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compare.py 2.4 KB

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  1. import matplotlib.pyplot as plt
  2. import numpy as np
  3. import seaborn as sns
  4. from ChildProject.projects import ChildProject
  5. from ChildProject.annotations import AnnotationManager
  6. from ChildProject.metrics import segments_to_grid, conf_matrix
  7. categories = ['Adult', 'Youngster', 'Junk']
  8. # load VanDam dataset
  9. project = ChildProject('vandam-data')
  10. am = AnnotationManager(project)
  11. annotations = am.annotations[am.annotations['set'].isin(['vtc', 'zoo'])]
  12. segments = am.get_collapsed_segments(annotations)
  13. # map VTC speakers to a two-class categorization
  14. vtc_segments = segments.loc[segments['set'] == 'vtc']
  15. vtc_segments['speaker_age'] = vtc_segments['speaker_type'].replace({
  16. 'MAL': 'Adult',
  17. 'FEM': 'Adult',
  18. 'CHI': 'Youngster',
  19. 'OCH': 'Youngster'
  20. })
  21. # map Zooniverse-extracted speakers to the same two-class categorization
  22. zoo_segments = segments.loc[segments['set'] == 'zoo']
  23. zoo_segments['speaker_age'] = zoo_segments['speaker_age'].replace({
  24. 'Baby': 'Youngster',
  25. 'Child': 'Youngster',
  26. 'Adolescent': 'Adult'
  27. })
  28. # create matrices indicating if any of the classes is active at every 50 ms time step (unitizing)
  29. vtc = segments_to_grid(
  30. vtc_segments, 0, segments['segment_offset'].max(), 50, 'speaker_age', categories, none = True
  31. )
  32. zoo = segments_to_grid(
  33. zoo_segments, 0, segments['segment_offset'].max(), 50, 'speaker_age', categories, none = True
  34. )
  35. # keep only the units that have been classified on Zooniverse
  36. vtc = vtc[zoo[:,-1] == 0][:,:-1]
  37. zoo = zoo[zoo[:,-1] == 0][:,:-1]
  38. # compute and show confusion matrices
  39. confusion_counts = conf_matrix(vtc, zoo)
  40. plt.rcParams.update({'font.size': 12})
  41. plt.rc('xtick', labelsize = 10)
  42. plt.rc('ytick', labelsize = 10)
  43. fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(6.4*2, 4.8))
  44. confusion = confusion_counts/np.sum(vtc, axis = 0)[:,None]
  45. sns.heatmap(confusion, annot = True, fmt = '.2f', ax = axes[0], cmap = 'Reds')
  46. axes[0].set_xlabel('zoo')
  47. axes[0].set_ylabel('vtc')
  48. axes[0].xaxis.set_ticklabels(categories)
  49. axes[0].yaxis.set_ticklabels(categories)
  50. confusion_counts = np.transpose(confusion_counts)
  51. confusion = confusion_counts/np.sum(zoo, axis = 0)[:,None]
  52. sns.heatmap(confusion, annot = True, fmt = '.2f', ax = axes[1], cmap = 'Reds')
  53. axes[1].set_xlabel('vtc')
  54. axes[1].set_ylabel('zoo')
  55. axes[1].xaxis.set_ticklabels(categories)
  56. axes[1].yaxis.set_ticklabels(categories)
  57. plt.savefig('annotations/comparison.png', bbox_inches = 'tight')