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silhouette coefficient
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@raphaelchang
raphaelchang committed Oct 29, 2019
1 parent 18a0a13 commit 93de4a8
Showing 1 changed file with 116 additions and 3 deletions.
119 changes: 116 additions & 3 deletions scripts/matching_eval_plot.py
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import seaborn as sns
import argparse
import os
import sklearn.metrics
import random

parser = argparse.ArgumentParser(description='Plot matching evaluation results')
parser.add_argument('results_path', help='matching results file, source bag file, or working directory')
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ax3.set_zlim([0, maxz])
ax3.view_init(elev=100, azim=270)

ax4.set_title('Distribution of matches over changes in radial distance')
ax4.set_ylabel('Number of matches')
# ax4.set_title('Distribution of matches over changes in radial distance')
# ax4.set_ylabel('Number of matches')
# ax4.set_xlabel('Delta radial distance')
# ax4.hist(good_radial_distances[:, 0].ravel(), color='c')

# match_dict = [[] for i in range(10)]
# for row in good_radial_distances:
# r = int(min(row[0], 0.499999) / 0.05)
# match_dict[r].append((row[1], 0))
# for row in bad_radial_distances:
# r = int(min(row[0], 0.499999) / 0.05)
# match_dict[r].append((row[1], 1))
# si_bins = [0] * 10
# for r in range(len(match_dict)):
# match_dict[r] = sorted(match_dict[r], key=lambda x: x[0])
# if len(match_dict[r]) <= 1:
# si_bins[r] = 1
# continue
# good_nn = 0
# for i in range(len(match_dict[r])):
# if i == 0:
# if match_dict[r][i][1] == match_dict[r][i + 1]:
# good_nn += 1
# elif i == len(match_dict[r]) - 1:
# if match_dict[r][i][1] == match_dict[r][i - 1]:
# good_nn += 1
# else:
# if match_dict[r][i][0] - match_dict[r][i - 1][0] > match_dict[r][i + 1][0] - match_dict[r][i][0]:
# if match_dict[r][i + 1][1] == match_dict[r][i][1]:
# good_nn += 1
# else:
# if match_dict[r][i][1] == match_dict[r][i - 1][1]:
# good_nn += 1
# si_bins[r] = good_nn / float(len(match_dict[r]))
# ax4.plot([i for i in range(0, len(match_dict))], [si_bins[i] for i in range(0, len(match_dict))])

ax4.set_title('Match separability over changes in radial distance')
ax4.set_ylabel('Silhouette coefficient')
ax4.set_xlabel('Delta radial distance')
ax4.hist(good_radial_distances[:, 0].ravel(), color='c')
num_bins = 50
num_samples = 5000
sr = [0 for i in range(num_bins)]
X_good = [np.array([]) for i in range(num_bins)]
labels_good = [np.array([]) for i in range(num_bins)]
X_bad = [np.array([]) for i in range(num_bins)]
labels_bad = [np.array([]) for i in range(num_bins)]
valid_good = [False for i in range(num_bins)]
valid_bad = [False for i in range(num_bins)]
valid = [False for i in range(num_bins)]
for row in good_radial_distances:
r = int(min(row[0], 0.499999) / (0.5 / num_bins))
X_good[r] = np.append(X_good[r], row[1])
labels_good[r] = np.append(labels_good[r], 0)
valid_good[r] = True
for row in bad_radial_distances:
r = int(min(row[0], 0.499999) / (0.5 / num_bins))
X_bad[r] = np.append(X_bad[r], row[1])
labels_bad[r] = np.append(labels_bad[r], 1)
valid_bad[r] = True
for i in range(num_bins):
valid[i] = valid_good[i] and valid_bad[i]
if not valid[i]:
continue
idx_good = np.arange(len(X_good[i]))
idx_bad = np.arange(len(X_bad[i]))
if len(X_good[i]) > num_samples:
idx_good = np.random.choice(np.arange(len(X_good[i])), num_samples, replace=False)
if len(X_bad[i]) > num_samples:
idx_bad = np.random.choice(np.arange(len(X_bad[i])), num_samples, replace=False)
sr[i] = sklearn.metrics.silhouette_score(np.concatenate((X_good[i][idx_good], X_bad[i][idx_bad])).reshape(-1, 1), np.concatenate((labels_good[i][idx_good], labels_bad[i][idx_bad])), metric = 'l1')
ax4.plot([i * 0.5 / num_bins + 0.5 / num_bins / 2 for i in range(0, len(sr)) if valid[i]], [sr[i] for i in range(0, len(sr)) if valid[i]])

