Welcome to QuaPy’s documentation!¶
QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation) written in Python.
Introduction¶
QuaPy roots on the concept of data sample, and provides implementations of most important concepts in quantification literature, such as the most important quantification baselines, many advanced quantification methods, quantification-oriented model selection, many evaluation measures and protocols used for evaluating quantification methods. QuaPy also integrates commonly used datasets and offers visualization tools for facilitating the analysis and interpretation of results.
A quick example:¶
The following script fetchs a Twitter dataset, trains and evaluates an Adjusted Classify & Count model in terms of the Mean Absolute Error (MAE) between the class prevalences estimated for the test set and the true prevalences of the test set.
import quapy as qp
from sklearn.linear_model import LogisticRegression
dataset = qp.datasets.fetch_twitter('semeval16')
# create an "Adjusted Classify & Count" quantifier
model = qp.method.aggregative.ACC(LogisticRegression())
model.fit(dataset.training)
estim_prevalences = model.quantify(dataset.test.instances)
true_prevalences = dataset.test.prevalence()
error = qp.error.mae(true_prevalences, estim_prevalences)
print(f'Mean Absolute Error (MAE)={error:.3f}')
Quantification is useful in scenarios of prior probability shift. In other words, we would not be interested in estimating the class prevalences of the test set if we could assume the IID assumption to hold, as this prevalence would simply coincide with the class prevalence of the training set. For this reason, any Quantification model should be tested across samples characterized by different class prevalences. QuaPy implements sampling procedures and evaluation protocols that automates this endeavour. See the Evaluation for detailed examples.
Features¶
Implementation of most popular quantification methods (Classify-&-Count variants, Expectation-Maximization, SVM-based variants for quantification, HDy, QuaNet, and Ensembles).
Versatile functionality for performing evaluation based on artificial sampling protocols.
Implementation of most commonly used evaluation metrics (e.g., MAE, MRAE, MSE, NKLD, etc.).
- Popular datasets for Quantification (textual and numeric) available, including:
32 UCI Machine Learning datasets.
11 Twitter Sentiment datasets.
3 Reviews Sentiment datasets.
Native supports for binary and single-label scenarios of quantification.
Model selection functionality targeting quantification-oriented losses.
Visualization tools for analysing results.