├── DTCR_code ├── Coffee │ ├── Coffee_TEST │ └── Coffee_TRAIN ├── Model │ ├── checkpoint │ ├── model.ckpt.data-00000-of-00001 │ └── model.ckpt.index ├── drnn.py ├── load_model_Coffee.py ├── rnn_cell_extensions.py ├── train_model.py └── utils.py ├── Learning Representations for Time Series Clustering.pdf ├── README.md └── Supplementary Material_Learning Representations for Time Series Clustering.pdf /DTCR_code/Coffee/Coffee_TEST: -------------------------------------------------------------------------------- 1 | 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29 | -------------------------------------------------------------------------------- /DTCR_code/Model/checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "model.ckpt" 2 | all_model_checkpoint_paths: "model.ckpt" 3 | -------------------------------------------------------------------------------- /DTCR_code/Model/model.ckpt.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qianlima-lab/DTCR/e4ecce48c0fd1fe3c017cccf1fd9203efdddbb6b/DTCR_code/Model/model.ckpt.data-00000-of-00001 -------------------------------------------------------------------------------- /DTCR_code/Model/model.ckpt.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qianlima-lab/DTCR/e4ecce48c0fd1fe3c017cccf1fd9203efdddbb6b/DTCR_code/Model/model.ckpt.index -------------------------------------------------------------------------------- /DTCR_code/drnn.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import copy 3 | import itertools 4 | import numpy as np 5 | import tensorflow as tf 6 | from tensorflow.python.ops import rnn 7 | 8 | def dRNN(cell, inputs, rate, scope='default'): 9 | """ 10 | This function constructs a layer of dilated RNN. 11 | Inputs: 12 | cell -- the dilation operations is implemented independent of the RNN cell. 13 | In theory, any valid tensorflow rnn cell should work. 14 | inputs -- the input for the RNN. inputs should be in the form of 15 | a list of 'n_steps' tenosrs. Each has shape (batch_size, input_dims) 16 | rate -- the rate here refers to the 'dilations' in the orginal WaveNet paper. 17 | scope -- variable scope. 18 | Outputs: 19 | outputs -- the outputs from the RNN. 20 | """ 21 | n_steps = len(inputs) 22 | if rate < 0 or rate >= n_steps: 23 | raise ValueError('The \'rate\' variable needs to be adjusted.') 24 | print "Building layer: %s, input length: %d, dilation rate: %d, input dim: %d." % ( 25 | scope, n_steps, rate, inputs[0].get_shape()[1]) 26 | 27 | # make the length of inputs divide 'rate', by using zero-padding 28 | EVEN = (n_steps % rate) == 0 29 | if not EVEN: 30 | # Create a tensor in shape (batch_size, input_dims), which all elements are zero. 31 | # This is used for zero padding 32 | zero_tensor = tf.zeros_like(inputs[0]) 33 | dialated_n_steps = n_steps // rate + 1 34 | print "=====> %d time points need to be padded. " % ( 35 | dialated_n_steps * rate - n_steps) 36 | print "=====> Input length for sub-RNN: %d" % (dialated_n_steps) 37 | for i_pad in xrange(dialated_n_steps * rate - n_steps): 38 | inputs.append(zero_tensor) 39 | else: 40 | dialated_n_steps = n_steps // rate 41 | print "=====> Input length for sub-RNN: %d" % (dialated_n_steps) 42 | 43 | # now the length of 'inputs' divide rate 44 | # reshape it in the format of a list of tensors 45 | # the length of the list is 'dialated_n_steps' 46 | # the shape of each tensor is [batch_size * rate, input_dims] 47 | # by stacking tensors that "colored" the same 48 | 49 | # Example: 50 | # n_steps is 5, rate is 2, inputs = [x1, x2, x3, x4, x5] 51 | # zero-padding --> [x1, x2, x3, x4, x5, 0] 52 | # we want to have --> [[x1; x2], [x3; x4], [x_5; 0]] 53 | # which the length is the ceiling of n_steps/rate 54 | dilated_inputs = [tf.concat(inputs[i * rate:(i + 1) * rate], 55 | axis=0) for i in range(dialated_n_steps)] 56 | # building a dialated RNN with reformated (dilated) inputs 57 | dilated_outputs, _ = tf.contrib.rnn.static_rnn( 58 | cell, dilated_inputs, 59 | dtype=tf.float32, scope=scope) 60 | # reshape output back to the input format as a list of tensors with shape [batch_size, input_dims] 61 | # split each element of the outputs from size [batch_size*rate, input_dims] to 62 | # [[batch_size, input_dims], [batch_size, input_dims], ...] with length = rate 63 | #tf.split(output, rate, axis=0) 64 | splitted_outputs = [tf.split(output, rate, axis=0) 65 | for output in dilated_outputs] 66 | unrolled_outputs = [output 67 | for sublist in splitted_outputs for output in sublist] 68 | # remove padded zeros 69 | outputs = unrolled_outputs[:n_steps] 70 | return outputs 71 | 72 | 73 | def multi_dRNN_with_dilations(cells, inputs, dilations): 74 | """ 75 | This function constucts a multi-layer dilated RNN. 76 | Inputs: 77 | cells -- A list of RNN cells. 