removing scripts

scripts/plots.py

@@ -1,77 +0,0 @@
-from ChildProject.projects import ChildProject
-from ChildProject.annotations import AnnotationManager
-from ChildProject.metrics import gamma, segments_to_grid
-
-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
-
-speakers = ['CHI', 'OCH', 'FEM', 'MAL']
-
-project = ChildProject('.')
-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)]
-segments.sort_values(['segment_onset', 'segment_offset']).to_csv('test.csv', index = False)
-#print(gamma(segments, column = 'speaker_type'))
-
-print('creating grids')
-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)
-print('done creating grids')
-
-speakers.extend(['overlap', 'none'])
-
-def get_pick(row):
-    for cat in reversed(speakers):
-        if row[cat]:
-            return cat
-
-def conf_matrix(horizontal, vertical, categories):
-    vertical = pd.DataFrame(vertical, columns = categories)
-    vertical['pick'] = vertical.apply(
-        get_pick,
-        axis = 1
-    )
-    vertical = vertical['pick'].values
-
-    horizontal = pd.DataFrame(horizontal, columns = categories)
-    horizontal['pick'] = horizontal.apply(
-        get_pick,
-        axis = 1
-    )
-    horizontal = horizontal['pick'].values
-
-    confusion = confusion_matrix(vertical, horizontal, labels = categories)
-    confusion = normalize(confusion, axis = 1, norm = 'l1')
-
-    return confusion
-
-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 = conf_matrix(its, vtc, speakers)
-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 = conf_matrix(vtc, its, speakers)
-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')

scripts/recall.py

@@ -1,84 +0,0 @@
-from ChildProject.projects import ChildProject
-from ChildProject.annotations import AnnotationManager
-from ChildProject.metrics import segments_to_annotation
-
-from pyannote.metrics.detection import DetectionPrecisionRecallFMeasure
-
-import numpy as np
-import pandas as pd
-from sklearn.metrics import confusion_matrix
-from sklearn.preprocessing import normalize
-
-import random
-
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-speakers = ['CHI', 'OCH', 'FEM', 'MAL']
-sets = ['its', 'vtc (conf 50%)', 'vtc (drop 50%)', 'vtc (conf 75%)', 'vtc (drop 75%)']
-
-project = ChildProject('.')
-am = AnnotationManager(project)
-am.read()
-
-def confusion(segments, prob):
-    segments['speaker_type'] = segments['speaker_type'].apply(
-        lambda s: random.choice(speakers) if random.random() < prob else s
-    )
-    return segments
-
-def drop(segments, prob):
-    return segments.sample(frac = 1-prob)
-
-intersection = AnnotationManager.intersection(am.annotations, ['vtc', 'its'])
-segments = am.get_collapsed_segments(intersection)
-segments = segments[segments['speaker_type'].isin(speakers)]
-segments.sort_values(['segment_onset', 'segment_offset']).to_csv('test.csv', index = False)
-
-conf50 = segments[segments['set'] == 'vtc'].copy()
-conf50 = confusion(conf50, 0.5)
-conf50['set'] = 'vtc (conf 50%)'
-
-conf75 = segments[segments['set'] == 'vtc'].copy()
-conf75 = confusion(conf75, 0.75)
-conf75['set'] = 'vtc (conf 75%)'
-
-drop50 = segments[segments['set'] == 'vtc'].copy()
-drop50 = drop(drop50, 0.5)
-drop50['set'] = 'vtc (drop 50%)'
-
-drop75 = segments[segments['set'] == 'vtc'].copy()
-drop75 = drop(drop75, 0.75)
-drop75['set'] = 'vtc (drop 75%)'
-
-segments = pd.concat([segments, conf50, conf75, drop50, drop75])
-
-metric = DetectionPrecisionRecallFMeasure()
-
-scores = []
-for speaker in speakers:
-    ref = segments_to_annotation(segments[(segments['set'] == 'vtc') & (segments['speaker_type'] == speaker)], 'speaker_type')
-
-    for s in sets:
-        hyp = segments_to_annotation(segments[(segments['set'] == s) & (segments['speaker_type'] == speaker)], 'speaker_type')
-        detail = metric.compute_components(ref, hyp)
-        precision, recall, f = metric.compute_metrics(detail)
-
-        scores.append({
-            'set': s,
-            'speaker': speaker,
-            'recall': recall,
-            'precision': precision,
-            'f': f
-        })
-
-scores = pd.DataFrame(scores)
-scores.to_csv('scores.csv', index = False)
-
-plt.rcParams.update({'font.size': 12})
-plt.rc('xtick', labelsize = 10)
-plt.rc('ytick', labelsize = 10)
-
-fig, axes = plt.subplots(nrows = 2, ncols = 2, figsize=(6.4*2, 4.8*2))
-
-plt.savefig('Fig4.pdf', bbox_inches = 'tight')