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generate_dataset_ucr.py
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import utils.dtw as dtw
import time
import numpy as np
import math
import csv
import os
import sys
from utils.constants import nb_classes, class_modifier_add, class_modifier_multi, max_seq_len
from utils.proto_select import selector_selector, random_selection, center_selection, k_centers_selection, border_selection, spanning_selection
def get_dtwfeatures(proto_data, proto_number, local_sample):
local_sample_length = np.shape(local_sample)[0]
features = np.zeros((local_sample_length, proto_number))
for prototype in range(proto_number):
local_proto = proto_data[prototype]
output, cost, DTW, path = dtw.dtw(local_proto, local_sample, extended=True)
for f in range(local_sample_length):
features[f, prototype] = cost[path[0][f]][path[1][f]]
return features
def read_dtw_matrix(version):
if not os.path.exists(os.path.join("data", "all-"+version+"-dtw-matrix.txt")):
exit("Please run cross_dtw.py first")
return np.genfromtxt(os.path.join("data", "all-"+version+"-dtw-matrix.txt"), delimiter=' ')
if __name__ == "__main__":
if len(sys.argv) < 5:
print("Error, Syntax: {0} [version] [prototype selection] [classwise/independent] [prototype number]".format(sys.argv[0]))
exit()
version = sys.argv[1]
selection = sys.argv[2]
classwise = sys.argv[3]
proto_number = int(sys.argv[4])
print("Starting: {} {} {}".format(version, selection, classwise))
# load settings
full_train_file = os.path.join("data", version + "_TRAIN")
full_test_file = os.path.join("data", version + "_TEST")
# load data
full_train = np.genfromtxt(full_train_file, delimiter=',')
full_test = np.genfromtxt(full_test_file, delimiter=',')
no_classes = nb_classes(version)
# print(proto_number)
train_max = np.max(full_train[:,1:])
train_min = np.min(full_train[:,1:])
train_data = 2. * (full_train[:,1:] - train_min) / (train_max - train_min) - 1.
train_labels = (full_train[:,0] + class_modifier_add(version))*class_modifier_multi(version)
train_number = np.shape(train_labels)[0]
#print(np.shape(train_data))
#print(np.shape(train_labels))
test_data = 2. * (full_test[:,1:] - train_min) / (train_max - train_min) - 1.
test_labels = (full_test[:,0] + class_modifier_add(version))*class_modifier_multi(version)
#print(np.shape(test_data))
#print(np.shape(test_labels))
test_number = np.shape(test_labels)[0]
seq_length = max_seq_len(version)
train_data = train_data.reshape((-1,seq_length, 1))
test_data = test_data.reshape((-1, seq_length, 1))
distances = train_number if selection == "random" else read_dtw_matrix(version)
if classwise == "classwise":
proto_loc = np.zeros(0, dtype=np.int32)
proto_factor = int(proto_number / no_classes)
for c in range(no_classes):
cw = np.where(train_labels == c)[0]
if selection == "random":
cw_distances = []
else:
cw_distances = distances[cw]
cw_distances = cw_distances[:,cw]
cw_proto = selector_selector(selection, proto_factor, cw_distances)
proto_loc = np.append(proto_loc, cw[cw_proto])
else:
proto_loc = selector_selector(selection, proto_number, distances)
proto_data = train_data[proto_loc]
print(proto_loc)
# start generation
test_label_fileloc = os.path.join("data", "all-test-label-{}-{}-{}-{}.txt".format(version, selection, classwise, proto_number))
test_raw_fileloc = os.path.join("data", "all-raw-test-data-{}-{}-{}-{}.txt".format(version, selection, classwise, proto_number))
test_dtw_fileloc = os.path.join("data", "all-dtw_features-test-data-{}-{}-{}-{}.txt".format(version, selection, classwise, proto_number))
test_combined_fileloc = os.path.join("data", "all-dtw_features-plus-raw-test-data-{}-{}-{}-{}.txt".format(version, selection, classwise, proto_number))
train_label_fileloc = os.path.join("data", "all-train-label-{}-{}-{}-{}.txt".format(version, selection, classwise, proto_number))
train_raw_fileloc = os.path.join("data", "all-raw-train-data-{}-{}-{}-{}.txt".format(version, selection, classwise, proto_number))
train_dtw_fileloc = os.path.join("data", "all-dtw_features-train-data-{}-{}-{}-{}.txt".format(version, selection, classwise, proto_number))
train_combined_fileloc = os.path.join("data", "all-dtw_features-plus-raw-train-data-{}-{}-{}-{}.txt".format(version, selection, classwise, proto_number))
# test set
with open(test_label_fileloc, 'w') as test_label_file, open(test_raw_fileloc, 'w') as test_raw_file, open(
test_dtw_fileloc, 'w') as test_dtw_file, open(test_combined_fileloc, 'w') as test_combined_file:
writer_test_label = csv.writer(test_label_file, quoting=csv.QUOTE_NONE, delimiter=" ")
writer_test_raw = csv.writer(test_raw_file, quoting=csv.QUOTE_NONE, delimiter=" ")
writer_test_dtw = csv.writer(test_dtw_file, quoting=csv.QUOTE_NONE, delimiter=" ")
writer_test_combined = csv.writer(test_combined_file, quoting=csv.QUOTE_NONE, delimiter=" ")
for sample in range(test_number):
local_sample = test_data[sample]
features = get_dtwfeatures(proto_data, proto_number, local_sample)
class_value = test_labels[sample]
# write files
feature_flat = features.reshape(seq_length * proto_number)
local_sample_flat = local_sample.reshape(seq_length)
writer_test_raw.writerow(local_sample_flat)
writer_test_dtw.writerow(feature_flat)
writer_test_combined.writerow(np.append(local_sample_flat, feature_flat))
writer_test_label.writerow(["{}-{}_test.png".format(class_value, sample), class_value])
if sample % (test_number // 16) == 0:
print("{} {}%: Test < {} Done".format(version, str(round(100. * sample / test_number, 1)),str(sample)))
print("{}: Test Done".format(version))
# train set
with open(train_label_fileloc, 'w') as train_label_file, open(train_raw_fileloc, 'w') as train_raw_file, open(
train_dtw_fileloc, 'w') as train_dtw_file, open(train_combined_fileloc, 'w') as train_combined_file:
writer_train_label = csv.writer(train_label_file, quoting=csv.QUOTE_NONE, delimiter=" ")
writer_train_raw = csv.writer(train_raw_file, quoting=csv.QUOTE_NONE, delimiter=" ")
writer_train_dtw = csv.writer(train_dtw_file, quoting=csv.QUOTE_NONE, delimiter=" ")
writer_train_combined = csv.writer(train_combined_file, quoting=csv.QUOTE_NONE, delimiter=" ")
for sample in range(train_number):
local_sample = train_data[sample]
features = get_dtwfeatures(proto_data, proto_number, local_sample)
class_value = train_labels[sample]
# write files
feature_flat = features.reshape(seq_length * proto_number)
local_sample_flat = local_sample.reshape(seq_length)
writer_train_raw.writerow(local_sample_flat)
writer_train_dtw.writerow(feature_flat)
writer_train_combined.writerow(np.append(local_sample_flat, feature_flat))
writer_train_label.writerow(["{}-{}_train.png".format(class_value, sample), class_value])
if sample % (train_number // 16) == 0:
print("{} {}%: Training < {} Done".format(version, str(round(100. * sample / train_number,1)),str(sample)))
print("{}: Training Done".format(version))
print("Done")