Empulse: Cost-Sensitive Learning in Python
Not every mistake costs the same.
Accuracy, F1 and AUC treat a false positive and a false negative as equally bad. Your business does not. Flagging a loyal customer as a churner wastes a discount; missing a real churner loses their entire lifetime value. A model tuned for accuracy quietly optimises the wrong thing.
Empulse is a Python package for cost-sensitive classification and value-driven (profit-driven) machine learning. It lets you write down what each outcome is actually worth, then use that same definition to evaluate models, train them, and set their decision threshold — all as ordinary scikit-learn estimators.
Installation
Empulse requires Python 3.11 or higher.
pip install empulse
To pull in every optional dependency at once:
pip install empulse[optional]
A model that makes money
On a real telecom churn dataset, an ordinary logistic regression reaches 0.93 ROC AUC and 89% accuracy — and earns 2.28 per customer once the retention campaign is priced in. Training the same data against the cost matrix turns that into a profit of 3.29 per customer.
import pandas as pd
from empulse.datasets import fetch_iranian_churn
from empulse.metrics import Cost, Metric
from empulse.models import CSLogitClassifier
dataset = fetch_iranian_churn(backend=pd)
X, y = dataset.data, dataset.target
clv = dataset.instance_costs['clv'] # each customer's lifetime value
# The dataset ships a cost matrix; turn it into a metric
expected_cost = Metric(dataset.cost_matrix, Cost())
# Train a model that optimises it directly
model = CSLogitClassifier(loss=expected_cost)
model.fit(X, y, clv=clv)
print(expected_cost(y, model.predict_proba(X)[:, 1], clv=clv))
Define your own cost matrix just as easily:
from empulse.metrics import Cost, CostMatrix, Metric
# A spam filter: a legitimate email marked as spam is 5x worse than spam getting through
cost_matrix = (
CostMatrix().add_fp_cost('missed_email').add_fn_cost('wasted_time').set_default(missed_email=5, wasted_time=1)
)
expected_cost = Metric(cost_matrix, Cost())
Read the 5-minute quickstart or the full tutorial.
Features
| Profit-driven metrics | Ready-made metrics for churn, acquisition and credit scoring, or build your own from a symbolic cost matrix. |
| Cost-sensitive models | Logistic regression, gradient boosting, trees, forests, bagging, minimax probability machines and evolutionary trees — all trained on your cost matrix. |
| Threshold tuning | Pick the cut-off that maximises profit instead of defaulting to 0.5, or classify the top-k% by score. |
| Instance-dependent costs | Costs that differ per row are routed through pipelines and cross-validation automatically via scikit-learn metadata routing. |
| Robustness | RobustCSClassifier detects and imputes outliers in noisy instance-dependent costs. |
| Samplers | Cost-proportionate resampling and bias mitigation to make any estimator cost-sensitive. |
| Optimizers | L-BFGS-B, SGD/Adam/RMSProp with learning-rate schedules, and genetic/memetic algorithms for non-smooth objectives. |
| Datasets | Cost-sensitive datasets, each shipping its own cost matrix. |
Works with scikit-learn
Every estimator follows scikit-learn conventions, so it drops into Pipeline, GridSearchCV and
cross_val_score unchanged, and every metric can be wrapped with make_scorer.
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from empulse.models import CSLogitClassifier
pipeline = Pipeline([
('scaler', StandardScaler()),
('model', CSLogitClassifier()),
])
search = GridSearchCV(pipeline, {'model__C': [0.1, 1, 10]})
search.fit(X, y, model__fp_cost=10, model__fn_cost=1)
Documentation
Full documentation at empulse.readthedocs.io:
- Getting Started — install, quickstart, and which tool you need
- Tutorial — a complete worked churn example
- User Guide — in-depth guides per component
- API Reference — every class and function
Citing
If you use Empulse in your research, please cite it via its Zenodo DOI.
License
AGPLv3 — see LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for empulse 0.13.0
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