs/v0.0.33.md`. + +## Publish to PyPI + +```bash +git checkout main && git pull +git tag v0.0.33 +git push origin v0.0.33 +``` + +Ensure `TABPFN_TOKEN` is set in GitHub repository secrets for CI. diff --git a/docs/changelogs/index.md b/docs/changelogs/index.md index 364df27..41737a4 100644 --- a/docs/changelogs/index.md +++ b/docs/changelogs/index.md @@ -2,6 +2,7 @@ Welcome to the TimeCopilot Changelog. Here, you will find a comprehensive list of all the changes, updates, and improvements made to the TimeCopilot project. This section is designed to keep you informed about the latest features, bug fixes, and enhancements as we continue to develop and refine the TimeCopilot experience. Stay tuned for regular updates and feel free to explore the details of each release below. +- [v0.0.33](v0.0.33.md) - [v0.0.32](v0.0.32.md) - [v0.0.31](v0.0.31.md) - [v0.0.30](v0.0.30.md) diff --git a/docs/changelogs/v0.0.33.md b/docs/changelogs/v0.0.33.md new file mode 100644 index 0000000..d56133e --- /dev/null +++ b/docs/changelogs/v0.0.33.md @@ -0,0 +1,38 @@ +### Features + +* **TabPFN-3 foundation model**: Added support for TabPFN-3 via `foundationforecast>=0.1.6`. Use the existing `TabPFN` class with `model_path`; TabPFN-2 remains the default. See [#25](https://github.com/TimeCopilot/foundationforecast/pull/25). + + ```python + import pandas as pd + from tabpfn_time_series import TabPFNMode + from timecopilot.models.foundation.tabpfn import TABPFN_V2_MODEL, TABPFN_V3_MODEL, TabPFN + + df = pd.read_csv( + "https://timecopilot.s3.amazonaws.com/public/data/air_passengers.csv", + parse_dates=["ds"], + ) + + model = TabPFN( + model_path=TABPFN_V3_MODEL, + mode=TabPFNMode.LOCAL, + context_length=32768, + alias="TabPFN-3", + ) + fcst_df = model.forecast(df, h=12, freq="MS", level=[0, 20, 40, 60, 80]) + ``` + + **License note:** TabPFN-2.6+ and TabPFN-3 weights use the TabPFN Non-Commercial license. First LOCAL use requires accepting terms at [ux.priorlabs.ai](https://ux.priorlabs.ai) (`TABPFN_TOKEN`). TabPFN-3 is LOCAL-only today. + +* **TabPFN family notebook**: Added [`tabpfn-family` example notebook](../examples/tabpfn-family.ipynb) comparing TabPFN-2 and TabPFN-3 with prediction intervals. + +### Dependencies + +* Bumped `foundationforecast` from `>=0.1.3` to `>=0.1.6` (TabPFN-3 via `model_path`, `tabpfn-time-series>=1.2.0`). + +### CI + +* Added `TABPFN_TOKEN` secret and TabPFN checkpoint cache for LOCAL integration tests. + +--- + +**Full Changelog**: https://github.com/TimeCopilot/timecopilot/compare/v0.0.32...v0.0.33 diff --git a/docs/examples/index.md b/docs/examples/index.md index b29b340..75ff7bb 100644 --- a/docs/examples/index.md +++ b/docs/examples/index.md @@ -29,6 +29,7 @@ For model API details, see the [Model Hub](../model-hub.md). | [Compare Foundation Models](ts-foundation-models-comparison-quickstart.ipynb) | Benchmark multiple foundation models side by side | Python 3.10+; GPU optional | | [Chronos Family](chronos-family.ipynb) | Forecast with Chronos 1.x and 2.x checkpoints | Python 3.10+ | | [TimesFM Family](timesfm-family.ipynb) | Forecast with TimesFM 1.0, 2.0, 2.5, and 3.0 with prediction intervals | Python 3.10+ | +| [TabPFN Family](tabpfn-family.ipynb) | Forecast with TabPFN-2 and TabPFN-3 with prediction intervals | Python 3.10–3.12; `TABPFN_TOKEN` for LOCAL | | [TiRex Family](tirex-family.ipynb) | Forecast with TiRex 1.0 and 2.0 | Python 3.11+ | | [Toto Family](toto-family.ipynb) | Forecast with Toto 1.0 and 2.0 | Python 3.10+ | | [Finetuning](finetuning.ipynb) | Adapt Chronos 2 