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d_shared_code_masked_skip_connection_seq2seq.py
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d_shared_code_masked_skip_connection_seq2seq.py
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import pathlib
import copy
import sys
import tensorflow as tf
import numpy as np
from tensorflow.python.ops import init_ops
from tensorflow.python.ops.rnn_cell_impl import _Linear, LSTMStateTuple, GRUCell, LSTMCell
from tensorflow.python.ops import variable_scope as vs
from utils import *
from tensorflow.contrib import seq2seq
class RLSTMCell(tf.nn.rnn_cell.BasicLSTMCell):
"""Basic LSTM recurrent network cell.
The implementation is based on: http://arxiv.org/abs/1409.2329.
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.
For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell}
that follows.
"""
def __init__(self, num_units, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, dense=None):
"""Initialize the basic LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
Must set to `0.0` manually when restoring from CudnnLSTM-trained
checkpoints.
state_is_tuple: If True, accepted and returned states are 2-tuples of
the `c_state` and `m_state`. If False, they are concatenated
along the column axis. The latter behavior will soon be deprecated.
activation: Activation function of the inner states. Default: `tanh`.
reuse: (optional) Python boolean describing whether to reuse variables
in an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
When restoring from CudnnLSTM-trained checkpoints, must use
CudnnCompatibleLSTMCell instead.
"""
super(tf.nn.rnn_cell.BasicLSTMCell, self).__init__(_reuse=reuse)
if not state_is_tuple:
tf.logging.warn("%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation or tf.nn.tanh
self._linear = None
self._dense = dense
def call(self, inputs, state):
"""Long short-term memory cell (LSTM).
Args:
inputs: `2-D` tensor with shape `[batch_size x input_size]`.
state: An `LSTMStateTuple` of state tensors, each shaped
`[batch_size x self.state_size]`, if `state_is_tuple` has been set to
`True`. Otherwise, a `Tensor` shaped
`[batch_size x 2 * self.state_size]`.
Returns:
A pair containing the new hidden state, and the new state (either a
`LSTMStateTuple` or a concatenated state, depending on
`state_is_tuple`).
"""
sigmoid = tf.sigmoid
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = tf.split(value=state, num_or_size_splits=2, axis=1)
if self._linear is None:
self._linear = _Linear([inputs, h], 4 * self._num_units, True)
i, j, f, o = tf.split(value=self._linear([inputs, h]), num_or_size_splits=4, axis=1)
new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
new_h_1 = self._activation(new_c) * sigmoid(o)
w_h, b_h = self.weight_bias([self._num_units, self._num_units], [self._num_units])
new_h_2 = sigmoid(tf.matmul(h, w_h) + b_h)
new_h = new_h_1 + new_h_2
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = tf.concat([new_c, new_h], 1)
return new_h, new_state
def weight_bias(self, W_shape, b_shape, bias_init=0.1):
"""Fully connected highway layer adopted from
https://github.com/fomorians/highway-fcn/blob/master/main.py
"""
W = tf.get_variable("weight_h_2", shape=W_shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
b = tf.get_variable("bias_h_2", shape=b_shape)
return W, b
class RSLSTMCell(tf.nn.rnn_cell.BasicLSTMCell):
"""Basic LSTM recurrent network cell.
The implementation is based on: http://arxiv.org/abs/1409.2329.
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.
For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell}
that follows.
"""
def __init__(self, num_units, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, dense=None,
file_name='tweet', type='enc', component=1, partition=1):
"""Initialize the basic LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
Must set to `0.0` manually when restoring from CudnnLSTM-trained
checkpoints.
state_is_tuple: If True, accepted and returned states are 2-tuples of
the `c_state` and `m_state`. If False, they are concatenated
along the column axis. The latter behavior will soon be deprecated.
activation: Activation function of the inner states. Default: `tanh`.
reuse: (optional) Python boolean describing whether to reuse variables
in an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
When restoring from CudnnLSTM-trained checkpoints, must use
CudnnCompatibleLSTMCell instead.
"""
super(tf.nn.rnn_cell.BasicLSTMCell, self).__init__(_reuse=reuse)
if not state_is_tuple:
tf.logging.warn("%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation or tf.nn.tanh
self._linear = None
self._dense = dense
self._step = 0
self._file_name = file_name
self._type = type
self._component = component
self._partition = partition
if not os.path.exists('./weight/' + self._file_name):
os.makedirs('./weight/' + self._file_name)
if not os.path.exists('./weight/' + self._file_name + '/' + str(self._partition)):
os.makedirs('./weight/' + self._file_name + '/' + str(self._partition))
if not os.path.exists('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component)):
os.makedirs('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component))
if not os.path.exists('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(
self._component) + '/' + self._type):
os.makedirs('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(
self._component) + '/' + self._type)
def call(self, inputs, state):
"""Long short-term memory cell (LSTM).
