Patient Rule Induction Method for Python

This module implements the Patient Rule Induction Method (PRIM) for scenario discovery in Python. This is a standalone version of the PRIM algorithm implemented in the EMA Workbench by Jan Kwakkel, which is based on the sdtoolkit R package developed by RAND Corporation. All credit goes to Jan Kwakkel for developing the original code. This standalone version of PRIM was created and maintained by David Hadka.

Licensed under the GNU General Public License, version 3 or later.

Installation

To install the latest PRIM release, run the following command:

    pip install prim

To install the latest development version of PRIM, run the following commands:

    pip install -U build setuptools
    git clone https://github.com/Project-Platypus/PRIM.git
    cd PRIM
    python -m build

Usage

Below shows the interactive use of the PRIM module for finding the first box. In this example, we are interested in cases where the response is greater than 0.5 (as indicated by the threshold and threshold_type arguments). After creating the Prim object, we invoke find_box() to find the first box containing cases of interest followed by box.show_tradeoff() to display the tradeoff between coverage and density for each peeling/pasting trajectory.

    import prim
    import pandas as pd
    import matplotlib.pyplot as plt

    df = pd.DataFrame(np.random.rand(1000, 3), columns=["x1", "x2", "x3"])
    response = df["x1"]*df["x2"] + 0.2*df["x3"]
    
    p = prim.Prim(df, response, threshold=0.5, threshold_type=">")
    
    box = p.find_box()
    box.show_tradeoff()
    
    plt.show()

You can interact with the tradeoff plot by hovering the mouse over points to view the stats, as shown below.

Clicking a point shows additional details in a separate window.

This module extends EMA Workbench's support for categorical data by allowing the categorical data to be plotted in the pairwise scatter plot:

Also note the Prev / Next buttons on this window allowing navigation to adjacent peeling trajectories without having to return to the tradeoff plot.