78 | inputs -- A list of 'n_steps' tensors, each has shape (batch_size, input_dims). 79 | dilations -- A list of integers with the same length of 'cells' indicates the dilations for each layer. 80 | Outputs: 81 | x -- A list of 'n_steps' tensors, as the outputs for the top layer of the multi-dRNN. 82 | """ 83 | assert (len(cells) == len(dilations)) 84 | outputs = [] 85 | output = [] 86 | x = copy.copy(inputs) 87 | i = 0 88 | for cell, dilation in zip(cells, dilations): 89 | scope_name = "multi_dRNN_dilation_%d" % i 90 | i +=1 91 | x= dRNN(cell, x, dilation, scope=scope_name) 92 | outputs.append(x) 93 | x_trans = tf.stack(x,axis=0) 94 | x_trans = tf.transpose(x_trans, [1,0,2]) 95 | output.append(x_trans) 96 | return outputs,output 97 | 98 | 99 | def _contruct_cells(hidden_structs, cell_type): 100 | """ 101 | This function contructs a list of cells. 102 | """ 103 | # error checking 104 | if cell_type not in ["RNN", "LSTM", "GRU"]: 105 | raise ValueError("The cell type is not currently supported.") 106 | 107 | # define cells 108 | cells = [] 109 | for hidden_dims in hidden_structs: 110 | if cell_type == "RNN": 111 | cell = tf.contrib.rnn.BasicRNNCell(hidden_dims) 112 | elif cell_type == "LSTM": 113 | cell = tf.contrib.rnn.BasicLSTMCell(hidden_dims) 114 | elif cell_type == "GRU": 115 | cell = tf.contrib.rnn.GRUCell(hidden_dims) 116 | cells.append(cell) 117 | 118 | return cells 119 | 120 | def _rnn_reformat(x, input_dims, n_steps): 121 | """ 122 | This function reformat input to the shape that standard RNN can take. 123 | 124 | Inputs: 125 | x -- a tensor of shape (batch_size, n_steps, input_dims). 126 | Outputs: 127 | x_reformat -- a list of 'n_steps' tenosrs, each has shape (batch_size, input_dims). 128 | """ 129 | # permute batch_size and n_steps 130 | x_ = tf.transpose(x, [1, 0, 2]) 131 | # reshape to (n_steps*batch_size, input_dims) 132 | x_ = tf.reshape(x_, [-1, input_dims]) 133 | # split to get a list of 'n_steps' tensors of shape (batch_size, input_dims) 134 | x_reformat = tf.split(x_, n_steps, 0) 135 | return x_reformat 136 | 137 | def drnn_layer_final(x, 138 | hidden_structs, 139 | dilations, 140 | n_steps, 141 | input_dims, 142 | cell_type): 143 | """ 144 | This function construct a multilayer dilated RNN for classifiction. 145 | Inputs: 146 | x -- a tensor of shape (batch_size, n_steps, input_dims). 147 | hidden_structs -- a list, each element indicates the hidden node dimension of each layer. 148 | dilations -- a list, each element indicates the dilation of each layer. 149 | n_steps -- the length of the sequence. 150 | n_classes -- the number of classes for the classification. 151 | input_dims -- the input dimension. 152 | cell_type -- the type of the RNN cell, should be in ["RNN", "LSTM", "GRU"]. 153 | 154 | Outputs: 155 | pred -- the prediction logits at the last timestamp and the last layer of the RNN. 156 | 'pred' does not pass any output activation functions. 157 | """ 158 | # error checking 159 | assert (len(hidden_structs) == len(dilations)) 160 | 161 | # reshape inputs 162 | x_reformat = _rnn_reformat(x, input_dims, n_steps) 163 | 164 | # construct a list of cells 165 | cells = _contruct_cells(hidden_structs, cell_type) 166 | 167 | # define dRNN structures 168 | outputs, output = multi_dRNN_with_dilations(cells, x_reformat, dilations) 169 | 170 | return outputs, output -------------------------------------------------------------------------------- /DTCR_code/load_model_Coffee.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu Sep 8 15:29:48 2016 4 | 5 | @author: root 6 | """ 7 | import tensorflow as tf 8 | import utils 9 | import math 10 | import sys 11 | import os 12 | import numpy as np 13 | import copy 14 | import drnn 15 | import rnn_cell_extensions 16 | from tensorflow.python.ops import variable_scope 17 | from sklearn import metrics 18 | from sklearn.cluster import KMeans 19 | from numpy import linalg as LA 20 | import warnings 21 | import shutil 22 | 23 | 24 | class Config(object): 25 | """Train config.""" 