Args:
inputs: `2-D` tensor with shape `[batch_size x input_size]`.
state: An `LSTMStateTuple` of state tensors, each shaped
`[batch_size x self.state_size]`, if `state_is_tuple` has been set to
`True`. Otherwise, a `Tensor` shaped
`[batch_size x 2 * self.state_size]`.
Returns:
A pair containing the new hidden state, and the new state (either a
`LSTMStateTuple` or a concatenated state, depending on
`state_is_tuple`).
"""
sigmoid = tf.sigmoid
self._step = self._step + 1
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = tf.split(value=state, num_or_size_splits=2, axis=1)
if self._linear is None:
self._linear = _Linear([inputs, h], 4 * self._num_units, True)
i, j, f, o = tf.split(value=self._linear([inputs, h]), num_or_size_splits=4, axis=1)
new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
new_h_1 = self._activation(new_c) * sigmoid(o)
w_h, b_h = self.weight_bias([self._num_units, self._num_units], [self._num_units])
new_h_2 = sigmoid(tf.matmul(h, w_h) + b_h)
masked_w1, masked_w2 = self.masked_weight(_load=False)
new_h = new_h_1 * masked_w1 + new_h_2 * masked_w2
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = tf.concat([new_c, new_h], 1)
return new_h, new_state
def weight_bias(self, W_shape, b_shape, bias_init=0.1):
"""Fully connected highway layer adopted from
https://github.com/fomorians/highway-fcn/blob/master/main.py
"""
W = tf.get_variable("weight_h_2", shape=W_shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
b = tf.get_variable("bias_h_2", shape=b_shape)
return W, b
def masked_weight(self, _load=False):
if _load == False:
masked_W1 = np.random.randint(2, size=1)
if masked_W1 == 0:
masked_W2 = 1
else:
masked_W2 = np.random.randint(2, size=1)
np.save('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component) + '/'
+ self._type + '/W1_' + str(self._step), masked_W1)
np.save('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component) + '/'
+ self._type + '/W2_' + str(self._step), masked_W2)
else:
masked_W1 = np.load('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component)
+ '/' + str(self._type) + '/W1_' + str(self._step) + '.npy')
masked_W2 = np.load('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component)
+ '/' + str(self._type) + '/W2_' + str(self._step) + '.npy')
tf_mask_W1 = tf.constant(masked_W1, dtype=tf.float32)
tf_mask_W2 = tf.constant(masked_W2, dtype=tf.float32)
return tf_mask_W1, tf_mask_W2
class RKLSTMCell(tf.nn.rnn_cell.BasicLSTMCell):
"""Basic LSTM recurrent network cell.
The implementation is based on: http://arxiv.org/abs/1409.2329.
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
It does not allow cell clipping, a projection layer, and does not
use peep-hole connections: it is the basic baseline.
For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell}
that follows.
"""
def __init__(self, num_units, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, dense=None,
file_name='tweet', type='enc', component=1, partition=1, skip_size=5):
"""Initialize the basic LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
Must set to `0.0` manually when restoring from CudnnLSTM-trained
checkpoints.
state_is_tuple: If True, accepted and returned states are 2-tuples of
the `c_state` and `m_state`. If False, they are concatenated
along the column axis. The latter behavior will soon be deprecated.
activation: Activation function of the inner states. Default: `tanh`.
reuse: (optional) Python boolean describing whether to reuse variables
in an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
When restoring from CudnnLSTM-trained checkpoints, must use
CudnnCompatibleLSTMCell instead.
"""
super(tf.nn.rnn_cell.BasicLSTMCell, self).__init__(_reuse=reuse)
if not state_is_tuple:
tf.logging.warn("%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
self._num_units = num_units
self._forget_bias = forget_bias
self._state_is_tuple = state_is_tuple
self._activation = activation or tf.nn.tanh
self._linear = None
self._dense = dense
self._step = 0
self._file_name = file_name
self._type = type
self._component = component
self._partition = partition
self._skip_size = skip_size
if not os.path.exists('./weight/' + self._file_name):
os.makedirs('./weight/' + self._file_name)
if not os.path.exists('./weight/' + self._file_name + '/' + str(self._partition)):
os.makedirs('./weight/' + self._file_name + '/' + str(self._partition))
if not os.path.exists('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component)):
os.makedirs('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component))
if not os.path.exists('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component) + '/' + self._type):
os.makedirs('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component) + '/' + self._type)
def call(self, inputs, state):
"""Long short-term memory cell (LSTM).
