def __init__(self, EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE,
use_negsampling, train_files, batch_size):
"""
:param EMBEDDING_DIM:
:param HIDDEN_SIZE:
:param ATTENTION_SIZE:
:param use_negsampling:
"""
self.embedding_dim = EMBEDDING_DIM
self.hidden_size = HIDDEN_SIZE
self.attention_size = ATTENTION_SIZE
self.use_negsampling = use_negsampling
self.data_iterator = DataIterator(train_files, batch_size)
def load_beddata(genome, bed_file, use_meta, use_gencode, input_dir, is_sorted, chrom=None):
bed = BedTool(bed_file)
if not is_sorted:
print('Sorting BED file')
bed = bed.sort()
is_sorted = True
blacklist = make_blacklist()
print('Determining which windows are valid')
bed_intersect_blacklist_count = bed.intersect(blacklist, wa=True, c=True, sorted=is_sorted)
if chrom:
nonblacklist_bools = np.array([i.chrom==chrom and i.count==0 for i in bed_intersect_blacklist_count])
else:
nonblacklist_bools = np.array([i.count==0 for i in bed_intersect_blacklist_count])
print('Filtering away blacklisted windows')
bed_filtered = bed.intersect(blacklist, wa=True, v=True, sorted=is_sorted)
if chrom:
print('Filtering away windows not in chromosome:', chrom)
bed_filtered = subset_chroms([chrom], bed_filtered)
print('Generating test data iterator')
bigwig_names, bigwig_files_list = load_bigwigs([input_dir])
bigwig_files = bigwig_files_list[0]
if use_meta:
meta_names, meta_list = load_meta([input_dir])
meta = meta_list[0]
else:
meta = []
meta_names = None
shift = 0
if use_gencode:
cpg_bed = BedTool('resources/cpgisland.bed.gz')
cds_bed = BedTool('resources/wgEncodeGencodeBasicV19.cds.merged.bed.gz')
intron_bed = BedTool('resources/wgEncodeGencodeBasicV19.intron.merged.bed.gz')
promoter_bed = BedTool('resources/wgEncodeGencodeBasicV19.promoter.merged.bed.gz')
utr5_bed = BedTool('resources/wgEncodeGencodeBasicV19.utr5.merged.bed.gz')
utr3_bed = BedTool('resources/wgEncodeGencodeBasicV19.utr3.merged.bed.gz')
peaks_cpg_bedgraph = bed_filtered.intersect(cpg_bed, wa=True, c=True)
peaks_cds_bedgraph = bed_filtered.intersect(cds_bed, wa=True, c=True)
peaks_intron_bedgraph = bed_filtered.intersect(intron_bed, wa=True, c=True)
peaks_promoter_bedgraph = bed_filtered.intersect(promoter_bed, wa=True, c=True)
peaks_utr5_bedgraph = bed_filtered.intersect(utr5_bed, wa=True, c=True)
peaks_utr3_bedgraph = bed_filtered.intersect(utr3_bed, wa=True, c=True)
data_bed = [(window.chrom, window.start, window.stop, 0, bigwig_files, np.append(meta, np.array([cpg.count, cds.count, intron.count, promoter.count, utr5.count, utr3.count], dtype=bool)))
for window, cpg, cds, intron, promoter, utr5, utr3 in
itertools.izip(bed_filtered, peaks_cpg_bedgraph,peaks_cds_bedgraph,peaks_intron_bedgraph,peaks_promoter_bedgraph,peaks_utr5_bedgraph,peaks_utr3_bedgraph)]
else:
data_bed = [(window.chrom, window.start, window.stop, shift, bigwig_files, meta)
for window in bed_filtered]
#from data_iter import DataIterator
from data_iter import DataIterator
bigwig_rc_order = get_bigwig_rc_order(bigwig_names)
datagen_bed = DataIterator(data_bed, genome, 100, L, bigwig_rc_order, shuffle=False)
return bigwig_names, meta_names, datagen_bed, nonblacklist_bools
class Model_DIN_MOGUJIE(object):
def __init__(self, EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE,
use_negsampling, train_files, batch_size):
"""
:param EMBEDDING_DIM:
:param HIDDEN_SIZE:
:param ATTENTION_SIZE:
:param use_negsampling:
"""
self.embedding_dim = EMBEDDING_DIM
self.hidden_size = HIDDEN_SIZE
self.attention_size = ATTENTION_SIZE
self.use_negsampling = use_negsampling
self.data_iterator = DataIterator(train_files, batch_size)
def build_inputs(self):
"""
iterator the training data and parse to xdl format
:return:
"""
datas = self.data_iterator.next_batch()
return datas
def build_network(self):
"""
build model structure
:return:
"""
@xdl.tf_wrapper(is_training=True)
def tf_train_model(*inputs):
with tf.variable_scope("deep_layer", reuse=tf.AUTO_REUSE):
#reshape sparse tensor back??
