Metadata-Version: 2.4
Name: tscglue
Version: 0.1.1
Summary: Automatic Time Series Classification
Requires-Python: <3.14,>=3.12
Requires-Dist: aeon>=1.3.0
Requires-Dist: click
Requires-Dist: huggingface-hub
Requires-Dist: imblearn
Requires-Dist: polars
Requires-Dist: pyarrow
Requires-Dist: pytorch-lightning
Requires-Dist: scikit-learn
Requires-Dist: seaborn
Requires-Dist: statsmodels
Requires-Dist: tabicl
Requires-Dist: torch
Requires-Dist: tqdm
Requires-Dist: tsfresh
Provides-Extra: dev
Requires-Dist: awscli; extra == 'dev'
Requires-Dist: boto3; extra == 'dev'
Requires-Dist: isort; extra == 'dev'
Requires-Dist: moto[s3]; extra == 'dev'
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Requires-Dist: s3fs; extra == 'dev'
Provides-Extra: notebooks
Requires-Dist: accelerate; extra == 'notebooks'
Requires-Dist: catboost; extra == 'notebooks'
Requires-Dist: chronos-forecasting; extra == 'notebooks'
Requires-Dist: hvplot; extra == 'notebooks'
Requires-Dist: ipykernel; extra == 'notebooks'
Requires-Dist: jupyter; extra == 'notebooks'
Requires-Dist: jupyterlab; extra == 'notebooks'
Requires-Dist: lightgbm; extra == 'notebooks'
Requires-Dist: mantis-tsfm; extra == 'notebooks'
Requires-Dist: pandas; extra == 'notebooks'
Requires-Dist: transformers; extra == 'notebooks'
Description-Content-Type: text/markdown

# TSCGlue

Automatic Time Series Classification library built on top of aeon and scikit-learn.

## Installation

```bash
pip install tscglue
```

## Quick Start

```python
from tscglue import utils
from tscglue.models import TSCGlue
from sklearn.metrics import accuracy_score

# Load a time series classification dataset
X_train, y_train, X_test, y_test = utils.load_dataset("ArrowHead")

# Create and train the model
model = TSCGlue(
    random_state=270,
    k_folds=10,
    n_jobs=-1
)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.4f}")
```