and TimeGPT to your data | Python 3.10+; GPU recommended | diff --git a/docs/examples/tabpfn-family.ipynb b/docs/examples/tabpfn-family.ipynb new file mode 100644 index 0000000..bda32bd --- /dev/null +++ b/docs/examples/tabpfn-family.ipynb @@ -0,0 +1,858 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "29edac33", + "metadata": {}, + "source": [ + "# Forecast Using the TabPFN Family\n" + ] + }, + { + "cell_type": "markdown", + "id": "b2fa5716", + "metadata": {}, + "source": [ + "TimeCopilot ships a single `TabPFN` class that supports TabPFN-2 (default) and TabPFN-3 checkpoints. Pass `model_path` to select the checkpoint:\n", + "\n", + "| Version | `model_path` | Notes |\n", + "|---------|-------------|-------|\n", + "| **TabPFN-2** (default) | `tabpfn-v2-regressor-2noar4o2.ckpt` | TabPFN NC license |\n", + "| **TabPFN-3** | `tabpfn-v3-regressor-v3_20260506_timeseries.ckpt` | Finetuned TS checkpoint; TabPFN NC license; LOCAL only |\n", + "\n", + "*Requirements*:\n", + "\n", + " - Python 3.10 \u2013 3.12\n", + " - LOCAL mode (default when CUDA is available): accept terms at [ux.priorlabs.ai](https://ux.priorlabs.ai) and set `TABPFN_TOKEN`\n", + " - TabPFN-3 recommends `context_length=32768`; TabPFN-2 defaults to 4096\n", + "\n", + "**License:** TabPFN-2.6+ and TabPFN-3 weights use the TabPFN Non-Commercial license. Commercial production requires a [Prior Labs license or API](https://docs.priorlabs.ai/models).\n", + "\n", + "In this example we compare TabPFN-2 and TabPFN-3 on event pageview data.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8b280b18", + "metadata": {}, + "source": [ + "## Import libraries\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c371b7eb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T18:44:11.802516Z", + "iopub.status.busy": "2026-09-14T18:44:11.802413Z", + "iopub.status.idle": "2026-09-14T18:44:17.912816Z", + "shell.execute_reply": "2026-09-14T18:44:17.912497Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/azul/projects/foundationforecast/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:datasets:JAX version 0.7.1 available.\n" + ] + } + ], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "import pandas as pd\n", + "from dotenv import load_dotenv\n", + "\n", + "from timecopilot import TimeCopilotForecaster\n", + "\n", + "load_dotenv(Path(\".env\"))\n", + "\n", + "if sys.version_info >= (3, 13):\n", + " raise RuntimeError(\"TabPFN requires Python < 3.13\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "4724fbc4", + "metadata": {}, + "source": [ + "## Load the dataset\n", + "\n", + "The DataFrame must include at least the following columns:\n", + "- unique_id: Unique identifier for each time series (string)\n", + "- ds: Date column (datetime format)\n", + "- y: Target variable for forecasting (float format)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "801b50be", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T18:44:17.915071Z", + "iopub.status.busy": "2026-09-14T18:44:17.914890Z", + "iopub.status.idle": "2026-09-14T18:44:18.490177Z", + "shell.execute_reply": "2026-09-14T18:44:18.489535Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TimeCopilotForecaster.plot(df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "7a50356d", + "metadata": {}, + "source": [ + "## Import the models\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8237cd39", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T18:44:18.903953Z", + "iopub.status.busy": "2026-09-14T18:44:18.903882Z", + "iopub.status.idle": "2026-09-14T18:44:18.905435Z", + "shell.execute_reply": "2026-09-14T18:44:18.905119Z" + } + }, + "outputs": [], + "source": [ + "from tabpfn_time_series import TabPFNMode\n", + "\n", + "from timecopilot.models.foundation.tabpfn import TABPFN_V2_MODEL, TABPFN_V3_MODEL, TabPFN\n" + ] + }, + { + "cell_type": "markdown", + "id": "b53fbb9f", + "metadata": {}, + "source": [ + "## Create a TimeCopilotForecaster\n", + "\n", + "We compare TabPFN-2 with TabPFN-3 in LOCAL mode. Each model gets a distinct `alias`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a6ecf9c0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T18:44:18.906341Z", + "iopub.status.busy": "2026-09-14T18:44:18.906286Z", + "iopub.status.idle": "2026-09-14T18:44:18.908015Z", + "shell.execute_reply": "2026-09-14T18:44:18.907751Z" + } + }, + "outputs": [], + "source": [ + "models = [\n", + " TabPFN(\n", + " model_path=TABPFN_V2_MODEL,\n", + " mode=TabPFNMode.LOCAL,\n", + " alias=\"TabPFN-2\",\n", + " ),\n", + " TabPFN(\n", + " model_path=TABPFN_V3_MODEL,\n", + " mode=TabPFNMode.LOCAL,\n", + " context_length=32768,\n", + " alias=\"TabPFN-3\",\n", + " ),\n", + "]\n", + "\n", + "tcf = TimeCopilotForecaster(models=models)\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a98344a", + "metadata": {}, + "source": [ + "## Generate forecast\n", + "\n", + "TabPFN outputs nine fixed quantiles (0.1 through 0.9). Pass `level=[0, 20, 40, 60, 80]` to request all supported prediction intervals.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "49a609e6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T18:44:18.908916Z", + "iopub.status.busy": "2026-09-14T18:44:18.908866Z", + "iopub.status.idle": "2026-09-14T18:44:53.068317Z", + "shell.execute_reply": "2026-09-14T18:44:53.049636Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Predicting time series: 0%| | 0/6 [00:00" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tcf.plot(df, cv_df.drop(columns=[\"cutoff\", \"y\"]), level=[80])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "03db9715", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T18:44:54.872569Z", + "iopub.status.busy": "2026-09-14T18:44:54.872487Z", + "iopub.status.idle": "2026-09-14T18:44:54.985277Z", + "shell.execute_reply": "2026-09-14T18:44:54.984860Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2Black Friday2024-11-302024-09-011105812908.76464812908.76464812908.7646489775.41992216364.1406257223.555664...6755.5000006755.5000005224.0512709573.4150394197.26611315283.3603523451.47460919639.7949222746.73535224622.894531
3Black Friday2024-12-312024-09-0135483868.7465823868.7465823868.7465823513.1193854233.5439453093.505371...3064.2814943064.2814942702.1047363447.5742192388.7438963879.0815432119.6474614505.3891601864.8121345696.427734
4Black Friday2025-01-312024-09-0117242284.3225102284.3225102284.3225102164.5927732436.9353032059.958984...2201.5971682201.5971682054.8662112375.4028321918.0677492579.7465821779.7249762839.1384281587.3757323371.126953
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" + ], + "text/plain": [ + " unique_id ds cutoff y TabPFN-2 TabPFN-2-lo-0 \\\n", + "0 Black Friday 2024-09-30 2024-09-01 2607 2283.965088 2283.965088 \n", + "1 Black Friday 2024-10-31 2024-09-01 2470 3523.283203 3523.283203 \n", + "2 Black Friday 2024-11-30 2024-09-01 11058 12908.764648 12908.764648 \n", + "3 Black Friday 2024-12-31 2024-09-01 3548 3868.746582 3868.746582 \n", + "4 Black Friday 2025-01-31 2024-09-01 1724 2284.322510 2284.322510 \n", + "\n", + " TabPFN-2-hi-0 TabPFN-2-lo-20 TabPFN-2-hi-20 TabPFN-2-lo-40 ... \\\n", + "0 