Args:
inputs: `2-D` tensor with shape `[batch_size x input_size]`.
state: An `LSTMStateTuple` of state tensors, each shaped
`[batch_size x self.state_size]`, if `state_is_tuple` has been set to
`True`. Otherwise, a `Tensor` shaped
`[batch_size x 2 * self.state_size]`.
Returns:
A pair containing the new hidden state, and the new state (either a
`LSTMStateTuple` or a concatenated state, depending on
`state_is_tuple`).
"""
sigmoid = tf.sigmoid
self._step = self._step + 1
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = tf.split(value=state, num_or_size_splits=2, axis=1)
if self._linear is None:
self._linear = _Linear([inputs, h], 4 * self._num_units, True)
i, j, f, o = tf.split(value=self._linear([inputs, h]), num_or_size_splits=4, axis=1)
new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
new_h_cnt = self._activation(new_c) * sigmoid(o)
if self._step % self._skip_size == 0:
w_h_skip, b_h_skip = self.weight_bias([self._num_units, self._num_units], [self._num_units])
new_h_skip = sigmoid(tf.matmul(h, w_h_skip) + b_h_skip)
masked_w1, masked_w2 = self.masked_weight(_load=False)
new_h = new_h_cnt * masked_w1 + new_h_skip * masked_w2
else:
new_h = new_h_cnt
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = tf.concat([new_c, new_h], 1)
return new_h, new_state
def weight_bias(self, W_shape, b_shape, bias_init=0.1):
"""Fully connected highway layer adopted from
https://github.com/fomorians/highway-fcn/blob/master/main.py
"""
W = tf.get_variable("weight_h_2", shape=W_shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
b = tf.get_variable("bias_h_2", shape=b_shape)
return W, b
def masked_weight(self, _load=False):
if _load==False:
masked_W1 = np.random.randint(2, size=1)
if masked_W1 == 0:
masked_W2 = 1
else:
masked_W2 = np.random.randint(2, size=1)
np.save('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component) + '/'
+ self._type + '/W1_' + str(self._step), masked_W1)
np.save('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component) + '/'
+ self._type + '/W2_' + str(self._step), masked_W2)
else:
masked_W1 = np.load('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component)
+ '/' + str(self._type) + '/W1_' + str(self._step) + '.npy')
masked_W2 = np.load('./weight/' + self._file_name + '/' + str(self._partition) + '/' + str(self._component)
+ '/' + str(self._type) + '/W2_' + str(self._step) + '.npy')
tf_mask_W1 = tf.constant(masked_W1, dtype=tf.float32)
tf_mask_W2 = tf.constant(masked_W2, dtype=tf.float32)
return tf_mask_W1, tf_mask_W2
class RSGRUCell(tf.nn.rnn_cell.GRUCell):
"""Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078).
Args:
num_units: int, The number of units in the GRU cell.
activation: Nonlinearity to use. Default: `tanh`.
reuse: (optional) Python boolean describing whether to reuse variables
in an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
kernel_initializer: (optional) The initializer to use for the weight and
projection matrices.
bias_initializer: (optional) The initializer to use for the bias.
"""
def __init__(self, num_units, activation=None, reuse=None, kernel_initializer=None, bias_initializer=None):
super(tf.nn.rnn_cell.GRUCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._activation = activation or tf.nn.tanh
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
self._gate_linear = None
self._candidate_linear = None
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def call(self, inputs, state):
"""Gated recurrent unit (GRU) with nunits cells."""
sigmoid = tf.sigmoid
if self._gate_linear is None:
bias_ones = self._bias_initializer
if self._bias_initializer is None:
bias_ones = init_ops.constant_initializer(1.0, dtype=inputs.dtype)
with vs.variable_scope("gates"): # Reset gate and update gate.
