Empulse
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 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 | Real-world 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 0.12.0
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