def evaluate_file(self, das_file, ttree_file):
"""Evaluate the reranking classifier on a given pair of DA/tree files (show the
total Hamming distance and total number of DAIs)
@param das_file: DA file path
@param ttree_file: trees/sentences file path
@return: a tuple (total DAIs, distance)
"""
das = read_das(das_file)
ttree_doc = read_ttrees(ttree_file)
if self.mode == 'tokens':
tokens = tokens_from_doc(ttree_doc, self.language, self.selector)
trees = self._tokens_to_flat_trees(tokens)
elif self.mode == 'tagged_lemmas':
tls = tagged_lemmas_from_doc(ttree_doc, self.language, self.selector)
trees = self._tokens_to_flat_trees(tls)
else:
trees = trees_from_doc(ttree_doc, self.language, self.selector)
da_len = 0
dist = 0
for da, tree in zip(das, trees):
da_len += len(da)
dist += self.dist_to_da(da, [tree])[0]
return da_len, dist
def evaluate_file(self, das_file, ttree_file):
"""Evaluate the reranking classifier on a given pair of DA/tree files (show the
total Hamming distance and total number of DAIs)
@param das_file: DA file path
@param ttree_file: trees/sentences file path
@return: a tuple (total DAIs, distance)
"""
das = read_das(das_file)
ttree_doc = read_ttrees(ttree_file)
if self.mode == 'tokens':
tokens = tokens_from_doc(ttree_doc, self.language, self.selector)
trees = self._tokens_to_flat_trees(tokens)
elif self.mode == 'tagged_lemmas':
tls = tagged_lemmas_from_doc(ttree_doc, self.language,
self.selector)
trees = self._tokens_to_flat_trees(tls)
else:
trees = trees_from_doc(ttree_doc, self.language, self.selector)
da_len = 0
dist = 0
for da, tree in zip(das, trees):
da_len += len(da)
dist += self.dist_to_da(da, [tree])[0]
return da_len, dist
def _load_contexts(self, das, context_file):
"""Load input context utterances from a .yaml.gz/.pickle.gz/.txt file and add them to the
given DAs (each returned item is then a tuple of context + DA)."""
# read contexts, combine them with corresponding DAs for easier handling
if context_file is None:
raise ValueError('Expected context utterances file name!')
log_info('Reading context utterances from %s...' % context_file)
if context_file.endswith('.txt'):
contexts = read_tokens(context_file)
else:
contexts = tokens_from_doc(read_ttrees(context_file), self.language, self.selector)
return [(context, da) for context, da in zip(contexts, das)]
def _load_contexts(self, das, context_file):
"""Load input context utterances from a .yaml.gz/.pickle.gz/.txt file and add them to the
given DAs (each returned item is then a tuple of context + DA)."""
# read contexts, combine them with corresponding DAs for easier handling
if context_file is None:
raise ValueError('Expected context utterances file name!')
log_info('Reading context utterances from %s...' % context_file)
if context_file.endswith('.txt'):
contexts = read_tokens(context_file)
else:
contexts = tokens_from_doc(read_ttrees(context_file), self.language, self.selector)
return [(context, da) for context, da in zip(contexts, das)]
def _load_trees(self, ttree_file, selector=None):
"""Load input trees/sentences from a .yaml.gz/.pickle.gz (trees) or .txt (sentences) file."""
log_info('Reading t-trees/sentences from ' + ttree_file + '...')
if ttree_file.endswith('.txt'):
if not self.use_tokens:
raise ValueError("Cannot read trees from a .txt file (%s)!" % ttree_file)
return read_tokens(ttree_file)
else:
ttree_doc = read_ttrees(ttree_file)
if selector is None:
selector = self.selector
if self.use_tokens:
return tokens_from_doc(ttree_doc, self.language, selector)
else:
return trees_from_doc(ttree_doc, self.language, selector)
def _load_trees(self, ttree_file, selector=None):
"""Load input trees/sentences from a .yaml.gz/.pickle.gz (trees) or .txt (sentences) file."""
log_info('Reading t-trees/sentences from ' + ttree_file + '...')
