Welcome to GalaxySpectrumClassifier

Installation

GalaxySpectrumClassifier requires Python 3.13 or newer. The package pulls in the scientific Python stack used by the trainers, including pandas, scikit-learn, torch, skorch, and torchvision.

The Python package can be installed from PyPI:

python -m pip install GalaxySpectrumClassifier

To install directly from a checkout of this repository:

git clone git@github.com:ssciwr/GalaxySpectrumClassifier.git
cd GalaxySpectrumClassifier
python -m pip install .

Development installation

If you want to contribute to the development of GalaxySpectrumClassifier, we recommend the following editable installation from this repository:

git clone git@github.com:ssciwr/GalaxySpectrumClassifier.git
cd GalaxySpectrumClassifier
python -m pip install --editable .[tests]

Having done so, the test suite can be run using pytest:

python -m pytest

Usage overview

GalaxySpectrumClassifier provides a small set of configurable building blocks for training galaxy-spectrum classifiers and related tabular models:

• TabularDataset presents a directory of tabular files as one indexed dataset.
• SimpleTrainer fits models after converting a dataset to full X, y arrays.
• EpochTrainer trains torch modules over repeated epochs through skorch.

Most objects can be created directly from dictionaries, which makes YAML files a convenient way to describe an experiment. The configs/ directory contains examples for sklearn and skorch-based training.

import yaml

from GalaxySpectrumClassifier import SimpleTrainer, TabularDataset

with open("configs/binary_classsifier_simple_example.yaml") as stream:
    config = yaml.safe_load(stream)

dataset = TabularDataset.from_config(config["dataset"] | {"label_columns": "source"})
trainer = SimpleTrainer.from_config(config["trainer"])

trainer.fit(dataset)
scores = trainer.evaluate(dataset)
trainer.save_snapshot("example-run")

Config-driven trainers

Trainer configuration uses dotted import paths for models, metrics, callbacks, calibrators, optimizers, losses, and other pluggable pieces. For example, model_type: sklearn.ensemble.RandomForestClassifier builds an sklearn random forest, while model_type: skorch.NeuralNetClassifier builds a skorch-wrapped torch network. Nested values of the form {"type": "package.Object"} are resolved to live Python objects, which is useful for torch modules and losses in YAML.

SimpleTrainer is intended for estimators that can train on materialized feature and target arrays. It supports sklearn-style estimators, skorch estimators, optional sklearn calibration wrappers, task-aware metrics, snapshots, and standalone model export.

EpochTrainer owns separate training, validation, and test dataset configuration. It is the better fit for torch models that should train in batches over multiple epochs, with skorch callbacks, checkpointing, early stopping, learning-rate schedulers, metrics, snapshots, and model export.

Tabular datasets

TabularDataset treats each row in a directory of tabular files as one sample. It currently supports registered tabular formats such as CSV and parquet through PyArrow-backed handlers. Files are ordered consistently and rows can be indexed like a torch dataset.

The dataset configuration names the data path, read options, file suffix, and target column or columns:

from GalaxySpectrumClassifier import TabularDataset

dataset = TabularDataset(
    path="data/classification_v2",
    dataformat="csv",
    suffix=".csv",
    label_columns="source",
)

features, target = dataset[0]

Optional pre_filter and pre_transform hooks run once into an on-disk cache, while transform prepares rows at retrieval time. These hooks can also be configured with dotted import paths so they can live in YAML alongside the rest of the experiment.

Torch, sklearn, and skorch

The trainers are designed to bridge sklearn-style and torch-style workflows. Use sklearn estimators directly with SimpleTrainer when the model consumes full arrays. Use skorch estimators with SimpleTrainer when a torch module can still be trained through the sklearn estimator interface.

For longer neural-network training runs, use EpochTrainer. It builds the appropriate skorch wrapper for the configured task:

• NeuralNetBinaryClassifier for binary classification.
• NeuralNetClassifier for multiclass classification.
• NeuralNetRegressor for regression.

This keeps torch modules usable in sklearn-like workflows while still allowing batch loading, callbacks, checkpointing, and metrics during epoch-based training.

Acknowledgments

This repository was set up using the SSC Cookiecutter for Python Packages.