sequence_emb = inputs[0]
sequence_emb = tf.reshape(
inputs[0], [-1, max_sequence_length, EMBEDDING_DIM])
sequence_emb.set_shape([None, None, EMBEDDING_DIM])
itemid_emb = inputs[1]
itemid_emb = tf.reshape(inputs[1], [-1, EMBEDDING_DIM])
itemid_emb.set_shape([None, EMBEDDING_DIM])
itemid_token_mask = tf.cast(inputs[4], tf.bool)
sequence_token_mask = tf.cast(
tf.slice(inputs[3], [0, 0],
[batch_size, reduce_sequence_length]), tf.bool)
self.logits_deep = self.bulid_attention_layers(
sequence_emb, sequence_token_mask, itemid_emb,
itemid_token_mask, 'attention')
self.logits = tf.nn.softmax(self.logits_deep) + 0.00000001
#loss / acc
labels = tf.slice(inputs[2], [0], tf.shape(self.logits))
losses = tf.nn.sigmoid_cross_entropy_with_logits(
labels=labels, logits=self.logits)
self.loss = tf.reduce_mean(losses, name='loss')
self.accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.round(self.logits), labels),
tf.float32))
#train_ops = self.train_ops() #
return self.loss, self.accuracy
#return train_ops[0], train_ops[1:]
#get data batch
datas = self.build_inputs()
#
train_ops = tf_train_model(*self.xdl_embedding(datas))
return train_ops
def xdl_embedding(self, datas): #data[0]=;data[1]=item_seq
"""
稀疏部分的定义
:param datas:
:return:
"""
results = []
seq_emb = xdl.embedding(
"item_embedding", datas[0],
xdl.VarianceScaling(scale=1.0,
mode="fan_avg",
distribution="normal",
seed=3), EMBEDDING_DIM, EMBEDDING_SIZE, 'sum')
item_emb = xdl.embedding(
"item_embedding", datas[1],
xdl.VarianceScaling(scale=1.0,
mode="fan_avg",
distribution="normal",
seed=3), EMBEDDING_DIM, EMBEDDING_SIZE, 'sum')
results.append(seq_emb)
results.append(item_emb)
#return results + datas[7:]
return results + datas[2:] #0,1,2,3,4
def bulid_attention_layers(self, u_emb, all_token_mask, itemid_emb,
temid_token_mask, scope):
"""
:param u_emb:
:param all_token_mask:
:param itemid_emb:
:param temid_token_mask:
:param scope:
:return:
"""
all_u_emb = tf.reduce_sum(u_emb, 1)
itemid_emb = tf.squeeze(itemid_emb, 1)
logger('item squeeze shape:{0}'.format(itemid_emb.get_shape()))
with tf.name_scope('linear_layer'):
outputs = bn_dense_layer(u_emb, 128, True, 0.01, 'multi_logits',
'elu', False, 5e-5, 0.75, is_train)
"""
outputs= directional_attention_with_dense(
self.item_his_eb, all_token_mask,None, 'dir_attn',
0.75, self.is_train,5e-5, 'elu' , None,name='s1_attention')
tf.summary.histogram('GRU_outputs', outputs)
"""
self.aux_loss = 0.