2283.965088 2177.424561 2420.626465 2081.235596 ... \n", + "1 3523.283203 3156.973877 3964.171875 2814.589111 ... \n", + "2 12908.764648 9775.419922 16364.140625 7223.555664 ... \n", + "3 3868.746582 3513.119385 4233.543945 3093.505371 ... \n", + "4 2284.322510 2164.592773 2436.935303 2059.958984 ... \n", + "\n", + " TabPFN-3-lo-0 TabPFN-3-hi-0 TabPFN-3-lo-20 TabPFN-3-hi-20 \\\n", + "0 2306.467773 2306.467773 2169.607178 2497.994629 \n", + "1 3155.802979 3155.802979 2800.380371 3559.806152 \n", + "2 6755.500000 6755.500000 5224.051270 9573.415039 \n", + "3 3064.281494 3064.281494 2702.104736 3447.574219 \n", + "4 2201.597168 2201.597168 2054.866211 2375.402832 \n", + "\n", + " TabPFN-3-lo-40 TabPFN-3-hi-40 TabPFN-3-lo-60 TabPFN-3-hi-60 \\\n", + "0 2056.207275 2797.267334 1941.594482 3244.148438 \n", + "1 2491.012451 4029.166504 2239.562500 4868.718750 \n", + "2 4197.266113 15283.360352 3451.474609 19639.794922 \n", + "3 2388.743896 3879.081543 2119.647461 4505.389160 \n", + "4 1918.067749 2579.746582 1779.724976 2839.138428 \n", + "\n", + " TabPFN-3-lo-80 TabPFN-3-hi-80 \n", + "0 1798.154785 4146.946777 \n", + "1 1980.670654 6789.798340 \n", + "2 2746.735352 24622.894531 \n", + "3 1864.812134 5696.427734 \n", + "4 1587.375732 3371.126953 \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cd98b33f", + "metadata": {}, + "source": [ + "## Evaluation\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a5a05f1e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-09-14T18:44:54.986645Z", + "iopub.status.busy": "2026-09-14T18:44:54.986591Z", + "iopub.status.idle": "2026-09-14T18:44:55.857925Z", + "shell.execute_reply": "2026-09-14T18:44:55.857554Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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metricmasescaled_crps
TabPFN-30.7890.254
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" + ], + "text/plain": [ + "metric mase scaled_crps\n", + "TabPFN-3 0.789 0.254\n", + "TabPFN-2 1.020 0.279" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from functools import partial\n", + "\n", + "from utilsforecast.evaluation import evaluate\n", + "from utilsforecast.losses import mase, scaled_crps\n", + "\n", + "eval_df = evaluate(\n", + " cv_df.drop(columns=[\"cutoff\"]),\n", + " train_df=df.query(\"ds <= '2024-08-31'\"),\n", + " metrics=[partial(mase, seasonality=12), scaled_crps],\n", + " level=level,\n", + ")\n", + "eval_df.groupby(\"metric\").mean(numeric_only=True).T.sort_values(\n", + " by=\"scaled_crps\"\n", + ").round(3)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/model-hub.md b/docs/model-hub.md index d6f09f2..b847bb8 100644 --- a/docs/model-hub.md +++ b/docs/model-hub.md @@ -51,7 +51,7 @@ TimeCopilot provides a unified interface to state-of-the-art foundation models f - [PatchTST-FM](api/models/foundation/models.md#timecopilot.models.foundation.patchtst_fm) ([arXiv:2602.06909](https://arxiv.org/abs/2602.06909)) - [Sundial](api/models/foundation/models.md#timecopilot.models.foundation.sundial) ([arXiv:2502.00816](https://arxiv.org/pdf/2502.00816)) - [T0](api/models/foundation/models.md#timecopilot.models.foundation.t0) ([model card](https://huggingface.co/theforecastingcompany/t0-alpha)) -- [TabPFN](api/models/foundation/models.md#timecopilot.models.foundation.tabpfn) ([arXiv:2501.02945](https://arxiv.org/abs/2501.02945)) +- [TabPFN](api/models/foundation/models.md#timecopilot.models.foundation.tabpfn) ([arXiv:2501.02945](https://arxiv.org/abs/2501.02945); TabPFN-2 default, TabPFN-3 via `model_path`; [NC