import tensorflow.keras as keras from tensorflow.keras.utils import to_categorical from kerastuner.applications.inceptiontime import Hyperinceptiontime from kerastuner import RandomSearch, Hyperband from utils.uts_classification.utils import readmts_uci_har, transform_labels import numpy as np import os os.environ["CUDA_VISIBLE_DEVICES"] = "0" # 加载UCI_HAR_Dataset file_name = '../../datasets/mts_data/UCI_HAR_Dataset' x_train, y_train, x_test, y_test = readmts_uci_har(file_name) data = np.concatenate((x_train, x_test), axis=0) label = np.concatenate((y_train, y_test), axis=0) N = data.shape[0] ind = int(N * 0.9) x_train = data[:ind] y_train = label[:ind] x_test = data[ind:] y_test = label[ind:] y_train, y_test = transform_labels(y_train, y_test) NUM_CLASSES = 6 y_train = to_categorical(y_train, NUM_CLASSES) y_test = to_categorical(y_test, NUM_CLASSES) print(x_train.shape) print(y_train.shape) print(x_test.shape) print(y_test.shape) print(y_train[:3])
def __init__(self, config):
super(UtsClassificationDataLoader, self).__init__(config)
if config.dataset.type == 'uts':
if config.dataset.name == 'AFClassification':
from utils.AFClassication.data import loaddata
(X_train,
y_train), (Xval, yval), (final_testset, final_testtarget), (
R_train, Rval,
Rtest), (P_train, Pval,
Ptest), (Q_train, Qval,
Qtest), (T_train, Tval,
Ttest) = loaddata()
X_train = X_train[0]
X_val = Xval[0]
y_val = yval
X_test = final_testset[0]
y_test = final_testtarget
self.nb_classes = 3
self.y_train = y_train
self.y_test = y_test
self.X_val = X_val.reshape((X_val.shape[0], X_val.shape[1], 1))
self.y_val = y_val
self.y_true = np.argmax(y_test, axis=1)
elif config.dataset.name == 'ptbdb':
file_path = './datasets/uts_data/ptbdb/'
X_train, y_train, X_val, y_val, X_test, y_test = readmts_ptb_aug(
file_path)
self.nb_classes = len(
np.unique(np.concatenate((y_train, y_val, y_test),
axis=0)))
y_train, y_val, y_test = transform_labels(
y_train, y_test, y_val)
self.X_val = X_val.reshape(
(self.X_val.shape[0], self.X_val.shape[1], 1))
enc = sklearn.preprocessing.OneHotEncoder()
enc.fit(
np.concatenate((y_train, y_val, y_test),
axis=0).reshape(-1, 1))
self.y_train = enc.transform(y_train.reshape(-1, 1)).toarray()
self.y_val = enc.transform(self.y_val.reshape(-1, 1)).toarray()
self.y_test = enc.transform(y_test.reshape(-1, 1)).toarray()
else:
file_name = 'datasets/uts_data/' + config.dataset.name + '/' + config.dataset.name
X_train, y_train = readucr(file_name + '_TRAIN.txt')
X_test, y_test = readucr(file_name + '_TEST.txt')
self.nb_classes = len(
np.unique(np.concatenate((y_train, y_test), axis=0)))
# make the min to zero of labels
y_train, y_test = transform_labels(y_train, y_test)
else:
if config.dataset.name == 'UCI_HAR_Dataset':
file_name = 'datasets/mts_data/' + config.dataset.name
X_train, y_train, X_test, y_test = readmts_uci_har(file_name)
# 调整划分比例
data = np.concatenate((X_train, X_test), axis=0)
label = np.concatenate((y_train, y_test), axis=0)
N = data.shape[0]
ind = int(N * 0.9)
X_train = data[:ind]
y_train = label[:ind]
X_test = data[ind:]
y_test = label[ind:]
self.nb_classes = 6
# make the min to zero of labels
y_train, y_test = transform_labels(y_train, y_test)
elif config.dataset.name == 'Challeng2018':
from utils.AFClassication.data_challenge2018 import loaddata
(X_train, y_train), (Xval,
yval), (final_testset,
final_testtarget) = loaddata()
X_val = Xval
X_test = final_testset
y_val = yval
y_test = final_testtarget
self.nb_classes = 9
self.X_val = X_val
self.y_train = y_train
self.y_test = y_test
self.y_val = y_val
self.y_true = np.argmax(y_test, axis=1)
else:
file_name = 'datasets/mts_data/' + config.dataset.name + '/' + config.dataset.name
X_train, y_train, X_test, y_test, self.nb_classes = readmts(
file_name)
if config.dataset.name not in [
'ptbdb', 'AFClassification', 'Challeng2018'
]:
# save orignal y because later we will use binary
self.y_true = y_test.astype(np.int64)
# transform the labels from integers to one hot vectors
enc = sklearn.preprocessing.OneHotEncoder()
enc.fit(np.concatenate((y_train, y_test), axis=0).reshape(-1, 1))
self.y_train = enc.transform(y_train.reshape(-1, 1)).toarray()
self.y_test = enc.transform(y_test.reshape(-1, 1)).toarray()
if config.dataset.type == 'uts':
self.X_train = X_train.reshape(
(X_train.shape[0], X_train.shape[1], 1))
self.X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
else:
self.X_train = X_train
self.X_test = X_test
self.train_size = self.X_train.shape[0]
self.test_size = self.X_test.shape[0]
self.input_shape = self.X_train.shape[1:]
if (self.config.model.name == "tlenet"):
from models.classification.tlenet import Classifier_TLENET
self.X_train, self.y_train, self.X_test, self.y_test, self.tot_increase_num, \
self.input_shape, self.nb_classes = Classifier_TLENET().pre_processing(self.X_train, self.y_train, self.X_test, self.y_test)
print(self.input_shape)
print("********************************")