self._gate_linear = _Linear([inputs, state], 2 * self._num_units, True, bias_initializer=bias_ones,
kernel_initializer=self._kernel_initializer)
value = sigmoid(self._gate_linear([inputs, state]))
r, u = tf.split(value=value, num_or_size_splits=2, axis=1)
r_state = r * state
if self._candidate_linear is None:
with vs.variable_scope("candidate"):
self._candidate_linear = _Linear([inputs, r_state], self._num_units, True,
bias_initializer=self._bias_initializer,
kernel_initializer=self._kernel_initializer)
c = self._activation(self._candidate_linear([inputs, r_state]))
new_h = (u * state + (1 - u) * c)
return new_h, new_h
def weight_bias(self, W_shape, b_shape, bias_init=0.1):
"""Fully connected highway layer adopted from
https://github.com/fomorians/highway-fcn/blob/master/main.py
"""
W = tf.get_variable("weight_h_2", shape=W_shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
b = tf.get_variable("bias_h_2", shape=b_shape)
return W, b
def Model(_abnormal_data, _abnormal_label, _hidden_num, _elem_num, _file_name, _partition):
tf.reset_default_graph()
g = tf.Graph()
with g.as_default():
# placeholder list
p_input = tf.placeholder(tf.float32, shape=(batch_num, _abnormal_data.shape[1], _abnormal_data.shape[2]))
# p_inputs = [tf.squeeze(t, [1]) for t in tf.split(p_input, _abnormal_data.shape[1], 1)]
# Regularizer signature
l1_regularizer = tf.contrib.layers.l1_regularizer(scale=0.005, scope=None)
# Projection layer
projection_layer = tf.layers.Dense(units=_elem_num, use_bias=True)
# with tf.device('/device:GPU:0'):
d_enc = {}
with tf.variable_scope('encoder'):
for j in range(ensemble_space):
# create RNN cell
if cell_type == 0:
enc_cell = tf.nn.rnn_cell.BasicRNNCell(_hidden_num)
if cell_type == 1:
pure_enc_cell = LSTMCell(_hidden_num)
residual_enc_cell = RLSTMCell(_hidden_num)
# enc_cell = RSLSTMCell(_hidden_num, file_name=_file_name, type='enc', partition=_partition,
# component=j, reuse=tf.AUTO_REUSE)
enc_cell = RKLSTMCell(_hidden_num, file_name=_file_name, type='enc', partition=_partition,
component=j, reuse=tf.AUTO_REUSE)
if cell_type == 2:
pure_enc_cell = GRUCell(_hidden_num)
enc_cell = RSGRUCell(_hidden_num)
if j == 0:
d_enc['enc_output_{0}'.format(j)], d_enc['enc_state_{0}'.format(j)] = tf.nn.dynamic_rnn(
pure_enc_cell, p_input, dtype=tf.float32)
elif j == 1:
d_enc['enc_output_{0}'.format(j)], d_enc['enc_state_{0}'.format(j)] = tf.nn.dynamic_rnn(
residual_enc_cell, p_input, dtype=tf.float32)
else:
d_enc['enc_output_{0}'.format(j)], d_enc['enc_state_{0}'.format(j)] = tf.nn.dynamic_rnn(enc_cell,
p_input,
dtype=tf.float32)
# shared_state_c = tf.concat([d_enc['enc_state_{0}'.format(j)].c for j in range(ensemble_space)], axis=1)
# shared_state_h = tf.concat([d_enc['enc_state_{0}'.format(j)].h for j in range(ensemble_space)], axis=1)
w_c = tf.Variable(tf.zeros([_hidden_num, _hidden_num]))
b_c = tf.Variable(tf.zeros([_hidden_num]))
w_h = tf.Variable(tf.zeros([_hidden_num, _hidden_num]))
b_h = tf.Variable(tf.zeros([_hidden_num]))
shared_state_c = tf.concat([tf.matmul(d_enc['enc_state_{0}'.format(j)].c, w_c) + b_c for j in range(ensemble_space)], axis=1)
shared_state_h = tf.concat([tf.matmul(d_enc['enc_state_{0}'.format(j)].h, w_h) + b_h for j in range(ensemble_space)], axis=1)
if compress:
compress_state = tf.layers.Dense(units=_hidden_num, activation=tf.tanh, use_bias=True)
shared_state_c = compress_state(shared_state_c)
shared_state_h = compress_state(shared_state_h)
shared_state = LSTMStateTuple(shared_state_c, shared_state_h)
# with tf.device('/device:GPU:1'):
d_dec = {}
with tf.variable_scope('decoder') as vs:
if decode_without_input:
dec_input = tf.zeros([p_input.shape[0], p_input.shape[1], p_input.shape[2]], dtype=tf.float32)
for k in range(ensemble_space):
# create RNN cell
if cell_type == 0:
dec_cell = tf.nn.rnn_cell.BasicRNNCell(_hidden_num)
if cell_type == 1:
if compress:
pure_dec_cell = LSTMCell(_hidden_num)
residual_dec_cell = RLSTMCell(_hidden_num)
dec_cell = RSLSTMCell(_hidden_num, file_name=_file_name, type='dec', partition=_partition,
component=k, reuse=tf.AUTO_REUSE)
else:
pure_dec_cell = LSTMCell(_hidden_num * ensemble_space)