license](https://docs.priorlabs.ai/models)) - [TiRex / TiRex-2](api/models/foundation/models.md#timecopilot.models.foundation.tirex) ([arXiv:2505.23719](https://arxiv.org/abs/2505.23719), [arXiv:2607.01204](https://arxiv.org/abs/2607.01204)) - [TimeGPT](api/models/foundation/models.md#timecopilot.models.foundation.timegpt) ([arXiv:2310.03589](https://arxiv.org/abs/2310.03589)) - [TimesFM](api/models/foundation/models.md#timecopilot.models.foundation.timesfm) ([arXiv:2310.10688](https://arxiv.org/abs/2310.10688); [3.0 license](https://huggingface.co/google/timesfm-3.0-pytorch/blob/main/LICENSE)) diff --git a/mkdocs.yml b/mkdocs.yml index 648a417..27f98e0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -38,6 +38,7 @@ nav: - Chronos: examples/chronos-family.ipynb - TimesFM: examples/timesfm-family.ipynb - TiRex: examples/tirex-family.ipynb + - TabPFN: examples/tabpfn-family.ipynb - Toto: examples/toto-family.ipynb - Finetuning: examples/finetuning.ipynb - Benchmarks and Ensembles: @@ -62,6 +63,7 @@ nav: - experiments/fev.md - Changelogs: - changelogs/index.md + - changelogs/v0.0.33.md - changelogs/v0.0.32.md - changelogs/v0.0.31.md - changelogs/v0.0.30.md diff --git a/pyproject.toml b/pyproject.toml index 47609fa..88b0cf2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ dev = [ "pytest-rerunfailures>=15.1", "pytest-xdist>=3.8.0", "pytest>=8.4.2", + "python-dotenv>=1.0", "s3fs>=2025.3.0", "sktime>=0.40.1", "timecopilot-gift-eval>=0.3.0", @@ -68,7 +69,7 @@ dependencies = [ "catboost>=1.2.10", "datasets>=4.1.1", "fire", - "foundationforecast>=0.1.3", + "foundationforecast>=0.1.6", "fsspec>=2025.9.0", "huggingface-hub>=0.36.2,<2.0", "hydra-core>=1.3.2", @@ -107,7 +108,7 @@ license = "MIT" name = "timecopilot" readme = "README.md" requires-python = ">=3.10" -version = "0.0.32" +version = "0.0.33" [project.optional-dependencies] distributed = [ @@ -171,3 +172,7 @@ select = ["B", "E", "F", "I", "SIM", "UP"] [tool.ruff.lint.isort] known-local-folder = ["timecopilot"] no-lines-before = ["local-folder"] + +[tool.uv.sources] +# Remove after foundationforecast 0.1.6 is on PyPI, then: uv lock --upgrade-package foundationforecast +foundationforecast = {branch = "feat/tabpfn-ts-3", git = "https://github.com/TimeCopilot/foundationforecast"} diff --git a/tests/models/conftest.py b/tests/models/conftest.py index eded3cc..c13760f 100644 --- a/tests/models/conftest.py +++ b/tests/models/conftest.py @@ -167,8 +167,26 @@ def disable_mps_session(monkeypatch): models.append(PatchTSTFM(context_length=2_048)) if sys.version_info < (3, 13): - from tabpfn_time_series import TabPFNMode + from contextlib import contextmanager + + import numpy as np + import pandas as pd + from tabpfn_time_series import TimeSeriesDataFrame from timecopilot.models.foundation.tabpfn import TabPFN - models.append(TabPFN(mode=TabPFNMode.MOCK)) + class _MockTabPFNPredictor: + def predict(self, train_tsdf, test_tsdf, quantiles=None): + result = {"target": np.full(len(test_tsdf), 1.0)} + if quantiles is not None: + for q in quantiles: + result[q] = np.full(len(test_tsdf), q) + return TimeSeriesDataFrame(pd.DataFrame(result, index=test_tsdf.index)) + + @contextmanager + def _mock_get_model(_self): + yield _MockTabPFNPredictor() + + tabpfn_model = TabPFN() + tabpfn_model._get_model = lambda: _mock_get_model(tabpfn_model) # type: ignore[method-assign, assignment] + models.append(tabpfn_model) diff --git a/tests/models/foundation/test_tabpfn.py b/tests/models/foundation/test_tabpfn.py new file mode 100644 index 0000000..a84fc36 --- /dev/null +++ b/tests/models/foundation/test_tabpfn.py @@ -0,0 +1,144 @@ +from __future__ import annotations + +import os +from pathlib import Path +from unittest.mock import MagicMock, patch + +import pytest +from dotenv import load_dotenv + +pytest.importorskip("tabpfn_time_series", reason="TabPFN requires Python < 3.13") + +from tabpfn.errors import TabPFNLicenseError # noqa: E402 +from tabpfn_time_series import TabPFNMode # noqa: E402 +from utilsforecast.data import generate_series # noqa: E402 + +from timecopilot.models.foundation.tabpfn import ( # noqa: E402 + TABPFN_V2_MODEL, + TABPFN_V3_MODEL, + TabPFN, +) + +load_dotenv(Path(__file__).resolve().parents[3] / ".env") + +pytestmark = pytest.mark.models + +DEFAULT_QUANTILES = [round(i * 0.1, 1) for i in range(1, 10)] +DEFAULT_LEVEL = [0, 20, 40, 60, 80] + +TABPFN_CASES = [ + pytest.param(TABPFN_V2_MODEL, 4096, "TabPFN-2", id="v2"), + pytest.param(TABPFN_V3_MODEL, 32768, "TabPFN-3", id="v3"), +] + + +def _require_tabpfn_token() -> None: + if not os.environ.get("TABPFN_TOKEN"): + pytest.skip("TABPFN_TOKEN not set") + + +def test_tabpfn_default_model_path() -> None: + model = TabPFN() + assert model.model_path == TABPFN_V2_MODEL + + +def test_tabpfn_v3_model_path() -> None: + model = TabPFN(model_path=TABPFN_V3_MODEL, context_length=32768) + assert model.model_path == TABPFN_V3_MODEL + assert model.context_length == 32768 + + +def test_tabpfn_predictor_receives_model_path() -> None: + with patch( + "foundationforecast.models.tabpfn.TabPFNTimeSeriesPredictor" + ) as predictor_cls: + predictor_cls.return_value = MagicMock() + model = TabPFN(model_path=TABPFN_V3_MODEL, mode=TabPFNMode.LOCAL) + with model._get_model(): + pass + predictor_cls.assert_called_once_with( + tabpfn_mode=TabPFNMode.LOCAL, + tabpfn_config={"model_path": TABPFN_V3_MODEL}, + ) + + +@pytest.fixture(scope="module") +def tabpfn_df(): + return generate_series(n_series=1, freq="D", min_length=30, max_length=30) + + +def _make_model(model_path: str, context_length: int, alias: str) -> TabPFN: + return TabPFN( + model_path=model_path, + mode=TabPFNMode.LOCAL, + context_length=context_length, + alias=alias, + ) + + +def _forecast_or_skip_v3(model: TabPFN, *args, **kwargs): + try: + return model.forecast(*args, **kwargs) + except TabPFNLicenseError as exc: + if model.model_path == TABPFN_V3_MODEL: + pytest.skip( + "TabPFN-3 license not accepted; accept at https://ux.priorlabs.ai" + ) + raise exc + + +@pytest.mark.parametrize("model_path,context_length,alias", TABPFN_CASES) +def test_tabpfn_local_point_forecast( + tabpfn_df, model_path: str, context_length: int, alias: str +) -> None: + _require_tabpfn_token() + fcst = _forecast_or_skip_v3( + _make_model(model_path, context_length, alias), + tabpfn_df, + h=3, + freq="D", + ) + assert fcst.shape == (3, 3) + assert alias in fcst.columns + + +@pytest.mark.parametrize("model_path,context_length,alias", TABPFN_CASES) +def test_tabpfn_local_quantile_forecast( + tabpfn_df, model_path: str, context_length: int, alias: str +) -> None: + _require_tabpfn_token() + fcst = _forecast_or_skip_v3( + _make_model(model_path, context_length, alias), + tabpfn_df, + h=3, + freq="D", + quantiles=DEFAULT_QUANTILES, + ) + q_cols = [f"{alias}-q-{int(100 * q)}" for q in DEFAULT_QUANTILES] + assert len(fcst.columns) == 3 + len(q_cols) + assert all(col in fcst.columns for col in q_cols) + assert not any("-lo-" in col or "-hi-" in col for col in fcst.columns) + for c1, c2 in zip(q_cols[:-1], q_cols[1:], strict=False): + assert fcst[c1].le(fcst[c2]).mean() >= 0.8 + + +@pytest.mark.parametrize("model_path,context_length,alias", TABPFN_CASES) +def test_tabpfn_local_level_forecast( + tabpfn_df, model_path: str, context_length: int, alias: str +) -> None: + _require_tabpfn_token() + fcst = _forecast_or_skip_v3( + _make_model(model_path, context_length, alias), + tabpfn_df, + h=3, + freq="D", + level=DEFAULT_LEVEL, + ) + lv_cols = [] + for lv in DEFAULT_LEVEL: + lv_cols.extend([f"{alias}-lo-{lv}", f"{alias}-hi-{lv}"]) + assert len(fcst.columns) == 3 + len(lv_cols) + assert all(col in fcst.columns for col in lv_cols) + assert not any("-q-" in col for col in fcst.columns) + for lo, hi in zip(lv_cols[2::2], lv_cols[3::2], strict=False): + assert fcst[lo].le(fcst[hi]).all() diff --git a/timecopilot/models/foundation/tabpfn.py b/timecopilot/models/foundation/tabpfn.py index 0858ff3..10dbc97 100644 --- a/timecopilot/models/foundation/tabpfn.py +++ b/timecopilot/models/foundation/tabpfn.py @@ -1,3 +1,4 @@ +from foundationforecast.models.tabpfn import TABPFN_V2_MODEL, TABPFN_V3_MODEL from foundationforecast.models.tabpfn import TabPFN as _TabPFN from ..utils.forecaster import Forecaster @@ -7,4 +8,4 @@ class TabPFN(_TabPFN, Forecaster): pass -__all__ = ["TabPFN"] +__all__ = ["TABPFN_V2_MODEL", "TABPFN_V3_MODEL", "TabPFN"] diff --git a/uv.lock b/uv.lock index 8515e6b..4d8715b 100644 --- a/uv.lock +++ b/uv.lock @@ -1638,6 +1638,21 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024, upload-time = 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released API, and drop the release checklist. Co-authored-by: Cursor --- docs/RELEASE.md | 34 -------------------------- docs/changelogs/v0.0.33.md | 4 +-- pyproject.toml | 4 --- tests/models/foundation/test_tabpfn.py | 11 ++++++++- uv.lock | 8 ++++-- 5 files changed, 18 insertions(+), 43 deletions(-) delete mode 100644 docs/RELEASE.md diff --git a/docs/RELEASE.md b/docs/RELEASE.md deleted file mode 100644 index d64dcea..0000000 --- a/docs/RELEASE.md +++ /dev/null @@ -1,34 +0,0 @@ -# Release checklist (v0.0.33) - -Depends on **foundationforecast v0.1.6** (TabPFN-3). See the [foundationforecast release checklist](https://github.com/TimeCopilot/foundationforecast/blob/main/docs/RELEASE.md). - -## Before tagging - -1. Merge PR for TabPFN-3 support into `main`. -2. Confirm `foundationforecast` **0.1.6** is on PyPI. -3. Remove the temporary git source from `pyproject.toml`: - - ```toml - [tool.uv.sources] - foundationforecast = { git = "...", branch = "feat/tabpfn-ts-3" } - ``` - -4. Refresh the lock file: - - ```bash - uv lock --upgrade-package foundationforecast - uv sync - uv run pytest tests/models/foundation/test_tabpfn.py -m models - ``` - -5. Verify version `0.0.33` in `pyproject.toml` and changelog `docs/changelogs/v0.0.33.md`. - -## Publish to PyPI - -```bash -git checkout main && git pull -git tag v0.0.33 -git push origin v0.0.33 -``` - -Ensure `TABPFN_TOKEN` is set in GitHub repository secrets for CI. diff --git a/docs/changelogs/v0.0.33.md b/docs/changelogs/v0.0.33.md index d56133e..8c49573 100644 --- a/docs/changelogs/v0.0.33.md +++ b/docs/changelogs/v0.0.33.md @@ -1,6 +1,6 @@ ### Features -* **TabPFN-3 foundation model**: Added support for TabPFN-3 via `foundationforecast>=0.1.6`. Use the existing `TabPFN` class with `model_path`; TabPFN-2 remains the default. See [#25](https://github.com/TimeCopilot/foundationforecast/pull/25). +* **TabPFN-3 foundation model**: Added support for TabPFN-3 via `foundationforecast>=0.1.6`. Use the existing `TabPFN` class with `model_path`; TabPFN-2 remains the default. ```python import pandas