if ttree_file.endswith('.txt'):
if not self.use_tokens:
raise ValueError("Cannot read trees from a .txt file (%s)!" %
ttree_file)
return read_tokens(ttree_file)
else:
ttree_doc = read_ttrees(ttree_file)
if selector is None:
selector = self.selector
if self.use_tokens:
return tokens_from_doc(ttree_doc, self.language, selector)
else:
return trees_from_doc(ttree_doc, self.language, selector)
def seq2seq_gen(args):
"""Sequence-to-sequence generation"""
def write_trees_or_tokens(output_file, das, gen_trees, base_doc, language,
selector):
"""Decide to write t-trees or tokens based on the output file name."""
if output_file.endswith('.txt'):
gen_toks = [t.to_tok_list() for t in gen_trees]
postprocess_tokens(gen_toks, das)
write_tokens(gen_toks, output_file)
else:
write_ttrees(
create_ttree_doc(gen_trees, base_doc, language, selector),
output_file)
ap = ArgumentParser(prog=' '.join(sys.argv[0:2]))
ap.add_argument('-e',
'--eval-file',
type=str,
help='A ttree/text file for evaluation')
ap.add_argument(
'-a',
'--abstr-file',
type=str,
help=
'Lexicalization file (a.k.a. abstraction instructions, for postprocessing)'
)
ap.add_argument('-r',
'--ref-selector',
type=str,
default='',
help='Selector for reference trees in the evaluation file')
ap.add_argument(
'-t',
'--target-selector',
type=str,
default='',
help='Target selector for generated trees in the output file')
ap.add_argument('-d',
'--debug-logfile',
type=str,
help='Debug output file name')
ap.add_argument('-w',
'--output-file',
type=str,
help='Output tree/text file')
ap.add_argument('-D',
'--delex-output-file',
type=str,
help='Output file for trees/text before lexicalization')
ap.add_argument('-b',
'--beam-size',
type=int,
help='Override beam size for beam search decoding')
ap.add_argument('-c',
'--context-file',
type=str,
help='Input ttree/text file with context utterances')
ap.add_argument('seq2seq_model_file',
type=str,
help='Trained Seq2Seq generator model')
ap.add_argument('da_test_file', type=str, help='Input DAs for generation')
args = ap.parse_args(args)
if args.debug_logfile:
set_debug_stream(file_stream(args.debug_logfile, mode='w'))
# load the generator
tgen = Seq2SeqBase.load_from_file(args.seq2seq_model_file)
if args.beam_size is not None:
tgen.beam_size = args.beam_size
# read input files (DAs, contexts)
das = read_das(args.da_test_file)
if args.context_file:
if not tgen.use_context and not tgen.context_bleu_weight:
log_warn(
'Generator is not trained to use context, ignoring context input file.'
)
else:
if args.context_file.endswith('.txt'):
contexts = read_tokens(args.context_file)
else:
contexts = tokens_from_doc(read_ttrees(args.context_file),
tgen.language, tgen.selector)
das = [(context, da) for context, da in zip(contexts, das)]
elif tgen.use_context or tgen.context_bleu_weight:
log_warn('Generator is trained to use context. ' +
'Using empty contexts, expect lower performance.')
das = [([], da) for da in das]
# generate
log_info('Generating...')
gen_trees = []
for num, da in enumerate(das, start=1):
log_debug("\n\nTREE No. %03d" % num)
gen_trees.append(tgen.generate_tree(da))
if num % 100 == 0:
log_info("Generated tree %d" % num)
log_info(tgen.get_slot_err_stats())
if args.delex_output_file is not None:
log_info('Writing delex output...')
write_trees_or_tokens(args.delex_output_file, das, gen_trees, None,
tgen.language, args.target_selector
or tgen.selector)
# evaluate the generated trees against golden trees (delexicalized)
eval_doc = None
if args.eval_file and not args.eval_file.endswith('.txt'):
eval_doc = read_ttrees(args.eval_file)
evaler = Evaluator()
evaler.process_eval_doc(eval_doc, gen_trees, tgen.language,
args.ref_selector, args.target_selector
or tgen.selector)
# lexicalize, if required
if args.abstr_file and tgen.lexicalizer:
log_info('Lexicalizing...')
tgen.lexicalize(gen_trees, args.abstr_file)
# we won't need contexts anymore, but we do need DAs
if tgen.use_context or tgen.context_bleu_weight:
das = [da for _, da in das]
# evaluate the generated & lexicalized tokens (F1 and BLEU scores)
if args.eval_file and args.eval_file.endswith('.txt'):
eval_tokens(das, read_tokens(args.eval_file, ref_mode=True),
[t.to_tok_list() for t in gen_trees])
# write output .yaml.gz or .txt
if args.output_file is not None:
log_info('Writing output...')
write_trees_or_tokens(args.output_file, das, gen_trees, eval_doc,
tgen.language, args.target_selector
or tgen.selector)
def _init_training(self, das, trees, data_portion):
"""Initialize training.
