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

#!/usr/bin/env python3

import pandas as pd
import pickle
import numpy as np

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

def set_size(width, fraction=1, ratio = None):
    fig_width_pt = width * fraction
    inches_per_pt = 1 / 72.27
    if ratio is None:
        ratio = (5 ** 0.5 - 1) / 2
    fig_width_in = fig_width_pt * inches_per_pt
    fig_height_in = fig_width_in * ratio
    return fig_width_in, fig_height_in

parser = argparse.ArgumentParser(description = 'plot_pred')
parser.add_argument('data')
parser.add_argument('fit')
parser.add_argument('output')
args = parser.parse_args()

with open(args.data, 'rb') as fp:
    data = pickle.load(fp)

fit = pd.read_parquet(args.fit)

fig = plt.figure(figsize=set_size(450, 1, 1))
axes = [fig.add_subplot(4,4,i+1) for i in range(4*4)]

speakers = ['CHI', 'OCH', 'FEM', 'MAL']

n_groups = data['n_groups']

for i in range(4*4):
    ax = axes[i]
    row = i//4+1
    col = i%4+1
    label = f'{col}.{row}'

    #if args.group is None:
    #    data = np.hstack([fit[f'alphas.{k}.{label}']/(fit[f'alphas.{k}.{label}']+fit[f'betas.{k}.{label}']).values for k in range(1,n_groups+1)])
    #else:
    #    data = fit[f'alphas.{args.group}.{label}']/(fit[f'alphas.{args.group}.{label}']+fit[f'betas.{args.group}.{label}']).values
    #data = np.hstack([(fit[f'group_mus.{k}.{label}']).values for k in range(1,59)])
    #data = fit[f'mus.{label}'].values
    data = np.hstack([fit[f'probs.{k+1}.{label}'].values for k in range(n_groups)])
  
    ax.set_xticks([])
    ax.set_xticklabels([])
    ax.set_yticks([])
    ax.set_yticklabels([])
    ax.set_ylim(0,5)
    ax.set_xlim(0,1)

    low = np.quantile(data, 0.0275)
    high = np.quantile(data, 0.975)

    if row == 1:
        ax.xaxis.tick_top()
        ax.set_xticks([0.5])
        ax.set_xticklabels([speakers[col-1]])

    if row == 4:
        ax.set_xticks(np.linspace(0.25,1,3, endpoint = False))
        ax.set_xticklabels(np.linspace(0.25,1,3, endpoint = False))

    if col == 1:
        ax.set_yticks([2.5])
        ax.set_yticklabels([speakers[row-1]])

    ax.hist(data, bins = np.linspace(0,1,40), density = True, histtype = 'step')
    ax.axvline(np.mean(data), linestyle = '--', linewidth = 0.5, color = '#333', alpha = 1)
    ax.text(0.5, 4.5, f'{low:.2f} - {high:.2f}', ha = 'center', va = 'center')

fig.suptitle("$p_{ij}$ distribution")
fig.subplots_adjust(wspace = 0, hspace = 0)

plt.savefig(args.output)

plt.show()