as pd @@ -18,7 +18,7 @@ context_length=32768, alias="TabPFN-3", ) - fcst_df = model.forecast(df, h=12, freq="MS", level=[0, 20, 40, 60, 80]) + assert model.model_path == TABPFN_V3_MODEL ``` **License note:** TabPFN-2.6+ and TabPFN-3 weights use the TabPFN Non-Commercial license. First LOCAL use requires accepting terms at [ux.priorlabs.ai](https://ux.priorlabs.ai) (`TABPFN_TOKEN`). TabPFN-3 is LOCAL-only today. diff --git a/pyproject.toml b/pyproject.toml index 88b0cf2..12f9254 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -172,7 +172,3 @@ select = ["B", "E", "F", "I", "SIM", "UP"] [tool.ruff.lint.isort] known-local-folder = ["timecopilot"] no-lines-before = ["local-folder"] - -[tool.uv.sources] -# Remove after foundationforecast 0.1.6 is on PyPI, then: uv lock --upgrade-package foundationforecast -foundationforecast = {branch = "feat/tabpfn-ts-3", git = "https://github.com/TimeCopilot/foundationforecast"} diff --git a/tests/models/foundation/test_tabpfn.py b/tests/models/foundation/test_tabpfn.py index a84fc36..fb049b8 100644 --- a/tests/models/foundation/test_tabpfn.py +++ b/tests/models/foundation/test_tabpfn.py @@ -43,11 +43,20 @@ def test_tabpfn_default_model_path() -> None: def test_tabpfn_v3_model_path() -> None: - model = TabPFN(model_path=TABPFN_V3_MODEL, context_length=32768) + model = TabPFN( + model_path=TABPFN_V3_MODEL, + context_length=32768, + mode=TabPFNMode.LOCAL, + ) assert model.model_path == TABPFN_V3_MODEL assert model.context_length == 32768 +def test_tabpfn_v3_rejects_client_mode() -> None: + with pytest.raises(ValueError, match="LOCAL-only"): + TabPFN(model_path=TABPFN_V3_MODEL, mode=TabPFNMode.CLIENT) + + def test_tabpfn_predictor_receives_model_path() -> None: with patch( "foundationforecast.models.tabpfn.TabPFNTimeSeriesPredictor" diff --git a/uv.lock b/uv.lock index 4d8715b..a2c2276 100644 --- a/uv.lock +++ b/uv.lock @@ -1769,7 +1769,7 @@ wheels = [ [[package]] name = "foundationforecast" version = "0.1.6" -source = { git = "https://github.com/TimeCopilot/foundationforecast?branch=feat%2Ftabpfn-ts-3#f70eb3f05788cd5cb8f2121a85bbc3800d062a82" } +source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "gluonts", extra = ["torch"] }, { name = "huggingface-hub" }, @@ -1792,6 +1792,10 @@ dependencies = [ { name = "transformers" }, { name = "utilsforecast" }, ] +sdist = { url = "https://files.pythonhosted.org/packages/d9/f9/174c146ac7078c7ab443e0aaddb210c11368d47f1483723f5a6f01db71d6/foundationforecast-0.1.6.tar.gz", hash = "sha256:eaab910fcc4980970f4135ee2a5bcf3cfe388c0af2898a2923bd69ecf18b2468", size = 3629335, upload-time = "2026-09-14T20:32:11.645Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2d/28/f4044aa413653dd62f3869fd4d91cbba145156308636aa519af1b26ae7ff/foundationforecast-0.1.6-py3-none-any.whl", hash = "sha256:4da896a9f33a88991ea040ad465058449836e8d558f295fb1ad39c26121c5f24", size = 71084, upload-time = "2026-09-14T20:32:09.967Z" }, +] [[package]] name = "frozenlist" @@ -7689,7 +7693,7 @@ requires-dist = [ { name = "dask", marker = "extra == 'distributed'", specifier = "<=2024.12.1" }, { name = "datasets", specifier = ">=4.1.1" }, { name = "fire" }, - { name = "foundationforecast", git = "https://github.com/TimeCopilot/foundationforecast?branch=feat%2Ftabpfn-ts-3" }, + { name = "foundationforecast", specifier = ">=0.1.6" }, { name = "fsspec", specifier = ">=2025.9.0" }, { name = "fugue", extras = ["dask", "ray", "spark"], marker = "extra == 'distributed'", specifier = ">=0.9.0" }, { name = "huggingface-hub", specifier = ">=0.36.2,<2.0" },