Skip to main content

The Python Data Valuation Library

Project description

pyDVL Logo

A library for data valuation.

Build Status
PyPI - License

Docs

pyDVL collects algorithms for Data Valuation and Influence Function computation.

Data Valuation is the task of estimating the intrinsic value of a data point wrt. the training set, the model and a scoring function. We currently implement methods from the following papers:

Influence Functions compute the effect that single points have on an estimator / model. We implement methods from the following papers:

Installation

To install the latest release use:

$ pip install pyDVL

You can also install the latest development version from TestPyPI:

pip install pyDVL --index-url https://test.pypi.org/simple/

For more instructions and information refer to Installing pyDVL in the documentation.

Usage

The steps required to compute values for your samples are:

  1. Create a Dataset object with your train and test splits.
  2. Create an instance of a SupervisedModel (basically any sklearn compatible predictor)
  3. Create a Utility object to wrap the Dataset, the model and a scoring function.
  4. Use one of the methods defined in the library to compute the values.

This is how it looks for Truncated Montecarlo Shapley, an efficient method for Data Shapley values:

import numpy as np
from pydvl.utils import Dataset, Utility
from pydvl.value.shapley import compute_shapley_values
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

X, y = np.arange(100).reshape((50, 2)), np.arange(50)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.5, random_state=16
)
dataset = Dataset(X_train, y_train, X_test, y_test)
model = LinearRegression()
utility = Utility(model, dataset)
values = compute_shapley_values(
    u=utility, n_iterations=100, mode="truncated_montecarlo"
)

For more instructions and information refer to Getting Started in the documentation. We provide several examples with details on the algorithms and their applications.

Caching

pyDVL offers the possibility to cache certain results and speed up computation. It uses Memcached For that.

You can run it either locally or, using Docker:

docker container run --rm -p 11211:11211 --name pydvl-cache -d memcached:latest

You can read more in the caching module's documentation.

Contributing

Please open new issues for bugs, feature requests and extensions. You can read about the structure of the project, the toolchain and workflow in the guide for contributions.

License

pyDVL is distributed under LGPL-3.0. A complete version can be found in two files: here and here.

All contributions will be distributed under this license.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyDVL-0.4.0.tar.gz (86.4 kB view hashes)

Uploaded source

Built Distribution

pyDVL-0.4.0-py3-none-any.whl (98.0 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page