catasta 0.3.0

Catasta is a Python library designed to simplify and accelerate the process of machine learning model experimentation. It encapsulates the complexities of model training and evaluation, offering researchers and developers a straightforward pipeline for rapid model assessment with minimal setup required.

Catasta: Streamlined Model Experimentation

Catasta is a Python library designed to simplify the process of Machine Learning model experimentation. Optimization, training, evaluation and inference all in one place!

[!WARNING] :construction: Catasta is in early development :construction:

Expect breaking changes on every release until v1.0.0 is reached.

The documentation is under development.

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With Catasta, you can build a model like an archway... Let me explain:

Optimization

First, set the foundations of the model with the Foundation class. This class uses the popular and supercool optuna library to optimize a model given a hyperparameter space and an objective function.

hp_space = {
    "n_patches": (2, 7),
    "d_model": (8, 16),
    "n_layers": (1, 2),
    "n_heads": (1, 2),
    "feedforward_dim": (8, 16),
    "head_dim": (4, 8),
    "dropout": (0.0, 0.5),
    "layer_norm": (True, False),
}

foundation = Foundation(
    hyperparameter_space=hp_space,
    objective_function=objective,
    sampler="bogp",
    n_trials=100,
    direction="maximize",
    use_secretary=True,
    catch_exceptions=True,
)

optimization_info = foundation.optimize()

Training

Set the scaffolds of your model with the Scaffold class. This class integrates a model and a dataset for training and evaluation.

model = FeedforwardRegressor(
    context_length=32,
    hidden_dims=[8, 16, 8],
    dropout=0.0,
    use_layer_norm=True,
    activation="relu",
)

dataset = CatastaDataset(
    root="path/to/dataset/",
    task="regression",
)

scaffold = Scaffold(
    model=model,
    dataset=dataset,
    optimizer="adamw",
    loss_function="mse",
)

scaffold.train(
    epochs=100,
    batch_size=256,
    lr=1e-3,
)

info = scaffold.evaluate()

Inference

Your archway is finished with the Archway class. This class runs the inference of the model given its saved path.

archway = Archway("path/to/saved/model/")

prediction = archway.predict(input)

Other modules

Catasta also has different modules that facilitate model experimentation.

• catasta.models offers a variety of pre-implemente Machine Learning models. All models are single-scripted, so feel free to copy and paste them anywhere.

• catasta.transformations let's you apply transformations to the data when its loaded to a dataset, such as window sliding, normalization...

• catasta.utils has several functions that are useful for model optimization and training.

Installation

Install via pip

Catasta is available as a PyPi package:

pip install catasta

Install from source

Clone the repository

git clone https://github.com/vistormu/catasta

and install the dependencies

pip install -r requirements.txt