def main():
args = check_argv()
print datetime.datetime.now()
print "Reading:", args.npz_fn
npz = np.load(args.npz_fn)
print datetime.datetime.now()
# if args.normalize:
# print "Normalizing embeddings"
# else:
print "Ordering embeddings"
n_embeds = 0
X = []
ids = []
for label in sorted(npz):
ids.append(label)
X.append(npz[label])
n_embeds += 1
X = np.array(X)
print "No. embeddings:", n_embeds
print "Embedding dimensionality:", X.shape[1]
print datetime.datetime.now()
print "Calculating distances"
distances = pdist(X, metric=args.metric)
print datetime.datetime.now()
print "Getting labels"
labels = []
for utt_id in ids:
word = "_".join(utt_id.split("_")[:-2])
labels.append(word)
if args.mean_ap:
print datetime.datetime.now()
print "Calculating mean average precision"
mean_ap, mean_prb, ap_dict = samediff.mean_average_precision(distances, labels)
print "Mean average precision:", mean_ap
print "Mean precision-recall breakeven:", mean_prb
print datetime.datetime.now()
print "Calculating average precision"
matches = samediff.generate_matches_array(labels)
ap, prb = samediff.average_precision(distances[matches == True], distances[matches == False])
print "Average precision:", ap
print "Precision-recall breakeven:", prb
print datetime.datetime.now()
def main():
args = check_argv()
print(datetime.now())
print("Reading:", args.npz_fn)
npz = np.load(args.npz_fn)
print(datetime.now())
print("Ordering embeddings")
n_embeds = 0
X = []
ids = []
for label in sorted(npz):
ids.append(label)
X.append(npz[label])
n_embeds += 1
X = np.array(X)
print("No. embeddings:", n_embeds)
print("Embedding dimensionality:", X.shape[1])
if args.mvn:
normed = (X - X.mean(axis=0)) / X.std(axis=0)
X = normed
print(datetime.now())
print("Calculating distances")
metric = args.metric
if metric == "kl":
import scipy.stats
metric = scipy.stats.entropy
distances = pdist(X, metric=metric)
print(datetime.now())
print("Getting labels and speakers")
labels = []
speakers = []
for utt_id in ids:
utt_id = utt_id.split("_")
word = utt_id[0]
speaker = utt_id[1]
labels.append(word)
speakers.append(speaker)
if args.mean_ap:
print(datetime.now())
print("Calculating mean average precision")
mean_ap, mean_prb, ap_dict = samediff.mean_average_precision(
distances, labels)
print("Mean average precision:", mean_ap)
print("Mean precision-recall breakeven:", mean_prb)
print(datetime.now())
print("Calculating average precision")
# matches = samediff.generate_matches_array(labels) # Temp
word_matches = samediff.generate_matches_array(labels)
speaker_matches = samediff.generate_matches_array(speakers)
print("No. same-word pairs:", sum(word_matches))
print("No. same-speaker pairs:", sum(speaker_matches))
sw_ap, sw_prb, swdp_ap, swdp_prb = samediff.average_precision_swdp(
distances[np.logical_and(word_matches, speaker_matches)],
distances[np.logical_and(word_matches, speaker_matches == False)],
distances[word_matches == False])
print("-" * 79)
print("Average precision: {:.8f}".format(sw_ap))
print("Precision-recall breakeven: {:.8f}".format(sw_prb))
print("SWDP average precision: {:.8f}".format(swdp_ap))
print("SWDP precision-recall breakeven: {:.8f}".format(swdp_prb))
print("-" * 79)
print(datetime.now())
def main():
args = check_argv()
print(datetime.now())
print("Reading:", args.npz_fn)
npz = np.load(args.npz_fn)
print(datetime.now())
# if args.normalize:
# print("Normalizing embeddings")
# else:
print("Ordering embeddings")
n_embeds = 0
X = []
ids = []
for label in sorted(npz):
ids.append(label)
X.append(npz[label])
n_embeds += 1
X = np.array(X)
print("No. embeddings:", n_embeds)
print("Embedding dimensionality:", X.shape[1])
if args.mvn:
normed = (X - X.mean(axis=0)) / X.std(axis=0)
X = normed
print(datetime.now())
print("Calculating distances")
distances = pdist(X, metric=args.metric)
print(datetime.now())
print("Getting labels")
labels = []
for utt_id in ids:
word = utt_id.split("_")[0]
labels.append(word)
if args.mean_ap:
print(datetime.now())
print("Calculating mean average precision")
mean_ap, mean_prb, ap_dict = samediff.mean_average_precision(
distances, labels)
print("Mean average precision:", mean_ap)
print("Mean precision-recall breakeven:", mean_prb)
print(datetime.now())
print("Calculating average precision")
matches = samediff.generate_matches_array(labels)
ap, prb = samediff.average_precision(distances[matches == True],
distances[matches == False])
print("Average precision:", ap)
print("Precision-recall breakeven:", prb)
print(datetime.now())