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cnnatt.py
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cnnatt.py
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from base import Model
from helper import *
import tensorflow as tf
"""
Abbreviations used in variable names:
Type: Entity type side informatoin
ProbY, RelAlias: Relation alias side information
Recommendation: View this file with tab size 8.
"""
class PCNNATT(Model):
def getBatches(self, data, shuffle = True):
"""
Generates batches of multiple bags
Parameters
----------
data: Data to be used for creating batches. Dataset as list of bags where each bag is a dictionary
shuffle: Decides whether to shuffle the data or not.
Returns
-------
Generator for creating batches.
"""
if shuffle: random.shuffle(data)
def get_sent_part(sub_pos, obj_pos, sents):
assert len(sub_pos) == len(obj_pos)
part_pos = []
for i in range(len(sub_pos)):
sent = sents[i]
pos1, pos2 = sub_pos[i], obj_pos[i]
pos1, pos2 = min(pos1, pos2), max(pos1, pos2)
if pos1 == pos2 or pos1 == 0 or pos2 == len(sent)-1:
pos1 = len(sent) // 4
pos2 = pos1 + len(sent) // 2
part_pos.append([pos1, pos2])
return part_pos
for chunk in getChunks(data, self.p.batch_size): # chunk = batch
batch = ddict(list)
num = 0
for i, bag in enumerate(chunk):
batch['X'] += bag['X']
batch['Pos1'] += bag['Pos1']
batch['Pos2'] += bag['Pos2']
batch['PartPos'] += get_sent_part(bag['SubPos'], bag['ObjPos'], bag['X'])
batch['Y'].append(bag['Y'])
old_num = num
num += len(bag['X'])
batch['sent_num'].append([old_num, num, i])
batch = dict(batch)
yield batch
def add_placeholders(self):
"""
Defines the placeholder required for the model
"""
self.input_x = tf.placeholder(tf.int32, shape=[None, None], name='input_data') # Tokens ids of sentences
self.input_y = tf.placeholder(tf.int32, shape=[None, None], name='input_labels') # Actual relation of the bag
self.input_pos1 = tf.placeholder(tf.int32, shape=[None, None], name='input_pos1') # Position ids wrt entity 1
self.input_pos2 = tf.placeholder(tf.int32, shape=[None, None], name='input_pos2') # Position ids wrt entity 2
self.part_pos = tf.placeholder(tf.int32, shape=[None, 2], name='input_part_pos') # Positions where sentence needs to be partitioned
self.x_len = tf.placeholder(tf.int32, shape=[None], name='input_len') # Number of words in sentences in a batch
self.sent_num = tf.placeholder(tf.int32, shape=[None, 3], name='sent_num') # Stores which sentences belong to which bag | [start_index, end_index, bag_number]
self.seq_len = tf.placeholder(tf.int32, shape=(), name='seq_len') # Max number of tokens in sentences in a batch
self.total_bags = tf.placeholder(tf.int32, shape=(), name='total_bags') # Total number of bags in a batch
self.total_sents = tf.placeholder(tf.int32, shape=(), name='total_sents') # Total number of sentences in a batch
self.dropout = tf.placeholder_with_default(self.p.dropout, shape=(), name='dropout') # Dropout used in GCN Layer
def pad_dynamic(self, X, pos1, pos2):
"""
Pads each batch during runtime.
