README.md

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:

https://uriahf.github.io/rtichoke_python/