Store input data, initialize 1-hot feature representations for input and output and
transform training data accordingly, initialize the classification neural network.
@param das: name of source file with training DAs, or list of DAs
@param trees: name of source file with corresponding trees/sentences, or list of trees
@param data_portion: portion of the training data to be used (0.0-1.0)
"""
# read input from files or take it directly from parameters
if not isinstance(das, list):
log_info('Reading DAs from ' + das + '...')
das = read_das(das)
if not isinstance(trees, list):
log_info('Reading t-trees from ' + trees + '...')
ttree_doc = read_ttrees(trees)
if self.mode == 'tokens':
tokens = tokens_from_doc(ttree_doc, self.language,
self.selector)
trees = self._tokens_to_flat_trees(tokens)
elif self.mode == 'tagged_lemmas':
tls = tagged_lemmas_from_doc(ttree_doc, self.language,
self.selector)
trees = self._tokens_to_flat_trees(tls, use_tags=True)
else:
trees = trees_from_doc(ttree_doc, self.language, self.selector)
elif self.mode in ['tokens', 'tagged_lemmas']:
trees = self._tokens_to_flat_trees(
trees, use_tags=self.mode == 'tagged_lemmas')
# make training data smaller if necessary
train_size = int(round(data_portion * len(trees)))
self.train_trees = trees[:train_size]
self.train_das = das[:train_size]
# ignore contexts, if they are contained in the DAs
if isinstance(self.train_das[0], tuple):
self.train_das = [da for (context, da) in self.train_das]
# delexicalize if DAs are lexicalized and we don't want that
if self.delex_slots:
self.train_das = [
da.get_delexicalized(self.delex_slots) for da in self.train_das
]
# add empty tree + empty DA to training data
# (i.e. forbid the network to keep any of its outputs "always-on")
train_size += 1
self.train_trees.append(TreeData())
empty_da = DA.parse('inform()')
self.train_das.append(empty_da)
self.train_order = range(len(self.train_trees))
log_info('Using %d training instances.' % train_size)
# initialize input features/embeddings
if self.tree_embs:
self.dict_size = self.tree_embs.init_dict(self.train_trees)
self.X = np.array([
self.tree_embs.get_embeddings(tree)
for tree in self.train_trees
])
else:
self.tree_feats = Features(['node: presence t_lemma formeme'])
self.tree_vect = DictVectorizer(sparse=False,
binarize_numeric=True)
self.X = [
self.tree_feats.get_features(tree, {})
for tree in self.train_trees
]
self.X = self.tree_vect.fit_transform(self.X)
# initialize output features
self.da_feats = Features(['dat: dat_presence', 'svp: svp_presence'])
self.da_vect = DictVectorizer(sparse=False, binarize_numeric=True)
self.y = [
self.da_feats.get_features(None, {'da': da})
for da in self.train_das
]
self.y = self.da_vect.fit_transform(self.y)
log_info('Number of binary classes: %d.' %
len(self.da_vect.get_feature_names()))
# initialize I/O shapes
if not self.tree_embs:
self.input_shape = list(self.X[0].shape)
else:
self.input_shape = self.tree_embs.get_embeddings_shape()
self.num_outputs = len(self.da_vect.get_feature_names())
# initialize NN classifier
self._init_neural_network()
# initialize the NN variables
self.session.run(tf.global_variables_initializer())
def seq2seq_gen(args):
"""Sequence-to-sequence generation"""
ap = ArgumentParser()
ap.add_argument('-e', '--eval-file', type=str, help='A ttree/text file for evaluation')
ap.add_argument('-a', '--abstr-file', type=str,
help='Lexicalization file (a.k.a. abstraction instructions, for postprocessing)')
ap.add_argument('-r', '--ref-selector', type=str, default='',
help='Selector for reference trees in the evaluation file')
ap.add_argument('-t', '--target-selector', type=str, default='',
help='Target selector for generated trees in the output file')
ap.add_argument('-d', '--debug-logfile', type=str, help='Debug output file name')
ap.add_argument('-w', '--output-file', type=str, help='Output tree/text file')
ap.add_argument('-b', '--beam-size', type=int,
help='Override beam size for beam search decoding')
ap.add_argument('-c', '--context-file', type=str,
help='Input ttree/text file with context utterances')
ap.add_argument('seq2seq_model_file', type=str, help='Trained Seq2Seq generator model')
ap.add_argument('da_test_file', type=str, help='Input DAs for generation')
args = ap.parse_args(args)
if args.debug_logfile:
set_debug_stream(file_stream(args.debug_logfile, mode='w'))
# load the generator
tgen = Seq2SeqBase.load_from_file(args.seq2seq_model_file)
if args.beam_size is not None:
tgen.beam_size = args.beam_size
# read input files
das = read_das(args.da_test_file)
if args.context_file:
if not tgen.use_context and not tgen.context_bleu_weight:
log_warn('Generator is not trained to use context, ignoring context input file.')