26 | batch_size = None 27 | hidden_size = [100, 50, 50] 28 | dilations = [1, 2, 4] 29 | num_steps = None 30 | embedding_size = None 31 | learning_rate = 1e-4 32 | cell_type = 'GRU' 33 | lamda = 1 34 | class_num = None 35 | denosing = True # False 36 | sample_loss = True # False 37 | 38 | 39 | def weight_variable(shape): 40 | initial = tf.truncated_normal(shape, stddev=0.01) 41 | return tf.Variable(initial) 42 | 43 | 44 | def bias_variable(shape): 45 | initial = tf.constant(0.1, shape=shape) 46 | return tf.Variable(initial) 47 | 48 | 49 | class RNN_clustering_model(object): 50 | 51 | def __init__(self, config): 52 | self.batch_size = config.batch_size 53 | self.hidden_size = config.hidden_size 54 | self.dilations = config.dilations 55 | self.num_steps = config.num_steps 56 | self.embedding_size = config.embedding_size 57 | self.cell_type = config.cell_type 58 | self.lamda = config.lamda 59 | self.class_num = config.class_num 60 | self.denosing = config.denosing 61 | self.sample_loss = config.sample_loss 62 | self.K = config.class_num 63 | 64 | # self.fully_units = config.fully_units 65 | 66 | def build_model(self): 67 | input = tf.placeholder(tf.float32, [None, self.num_steps], name='inputs') # input 68 | noise = tf.placeholder(tf.float32, [None, self.num_steps], name='noise') 69 | 70 | real_fake_label = tf.placeholder(tf.float32, [None, 2], name='real_fake_label') 71 | 72 | F_new_value = tf.placeholder(tf.float32, [None, self.K], name='F_new_value') 73 | # F = tf.Variable(tf.eye(self.batch_size,num_columns = self.K), trainable = False) 74 | F = tf.get_variable('F', shape=[self.batch_size, self.K], 75 | initializer=tf.orthogonal_initializer(gain=1.0, seed=None, dtype=tf.float32), 76 | trainable=False) 77 | 78 | # inputs has shape (batch_size, n_steps, embedding_size) 79 | inputs = tf.reshape(input, [-1, self.num_steps, self.embedding_size]) 80 | noises = tf.reshape(noise, [-1, self.num_steps, self.embedding_size]) 81 | 82 | # a list of 'n_steps' tenosrs, each has shape (batch_size, embedding_size) 83 | # encoder_inputs = utils._rnn_reformat(x = inputs, input_dims = self.embedding_size, n_steps = self.num_steps) 84 | 85 | # noise_input has shape (batch_size, n_steps, embedding_size) 86 | if self.denosing: 87 | print('Noise') 88 | noise_input = inputs + noises 89 | else: 90 | print('Non_noise') 91 | noise_input = inputs 92 | 93 | reverse_noise_input = tf.reverse(noise_input, axis=[1]) 94 | decoder_inputs = utils._rnn_reformat(x=noise_input, input_dims=self.embedding_size, n_steps=self.num_steps) 95 | targets = utils._rnn_reformat(x=inputs, input_dims=self.embedding_size, n_steps=self.num_steps) 96 | 97 | if self.cell_type == 'LSTM': 98 | raise ValueError('LSTMs have not support yet!') 99 | # cell = tf.contrib.rnn.BasicLSTMCell( np.sum(self.hidden_size) *2) 100 | elif self.cell_type == 'GRU': 101 | cell = tf.contrib.rnn.GRUCell(np.sum(self.hidden_size) * 2) 102 | 103 | cell = rnn_cell_extensions.LinearSpaceDecoderWrapper(cell, self.embedding_size) 104 | 105 | lf = None 106 | if self.sample_loss: 107 | print 108 | 'Sample Loss' 109 | 110 | def lf(prev, i): 111 | return prev 112 | 113 | # encoder_output has shape 'layer' list of tensor [batch_size, n_steps, hidden_size] 114 | with tf.variable_scope('fw'): 115 | _, encoder_output_fw = drnn.drnn_layer_final(noise_input, self.hidden_size, self.dilations, self.num_steps, 116 | self.embedding_size, self.cell_type) 117 | 118 | with tf.variable_scope('bw'): 119 | _, encoder_output_bw = drnn.drnn_layer_final(reverse_noise_input, self.hidden_size, self.dilations, 120 | self.num_steps, self.embedding_size, self.cell_type) 121 | 122 | if self.cell_type == 'LSTM': 123 | raise ValueError('LSTMs have not support yet!') 124 | elif self.cell_type == 'GRU': 125 | fw = [] 126 | bw = [] 127 | for i in range(len(self.hidden_size)): 128 | fw.append(encoder_output_fw[i][:, -1, :]) 129 | bw.append(encoder_output_bw[i][:, -1, :]) 130 | encoder_state_fw = tf.concat(fw, axis=1) 131 | encoder_state_bw = tf.concat(bw, axis=1) 132 | 133 | # encoder_state has shape [batch_size, sum(hidden_size)*2] 134 | encoder_state = tf.concat([encoder_state_fw, encoder_state_bw], axis=1) 135 | 136 | decoder_outputs, _ = tf.contrib.legacy_seq2seq.rnn_decoder(decoder_inputs=decoder_inputs, 137 | initial_state=encoder_state, cell=cell, 138 | loop_function=lf) 139 | 140 | if self.cell_type == 'LSTM': 141 | hidden_abstract = encoder_state.h 142 | elif self.cell_type == 'GRU': 143 | hidden_abstract = encoder_state 144 | 145 | # F_update 146 | F_update = tf.assign(F, F_new_value) 147 | 148 | real_hidden_abstract = tf.split(hidden_abstract, 2)[0] 149 | 150 | # W has shape [sum(hidden_size)*2, batch_size] 151 | W = tf.transpose(real_hidden_abstract) 152 | WTW = tf.matmul(real_hidden_abstract, W) 153 | FTWTWF = tf.matmul(tf.matmul(tf.transpose(F), WTW), F) 154 | 155 | with tf.name_scope("loss_reconstruct"): 156 | loss_reconstruct = tf.losses.mean_squared_error(labels=tf.split(targets, 2, axis=1)[0], predictions=tf.split(decoder_outputs, 2, axis=1)[0]) 