speaker-confusion-model/code/plots/algo_comparison.py

from email.errors import MalformedHeaderDefect
from logging import logMultiprocessing
import pandas as pd
import numpy as np

from matplotlib import pyplot as plt
import matplotlib

matplotlib.use("pgf")
matplotlib.rcParams.update(
    {
        "pgf.texsystem": "pdflatex",
        "font.family": "serif",
        "font.serif": "Times New Roman",
        "text.usetex": True,
        "pgf.rcfonts": False,
    }
)


import pickle
from os.path import join as opj

import argparse

parser = argparse.ArgumentParser()
parser.add_argument("--vtc")
parser.add_argument("--lena")
args = parser.parse_args()

data = {
    "lena": opj("output", f"{args.lena}.pickle"),
    "vtc": opj("output", f"{args.vtc}.pickle")   
}

for key in data:
    with open(data[key], "rb") as f:
        data[key] = pickle.load(f)

samples = {
    "vtc": np.load(opj("output", f"{args.vtc}.npz")),
    "lena": np.load(opj("output", f"{args.lena}.npz"))
}

labels = {
    "lena": "LENA",
    "vtc": "VTC"
}

speakers = ["CHI", "OCH", "FEM", "MAL"]
cb_colors = [
    "#377eb8",
    "#ff7f00",
    "#f781bf",
    "#4daf4a",
    "#a65628",
    "#984ea3",
    "#999999",
    "#e41a1c",
    "#dede00",
]

corpora = {
    'bergelson': 0, 'cougar': 1, 'fausey-trio': 2, 'lucid': 3, 'warlaumont': 4, 'winnipeg': 5
}
corpora_names = {
    corpora[corpus]: corpus
    for corpus in corpora
}

def algo_comparison(algo1, algo2, algo1_name, algo2_name):
    fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)

    for row in range(2):
        for col in range(2):
            i = row + 2 * col

            for corpus in set(algo1["corpus"]):
                mask = np.array([algo1["corpus"][k - 1] == corpus for k in algo1["children"]])
                vocs_algo1 = algo1["vocs"][mask,i]
                vocs_algo2 = algo2["vocs"][mask,i]

                axes[row, col].scatter(
                    vocs_algo1,
                    vocs_algo2,
                    s=0.5,
                    color=cb_colors[corpus - 1],
                )

            if row == 1 and col == 0:
                axes[row, col].set_xlabel(algo1_name)
                axes[row, col].set_ylabel(algo2_name)

            axes[row,col].set_xscale("log")
            axes[row,col].set_yscale("log")

            axes[row,col].set_xlim(20,5000)
            axes[row,col].set_ylim(20,5000)

            axes[row, col].axline((100, 100), (1000, 1000), color="black")
            axes[row, col].set_title(speakers[i], y=1, pad=-14)

    plt.subplots_adjust(wspace=0, hspace=0)
    fig.savefig(f"output/algo_comparison_{algo1_name}_{algo2_name}.eps", bbox_inches="tight")

algo_comparison(data["lena"], data["vtc"], "LENA", "VTC")