else:
if args.context_file.endswith('.txt'):
contexts = read_tokens(args.context_file)
else:
contexts = tokens_from_doc(read_ttrees(args.context_file),
tgen.language, tgen.selector)
das = [(context, da) for context, da in zip(contexts, das)]
# generate
log_info('Generating...')
gen_trees = []
for num, da in enumerate(das, start=1):
log_debug("\n\nTREE No. %03d" % num)
gen_trees.append(tgen.generate_tree(da))
log_info(tgen.get_slot_err_stats())
# evaluate the generated trees against golden trees (delexicalized)
eval_doc = None
if args.eval_file and not args.eval_file.endswith('.txt'):
eval_doc = read_ttrees(args.eval_file)
evaler = Evaluator()
evaler.process_eval_doc(eval_doc, gen_trees, tgen.language, args.ref_selector,
args.target_selector or tgen.selector)
# lexicalize, if required
if args.abstr_file and tgen.lexicalizer:
log_info('Lexicalizing...')
tgen.lexicalize(gen_trees, args.abstr_file)
# evaluate the generated & lexicalized tokens (F1 and BLEU scores)
if args.eval_file and args.eval_file.endswith('.txt'):
eval_tokens(das, read_tokens(args.eval_file, ref_mode=True), gen_trees)
# write output .yaml.gz or .txt
if args.output_file is not None:
log_info('Writing output...')
if args.output_file.endswith('.txt'):
write_tokens(gen_trees, args.output_file)
else:
write_ttrees(create_ttree_doc(gen_trees, eval_doc, tgen.language,
args.target_selector or tgen.selector),
args.output_file)
def seq2seq_gen(args):
"""Sequence-to-sequence generation"""
ap = ArgumentParser(prog=' '.join(sys.argv[0:2]))
ap.add_argument('-e', '--eval-file', type=str, help='A ttree/text file for evaluation')
ap.add_argument('-a', '--abstr-file', type=str,
help='Lexicalization file (a.k.a. abstraction instructions, for postprocessing)')
ap.add_argument('-r', '--ref-selector', type=str, default='',
help='Selector for reference trees in the evaluation file')
ap.add_argument('-t', '--target-selector', type=str, default='',
help='Target selector for generated trees in the output file')
ap.add_argument('-d', '--debug-logfile', type=str, help='Debug output file name')
ap.add_argument('-w', '--output-file', type=str, help='Output tree/text file')
ap.add_argument('-b', '--beam-size', type=int,
help='Override beam size for beam search decoding')
ap.add_argument('-c', '--context-file', type=str,
help='Input ttree/text file with context utterances')
ap.add_argument('seq2seq_model_file', type=str, help='Trained Seq2Seq generator model')
ap.add_argument('da_test_file', type=str, help='Input DAs for generation')
args = ap.parse_args(args)
if args.debug_logfile:
set_debug_stream(file_stream(args.debug_logfile, mode='w'))
# load the generator
tgen = Seq2SeqBase.load_from_file(args.seq2seq_model_file)
if args.beam_size is not None:
tgen.beam_size = args.beam_size
# read input files (DAs, contexts)
das = read_das(args.da_test_file)
if args.context_file:
if not tgen.use_context and not tgen.context_bleu_weight:
log_warn('Generator is not trained to use context, ignoring context input file.')