157 | 158 | with tf.name_scope("k-means_loss"): 159 | loss_k_means = tf.trace(WTW) - tf.trace(FTWTWF) 160 | 161 | with tf.name_scope("discriminative_loss"): 162 | weight1 = weight_variable(shape=[hidden_abstract.get_shape().as_list()[1], 128]) 163 | bias1 = bias_variable(shape=[128]) 164 | 165 | weight2 = weight_variable(shape=[128, 2]) 166 | bias2 = bias_variable(shape=[2]) 167 | 168 | hidden = tf.nn.relu(tf.matmul(hidden_abstract, weight1) + bias1) 169 | output = tf.matmul(hidden, weight2) + bias2 170 | predict = tf.reshape(output, shape=[-1, 2]) 171 | discriminative_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=real_fake_label)) 172 | 173 | with tf.name_scope("loss_total"): 174 | loss = loss_reconstruct + self.lamda / 2 * loss_k_means + discriminative_loss 175 | 176 | regularization_loss = 0.0 177 | for i in range(len(tf.trainable_variables())): 178 | regularization_loss += tf.nn.l2_loss(tf.trainable_variables()[i]) 179 | loss = loss + 1e-4 * regularization_loss 180 | input_tensors = { 181 | 'inputs': input, 182 | 'noise': noise, 183 | 'F_new_value': F_new_value, 184 | 'real_fake_label': real_fake_label 185 | } 186 | loss_tensors = { 187 | 'loss_reconstruct': loss_reconstruct, 188 | 'loss_k_means': loss_k_means, 189 | 'regularization_loss': regularization_loss, 190 | 'discriminative_loss': discriminative_loss, 191 | 'loss': loss 192 | } 193 | output_tensor = {'prediction': predict} 194 | return input_tensors, loss_tensors, hidden_abstract, F_update, output_tensor 195 | 196 | 197 | def run_model(filename, config): 198 | os.environ['CUDA_VISIBLE_DEVICES'] = '0' 199 | gpu_config = tf.ConfigProto() 200 | gpu_config.gpu_options.allow_growth = True 201 | 202 | testing_data, testing_label = utils.load_data(filename) 203 | testing_label, num_classes = utils.transfer_labels(testing_label) 204 | 205 | config.class_num = num_classes 206 | config.embedding_size = 1 207 | config.batch_size = testing_data.shape[0] 208 | config.num_steps = testing_data.shape[1] 209 | 210 | test_noise_data = np.zeros(shape=testing_data.shape) 211 | 212 | with tf.Session(config=gpu_config) as sess: 213 | model = RNN_clustering_model(config=config) 214 | input_tensors, loss_tensors, hidden_abstract, F_update, output_tensor = model.build_model() 215 | 216 | sess.run(tf.global_variables_initializer()) 217 | 218 | model_path = './Model/model.ckpt' 219 | saver = tf.train.Saver() 220 | saver.restore(sess, model_path) 221 | 222 | test_total_abstract = sess.run(hidden_abstract, 223 | feed_dict={input_tensors['inputs']: testing_data, 224 | input_tensors['noise']: test_noise_data 225 | }) 226 | 227 | test_hidden_val = np.array(test_total_abstract).reshape(-1, np.sum(config.hidden_size) * 2) 228 | km = KMeans(n_clusters=num_classes) 229 | km_idx = km.fit_predict(test_hidden_val) 230 | ri, nmi, acc = utils.evaluation(prediction=km_idx, label=testing_label) 231 | 232 | def main(): 233 | config = Config() 234 | filename = './Coffee/Coffee_TEST' 235 | config.lamda = 1e-1 236 | config.hidden_size = [100,50,50] 237 | config.dilations = [1,2,4] 238 | 239 | run_model(filename, config) 240 | 241 | if __name__ == "__main__": 242 | main() 243 | -------------------------------------------------------------------------------- /DTCR_code/rnn_cell_extensions.py: -------------------------------------------------------------------------------- 1 | 2 | """ Extensions to TF RNN class by una_dinosaria""" 3 | 4 | from __future__ import absolute_import 5 | from __future__ import division 6 | from __future__ import print_function 7 | 8 | import tensorflow as tf 9 | 10 | from tensorflow.contrib.rnn.python.ops.core_rnn_cell import RNNCell 11 | 12 | # The import for LSTMStateTuple changes in TF >= 1.2.0 13 | from pkg_resources import parse_version as pv 14 | if pv(tf.__version__) >= pv('1.2.0'): 15 | from tensorflow.contrib.rnn import LSTMStateTuple 16 | else: 17 | from tensorflow.contrib.rnn.python.ops.core_rnn_cell import LSTMStateTuple 18 | del pv 19 | 20 | from tensorflow.python.ops import variable_scope as vs 21 | 22 | import collections 23 | import math 24 | 25 | class LinearSpaceDecoderWrapper(RNNCell): 26 | """Operator adding a linear encoder to an RNN cell""" 27 | 28 | def __init__(self, cell, output_size): 29 | """Create a cell with with a linear encoder in space. 30 | 31 | Args: 32 | cell: an RNNCell. The input is passed through a linear layer. 33 | 34 | Raises: 35 | TypeError: if cell is not an RNNCell. 36 | """ 37 | if not isinstance(cell, RNNCell): 38 | raise TypeError("The parameter cell is not a RNNCell.") 