mlpr/logreg_analysis.py at main · TeamOGC/mlpr · GitHub

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from typing import Dict
from os import makedirs
import matplotlib.pyplot as plt
# from ogc import dimensionality_reduction as dr
from ogc.classifiers import LogisticRegression
from ogc import utilities
import numpy.typing as npt
import numpy as np
from project import TRAINING_DATA, ROOT_PATH
import logging
from pprint import pprint
logging.basicConfig(level=logging.DEBUG)

logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)

OUTPUT_PATH = ROOT_PATH + "../images/logreg_analysis/"
TABLES_OUTPUT_PATH = ROOT_PATH + "../tables/logreg_analysis/"
makedirs(OUTPUT_PATH, exist_ok=True)
makedirs(TABLES_OUTPUT_PATH, exist_ok=True)


def logreg_callback(prior, l, dimred, dataset_type, weighted, quadratic, logreg_prior=None):
    model = LogisticRegression.LogisticRegression(
        l, logreg_prior, weighted=weighted, quadratic=quadratic)
    DTR, LTR = TRAINING_DATA()
    if dataset_type == "Z-Norm":
        from ogc.utilities import ZNormalization as znorm
        DTR = znorm(DTR)[0]
    if dimred != None:
        from ogc import dimensionality_reduction as dr
        DTR = dr.PCA(DTR, dimred)[0]
    from ogc.utilities import Kfold
    kfold = Kfold(DTR, LTR, model, 5, prior=prior)
    return kfold


def main():
    fast_run = False
    if fast_run:
        priors = [("$\pi = 0.5$", 0.5)]
        l = [("$10^3$", 100)]
        dataset_types = [("RAW", None), ("Z-Norm", "Z-Norm")]
        dimred = [("No PCA", None)]
        weighted = [("Weighted", True), ("Unweighted", False)]
        quadratic = [("Quadratic", True), ("Linear", False)]
        logreg_priors = [("$\pi = 0.1$", 0.1)]
    else:
        priors = [("$\pi = 0.5$", 0.5), ("$\pi = 0.1$", 0.1),
                  ("$\pi = 0.9$", 0.9)]
        l = [("$10^3$", 100), ("$10^2$", 10), ("$10$", 1), ("$10^{-1}$", 0.1), ("$10^{-2}$", 0.01), ("$10^{-3}$",
                                                                                                     0.001), ("$10^{-4}$", 0.0001), ("$10^{-5}$", 0.00001)]
        dataset_types = [("RAW", None), ("Z-Norm", "Z-Norm")]
        dimred = [("No PCA", None), ("PCA $(m=5)$", 5)]
        weighted = [("Weighted", True), ("Unweighted", False)]
        quadratic = [("Quadratic", True), ("Linear", False)]
        logreg_priors = [("$\pi = 0.5$", 0.5),
                         ("$\pi = 0.1$", 0.1), ("$\pi = 0.9$", 0.9)]

    use_csv = False

    if use_csv:
        weighted_table = utilities.load_from_csv(TABLES_OUTPUT_PATH + "logreg_results_weighted.csv")
    else:
        _, weighted_table = utilities.grid_search(
            logreg_callback, priors, l, dimred, dataset_types, [weighted[0]], quadratic, logreg_priors)
        np.savetxt(TABLES_OUTPUT_PATH + "logreg_results_weighted.csv", weighted_table, delimiter=";",
                fmt="%s", header=";".join(["Prior", "Lambda", "PCA", "Dataset", "Weighted", "Type", "LogregPrior", "MinDCF"]))
    # if use_csv:
    #     table = utilities.load_from_csv(TABLES_OUTPUT_PATH + "logreg_results_unweighted.csv")
    # else:
    #     _, table = utilities.grid_search(
    #         logreg_callback, priors, l, dimred, dataset_types, [weighted[1]], quadratic)
    #     np.savetxt(TABLES_OUTPUT_PATH + "logreg_results_unweighted.csv", table, delimiter=";",
    #             fmt="%s", header=";".join(["Prior", "Lambda", "PCA", "Dataset", "Weighted", "Type", "MinDCF"]))


if __name__ == "__main__":
    import time
    start = time.time()
    try:
        main()
    finally:
        print(f"Time elapsed: {time.time() - start} seconds")