else:
if args.context_file.endswith('.txt'):
contexts = read_tokens(args.context_file)
else:
contexts = tokens_from_doc(read_ttrees(args.context_file),
tgen.language, tgen.selector)
das = [(context, da) for context, da in zip(contexts, das)]
elif tgen.use_context or tgen.context_bleu_weight:
log_warn('Generator is trained to use context. ' +
'Using empty contexts, expect lower performance.')
das = [([], da) for da in das]
# generate
log_info('Generating...')
gen_trees = []
for num, da in enumerate(das, start=1):
log_debug("\n\nTREE No. %03d" % num)
gen_trees.append(tgen.generate_tree(da))
if num % 100 == 0:
log_info("Generated tree %d" % num)
log_info(tgen.get_slot_err_stats())
# evaluate the generated trees against golden trees (delexicalized)
eval_doc = None
if args.eval_file and not args.eval_file.endswith('.txt'):
eval_doc = read_ttrees(args.eval_file)
evaler = Evaluator()
evaler.process_eval_doc(eval_doc, gen_trees, tgen.language, args.ref_selector,
args.target_selector or tgen.selector)
# lexicalize, if required
if args.abstr_file and tgen.lexicalizer:
log_info('Lexicalizing...')
tgen.lexicalize(gen_trees, args.abstr_file)
# we won't need contexts anymore, but we do need DAs
if tgen.use_context or tgen.context_bleu_weight:
das = [da for _, da in das]
# evaluate the generated & lexicalized tokens (F1 and BLEU scores)
if args.eval_file and args.eval_file.endswith('.txt'):
eval_tokens(das, read_tokens(args.eval_file, ref_mode=True),
[t.to_tok_list() for t in gen_trees])
# write output .yaml.gz or .txt
if args.output_file is not None:
log_info('Writing output...')
if args.output_file.endswith('.txt'):
gen_toks = [t.to_tok_list() for t in gen_trees]
postprocess_tokens(gen_toks, das)
write_tokens(gen_toks, args.output_file)
else:
write_ttrees(create_ttree_doc(gen_trees, eval_doc, tgen.language,
args.target_selector or tgen.selector),
args.output_file)
def seq2seq_gen(args):
"""Sequence-to-sequence generation"""
ap = ArgumentParser()
ap.add_argument('-e', '--eval-file', type=str, help='A ttree/text file for evaluation')
ap.add_argument('-a', '--abstr-file', type=str,
help='Lexicalization file (a.k.a. abstraction instsructions, for tokens only)')
ap.add_argument('-r', '--ref-selector', type=str, default='',
help='Selector for reference trees in the evaluation file')
ap.add_argument('-t', '--target-selector', type=str, default='',
help='Target selector for generated trees in the output file')
ap.add_argument('-d', '--debug-logfile', type=str, help='Debug output file name')
ap.add_argument('-w', '--output-file', type=str, help='Output tree/text file')
ap.add_argument('-b', '--beam-size', type=int,
help='Override beam size for beam search decoding')
ap.add_argument('-c', '--context-file', type=str,
help='Input ttree/text file with context utterances')
ap.add_argument('seq2seq_model_file', type=str, help='Trained Seq2Seq generator model')
ap.add_argument('da_test_file', type=str, help='Input DAs for generation')
args = ap.parse_args(args)
if args.debug_logfile:
set_debug_stream(file_stream(args.debug_logfile, mode='w'))
# load the generator
tgen = Seq2SeqBase.load_from_file(args.seq2seq_model_file)
if args.beam_size is not None:
tgen.beam_size = args.beam_size
# read input files
das = read_das(args.da_test_file)
if args.context_file:
if not tgen.use_context and not tgen.context_bleu_weight:
log_warn('Generator is not trained to use context, ignoring context input file.')
else:
if args.context_file.endswith('.txt'):
contexts = read_tokens(args.context_file)
else:
contexts = tokens_from_doc(read_ttrees(args.context_file),
tgen.language, tgen.selector)
das = [(context, da) for context, da in zip(contexts, das)]
# prepare evaluation
if args.eval_file is None or args.eval_file.endswith('.txt'): # just tokens
gen_doc = []
else: # Trees: depending on PyTreex
from pytreex.core.document import Document
eval_doc = read_ttrees(args.eval_file)
if args.ref_selector == args.target_selector:
gen_doc = Document()
else:
gen_doc = eval_doc
if args.eval_file:
tgen.init_slot_err_stats()
# generate
log_info('Generating...')