residual_dec_cell = RLSTMCell(_hidden_num * ensemble_space)
dec_cell = RSLSTMCell(_hidden_num * ensemble_space, file_name=_file_name, type='dec',
partition=_partition, component=k, reuse=tf.AUTO_REUSE)
if cell_type == 2:
if compress:
pure_dec_cell = GRUCell(_hidden_num)
dec_cell = RSGRUCell(_hidden_num)
else:
pure_dec_cell = GRUCell(_hidden_num * ensemble_space)
dec_cell = RSGRUCell(_hidden_num * ensemble_space)
if k == 0:
d_dec['dec_output_{0}'.format(k)], d_dec['dec_state_{0}'.format(k)] = tf.nn.dynamic_rnn(
pure_dec_cell, dec_input, initial_state=shared_state, dtype=tf.float32)
elif k == 1:
d_dec['dec_output_{0}'.format(k)], d_dec['dec_state_{0}'.format(k)] = tf.nn.dynamic_rnn(
residual_dec_cell, dec_input, initial_state=shared_state, dtype=tf.float32)
else:
d_dec['dec_output_{0}'.format(k)], d_dec['dec_state_{0}'.format(k)] = tf.nn.dynamic_rnn(
dec_cell, dec_input, initial_state=shared_state, dtype=tf.float32)
if reverse:
d_dec['dec_output_{0}'.format(k)] = d_dec['dec_output_{0}'.format(k)][::-1]
else:
dec_input = tf.zeros([p_input.shape[0], p_input.shape[2]], dtype=tf.float32)
for k in range(ensemble_space):
# create RNN cell
if cell_type == 0:
dec_cell = tf.nn.rnn_cell.BasicRNNCell(_hidden_num)
if cell_type == 1:
if compress:
pure_dec_cell = LSTMCell(_hidden_num)
residual_dec_cell = RLSTMCell(_hidden_num)
# dec_cell = RSLSTMCell(_hidden_num, file_name=_file_name, type='dec', partition=_partition,
# component=k, reuse=tf.AUTO_REUSE)
dec_cell = RKLSTMCell(_hidden_num, file_name=_file_name, type='dec', partition=_partition,
component=k, reuse=tf.AUTO_REUSE)
else:
pure_dec_cell = LSTMCell(_hidden_num * ensemble_space)
residual_dec_cell = RLSTMCell(_hidden_num * ensemble_space)
# dec_cell = RSLSTMCell(_hidden_num * ensemble_space, file_name=_file_name, type='dec',
# partition=_partition, component=k, reuse=tf.AUTO_REUSE)
dec_cell = RKLSTMCell(_hidden_num * ensemble_space, file_name=_file_name, type='dec',
partition = _partition, component=k, reuse=tf.AUTO_REUSE)
if cell_type == 2:
if compress:
pure_dec_cell = GRUCell(_hidden_num)
dec_cell = RSGRUCell(_hidden_num)
else:
pure_dec_cell = GRUCell(_hidden_num * ensemble_space)
dec_cell = RSGRUCell(_hidden_num * ensemble_space)
inference_helper = tf.contrib.seq2seq.InferenceHelper(
sample_fn=lambda outputs: outputs,
sample_shape=[_elem_num],
sample_dtype=tf.float32,
start_inputs=dec_input,
end_fn=lambda sample_ids: False)
if k == 0:
inference_decoder = tf.contrib.seq2seq.BasicDecoder(pure_dec_cell, inference_helper,
shared_state, output_layer=projection_layer)
elif k == 1:
inference_decoder = tf.contrib.seq2seq.BasicDecoder(residual_dec_cell, inference_helper,
shared_state, output_layer=projection_layer)
else:
inference_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, inference_helper, shared_state,
output_layer=projection_layer)
d_dec['dec_output_{0}'.format(k)], _, _ = tf.contrib.seq2seq.dynamic_decode(inference_decoder,
impute_finished=True,
maximum_iterations=
p_input.shape[1])
if reverse:
d_dec['dec_output_{0}'.format(k)] = d_dec['dec_output_{0}'.format(k)][::-1]
sum_of_difference = 0
for i in range(ensemble_space):
sum_of_difference += d_dec['dec_output_{0}'.format(i)][0] - p_input
loss = tf.reduce_mean(tf.square(sum_of_difference))
regularization_penalty = tf.contrib.layers.apply_regularization(l1_regularizer, [shared_state])
loss = loss + regularization_penalty
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
return g, p_input, d_dec, loss, optimizer, saver
def RunModel(_abnormal_data, _abnormal_label, _hidden_num, _elem_num, _file_name, _partition):
graph, p_input, d_dec, loss, optimizer, saver = Model(_abnormal_data, _abnormal_label, _hidden_num, _elem_num,
_file_name, _partition)
config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# Add ops to save and restore all the variables.
with tf.Session(graph=graph, config=config) as sess:
sess.run(tf.global_variables_initializer())
for i in range(iteration):
"""Random sequences.
Every sequence has size batch_num * step_num * elem_num
Each step number increases 1 by 1.
An initial number of each sequence is in the range from 0 to 19.