39 | 40 | self._cell = cell 41 | 42 | print( 'output_size = {0}'.format(output_size) ) 43 | print( 'state_size = {0}'.format(self._cell.state_size) ) 44 | 45 | # Tuple if multi-rnn 46 | if isinstance(self._cell.state_size,tuple): 47 | 48 | # Fine if GRU... 49 | insize = self._cell.state_size[-1] 50 | 51 | # LSTMStateTuple if LSTM 52 | if isinstance( insize, LSTMStateTuple ): 53 | insize = insize.h 54 | 55 | else: 56 | # Fine if not multi-rnn 57 | insize = self._cell.state_size 58 | 59 | self.w_out = tf.get_variable("proj_w_out", 60 | [insize, output_size], 61 | dtype=tf.float32, 62 | initializer=tf.random_uniform_initializer(minval=-0.04, maxval=0.04)) 63 | self.b_out = tf.get_variable("proj_b_out", [output_size], 64 | dtype=tf.float32, 65 | initializer=tf.random_uniform_initializer(minval=-0.04, maxval=0.04)) 66 | 67 | self.linear_output_size = output_size 68 | 69 | 70 | @property 71 | def state_size(self): 72 | return self._cell.state_size 73 | 74 | @property 75 | def output_size(self): 76 | return self.linear_output_size 77 | 78 | def __call__(self, inputs, state, scope=None): 79 | """Use a linear layer and pass the output to the cell.""" 80 | 81 | # Run the rnn as usual 82 | output, new_state = self._cell(inputs, state, scope) 83 | 84 | # Apply the multiplication to everything 85 | output = tf.matmul(output, self.w_out) + self.b_out 86 | 87 | return output, new_state 88 | -------------------------------------------------------------------------------- /DTCR_code/train_model.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import tensorflow as tf 4 | import utils 5 | import math 6 | import sys 7 | import os 8 | import numpy as np 9 | import copy 10 | import drnn 11 | import rnn_cell_extensions 12 | from tensorflow.python.ops import variable_scope 13 | from sklearn import metrics 14 | from sklearn.cluster import KMeans 15 | from numpy import linalg as LA 16 | import warnings 17 | 18 | 19 | class Config(object): 20 | """Train config.""" 21 | batch_size = None 22 | hidden_size = [100, 50, 50] 23 | dilations = [1, 2, 4] 24 | num_steps = None 25 | embedding_size = None 26 | learning_rate = 1e-4 27 | cell_type = 'GRU' 28 | lamda = 1 29 | class_num = None 30 | denosing = True # False 31 | sample_loss = True # False 32 | 33 | def weight_variable(shape): 34 | initial = tf.truncated_normal(shape, stddev=0.01) 35 | return tf.Variable(initial) 36 | 37 | 38 | def bias_variable(shape): 39 | initial = tf.constant(0.1, shape=shape) 40 | return tf.Variable(initial) 41 | 42 | 43 | class RNN_clustering_model(object): 44 | 45 | def __init__(self, config): 46 | self.batch_size = config.batch_size 47 | self.hidden_size = config.hidden_size 48 | self.dilations = config.dilations 49 | self.num_steps = config.num_steps 50 | self.embedding_size = config.embedding_size 51 | self.cell_type = config.cell_type 52 | self.lamda = config.lamda 53 | self.class_num = config.class_num 54 | self.denosing = config.denosing 55 | self.sample_loss = config.sample_loss 56 | self.K = config.class_num 57 | 58 | # self.fully_units = config.fully_units 59 | 60 | def build_model(self): 61 | input = tf.placeholder(tf.float32, [None, self.num_steps], name='inputs') # input 62 | noise = tf.placeholder(tf.float32, [None, self.num_steps], name='noise') 63 | 64 | real_fake_label = tf.placeholder(tf.float32, [None, 2], name='real_fake_label') 65 | 66 | F_new_value = tf.placeholder(tf.float32, [None, self.K], name='F_new_value') 67 | # F = tf.Variable(tf.eye(self.batch_size,num_columns = self.K), trainable = False) 68 | F = tf.get_variable('F', shape=[self.batch_size, self.K], 69 | initializer=tf.orthogonal_initializer(gain=1.0, seed=None, dtype=tf.float32), 70 | trainable=False) 71 | 72 | # inputs has shape (batch_size, n_steps, embedding_size) 73 | inputs = tf.reshape(input, [-1, self.num_steps, self.embedding_size]) 74 | noises = tf.reshape(noise, [-1, self.num_steps, self.embedding_size]) 75 | 76 | # a list of 'n_steps' tenosrs, each has shape (batch_size, embedding_size) 77 | # encoder_inputs = utils._rnn_reformat(x = inputs, input_dims = self.embedding_size, n_steps = self.num_steps) 78 | 79 | # noise_input has shape (batch_size, n_steps, embedding_size) 80 | if self.denosing: 81 | print('Noise') 82 | noise_input = inputs + noises 83 | else: 84 | print('Non_noise') 85 | noise_input = inputs 86 | 87 | reverse_noise_input = tf.reverse(noise_input, axis=[1]) 88 | decoder_inputs = utils._rnn_reformat(x=noise_input, input_dims=self.embedding_size, n_steps=self.num_steps) 89 | targets = utils._rnn_reformat(x=inputs, input_dims=self.embedding_size, n_steps=self.num_steps) 90 | 91 | if self.cell_type == 'LSTM': 