Source code for causalexplain.independence.mic

"""

This module contains functions to compute the Maximal Information Coefficient
between pairs of features in a dataframe. The MIC is a measure of the strength
of the linear or non-linear association between two variables. The MIC is
computed using the MINE statistics, which is a non-parametric method that
computes the MIC between two variables by estimating the mutual information
between them.

"""

from itertools import combinations

import numpy as np
import pandas as pd

from mlforge.progbar import ProgBar # type: ignore




def pairwise_mic(
        data: pd.DataFrame,
        alpha=0.6,
        c=15,
        to_return='mic',
        est="mic_approx",
        prog_bar=True,
        silent=False):
    """Compute the MIC/TIC for each pair of features in a dataframe."""
    assert est in ["mic_approx",
                   "mic_e"], "est must be either mic_approx or mic_e"

    # Check if 'minpy' package is installed
    try:
        from minepy import pstats
    except ModuleNotFoundError:
        raise ModuleNotFoundError(
            "Please install 'minepy' package to use the 'pairwise_mic' function")

    mic_p, tic_p = pstats(data.values.T, alpha=alpha, c=c, est=est)
    m = len(data.columns)
    mic, tic = np.ones((m, m)), np.ones((m, m))

    # pbar = tqdm(total=m*(m-1)/2, **
    #             tqdm_params("Computing MIC", prog_bar, silent=silent))
    pbar = ProgBar().start_subtask(m*(m-1)/2)

    # desc="Computing MIC", disable=not prog_bar,
    # position=1, leave=False)
    for i in range(m):
        for j in range(i+1, m):
            k = int(m*i - i*(i+1)/2 - i - 1 + j)
            mic[i, j] = mic_p[k]
            mic[j, i] = mic_p[k]
            tic[i, j] = tic_p[k]
            tic[j, i] = tic_p[k]
            pbar.update_subtask(1)

    if to_return == "tic":
        return tic
    return mic