"""
#do not use aux_loss
self.aux_loss = auxiliary_loss(outputs[:, :-1, :], u_emb[:, 1:, :],
self.noclk_item_his_eb[:, 1:, :],
all_token_mask[:, 1:], stag="gru")
"""
# Attention layer
with tf.name_scope('cross_attention_layer'):
att_outputs, alphas = din_fcn_attention(itemid_emb,
outputs,
128,
all_token_mask,
softmax_stag=1,
stag='1_1',
mode='LIST',
return_alphas=True)
tf.summary.histogram('att_outputs', att_outputs)
with tf.name_scope('single_attention_layer'):
final_state = single_attention(att_outputs,
all_token_mask,
'disan_sequence',
0.75,
is_train,
5e-5,
'elu',
None,
's1_attention',
is_multi_att=False,
attention_dim=None)
inp = tf.concat([
itemid_emb, all_u_emb, itemid_emb * all_u_emb,
all_u_emb - itemid_emb, final_state
], 1)
with tf.variable_scope('output_layer_last'):
logits_deep = self.build_fcn_net(inp, use_dice=True)
return logits_deep
def build_fcn_net(self, inp, use_dice=False):
bn1 = tf.layers.batch_normalization(inputs=inp, name='bn1')
dnn1 = tf.layers.dense(bn1, 200, activation=None, name='f1')
if use_dice:
dnn1 = dice(dnn1, name='dice_1')
else:
dnn1 = prelu(dnn1, 'prelu1')
dnn1 = tf.nn.dropout(dnn1, 0.75)
dnn2 = tf.layers.dense(dnn1, 80, activation=None, name='f2')
if use_dice:
dnn2 = dice(dnn2, name='dice_2')
else:
dnn2 = prelu(dnn2, 'prelu2')
dnn2 = tf.nn.dropout(dnn2, 0.9)
dnn3 = tf.layers.dense(dnn2, 1, activation=None, name='f3')[:, 0]
return dnn3
def run(self, train_ops, train_sess):
iter = 0
for epoch in range(1):
while not train_sess.should_stop():
values = train_sess.run(train_ops)
if values is None:
break
loss, acc, _ = values
add_metrics("loss", loss)
add_metrics("acc", acc)
add_metrics(
"time",
datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
iter += 1
#if (iter % test_iter) == 0:
# self.run_test(test_ops, test_sess)
if (iter % 100) == 0: # add timeline
run_option = xdl.RunOption()
run_option.perf = True
run_statistic = xdl.RunStatistic()
_ = train_sess.run(train_ops, run_option, run_statistic)
xdl.Timeline(run_statistic.perf_result).save(
'../ckpt/timeline.json-' + str(iter))
print('======print the timeline =====')
iter += 1
train_sess._finish = False
def make_features_multiTask(positive_windows, y_positive,
nonnegative_regions_bed, bigwig_files,
bigwig_names, genome, epochs, valid_chroms,
test_chroms):
chroms, chroms_sizes, genome_bed = get_genome_bed()
train_chroms = chroms
for chrom in valid_chroms + test_chroms:
train_chroms.remove(chrom)
genome_bed_train, genome_bed_valid, genome_bed_test = \
[subset_chroms(chroms_set, genome_bed) for chroms_set in
(train_chroms, valid_chroms, test_chroms)]
positive_windows_train = []
positive_windows_valid = []
positive_windows_test = []
positive_data_train = []
positive_data_valid = []
positive_data_test = []
import pdb
print 'Splitting positive windows into training, validation, and testing sets'
for positive_window, target_array in itertools.izip(
positive_windows, y_positive):
if len(positive_window.chrom) > 8:
pdb.set_trace()
chrom = positive_window.chrom
start = int(positive_window.start)
stop = int(positive_window.stop)
if chrom in test_chroms:
positive_windows_test.append(positive_window)
positive_data_test.append((chrom, start, stop, shift_size,
bigwig_files, [], target_array))
elif chrom in valid_chroms:
positive_windows_valid.append(positive_window)
positive_data_valid.append((chrom, start, stop, shift_size,
bigwig_files, [], target_array))
else:
positive_windows_train.append(positive_window)
positive_data_train.append((chrom, start, stop, shift_size,
bigwig_files, [], target_array))
positive_windows_train = BedTool(positive_windows_train)
positive_windows_valid = BedTool(positive_windows_valid)
positive_windows_test = BedTool(positive_windows_test)
import pdb
print 'Getting negative training examples'
negative_windows_train = BedTool.cat(*(epochs * [positive_windows]),
postmerge=False)
#negative_windows_train = BedTool.cat(*(10*[positive_windows]), postmerge=False)