92 | raise ValueError('LSTMs have not support yet!') 93 | 94 | elif self.cell_type == 'GRU': 95 | cell = tf.contrib.rnn.GRUCell(np.sum(self.hidden_size) * 2) 96 | 97 | cell = rnn_cell_extensions.LinearSpaceDecoderWrapper(cell, self.embedding_size) 98 | 99 | lf = None 100 | if self.sample_loss: 101 | print 102 | 'Sample Loss' 103 | 104 | def lf(prev, i): 105 | return prev 106 | 107 | # encoder_output has shape 'layer' list of tensor [batch_size, n_steps, hidden_size] 108 | with tf.variable_scope('fw'): 109 | _, encoder_output_fw = drnn.drnn_layer_final(noise_input, self.hidden_size, self.dilations, self.num_steps, 110 | self.embedding_size, self.cell_type) 111 | 112 | with tf.variable_scope('bw'): 113 | _, encoder_output_bw = drnn.drnn_layer_final(reverse_noise_input, self.hidden_size, self.dilations, 114 | self.num_steps, self.embedding_size, self.cell_type) 115 | 116 | if self.cell_type == 'LSTM': 117 | raise ValueError('LSTMs have not support yet!') 118 | elif self.cell_type == 'GRU': 119 | fw = [] 120 | bw = [] 121 | for i in range(len(self.hidden_size)): 122 | fw.append(encoder_output_fw[i][:, -1, :]) 123 | bw.append(encoder_output_bw[i][:, -1, :]) 124 | encoder_state_fw = tf.concat(fw, axis=1) 125 | encoder_state_bw = tf.concat(bw, axis=1) 126 | 127 | # encoder_state has shape [batch_size, sum(hidden_size)*2] 128 | encoder_state = tf.concat([encoder_state_fw, encoder_state_bw], axis=1) 129 | 130 | decoder_outputs, _ = tf.contrib.legacy_seq2seq.rnn_decoder(decoder_inputs=decoder_inputs, 131 | initial_state=encoder_state, cell=cell, 132 | loop_function=lf) 133 | 134 | if self.cell_type == 'LSTM': 135 | hidden_abstract = encoder_state.h 136 | elif self.cell_type == 'GRU': 137 | hidden_abstract = encoder_state 138 | 139 | # F_update 140 | F_update = tf.assign(F, F_new_value) 141 | 142 | real_hidden_abstract = tf.split(hidden_abstract, 2)[0] 143 | 144 | # W has shape [sum(hidden_size)*2, batch_size] 145 | W = tf.transpose(real_hidden_abstract) 146 | WTW = tf.matmul(real_hidden_abstract, W) 147 | FTWTWF = tf.matmul(tf.matmul(tf.transpose(F), WTW), F) 148 | 149 | with tf.name_scope("loss_reconstruct"): 150 | loss_reconstruct = tf.losses.mean_squared_error(labels=tf.split(targets, 2, axis=1)[0], 151 | predictions=tf.split(decoder_outputs, 2, axis=1)[0]) 152 | 153 | with tf.name_scope("k-means_loss"): 154 | loss_k_means = tf.trace(WTW) - tf.trace(FTWTWF) 155 | 156 | with tf.name_scope("discriminative_loss"): 157 | weight1 = weight_variable(shape=[hidden_abstract.get_shape().as_list()[1], 128]) 158 | bias1 = bias_variable(shape=[128]) 159 | 160 | weight2 = weight_variable(shape=[128, 2]) 161 | bias2 = bias_variable(shape=[2]) 162 | 163 | hidden = tf.nn.relu(tf.matmul(hidden_abstract, weight1) + bias1) 164 | output = tf.matmul(hidden, weight2) + bias2 165 | predict = tf.reshape(output, shape=[-1, 2]) 166 | discriminative_loss = tf.reduce_mean( 167 | tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=real_fake_label)) 168 | 169 | with tf.name_scope("loss_total"): 170 | loss = loss_reconstruct + self.lamda / 2 * loss_k_means + discriminative_loss 171 | 172 | regularization_loss = 0.0 173 | for i in range(len(tf.trainable_variables())): 174 | regularization_loss += tf.nn.l2_loss(tf.trainable_variables()[i]) 175 | loss = loss + 1e-4 * regularization_loss 176 | input_tensors = { 177 | 'inputs': input, 178 | 'noise': noise, 179 | 'F_new_value': F_new_value, 180 | 'real_fake_label': real_fake_label 181 | } 182 | loss_tensors = { 183 | 'loss_reconstruct': loss_reconstruct, 184 | 'loss_k_means': loss_k_means, 185 | 'regularization_loss': regularization_loss, 186 | 'discriminative_loss': discriminative_loss, 187 | 'loss': loss 188 | } 189 | output_tensor = {'prediction': predict} 190 | return input_tensors, loss_tensors, real_hidden_abstract, F_update, output_tensor 191 | 192 | 193 | def run_model(train_data_filename, config): 194 | os.environ['CUDA_VISIBLE_DEVICES'] = '0' 195 | gpu_config = tf.ConfigProto() 196 | gpu_config.gpu_options.allow_growth = True 197 | 198 | train_data, train_label = utils.load_data(train_data_filename) 199 | 200 | config.batch_size = train_data.shape[0] 201 | config.num_steps = train_data.shape[1] 202 | config.embedding_size = 1 203 | 204 | train_label, num_classes = utils.transfer_labels(train_label) 205 | config.class_num = num_classes 206 | 207 | print('Label:', np.unique(train_label)) 208 | 209 | with tf.Session(config=gpu_config) as sess: 210 | model = RNN_clustering_model(config=config) 211 | input_tensors, loss_tensors, real_hidden_abstract, F_update, output_tensor = model.build_model() 212 | train_op = tf.train.AdamOptimizer(config.learning_rate).minimize(loss_tensors['loss']) 213 | 214 | sess.run(tf.global_variables_initializer()) 215 | 216 | Epoch = 300 217 | 218 | for i in range(Epoch): 219 | # shuffle data and label 220 | indices = np.random.permutation(train_data.shape[0]) 221 | shuffle_data = train_data[indices] 222 | shuffle_label = train_label[indices] 223 | 224 | row = train_data.shape[0] 225 | batch_len = int(row / config.batch_size) 226 | left_row = row - batch_len * config.batch_size 227 | 228 | if left_row != 0: 229 | need_more = config.batch_size - left_row 230 | rand_idx = np.random.choice(np.arange(batch_len * config.batch_size), size=need_more) 231 | shuffle_data = np.concatenate((shuffle_data, shuffle_data[rand_idx]), axis=0) 232 | shuffle_label = np.concatenate((shuffle_label, shuffle_label[rand_idx]), axis=0) 233 | assert shuffle_data.shape[0] % config.batch_size == 0 234 | 235 | noise_data = np.random.normal(loc=0, scale=0.1, size=[shuffle_data.shape[0]*2, shuffle_data.shape[1]]) 236 | total_abstract = [] 237 | print('----------Epoch %d----------' % i) 238 | k = 0 239 | 240 | for input, _ in utils.next_batch(config.batch_size, shuffle_data): 241 | noise = noise_data[k * config.batch_size * 2: (k + 1) * config.batch_size * 2, :] 242 | fake_input, train_real_fake_labels = utils.get_fake_sample(input) 243 | loss_val, abstract, _ = sess.run( 244 | [loss_tensors['loss'], real_hidden_abstract, train_op], 245 | feed_dict={input_tensors['inputs']: np.concatenate((input, fake_input), axis=0), 246 | input_tensors['noise']: noise, 247 | input_tensors['real_fake_label']: train_real_fake_labels 248 | }) 249 | print(loss_val) 250 | total_abstract.append(abstract) 251 | k += 1 252 | if i % 10 == 0 and i != 0: 253 | part_hidden_val = np.array(abstract).reshape(-1, np.sum(config.hidden_size) * 2) 254 | W = part_hidden_val.T 255 | U, sigma, VT = np.linalg.svd(W) 256 | sorted_indices = np.argsort(sigma) 257 | topk_evecs = VT[sorted_indices[:-num_classes - 1:-1], :] 258 | F_new = topk_evecs.T 259 | sess.run(F_update, feed_dict={input_tensors['F_new_value']: F_new}) 260 | 261 | 262 | def main(): 263 | config = Config() 264 | # input your filename 265 | filename = './Coffee/Coffee_TRAIN' 266 | lamda = 1e-1 267 | hidden_sizes = [100, 50, 50] 268 | dilations = [1, 2, 4] 269 | config.lamda = lamda 270 | config.hidden_size = hidden_sizes 271 | config.dilations = dilations 272 | run_model(filename, config) 273 | 274 | 275 | if __name__ == "__main__": 276 | main() 277 | -------------------------------------------------------------------------------- /DTCR_code/utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu Sep 8 15:29:48 2016 4 | 5 | @author: root 6 | """ 7 | import tensorflow as tf 8 | import numpy as np 9 | import sys, time 10 | from scipy.misc import comb 11 | from sklearn import metrics 12 | 13 | 14 | def transfer_labels(labels): 15 | indexes = np.unique(labels) 16 | num_classes = indexes.shape[0] 17 | num_samples = labels.shape[0] 18 | 19 | for i in range(num_samples): 20 | new_label = np.argwhere(labels[i] == indexes)[0][0] 21 | labels[i] = new_label 22 | return labels, num_classes 23 | 24 | 25 | def get_fake_sample(data): 26 | sample_nums = data.shape[0] 27 | series_len = data.shape[1] 28 | mask = np.ones(shape=[sample_nums, series_len]) 29 | rand_list = np.zeros(shape=[sample_nums, series_len]) 30 | 31 | fake_position_nums = int(series_len * 0.2) 32 | fake_position = np.random.randint(low=0, high=series_len, size=[sample_nums, fake_position_nums]) 33 | 34 | for i in range(fake_position.shape[0]): 35 | for j in range(fake_position.shape[1]): 36 | mask[i, fake_position[i, j]] = 0 37 | 38 | for i in range(rand_list.shape[0]): 39 | count = 0 40 | for j in range(rand_list.shape[1]): 41 | if j in fake_position[i]: 42 | rand_list[i, j] = data[i, fake_position[i, count]] 43 | count += 1 44 | fake_data = data * mask + rand_list * (1 - mask) 45 | real_fake_labels = np.zeros(shape=[sample_nums * 2, 2]) 46 | for i in range(sample_nums * 2): 47 | if i < sample_nums: 48 | real_fake_labels[i, 0] = 1 49 | else: 50 | real_fake_labels[i, 1] = 1 51 | return fake_data, real_fake_labels 52 | 53 | 54 | def _rnn_reformat(x, input_dims, n_steps): 55 | """ 56 | This function reformat input to the shape that standard RNN can take. 57 | Inputs: 58 | x -- a tensor of shape (batch_size, n_steps, input_dims). 59 | Outputs: 60 | x_reformat -- a list of 'n_steps' tenosrs, each has shape (batch_size, input_dims). 