(ex. [8. 9. 10. 11. 12. 13. 14. 15])
"""
(loss_val, _) = sess.run([loss, optimizer], {p_input: _abnormal_data})
# print('iter %d:' % (i + 1), loss_val)
if save_model:
save_path = saver.save(sess, './saved_model/' + pathlib.Path(file_name).parts[
0] + '/shared_code_masked_skip_rnn_seq2seq_' + str(cell_type) + '_' + os.path.basename(
file_name) + '.ckpt')
print("Model saved in path: %s" % save_path)
result = {}
error = []
for k in range(ensemble_space):
(result['input_{0}'.format(k)], result['output_{0}'.format(k)]) = sess.run(
[p_input, d_dec['dec_output_{0}'.format(k)]], {p_input: _abnormal_data})
error.append(SquareErrorDataPoints(result['input_{0}'.format(k)], result['output_{0}'.format(k)][0]))
sess.close()
ensemble_errors = np.asarray(error)
anomaly_score = CalculateFinalAnomalyScore(ensemble_errors)
zscore = Z_Score(anomaly_score)
y_pred = CreateLabelBasedOnZscore(zscore, 3)
if not partition:
score_pred_label = np.c_[ensemble_errors, y_pred, _abnormal_label]
np.savetxt('./saved_result/' + pathlib.Path(file_name).parts[
0] + '/shared_code_masked_skip_rnn_seq2seq_' + os.path.basename(file_name) + '_score.txt', score_pred_label,
delimiter=',') # X is an array
precision, recall, f1 = CalculatePrecisionRecallF1Metrics(_abnormal_label, y_pred)
if not partition:
PrintPrecisionRecallF1Metrics(precision, recall, f1)
fpr, tpr, roc_auc = CalculateROCAUCMetrics(_abnormal_label, anomaly_score)
# PlotROCAUC(fpr, tpr, roc_auc)
if not partition:
print('roc_auc=' + str(roc_auc))
precision_curve, recall_curve, average_precision = CalculatePrecisionRecallCurve(_abnormal_label, anomaly_score)
# PlotPrecisionRecallCurve(precision_curve, recall_curve, average_precision)
if not partition:
print('pr_auc=' + str(average_precision))
cks = CalculateCohenKappaMetrics(_abnormal_label, y_pred)
if not partition:
print('cks=' + str(cks))
return anomaly_score, precision, recall, f1, roc_auc, average_precision, cks
if __name__ == '__main__':
batch_num = 1
hidden_num = 16
# step_num = 8
iteration = 30
learning_rate = 1e-3
multivariate = True
reverse = True
decode_without_input = False
compress = False
partition = True
save_model = False
# cell type 0 => BasicRNN, 1 => LSTM, 2 => GRU
cell_type = 1
try:
sys.argv[1]
sys.argv[2]
except IndexError:
ensemble_space = 20
for n in range(1, 7):
# file name parameter
dataset = n
if dataset == 1:
file_name = './GD/data/Genesis_AnomalyLabels.csv'
print(file_name)
k_partition = 40
abnormal_data, abnormal_label = ReadGDDataset(file_name)
elem_num = 18
if multivariate:
abnormal_data = np.expand_dims(abnormal_data, axis=0)
if partition:
splitted_data, splitted_label = PartitionTimeSeriesKPart(abnormal_data, abnormal_label, _part_number=k_partition)
final_error = []
for i in range(k_partition):
error_partition, precision_partition, recall_partition, f1_partition, roc_auc_partition, pr_auc_partition, cks = RunModel(splitted_data[i], splitted_label[i], hidden_num, elem_num)
final_error.append(error_partition)
# print('-----------------------------------------')
final_error = np.concatenate(final_error).ravel()
final_zscore = Z_Score(final_error)
y_pred = CreateLabelBasedOnZscore(final_zscore, 3)
precision, recall, f1 = CalculatePrecisionRecallF1Metrics(abnormal_label, y_pred)
PrintPrecisionRecallF1Metrics(precision, recall, f1)
_, _, roc_auc = CalculateROCAUCMetrics(abnormal_label, final_error)
print('roc_auc=' + str(roc_auc))
_, _, pr_auc = CalculatePrecisionRecallCurve(abnormal_label, final_error)
print('pr_auc=' + str(pr_auc))
cks = CalculateCohenKappaMetrics(abnormal_label, y_pred)
print('cohen_kappa=' + str(cks))
else:
error, precision, recall, f1, roc_auc, pr_auc, cks = RunModel(abnormal_data, abnormal_label, hidden_num, elem_num)
if dataset == 2:
file_name = './HSS/data/HRSS_anomalous_standard.csv'
print(file_name)
k_partition = 80
abnormal_data, abnormal_label = ReadHSSDataset(file_name)
elem_num = 18
if multivariate:
abnormal_data = np.expand_dims(abnormal_data, axis=0)
