Irregular time series made easy
Project description
pyrregular
| 📖 Documentation · ⚙️ Tutorials | |
|---|---|
| CI/CD | |
| Code | |
| Community | |
| Citation | coming soon... |
Installation
You can install via pip with:
pip install pyrregular
For third party models use:
pip install pyrregular[models]
Quick Guide
List datasets
If you want to see all the datasets available, you can use the list_datasets function:
from pyrregular import list_datasets
df = list_datasets()
Load a dataset
To load a dataset, you can use the load_dataset function. For example, to load the "Garment" dataset, you can do:
from pyrregular import load_dataset
df = load_dataset("Garment.h5")
The dataset is saved in the default os cache directory, which can be found with:
import pooch
print(pooch.os_cache("pyrregular"))
The repository is hosted at: https://huggingface.co/datasets/splandi/pyrregular/
Downstream tasks
Classification
To use the dataset for classification, you can just "densify" it:
from pyrregular import load_dataset
df = load_dataset("Garment.h5")
X, _ = df.irr.to_dense()
y, split = df.irr.get_task_target_and_split()
X_train, X_test = X[split != "test"], X[split == "test"]
y_train, y_test = y[split != "test"], y[split == "test"]
# We have ready-to-go models from various libraries:
from pyrregular.models.rocket import rocket_pipeline
model = rocket_pipeline
model.fit(X_train, y_train)
model.score(X_test, y_test)
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