61 | """ 62 | # permute batch_size and n_steps 63 | x_ = tf.transpose(x, [1, 0, 2]) 64 | # reshape to (n_steps*batch_size, input_dims) 65 | x_ = tf.reshape(x_, [-1, input_dims]) 66 | # split to get a list of 'n_steps' tensors of shape (batch_size, input_dims) 67 | x_reformat = tf.split(x_, n_steps, 0) 68 | return x_reformat 69 | 70 | 71 | def _rnn_reformat_denoise(x, input_dims, n_steps, batch_size): 72 | """ 73 | This function reformat input to the shape that standard RNN can take. 74 | Inputs: 75 | x -- a tensor of shape (batch_size, n_steps, input_dims). 76 | Outputs: 77 | x_reformat -- a list of 'n_steps' tenosrs, each has shape (batch_size, input_dims). 78 | """ 79 | # x_ = x + np.random.uniform(-1,1,(batch_size, n_steps, input_dims)) 80 | # x_ = x + np.random.normal(size=(batch_size, n_steps, input_dims)) 81 | # permute batch_size and n_steps 82 | x_ = tf.transpose(x, [1, 0, 2]) 83 | # reshape to (n_steps*batch_size, input_dims) 84 | x_ = tf.reshape(x_, [-1, input_dims]) 85 | # split to get a list of 'n_steps' tensors of shape (batch_size, input_dims) 86 | x_reformat = tf.split(x_, n_steps, 0) 87 | return x_reformat 88 | 89 | 90 | def load_data(filename): 91 | data_label = np.loadtxt(filename, delimiter=',') 92 | data = data_label[:, 1:] 93 | label = data_label[:, 0].astype(np.int32) 94 | return data, label 95 | 96 | 97 | def evaluation(prediction, label): 98 | acc = cluster_acc(label, prediction) 99 | nmi = metrics.normalized_mutual_info_score(label, prediction) 100 | ari = metrics.adjusted_rand_score(label, prediction) 101 | ri = rand_index_score(label, prediction) 102 | print((acc, nmi, ari, ri)) 103 | return ri, nmi, acc 104 | 105 | 106 | def rand_index_score(clusters, classes): 107 | tp_plus_fp = comb(np.bincount(clusters), 2).sum() 108 | tp_plus_fn = comb(np.bincount(classes), 2).sum() 109 | A = np.c_[(clusters, classes)] 110 | tp = sum(comb(np.bincount(A[A[:, 0] == i, 1]), 2).sum() 111 | for i in set(clusters)) 112 | fp = tp_plus_fp - tp 113 | fn = tp_plus_fn - tp 114 | tn = comb(len(A), 2) - tp - fp - fn 115 | return (tp + tn) / (tp + fp + fn + tn) 116 | 117 | 118 | def cluster_acc(y_true, y_pred): 119 | """ 120 | Calculate clustering accuracy. Require scikit-learn installed 121 | 122 | # Arguments 123 | y: true labels, numpy.array with shape `(n_samples,)` 124 | y_pred: predicted labels, numpy.array with shape `(n_samples,)` 125 | 126 | # Return 127 | accuracy, in [0,1] 128 | """ 129 | y_true = y_true.astype(np.int64) 130 | assert y_pred.size == y_true.size 131 | D = max(y_pred.max(), y_true.max()) + 1 132 | w = np.zeros((D, D), dtype=np.int64) 133 | for i in range(y_pred.size): 134 | w[y_pred[i], y_true[i]] += 1 135 | from sklearn.utils.linear_assignment_ import linear_assignment 136 | ind = linear_assignment(w.max() - w) 137 | return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size 138 | 139 | 140 | def next_batch(batch_size, data): 141 | # assert data.shape[0] == label.shape[0] 142 | row = data.shape[0] 143 | batch_len = int(row / batch_size) 144 | left_row = row - batch_len * batch_size 145 | 146 | for i in range(batch_len): 147 | batch_input = data[i * batch_size: (i + 1) * batch_size, :] 148 | yield (batch_input, False) 149 | 150 | if left_row != 0: 151 | need_more = batch_size - left_row 152 | need_more = np.random.choice(np.arange(row), size=need_more) 153 | yield (np.concatenate((data[-left_row:, :], data[need_more]), axis=0), True) 154 | 155 | 156 | -------------------------------------------------------------------------------- /Learning Representations for Time Series Clustering.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qianlima-lab/DTCR/e4ecce48c0fd1fe3c017cccf1fd9203efdddbb6b/Learning Representations for Time Series Clustering.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DTCR 2 | The paper "Learning Representations for Time Series Clustering" accepted by 2019 Neural Information Processing Systems (NeurIPS). 3 | 4 | Jiawei Zheng and Sen Li equally contributed to this work. 5 | # Code 6 | The code for DTCR. 7 | 8 | ## Dependencies 9 | * tensorflow 1.0 and above 10 | ## Usage 11 | This repository contains a demo of Coffee in the UCR dataset. You can run the command: 12 | * Train: python train_model.py 13 | * Test: python load_model_Coffee.py 14 | 15 | -------------------------------------------------------------------------------- /Supplementary Material_Learning Representations for Time Series Clustering.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/qianlima-lab/DTCR/e4ecce48c0fd1fe3c017cccf1fd9203efdddbb6b/Supplementary Material_Learning Representations for Time Series Clustering.pdf --------------------------------------------------------------------------------