if partition:
splitted_data, splitted_label = PartitionTimeSeriesKPart(abnormal_data, abnormal_label, _part_number=k_partition)
final_error = []
for i in range(k_partition):
error_partition, precision_partition, recall_partition, f1_partition, roc_auc_partition, pr_auc_partition, cks = RunModel(splitted_data[i], splitted_label[i], hidden_num, elem_num)
final_error.append(error_partition)
# print('-----------------------------------------')
final_error = np.concatenate(final_error).ravel()
final_zscore = Z_Score(final_error)
y_pred = CreateLabelBasedOnZscore(final_zscore, 3)
precision, recall, f1 = CalculatePrecisionRecallF1Metrics(abnormal_label, y_pred)
PrintPrecisionRecallF1Metrics(precision, recall, f1)
_, _, roc_auc = CalculateROCAUCMetrics(abnormal_label, final_error)
print('roc_auc=' + str(roc_auc))
_, _, pr_auc = CalculatePrecisionRecallCurve(abnormal_label, final_error)
print('pr_auc=' + str(pr_auc))
cks = CalculateCohenKappaMetrics(abnormal_label, y_pred)
print('cohen_kappa=' + str(cks))
else:
error, precision, recall, f1, roc_auc, pr_auc, cks = RunModel(abnormal_data, abnormal_label, hidden_num, elem_num)
if dataset == 3:
for root, dirs, _ in os.walk('./YAHOO/data'):
for dir in dirs:
k_partition = 10
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
abnormal_data, abnormal_label = ReadS5Dataset(file_name)
elem_num = 1
if multivariate:
abnormal_data = np.expand_dims(abnormal_data, axis=0)
if partition:
splitted_data, splitted_label = PartitionTimeSeriesKPart(abnormal_data,
abnormal_label,
_part_number=k_partition)
final_error = []
for i in range(k_partition):
error_partition, precision_partition, recall_partition, f1_partition, roc_auc_partition, pr_auc_partition, cks = RunModel(
splitted_data[i], splitted_label[i], hidden_num, elem_num,
_file_name=os.path.splitext(os.path.basename(file_name))[0], _partition=i)
final_error.append(error_partition)
# print('-----------------------------------------')
final_error = np.concatenate(final_error).ravel()
final_zscore = Z_Score(final_error)
y_pred = CreateLabelBasedOnZscore(final_zscore, 3)
precision, recall, f1 = CalculatePrecisionRecallF1Metrics(abnormal_label, y_pred)
PrintPrecisionRecallF1Metrics(precision, recall, f1)
_, _, roc_auc = CalculateROCAUCMetrics(abnormal_label, final_error)
print('roc_auc=' + str(roc_auc))
_, _, pr_auc = CalculatePrecisionRecallCurve(abnormal_label, final_error)
print('pr_auc=' + str(pr_auc))
cks = CalculateCohenKappaMetrics(abnormal_label, y_pred)
print('cohen_kappa=' + str(cks))
else:
error, precision, recall, f1, roc_auc, pr_auc, cks = RunModel(abnormal_data,
abnormal_label,
hidden_num, elem_num)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
if dataset == 4:
for root, dirs, _ in os.walk('./NAB/data'):
for dir in dirs:
k_partition = 10
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
abnormal_data, abnormal_label = ReadNABDataset(file_name)
elem_num = 1
if multivariate:
abnormal_data = np.expand_dims(abnormal_data, axis=0)
if partition:
splitted_data, splitted_label = PartitionTimeSeriesKPart(abnormal_data,
abnormal_label,
_part_number=k_partition)
final_error = []
for i in range(k_partition):
error_partition, precision_partition, recall_partition, f1_partition, roc_auc_partition, pr_auc_partition, cks = RunModel(
splitted_data[i], splitted_label[i], hidden_num, elem_num,
_file_name=os.path.splitext(os.path.basename(file_name))[0], _partition=i)
final_error.append(error_partition)
# print('-----------------------------------------')
final_error = np.concatenate(final_error).ravel()
final_zscore = Z_Score(final_error)
y_pred = CreateLabelBasedOnZscore(final_zscore, 3)
precision, recall, f1 = CalculatePrecisionRecallF1Metrics(abnormal_label, y_pred)
PrintPrecisionRecallF1Metrics(precision, recall, f1)
_, _, roc_auc = CalculateROCAUCMetrics(abnormal_label, final_error)
print('roc_auc=' + str(roc_auc))
_, _, pr_auc = CalculatePrecisionRecallCurve(abnormal_label, final_error)
print('pr_auc=' + str(pr_auc))