#pdb.set_trace()
negative_windows_train = negative_windows_train.shuffle(
g=genome_sizes_file,
incl=genome_bed_train.fn,
excl=nonnegative_regions_bed.fn,
noOverlapping=False,
seed=np.random.randint(-214783648, 2147483647))
#seed=np.random.randint(-21478364, 21474836))
print 'Getting negative validation examples'
negative_windows_valid = positive_windows_valid.shuffle(
g=genome_sizes_file,
incl=genome_bed_valid.fn,
excl=nonnegative_regions_bed.fn,
noOverlapping=False,
seed=np.random.randint(-214783648, 2147483647))
#seed=np.random.randint(-21478364, 21474836))
print 'Getting negative testing examples'
negative_windows_test = positive_windows_test.shuffle(
g=genome_sizes_file,
incl=genome_bed_test.fn,
excl=nonnegative_regions_bed.fn,
noOverlapping=False,
seed=np.random.randint(-214783648, 2147483647))
#seed=np.random.randint(-21478364, 21474836))
# Train
print 'Extracting data from negative training BEDs'
negative_targets = np.zeros(y_positive.shape[1])
negative_data_train = [(window.chrom, window.start, window.stop,
shift_size, bigwig_files, [], negative_targets)
for window in negative_windows_train]
# Validation
print 'Extracting data from negative validation BEDs'
negative_data_valid = [(window.chrom, window.start, window.stop,
shift_size, bigwig_files, [], negative_targets)
for window in negative_windows_valid]
# Test
print 'Extracting data from negative testing BEDs'
negative_data_test = [(window.chrom, window.start, window.stop, shift_size,
bigwig_files, [], negative_targets)
for window in negative_windows_test]
num_positive_train_windows = len(positive_data_train)
data_valid = negative_data_valid + positive_data_valid
data_test = negative_data_test + positive_data_test
print 'Shuffling training data'
data_train = []
for i in xrange(epochs):
epoch_data = []
epoch_data.extend(positive_data_train)
epoch_data.extend(
negative_data_train[i * num_positive_train_windows:(i + 1) *
num_positive_train_windows])
np.random.shuffle(epoch_data)
data_train.extend(epoch_data)
print 'Generating data iterators'
bigwig_rc_order = get_bigwig_rc_order(bigwig_names)
datagen_train = DataIterator(data_train, genome, batch_size, L,
bigwig_rc_order)
datagen_valid = DataIterator(data_valid, genome, batch_size, L,
bigwig_rc_order)
datagen_test = DataIterator(data_test, genome, batch_size, L,
bigwig_rc_order)
print len(datagen_train), 'training samples'
print len(datagen_valid), 'validation samples'
print len(datagen_test), 'test samples'
return datagen_train, datagen_valid, datagen_test, data_valid, data_test
def make_features_singleTask(chip_bed_list, nonnegative_regions_bed_list,
bigwig_files_list, bigwig_names, meta_list,
gencode, genome, epochs, negatives, valid_chroms,
test_chroms, valid_chip_bed_list,
valid_nonnegative_regions_bed_list,
valid_bigwig_files_list, valid_meta_list):
num_cells = len(chip_bed_list)
chroms, chroms_sizes, genome_bed = get_genome_bed()
train_chroms = chroms
for chrom in valid_chroms + test_chroms:
train_chroms.remove(chrom)
genome_bed_train, genome_bed_valid, genome_bed_test = \
[subset_chroms(chroms_set, genome_bed) for chroms_set in
(train_chroms, valid_chroms, test_chroms)]
print 'Splitting ChIP peaks into training, validation, and testing BEDs'
chip_bed_split_list = parmap.map(valid_test_split_wrapper, chip_bed_list,
valid_chroms, test_chroms)
chip_bed_train_list, chip_bed_valid_list, chip_bed_test_list = zip(
*chip_bed_split_list)
if valid_chip_bed_list: # the user specified a validation directory, must adjust validation data
valid_chip_bed_split_list = parmap.map(valid_test_split_wrapper,
valid_chip_bed_list,
valid_chroms, test_chroms)
_, chip_bed_valid_list, _ = zip(*valid_chip_bed_split_list)
else:
valid_nonnegative_regions_bed_list = nonnegative_regions_bed_list
valid_bigwig_files_list = bigwig_files_list
valid_meta_list = meta_list
positive_label = [True]
#Train
print 'Extracting data from positive training BEDs'
