mlquantify 0.1.20

Quantification Library

A Python Package for Quantification

---

mlquantify is a Python library for quantification, also known as supervised prevalence estimation, designed to estimate the distribution of classes within datasets. It offers a range of tools for various quantification methods, model selection tailored for quantification tasks, evaluation metrics, and protocols to assess quantification performance. Additionally, mlquantify includes popular datasets and visualization tools to help analyze and interpret results.

---

Latest Release

• Version 0.1.11: Inicial beta version. For a detailed list of changes, check the changelog.
• In case you need any help, refer to the User Guide.
• Explore the API documentation for detailed developer information.
• See also the library in the pypi site in pypi mlquantify

---

Installation

To install mlquantify, run the following command:

pip install mlquantify

If you only want to update, run the code below:

pip install --upgrade mlquantify

---

Contents

Section	Description
21 Quantification Methods	Methods for quantification, such as classify & Count Correct methods, Threshold Optimization, Mixture Models and more.
Dynamic class management	All methods are dynamic, and handles multiclass and binary problems, in case of binary it makes One-Vs-All (OVA) automatically.
Model Selection	Criteria and processes used to select the best model, such as grid-search for the case of quantification
Evaluation Metrics	Specific metrics used to evaluate quantification performance, (e.g., AE, MAE, NAE, SE, KLD, etc.).
Evaluation Protocols	Evaluation protocols used, based on sampling generation (e.g., APP, NPP, etc.)..
Comprehensive Documentation	Complete documentation of the project, including code, data, and results.

---

Quick example:

This code first loads the breast cancer dataset from sklearn, which is then split into training and testing sets. It uses the Expectation Maximisation Quantifier (EMQ) with a RandomForest classifier to predict class prevalence. After training the model, it evaluates performance by calculating and printing the absolute error and bias between the real and predicted prevalences.

from mlquantify.methods import EMQ
from mlquantify.metrics import MAE, NRAE
from mlquantify.utils import get_prev_from_labels

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

# Loading dataset from sklearn
features, target = load_breast_cancer(return_X_y=True)

#Splitting into train and test
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.3)

#Create the model, here it is the Expectation Maximisation Quantifier (EMQ) with a classifier
model = EMQ(RandomForestClassifier())
model.fit(X_train, y_train)

#Predict the class prevalence for X_test
pred_prevalence = model.predict(X_test)
real_prevalence = get_prev_from_labels(y_test)

#Get the error for the prediction
mae = MAE(real_prevalence, pred_prevalence)
nrae = NRAE(real_prevalence, pred_prevalence)

print(f"Mean Absolute Error -> {mae}")
print(f"Normalized Relative Absolute Error -> {nrae}")

---

Requirements

• Scikit-learn
• pandas
• numpy
• joblib
• tqdm
• matplotlib
• xlrd

---

Documentation

API is avaliable here

---