tgen.selector = args.target_selector # override target selector for generation
for num, da in enumerate(das, start=1):
log_debug("\n\nTREE No. %03d" % num)
tgen.generate_tree(da, gen_doc)
# evaluate
if args.eval_file is not None:
log_info(tgen.get_slot_err_stats())
# evaluate the generated tokens (F1 and BLEU scores)
if args.eval_file.endswith('.txt'):
lexicalize_tokens(gen_doc, lexicalization_from_doc(args.abstr_file))
eval_tokens(das, read_tokens(args.eval_file, ref_mode=True), gen_doc)
# evaluate the generated trees against golden trees
else:
eval_trees(das,
ttrees_from_doc(eval_doc, tgen.language, args.ref_selector),
ttrees_from_doc(gen_doc, tgen.language, args.target_selector),
eval_doc, tgen.language, tgen.selector)
# write output .yaml.gz or .txt
if args.output_file is not None:
log_info('Writing output...')
if args.output_file.endswith('.txt'):
write_tokens(gen_doc, args.output_file)
else:
write_ttrees(gen_doc, args.output_file)
def _init_training(self, das, trees, data_portion):
"""Initialize training.
Store input data, initialize 1-hot feature representations for input and output and
transform training data accordingly, initialize the classification neural network.
@param das: name of source file with training DAs, or list of DAs
@param trees: name of source file with corresponding trees/sentences, or list of trees
@param data_portion: portion of the training data to be used (0.0-1.0)
"""
# read input from files or take it directly from parameters
if not isinstance(das, list):
log_info('Reading DAs from ' + das + '...')
das = read_das(das)
if not isinstance(trees, list):
log_info('Reading t-trees from ' + trees + '...')
ttree_doc = read_ttrees(trees)
if self.mode == 'tokens':
tokens = tokens_from_doc(ttree_doc, self.language, self.selector)
trees = self._tokens_to_flat_trees(tokens)
elif self.mode == 'tagged_lemmas':
tls = tagged_lemmas_from_doc(ttree_doc, self.language, self.selector)
trees = self._tokens_to_flat_trees(tls, use_tags=True)
else:
trees = trees_from_doc(ttree_doc, self.language, self.selector)
elif self.mode in ['tokens', 'tagged_lemmas']:
trees = self._tokens_to_flat_trees(trees, use_tags=self.mode == 'tagged_lemmas')
# make training data smaller if necessary
train_size = int(round(data_portion * len(trees)))
self.train_trees = trees[:train_size]
self.train_das = das[:train_size]
# ignore contexts, if they are contained in the DAs
if isinstance(self.train_das[0], tuple):
self.train_das = [da for (context, da) in self.train_das]
# delexicalize if DAs are lexicalized and we don't want that
if self.delex_slots:
self.train_das = [da.get_delexicalized(self.delex_slots) for da in self.train_das]
# add empty tree + empty DA to training data
# (i.e. forbid the network to keep any of its outputs "always-on")
train_size += 1
self.train_trees.append(TreeData())
empty_da = DA.parse('inform()')
self.train_das.append(empty_da)
self.train_order = range(len(self.train_trees))
log_info('Using %d training instances.' % train_size)
# initialize input features/embeddings
if self.tree_embs:
self.dict_size = self.tree_embs.init_dict(self.train_trees)
self.X = np.array([self.tree_embs.get_embeddings(tree) for tree in self.train_trees])
else:
self.tree_feats = Features(['node: presence t_lemma formeme'])
self.tree_vect = DictVectorizer(sparse=False, binarize_numeric=True)
self.X = [self.tree_feats.get_features(tree, {}) for tree in self.train_trees]
self.X = self.tree_vect.fit_transform(self.X)
# initialize output features
self.da_feats = Features(['dat: dat_presence', 'svp: svp_presence'])
self.da_vect = DictVectorizer(sparse=False, binarize_numeric=True)
self.y = [self.da_feats.get_features(None, {'da': da}) for da in self.train_das]
self.y = self.da_vect.fit_transform(self.y)
log_info('Number of binary classes: %d.' % len(self.da_vect.get_feature_names()))
# initialize I/O shapes
if not self.tree_embs:
self.input_shape = list(self.X[0].shape)
else:
self.input_shape = self.tree_embs.get_embeddings_shape()
self.num_outputs = len(self.da_vect.get_feature_names())
# initialize NN classifier
self._init_neural_network()
# initialize the NN variables
self.session.run(tf.global_variables_initializer())