Parameters
----------
X: For each sentence in the bag, list of words
pos1: For each sentence in the bag, list position of words with respect to subject
pos2: For each sentence in the bag, list position of words with respect to object
Returns
-------
x_pad Padded words
x_len Number of sentences in each sentence,
pos1_pad Padded position 1
pos2_pad Padded position 2
seq_len Maximum sentence length in the batch
"""
seq_len, max_et = 0, 0
x_len = np.zeros((len(X)), np.int32)
for i, x in enumerate(X):
seq_len = max(seq_len, len(x))
x_len[i] = len(x)
x_pad, _ = self.padData(X, seq_len)
pos1_pad, _ = self.padData(pos1, seq_len)
pos2_pad, _ = self.padData(pos2, seq_len)
return x_pad, x_len, pos1_pad, pos2_pad, seq_len
def create_feed_dict(self, batch, wLabels=True, split='train'):
X, Y, pos1, pos2, sent_num, part_pos = batch['X'], batch['Y'], batch['Pos1'], batch['Pos2'], batch['sent_num'], batch['PartPos']
total_sents = len(batch['X'])
total_bags = len(batch['Y'])
x_pad, x_len, pos1_pad, pos2_pad, seq_len = self.pad_dynamic(X, pos1, pos2)
y_hot = self.getOneHot(Y, self.num_class)
feed_dict = {}
feed_dict[self.input_x] = np.array(x_pad)
feed_dict[self.input_pos1] = np.array(pos1_pad)
feed_dict[self.input_pos2] = np.array(pos2_pad)
feed_dict[self.x_len] = np.array(x_len)
feed_dict[self.seq_len] = seq_len
feed_dict[self.total_sents] = total_sents
feed_dict[self.total_bags] = total_bags
feed_dict[self.sent_num] = sent_num
feed_dict[self.part_pos] = np.array(part_pos)
if wLabels: feed_dict[self.input_y] = y_hot
if split != 'train': feed_dict[self.dropout] = 1.0
else: feed_dict[self.dropout] = self.p.dropout
return feed_dict
def get_adj(self, edgeList, batch_size, max_nodes, max_labels):
"""
Stores the adjacency matrix as indices and data for feeding to TensorFlow
Parameters
----------
edgeList: List of list of edges
batch_size: Number of bags in a batch
max_nodes: Maximum number of nodes in the graph
max_labels: Maximum number of edge labels in the graph
Returns
-------
adj_mat_ind indices of adjacency matrix
adj_mat_data data of adjacency matrix
"""
max_edges = 0
for edges in edgeList:
max_edges = max(max_edges, len(edges))
adj_mat_ind = np.zeros((max_labels, batch_size, max_edges, 2), np.int64)
adj_mat_data = np.zeros((max_labels, batch_size, max_edges), np.float32)
for lbl in range(max_labels):
for i, edges in enumerate(edgeList):
in_ind_temp, in_data_temp = [], []
for j, (src, dest, _, _) in enumerate(edges):
adj_mat_ind [lbl, i, j] = (src, dest)
adj_mat_data[lbl, i, j] = 1.0
return adj_mat_ind, adj_mat_data
def add_model(self):
"""
Creates the Computational Graph
Parameters
----------
Returns
-------
nn_out: Logits for each bag in the batch
accuracy: accuracy for the entire batch
"""
in_wrds, in_pos1, in_pos2 = self.input_x, self.input_pos1, self.input_pos2
with tf.variable_scope('Embeddings') as scope:
embed_init = getEmbeddings(self.wrd_list, self.p.embed_dim, self.p.embed_loc)
_wrd_embeddings = tf.get_variable('embeddings', initializer=embed_init, trainable=True, regularizer=self.regularizer) # Word Embeddings
wrd_pad = tf.zeros([1, self.p.embed_dim])
wrd_embeddings = tf.concat([wrd_pad, _wrd_embeddings], axis=0)
pos1_embeddings = tf.get_variable('pos1_embeddings', [self.max_pos, self.p.pos_dim], trainable=True, regularizer=self.regularizer)
pos2_embeddings = tf.get_variable('pos2_embeddings', [self.max_pos, self.p.pos_dim], trainable=True, regularizer=self.regularizer)
wrd_embed = tf.nn.embedding_lookup(wrd_embeddings, self.input_x)
pos1_embed = tf.nn.embedding_lookup(pos1_embeddings, self.input_pos1)
pos2_embed = tf.nn.embedding_lookup(pos2_embeddings, self.input_pos2)
conv_in = tf.expand_dims(tf.concat([wrd_embed, pos1_embed, pos2_embed], axis=2), axis=3)
conv_in_dim = self.p.embed_dim + 2 * self.p.pos_dim
with tf.variable_scope('Convolution') as scope:
padding = tf.constant([[0,0], [1,1], [0,0], [0,0]])
kernel = tf.get_variable('kernel', [self.p.filt_size, conv_in_dim, 1, self.p.num_filt], initializer=tf.truncated_normal_initializer(), regularizer=self.regularizer)
biases = tf.get_variable('biases', [self.p.num_filt], initializer=tf.random_normal_initializer(), regularizer=self.regularizer)
conv_in = tf.pad(conv_in, padding, 'CONSTANT')
conv = tf.nn.conv2d(conv_in, kernel, strides=[1, 1, 1, 1], padding='VALID')
convRes = tf.nn.relu(conv + biases, name=scope.name)
convRes = tf.squeeze(convRes, 2)
sent_rep = tf.reduce_max(convRes, axis = 1)
