rtichoke 0.1.27

interactive visualizations for performance of predictive models

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
• 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 the rtichoke blog!

Installation

You can install rtichoke from PyPI:

pip install rtichoke

Getting Started

To use rtichoke, you'll need two main inputs:

• probs: A dictionary containing your model's predicted probabilities.
• reals: A dictionary of the true binary outcomes.

Here's a quick example of how to create a ROC curve for a single model:

import numpy as np
import rtichoke as rk

# Sample data for a model. Note that the probabilities for the
# positive class (1) are generally higher than for the negative class (0).
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])}


# Create the ROC curve
fig = rk.create_roc_curve(
  probs=probs,
  reals=reals
)

fig.show()

Key Features

• Simple API: Create complex visualizations with just a few lines of code.
• Time-to-Event Analysis: Native support for models with time-dependent outcomes, including censoring and competing risks.
• Interactive Plots: Built on Plotly for interactive, publication-quality figures.
• Flexible Data Handling: Works seamlessly with NumPy and Polars.

Documentation

For a complete guide to the library, including a "Getting Started" tutorial and a full API reference, please see the official documentation.