cks = CalculateCohenKappaMetrics(abnormal_label, y_pred)
print('cohen_kappa=' + str(cks))
else:
error, precision, recall, f1, roc_auc, pr_auc, cks = RunModel(abnormal_data,
abnormal_label,
hidden_num, elem_num)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 5:
for root, dirs, _ in os.walk('./2D/test'):
for dir in dirs:
k_partition = 3
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for _, _, files in os.walk(root + '/' + dir):
for file in files:
file_name = os.path.join(root, dir, file)
print(file_name)
abnormal_data, abnormal_label = Read2DDataset(file_name)
elem_num = 2
if multivariate:
abnormal_data = np.expand_dims(abnormal_data, axis=0)
if partition:
splitted_data, splitted_label = PartitionTimeSeriesKPart(abnormal_data,
abnormal_label,
_part_number=k_partition)
final_error = []
for i in range(k_partition):
error_partition, precision_partition, recall_partition, f1_partition, roc_auc_partition, pr_auc_partition, cks = RunModel(
splitted_data[i], splitted_label[i], hidden_num, elem_num,
_file_name=os.path.splitext(os.path.basename(file_name))[0], _partition=i)
final_error.append(error_partition)
# print('-----------------------------------------')
final_error = np.concatenate(final_error).ravel()
final_zscore = Z_Score(final_error)
y_pred = CreateLabelBasedOnZscore(final_zscore, 3)
precision, recall, f1 = CalculatePrecisionRecallF1Metrics(abnormal_label, y_pred)
PrintPrecisionRecallF1Metrics(precision, recall, f1)
_, _, roc_auc = CalculateROCAUCMetrics(abnormal_label, final_error)
print('roc_auc=' + str(roc_auc))
_, _, pr_auc = CalculatePrecisionRecallCurve(abnormal_label, final_error)
print('pr_auc=' + str(pr_auc))
cks = CalculateCohenKappaMetrics(abnormal_label, y_pred)
print('cohen_kappa=' + str(cks))
else:
error, precision, recall, f1, roc_auc, pr_auc, cks = RunModel(abnormal_data,
abnormal_label,
hidden_num, elem_num)
s_precision.append(precision)
s_recall.append(recall)
s_f1.append(f1)
s_roc_auc.append(roc_auc)
s_pr_auc.append(pr_auc)
s_cks.append(cks)
print('########################################')
avg_precision = CalculateAverageMetric(s_precision)
print('avg_precision=' + str(avg_precision))
avg_recall = CalculateAverageMetric(s_recall)
print('avg_recall=' + str(avg_recall))
avg_f1 = CalculateAverageMetric(s_f1)
print('avg_f1=' + str(avg_f1))
avg_roc_auc = CalculateAverageMetric(s_roc_auc)
print('avg_roc_auc=' + str(avg_roc_auc))
avg_pr_auc = CalculateAverageMetric(s_pr_auc)
print('avg_pr_auc=' + str(avg_pr_auc))
avg_cks = CalculateAverageMetric(s_cks)
print('avg_cks=' + str(avg_cks))
print('########################################')
if dataset == 6:
k_partition = 3
s_precision = []
s_recall = []
s_f1 = []
s_roc_auc = []
s_pr_auc = []
s_cks = []
for root, dirs, _ in os.walk('./UAH/'):
for dir in dirs:
folder_name = os.path.join(root, dir)
print(folder_name)
abnormal_data, abnormal_label = ReadUAHDataset(folder_name)
elem_num = 4
if multivariate:
abnormal_data = np.expand_dims(abnormal_data, axis=0)
if partition:
splitted_data, splitted_label = PartitionTimeSeriesKPart(abnormal_data, abnormal_label,
_part_number=k_partition)
final_error = []
for i in range(k_partition):
error_partition, precision_partition, recall_partition, f1_partition, roc_auc_partition, pr_auc_partition, cks = RunModel(
splitted_data[i], splitted_label[i], hidden_num, elem_num, _file_name=dir,
_partition=i)
final_error.append(error_partition)
# print('-----------------------------------------')
final_error = np.concatenate(final_error).ravel()
final_zscore = Z_Score(final_error)
y_pred = CreateLabelBasedOnZscore(final_zscore, 3)
print('########################################')
precision, recall, f1 = CalculatePrecisionRecallF1Metrics(abnormal_label, y_pred)
PrintPrecisionRecallF1Metrics(precision, recall, f1)
_, _, roc_auc = CalculateROCAUCMetrics(abnormal_label, final_error)
print('roc_auc=' + str(roc_auc))
_, _, pr_auc = CalculatePrecisionRecallCurve(abnormal_label, final_error)