df = pd.DataFrame({'Delta radial distance': ['{}-{}'.format(r * 0.05, (r + 1) * 0.05) for r in (np.minimum(np.hstack((good_radial_distances[:, 0], bad_radial_distances[:, 0])), 0.499999) / 0.05).astype(int)], 'Descriptor distance': np.hstack((good_radial_distances[:, 1], bad_radial_distances[:, 1])), 'Match': ['Good' for i in range(len(good_radial_distances))] + ['Bad' for i in range(len(bad_radial_distances))]})
sns.violinplot(x="Delta radial distance", y="Descriptor distance", hue="Match", data=df, split=True, ax=ax5, palette="Set2", inner="quart")
Expand Down Expand Up @@ -142,6 +211,7 @@
ax2.set_prop_cycle(color=[cm(1. * i / len(detdesclist)) for i in range(len(detdesclist))])
ax3.set_prop_cycle(color=[cm(1. * i / len(detdesclist)) for i in range(len(detdesclist))])
ax4.set_prop_cycle(color=[cm(1. * i / len(detdesclist)) for i in range(len(detdesclist))])
ax6.set_prop_cycle(color=[cm(1. * i / len(detdesclist)) for i in range(len(detdesclist))])

handles = []
for detdesc in detdesclist:
Expand Down Expand Up @@ -203,6 +273,49 @@
ax5.legend(handles=handles[0:], labels=labels[0:], fontsize='small')
ax5.set_title('Distribution of good and bad matches over descriptor distances')

ax6.set_title('Match separability over changes in radial distance')
ax6.set_ylabel('Silhouette coefficient')
ax6.set_xlabel('Delta radial distance')
handles = []
for detdesc in detdesclist:
num_bins = 50
num_samples = 1000
sr = [0 for i in range(num_bins)]
X_good = [np.array([]) for i in range(num_bins)]
labels_good = [np.array([]) for i in range(num_bins)]
X_bad = [np.array([]) for i in range(num_bins)]
labels_bad = [np.array([]) for i in range(num_bins)]
valid_good = [False for i in range(num_bins)]
valid_bad = [False for i in range(num_bins)]
valid = [False for i in range(num_bins)]
for row in good_radial_distances[detdesc]:
r = int(min(row[0], 0.499999) / (0.5 / num_bins))
X_good[r] = np.append(X_good[r], row[1])
labels_good[r] = np.append(labels_good[r], 0)
valid_good[r] = True
for row in bad_radial_distances[detdesc]:
r = int(min(row[0], 0.499999) / (0.5 / num_bins))
X_bad[r] = np.append(X_bad[r], row[1])
labels_bad[r] = np.append(labels_bad[r], 1)
valid_bad[r] = True
for i in range(num_bins):
valid[i] = valid_good[i] and valid_bad[i]
if not valid[i]:
continue
idx_good = np.arange(len(X_good[i]))
idx_bad = np.arange(len(X_bad[i]))
if len(X_good[i]) > num_samples:
idx_good = np.random.choice(np.arange(len(X_good[i])), num_samples, replace=False)
if len(X_bad[i]) > num_samples:
idx_bad = np.random.choice(np.arange(len(X_bad[i])), num_samples, replace=False)
sr[i] = sklearn.metrics.silhouette_score(np.concatenate((X_good[i][idx_good], X_bad[i][idx_bad])).reshape(-1, 1), np.concatenate((labels_good[i][idx_good], labels_bad[i][idx_bad])), metric = 'l1')
# sr[i] = sklearn.metrics.davies_bouldin_score(X[i].reshape(-1, 1), labels[i])
color = next(ax6._get_lines.prop_cycler)['color']
h, = ax6.plot([i * 0.5 / num_bins + 0.5 / num_bins / 2 for i in range(0, len(sr)) if valid[i]], [sr[i] for i in range(0, len(sr)) if valid[i]], color=color)
handles.append(h)
l1 = ax6.legend(handles, ['{}+{}'.format(det, desc) for det, desc in detdesclist], loc=1, title='Detector+Descriptor', fontsize='small')
ax6.add_artist(l1)

plt.show()

else:
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