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scpn_utils.py
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scpn_utils.py
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import torch, sys, h5py
import numpy as np
from nltk import ParentedTree
reload(sys)
sys.setdefaultencoding('utf8')
def is_paren(tok):
return tok == ")" or tok == "("
# given list of parse strings, output numpy array containing the transformations
def indexify_transformations(in_p, out_p, label_voc, args):
in_seqs = []
out_seqs = []
mismatch_inds = []
max_trans_size = 0
for idx in range(len(in_p)):
# very rarely, a tree is invalid
try:
in_tree = ParentedTree.fromstring(in_p[idx])
out_tree = ParentedTree.fromstring(out_p[idx])
except:
continue
if args.tree_dropout > 0:
tree_dropout(in_tree, args.tree_dropout, 0)
tree_dropout(out_tree, args.tree_dropout, 0)
elif args.tree_level_dropout > 0:
parse_tree_level_dropout(in_tree, args.tree_level_dropout)
parse_tree_level_dropout(out_tree, args.tree_level_dropout)
in_full_trans = deleaf(in_tree)
out_full_trans = deleaf(out_tree)
if max_trans_size < len(in_full_trans):
max_trans_size = len(in_full_trans)
if max_trans_size < len(out_full_trans):
max_trans_size = len(out_full_trans)
# only consider instances where input syntax differs from output syntax
if in_full_trans != out_full_trans:
# make sure everything is invocab
try:
x = [label_voc[z] for z in in_full_trans]
x = [label_voc[z] for z in out_full_trans]
in_seqs.append(in_full_trans)
out_seqs.append(out_full_trans)
mismatch_inds.append(idx)
except:
pass
# no syntactic transformations in the batch!
if len(in_seqs) == 0:
return None
# otherwise, indexify and return
else:
in_trans_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
out_trans_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
in_lengths = []
out_lengths = []
for idx in range(len(in_seqs)):
curr_in = in_seqs[idx]
in_trans_np[idx, :len(curr_in)] = [label_voc[z] for z in curr_in]
in_lengths.append(len(curr_in))
curr_out = out_seqs[idx]
out_trans_np[idx, :len(curr_out)] = [label_voc[z] for z in curr_out]
out_lengths.append(len(curr_out))
return in_trans_np, out_trans_np, mismatch_inds,\
np.array(in_lengths, dtype='int32'), np.array(out_lengths, dtype='int32')
#returns tokenized parse tree and removes leaf nodes (i.e. words)
def deleaf(tree):
nonleaves = ''
for w in str(tree).replace('\n', '').split():
w = w.replace('(', '( ').replace(')', ' )')
nonleaves += w + ' '
arr = nonleaves.split()
for n, i in enumerate(arr):
if n + 1 < len(arr):
tok1 = arr[n]
tok2 = arr[n + 1]
if not is_paren(tok1) and not is_paren(tok2):
arr[n + 1] = ""
nonleaves = " ".join(arr)
return nonleaves.split() + ['EOP']
#removes levels of parse tree belowe specifice level or random levels
#if level is None
def parse_tree_level_dropout(tree, treerate, level=None):
def parse_tree_level_dropout2(tree, level, mlevel):
if level == mlevel:
for idx, n in enumerate(tree):
if isinstance(n, ParentedTree):
tree[idx] = "(" + n.label() + ")"
else:
for n in tree:
parse_tree_level_dropout2(n, level + 1, mlevel)
h = tree.height()
if not level:
level = 0
for i in range(2, h):
if np.random.rand() <= treerate:
level = i
break
if level > 0:
parse_tree_level_dropout2(tree, 1, level)
else:
parse_tree_level_dropout2(tree, 1, level)
#dropout constituents from tree
def tree_dropout(tree, treerate, level):
if level == 0:
for n in tree:
tree_dropout(n, treerate, level + 1)
else:
for idx, n in enumerate(tree):
if np.random.rand(1)[0] <= treerate and isinstance(n, ParentedTree):
tree[idx] = "(" + n.label() + ")"
elif not isinstance(n, ParentedTree):
continue
else:
tree_dropout(n, treerate, level + 1)
# given list of parse strings, output numpy array containing the transformations
def parse_indexify_transformations(in_p, out_p, label_voc, args):
in_trimmed_seqs = []
in_seqs = []
out_trimmed_seqs = []
out_seqs = []
max_trans_size = 0
for idx in range(len(in_p)):
# very rarely, a tree is invalid
try:
in_trimmed = ParentedTree.fromstring(in_p[idx])
in_orig = ParentedTree.fromstring(in_p[idx])
out_trimmed = ParentedTree.fromstring(out_p[idx])
out_orig = ParentedTree.fromstring(out_p[idx])
except:
continue
out_dh = parse_tree_level_dropout(out_trimmed, args.tree_level_dropout)
parse_tree_level_dropout(in_trimmed, args.tree_level_dropout, level=out_dh)
in_orig = deleaf(in_orig)
in_trimmed = deleaf(in_trimmed)
out_orig = deleaf(out_orig)
out_trimmed = deleaf(out_trimmed)
if max_trans_size < len(in_orig):
max_trans_size = len(in_orig)
if max_trans_size < len(out_orig):
max_trans_size = len(out_orig)
# only consider instances where top-level of input parse != top-level output
if in_trimmed != out_trimmed:
# make sure everything is invocab
try:
x = [label_voc[z] for z in in_orig]
x = [label_voc[z] for z in out_orig]
in_seqs.append(in_orig)
out_seqs.append(out_orig)
out_trimmed_seqs.append(out_trimmed)
in_trimmed_seqs.append(in_trimmed)
except:
pass
# no syntactic transformations in the batch!
if len(in_seqs) == 0:
return None
# otherwise, indexify and return
else:
in_trans_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
out_trans_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
in_trimmed_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
out_trimmed_np = np.zeros((len(in_seqs), max_trans_size), dtype='int32')
in_lengths = []
out_lengths = []
out_trimmed_lengths = []
in_trimmed_lengths = []
for idx in range(len(in_seqs)):
curr_in = in_seqs[idx]
in_trans_np[idx, :len(curr_in)] = [label_voc[z] for z in curr_in]
in_lengths.append(len(curr_in))
curr_out = out_seqs[idx]
out_trans_np[idx, :len(curr_out)] = [label_voc[z] for z in curr_out]
out_lengths.append(len(curr_out))
curr_trimmed_in = in_trimmed_seqs[idx]
in_trimmed_np[idx, :len(curr_trimmed_in)] = [label_voc[z] for z in curr_trimmed_in]
in_trimmed_lengths.append(len(curr_trimmed_in))
curr_trimmed_out = out_trimmed_seqs[idx]
out_trimmed_np[idx, :len(curr_trimmed_out)] = [label_voc[z] for z in curr_trimmed_out]
out_trimmed_lengths.append(len(curr_trimmed_out))
# cut off extra padding
in_trans_np = in_trans_np[:, :np.max(in_lengths)]
out_trans_np = out_trans_np[:, :np.max(out_lengths)]
in_trimmed_np = in_trimmed_np[:, :np.max(in_trimmed_lengths)]
out_trimmed_np = out_trimmed_np[:, :np.max(out_trimmed_lengths)]
return in_trans_np, out_trans_np, in_trimmed_np, out_trimmed_np,\
np.array(in_lengths, dtype='int32'), np.array(out_lengths, dtype='int32'),\
np.array(in_trimmed_lengths, dtype='int32'), np.array(out_trimmed_lengths, dtype='int32')