rtichoke
rtichoke is a Python library for visualizing the performance of predictive models. It provides a flexible and intuitive way to create a variety of common evaluation plots, including:
- ROC Curves
- Precision-Recall Curves
- Gains and Lift Charts
- Calibration Curves
- Decision Curves
The library is designed to be easy to use while still offering a high degree of control over the final plots.
For some reproducible examples please visit rtichoke blog!
Installation
For a project managed with uv, add rtichoke with:
uv add rtichoke
Alternatively, install rtichoke from PyPI with pip:
pip install rtichoke
Getting Started
To use rtichoke, you'll usually need two main inputs:
probs: A dictionary containing model-predicted probabilities.reals: Observed outcomes, provided either as one array or as a dictionary keyed by population.
Here's a quick example of creating a ROC curve for a single model:
import numpy as np
import rtichoke as rk
probs = {
"Model A": np.array([0.1, 0.9, 0.4, 0.8, 0.3, 0.7, 0.2, 0.6])
}
reals = {
"Population": np.array([0, 1, 0, 1, 0, 1, 0, 1])
}
fig = rk.create_roc_curve(
probs=probs,
reals=reals,
)
fig.show()
Compare populations
When predictions and outcomes are both dictionaries with the same keys, rtichoke pairs them population-by-population. The populations do not need to have the same sample size.
probs = {
"Train": np.array([0.10, 0.90, 0.20, 0.80, 0.30, 0.70]),
"Test": np.array([0.15, 0.85, 0.25, 0.75]),
}
reals = {
"Train": np.array([0, 1, 0, 1, 0, 1]),
"Test": np.array([0, 1, 0, 0]),
}
fig = rk.create_calibration_curve(
probs=probs,
reals=reals,
)
fig.show()
Here, Train contains six observations and Test contains four. Each probability vector only needs to match the outcome vector for its own population.
Key Features
- Simple API: Create complex visualizations with a small amount of code.
- Time-to-Event Analysis: Support for time-dependent outcomes, including censoring and competing risks.
- Interactive Plots: Plotly-based interactive visualizations.
- Flexible Data Handling: Works with common Python array/data-frame workflows, including NumPy and Polars.
Documentation
The official documentation, including the Getting Started guide and API reference, is published at:
1 maintainer
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