Liam/add inference code and example

.github/workflows/publish.yml

@@ -0,0 +1,32 @@
+name: Publish to PyPI
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v5
+      - run: uv build
+      - uses: actions/upload-artifact@v4
+        with:
+          name: dist
+          path: dist/
+
+  publish:
+    needs: build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi
+      url: https://pypi.org/p/timee-ts
+    permissions:
+      id-token: write
+    steps:
+      - uses: actions/download-artifact@v4
+        with:
+          name: dist
+          path: dist/
+      - uses: pypa/gh-action-pypi-publish@release/v1

.gitignore

@@ -195,9 +195,9 @@ cython_debug/
 .abstra/
 
 # Visual Studio Code
-#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore
 #   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
-#   and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#   and can be added to the global gitignore or merged into this file. However, if you prefer,
 #   you could uncomment the following to ignore the entire vscode folder
 # .vscode/
 # Temporary file for partial code execution

.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: check-yaml
+      - id: check-toml
+      - id: check-added-large-files
+      - id: check-merge-conflict
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.8.4
+    hooks:
+      - id: ruff
+        args: [--fix]
+      - id: ruff-format

README.md

@@ -1 +1,61 @@
-# timee
+# TIMEE: Time Series Classification via In-Context Learning
+
+TIMEE is a pretrained transformer for time series classification. It classifies test
+series in a single forward pass given labeled training examples — no per-dataset
+training or fine-tuning required.
+
+**Paper:** *TIMEE: End-to-end Time Series Classification via In-Context Learning*
+
+## Installation
+
+```bash
+pip install timee-ts
+```
+
+Requirements: Python ≥ 3.10, PyTorch ≥ 2.0.
+
+## Quickstart
+
+```python
+from timee import TimeeClassifier
+import numpy as np
+
+clf = TimeeClassifier.from_pretrained()  # downloads from HuggingFace on first use
+
+# X: (n_samples, n_channels, seq_len) float32
+X_train = np.random.randn(20, 1, 256).astype(np.float32)
+y_train = np.array([0, 1] * 10)
+X_test  = np.random.randn(5, 1, 256).astype(np.float32)
+
+predictions, probabilities = clf.predict(X_train, y_train, X_test)
+```
+
+Labels can be any type (`int`, `str`, etc.). Datasets with more than 10 classes are
+handled automatically via one-vs-rest.
+
+## API
+
+### `TimeeClassifier.from_pretrained(path, device=None, use_ensemble=True)`
+
+Loads a checkpoint from a directory containing `model.safetensors`.
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `path` | `"liamsbhoo/timee"` | Local directory or HuggingFace Hub repo ID. |
+| `device` | auto | `"cuda"`, `"cpu"`, or `torch.device`. Defaults to CUDA > MPS > CPU. |
+| `use_ensemble` | `True` | 4-member preprocessing ensemble (interpolate×{256,512} × {raw, diff}). Set `False` for faster single-pass inference. |
+
+### `clf.predict(X_train, y_train, X_test)`
+
+Returns `(predictions, probabilities)`:
+- `predictions`: `(n_test,)`, same type as `y_train`
+- `probabilities`: `(n_test, n_classes)`, rows sum to 1
+
+## Citation
+
+```bibtex
+@inproceedings{timee2026,
+  title     = {TIMEE: End-to-end Time Series Classification via In-Context Learning},
+  year      = {2026},
+}
+```

pyproject.toml

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+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "timee-ts"
+version = "0.1.0"
+description = "End-to-end time series classification via in-context learning"
+readme = "README.md"
+requires-python = ">=3.10"
+license = { file = "LICENSE" }
+authors = [
+    { name = "Liam Shi Bin Hoo", email = "hoos@tf.uni-freiburg.de" },
+    { name = "Jaris Küken", email = "kuekenj@informatik.uni-freiburg.de" },
+]
+keywords = ["time-series", "classification", "in-context learning"]
+classifiers = [
+    "License :: OSI Approved :: Apache Software License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+]
+dependencies = [
+    "torch>=2.0",
+    "numpy>=1.24",
+    "scipy>=1.10",
+    "einops>=0.7",
+    "safetensors>=0.4",
+    "scikit-learn>=1.3",
+    "huggingface_hub>=0.20",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest",
+    "ruff",
+    "pre-commit",
+    "aeon",
+    "matplotlib",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/timee"]
+
+[tool.ruff]
+line-length = 100
+
+[tool.ruff.lint]
+select = ["E", "F", "I"]
+
+[tool.ruff.lint.per-file-ignores]
+"*.ipynb" = ["E402"]

src/timee/__init__.py

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+"""TIMEE: End-to-end time series classification via in-context learning."""
+
+from timee.classifier import TimeeClassifier
+
+__all__ = ["TimeeClassifier"]