positive_data_train_list = parmap.map(
extract_data_from_bed,
zip(chip_bed_train_list, bigwig_files_list, meta_list), True,
positive_label, gencode)
positive_data_train = list(itertools.chain(*positive_data_train_list))
#Validation
print 'Extracting data from positive validation BEDs'
positive_data_valid_list = parmap.map(
extract_data_from_bed,
zip(chip_bed_valid_list, valid_bigwig_files_list, valid_meta_list),
False, positive_label, gencode)
positive_data_valid = list(itertools.chain(*positive_data_valid_list))
print 'Shuffling positive training windows in negative regions'
train_noOverlap = True
train_randomseeds = np.random.randint(-214783648, 2147483647, num_cells)
positive_windows_train_list = parmap.map(data_to_bed,
positive_data_train_list)
negative_windows_train_list = parmap.map(
negative_shuffle_wrapper,
zip(positive_windows_train_list, nonnegative_regions_bed_list,
bigwig_files_list, train_randomseeds), genome_bed_train,
negatives * epochs, train_noOverlap)
print 'Shuffling positive validation windows in negative regions'
valid_randomseeds = np.random.randint(-214783648, 2147483647, num_cells)
positive_windows_valid_list = parmap.map(data_to_bed,
positive_data_valid_list)
negative_windows_valid_list = parmap.map(
negative_shuffle_wrapper,
zip(positive_windows_valid_list, nonnegative_regions_bed_list,
bigwig_files_list, valid_randomseeds), genome_bed_valid, negatives,
True)
negative_label = [False]
#Train
print 'Extracting data from negative training BEDs'
negative_data_train_list = parmap.map(
extract_data_from_bed,
zip(negative_windows_train_list, bigwig_files_list, meta_list), False,
negative_label, gencode)
negative_data_train = list(itertools.chain(*negative_data_train_list))
#Validation
print 'Extracting data from negative validation BEDs'
negative_data_valid_list = parmap.map(
extract_data_from_bed,
zip(negative_windows_valid_list, valid_bigwig_files_list,
valid_meta_list), False, negative_label, gencode)
negative_data_valid = list(itertools.chain(*negative_data_valid_list))
data_valid = negative_data_valid + positive_data_valid
print 'Shuffling training data'
num_negatives_per_epoch = negatives * len(positive_data_train)
np.random.shuffle(negative_data_train)
data_train = []
for i in xrange(epochs):
epoch_data = []
epoch_data.extend(positive_data_train)
epoch_data.extend(
negative_data_train[i * num_negatives_per_epoch:(i + 1) *
num_negatives_per_epoch])
np.random.shuffle(epoch_data)
data_train.extend(epoch_data)
print 'Generating data iterators'
from data_iter import DataIterator
bigwig_rc_order = get_bigwig_rc_order(bigwig_names)
datagen_train = DataIterator(data_train, genome, batch_size, L,
bigwig_rc_order)
datagen_valid = DataIterator(data_valid,
genome,
batch_size,
L,
bigwig_rc_order,
shuffle=True)
print len(datagen_train), 'training samples'
print len(datagen_valid), 'validation samples'
return datagen_train, datagen_valid
train = theano.function([x, mask], [loss,predictions], updates=updates)
validate = theano.function([x, mask], [loss_det,predictions_det])
def create_batch(idxs):
max_seq_len = max([len(tunes[i]) for i in idxs])
x = np.zeros((config.batch_size, max_seq_len), dtype='float32')
mask = np.zeros((config.batch_size, max_seq_len - 1), dtype='float32')
for i, j in enumerate(idxs):
x[i, :tune_lens[j]] = tunes[j]
mask[i, : tune_lens[j] - 1] = 1
return x, mask
train_data_iterator = DataIterator(tune_lens[train_idxs], train_idxs, config.batch_size, random_lens=False)
valid_data_iterator = DataIterator(tune_lens[valid_idxs], valid_idxs, config.batch_size, random_lens=False)
print 'Train model'
train_batches_per_epoch = ntrain_tunes / config.batch_size
max_niter = config.max_epoch * train_batches_per_epoch
losses_train = []
losTrain = []
HaccuracyT = []
HaccuracyV = []
nvalid_batches = nvalid_tunes / config.batch_size
losses_eval_valid = []
niter = 1
start_epoch = 0
prev_time = time.clock()