sent_rep_dim = self.p.num_filt
with tf.variable_scope('Sentence_attention') as scope:
sent_atten_q = tf.get_variable('sent_atten_q', [sent_rep_dim, 1] )
def getSentAtten(num):
bag_sents = sent_rep[num[0]: num[1]]
num_sents = num[1] - num[0]
if self.p.inc_attn:
sent_atten_wts = tf.nn.softmax(tf.reshape(tf.matmul(tf.tanh(bag_sents), sent_atten_q), [-1]))
bag_rep_ = tf.reshape(tf.matmul(tf.expand_dims(sent_atten_wts, 0), bag_sents), [sent_rep_dim])
else:
bag_rep_ = tf.reduce_mean(bag_sents, axis=0)
return bag_rep_
bag_rep = tf.map_fn(getSentAtten, self.sent_num, dtype=tf.float32)
with tf.variable_scope('FC1') as scope:
w_rel = tf.get_variable('w_rel', [sent_rep_dim, self.num_class], initializer=tf.truncated_normal_initializer(), regularizer=self.regularizer)
b_rel = tf.get_variable('b_rel', [self.num_class], initializer=tf.constant_initializer(0.0), regularizer=self.regularizer)
nn_out = tf.nn.xw_plus_b(bag_rep, w_rel, b_rel)
with tf.name_scope('Accuracy') as scope:
prob = tf.nn.softmax(nn_out)
y_pred = tf.argmax(prob, axis=1)
y_actual = tf.argmax(self.input_y, axis=1)
accuracy = tf.reduce_mean(tf.cast(tf.equal(y_pred, y_actual), tf.float32))
''' Debugging command:
Put the below command anywhere and get the values of the tensors (Use TF like PyTorch!)
res = debug_nn([de_out], self.create_feed_dict( next(self.getBatches(self.data['train'])) ) ); pdb.set_trace()
'''
return nn_out, accuracy
def __init__(self, params):
"""
Constructor for the main function. Loads data and creates computation graph.
Parameters
----------
params: Hyperparameters of the model
Returns
-------
"""
super(PCNNATT, self).__init__(params)
if __name__== "__main__":
parser = argparse.ArgumentParser(description='Improving Distantly-Supervised Neural Relation Extraction using Side Information')
parser.add_argument('-data', dest="dataset", required=True, help='Dataset to use')
parser.add_argument('-gpu', dest="gpu", default='0', help='GPU to use')
parser.add_argument('-nGate', dest="wGate", action='store_false', help='Include edgewise-gating in GCN')
parser.add_argument('-pos_dim', dest="pos_dim", default=16, type=int, help='Dimension of positional embeddings')
parser.add_argument('-filt_size',dest="filt_size", default=3, type=int, help='Size of filters used in Convolution Layer')
parser.add_argument('-num_filt', dest="num_filt", default=100, type=int, help='Number of filters used in Convolution Layer')
parser.add_argument('-drop', dest="dropout", default=0.8, type=float, help='Dropout for full connected layer')
parser.add_argument('-attn', dest="inc_attn", action='store_true', help='Include attention during instance aggregation')
parser.add_argument('-lr', dest="lr", default=0.001, type=float, help='Learning rate')
parser.add_argument('-l2', dest="l2", default=0.001, type=float, help='L2 regularization')
parser.add_argument('-epoch', dest="max_epochs", default=2, type=int, help='Max epochs')
parser.add_argument('-batch', dest="batch_size", default=32, type=int, help='Batch size')
parser.add_argument('-chunk', dest="chunk_size", default=1000, type=int, help='Chunk size')
parser.add_argument('-only_eval',dest="only_eval", action='store_true', help='Only Evaluate the pretrained model (skip training)')
parser.add_argument('-restore', dest="restore", action='store_true', help='Restore from the previous best saved model')
parser.add_argument('-opt', dest="opt", default='adam', help='Optimizer to use for training')
parser.add_argument('-eps', dest="eps", default=0.00000001, type=float, help='Value of epsilon')
parser.add_argument('-name', dest="name", default='test_'+str(uuid.uuid4()), help='Name of the run')
parser.add_argument('-seed', dest="seed", default=1234, type=int, help='Seed for randomization')
parser.add_argument('-logdir', dest="log_dir", default='./log/', help='Log directory')
parser.add_argument('-config', dest="config_dir", default='./config/', help='Config directory')
parser.add_argument('-embed_loc',dest="embed_loc", default='./glove/glove.6B.50d.txt', help='Log directory')
parser.add_argument('-embed_dim',dest="embed_dim", default=50, type=int, help='Dimension of embedding')
args = parser.parse_args()
if not args.restore: args.name = args.name
# Set GPU to use
set_gpu(args.gpu)
# Set seed
tf.set_random_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
# Create model computational graph
model = PCNNATT(args)
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
model.fit(sess)