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- #!/usr/bin/env python3
- from ChildProject.projects import ChildProject
- from ChildProject.annotations import AnnotationManager
- from ChildProject.metrics import segments_to_grid, conf_matrix
- import numpy as np
- import pandas as pd
- from sklearn.metrics import confusion_matrix
- from sklearn.preprocessing import normalize
- import seaborn as sns
- import matplotlib.pyplot as plt
- import sys
- speakers = ['CHI', 'OCH', 'FEM', 'MAL']
- path = sys.argv[1]
- project = ChildProject(path)
- am = AnnotationManager(project)
- am.read()
- intersection = AnnotationManager.intersection(am.annotations, ['vtc', 'its'])
- segments = am.get_collapsed_segments(intersection)
- segments = segments[segments['speaker_type'].isin(speakers)]
- vtc = segments_to_grid(segments[segments['set'] == 'vtc'], 0, segments['segment_offset'].max(), 100, 'speaker_type', speakers)
- its = segments_to_grid(segments[segments['set'] == 'its'], 0, segments['segment_offset'].max(), 100, 'speaker_type', speakers)
- speakers.extend(['overlap', 'none'])
- confusion_counts = conf_matrix(its, vtc, speakers)
- plt.rcParams.update({'font.size': 12})
- plt.rc('xtick', labelsize = 10)
- plt.rc('ytick', labelsize = 10)
- fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(6.4*2, 4.8))
- confusion = normalize(confusion_counts, axis = 1, norm = 'l1')
- sns.heatmap(confusion, annot = True, fmt = '.2f', ax = axes[0], cmap = 'Reds')
- axes[0].set_xlabel('its')
- axes[0].set_ylabel('vtc')
- axes[0].xaxis.set_ticklabels(speakers)
- axes[0].yaxis.set_ticklabels(speakers)
- confusion_counts = np.transpose(confusion_counts)
- confusion = normalize(confusion_counts, axis = 1, norm = 'l1')
- sns.heatmap(confusion, annot = True, fmt = '.2f', ax = axes[1], cmap = 'Reds')
- axes[1].set_xlabel('vtc')
- axes[1].set_ylabel('its')
- axes[1].xaxis.set_ticklabels(speakers)
- axes[1].yaxis.set_ticklabels(speakers)
- plt.savefig('Fig5.pdf', bbox_inches = 'tight')
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