def _init_training(self, das_file, ttree_file, data_portion):
"""Initialize training (read input data, fix size, initialize candidate generator
and planner)"""
# read input
log_info('Reading DAs from ' + das_file + '...')
das = read_das(das_file)
log_info('Reading t-trees from ' + ttree_file + '...')
ttree_doc = read_ttrees(ttree_file)
sents = sentences_from_doc(ttree_doc, self.language, self.selector)
trees = trees_from_doc(ttree_doc, self.language, self.selector)
# 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]
self.train_sents = sents[:train_size]
self.train_order = range(len(self.train_trees))
log_info('Using %d training instances.' % train_size)
# initialize candidate generator + planner if needed
if self.candgen_model is not None:
self.candgen = RandomCandidateGenerator.load_from_file(self.candgen_model)
self.sampling_planner = SamplingPlanner({'language': self.language,
'selector': self.selector,
'candgen': self.candgen})
if 'gen_cur_weights' in self.rival_gen_strategy:
assert self.candgen is not None
self.asearch_planner = ASearchPlanner({'candgen': self.candgen,
'language': self.language,
'selector': self.selector,
'ranker': self, })
def _init_training(self, das_file, ttree_file, data_portion):
"""Initialize training (read input data, fix size, initialize candidate generator
and planner)"""
# read input
log_info('Reading DAs from ' + das_file + '...')
das = read_das(das_file)
log_info('Reading t-trees from ' + ttree_file + '...')
ttree_doc = read_ttrees(ttree_file)
sents = sentences_from_doc(ttree_doc, self.language, self.selector)
trees = trees_from_doc(ttree_doc, self.language, self.selector)
# 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]
self.train_sents = sents[:train_size]
self.train_order = range(len(self.train_trees))
log_info('Using %d training instances.' % train_size)
# initialize candidate generator
if self.candgen_model is not None:
self.candgen = RandomCandidateGenerator.load_from_file(
self.candgen_model)
# self.sampling_planner = SamplingPlanner({'language': self.language,
# 'selector': self.selector,
# 'candgen': self.candgen})
# check if A*search planner is needed (i.e., any rival generation strategy requires it)
# and initialize it
if isinstance(self.rival_gen_strategy[0], tuple):
asearch_needed = any([
s in ['gen_cur_weights', 'gen_update']
for _, ss in self.rival_gen_strategy for s in ss
])
else:
asearch_needed = any([
s in ['gen_cur_weights', 'gen_update']
for s in self.rival_gen_strategy
])
if asearch_needed:
assert self.candgen is not None
self.asearch_planner = ASearchPlanner({
'candgen': self.candgen,
'language': self.language,
'selector': self.selector,
'ranker': self,
})
def _init_training(self, das_file, ttree_file, data_portion):
"""Initialize training (read input data, fix size, initialize candidate generator
and planner)"""
# read input
log_info('Reading DAs from ' + das_file + '...')
das = read_das(das_file)
log_info('Reading t-trees from ' + ttree_file + '...')
ttree_doc = read_ttrees(ttree_file)
sents = sentences_from_doc(ttree_doc, self.language, self.selector)
trees = trees_from_doc(ttree_doc, self.language, self.selector)
# 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]
self.train_sents = sents[:train_size]
self.train_order = range(len(self.train_trees))
log_info('Using %d training instances.' % train_size)
# initialize candidate generator
if self.candgen_model is not None:
self.candgen = RandomCandidateGenerator.load_from_file(self.candgen_model)
# self.sampling_planner = SamplingPlanner({'language': self.language,
# 'selector': self.selector,
# 'candgen': self.candgen})
# check if A*search planner is needed (i.e., any rival generation strategy requires it)
# and initialize it
if isinstance(self.rival_gen_strategy[0], tuple):
asearch_needed = any([s in ['gen_cur_weights', 'gen_update']
for _, ss in self.rival_gen_strategy
for s in ss])
else:
asearch_needed = any([s in ['gen_cur_weights', 'gen_update']
for s in self.rival_gen_strategy])
if asearch_needed:
assert self.candgen is not None
self.asearch_planner = ASearchPlanner({'candgen': self.candgen,
'language': self.language,
'selector': self.selector,
'ranker': self, })
def train(self, das_file, ttree_file):
"""``Training'' the generator (collect counts of DAIs and corresponding t-nodes).
@param da_file: file with training DAs
@param t_file: file with training t-trees (YAML or pickle)
"""
# read training data
log_info('Reading ' + ttree_file)
ttrees = ttrees_from_doc(read_ttrees(ttree_file), self.language, self.selector)
log_info('Reading ' + das_file)
das = read_das(das_file)
# collect counts
log_info('Collecting counts')
child_type_counts = {}
child_num_counts = defaultdict(Counter)
max_total_nodes = defaultdict(int)
max_level_nodes = defaultdict(Counter)
for ttree, da in zip(ttrees, das):
# counts for formeme/lemma given DAI
for dai in da:
for tnode in ttree.get_descendants():
if dai not in child_type_counts:
child_type_counts[dai] = defaultdict(Counter)
parent_id = self._parent_node_id(tnode.parent)
child_id = (tnode.formeme, tnode.t_lemma, tnode > tnode.parent)
child_type_counts[dai][parent_id][child_id] += 1
# counts for number of children
for tnode in ttree.get_descendants(add_self=1):
child_num_counts[self._parent_node_id(tnode)][len(tnode.get_children())] += 1
# counts for max. number of nodes
total_nodes = len(ttree.get_descendants(add_self=True))
for dai in da:
max_total_nodes[dai] = max((max_total_nodes[dai], total_nodes))
level_nodes = defaultdict(int)
for tnode in ttree.get_descendants(add_self=True):
level_nodes[tnode.get_depth()] += 1
for dai in da:
for level in level_nodes.iterkeys():
max_level_nodes[dai][level] = max((max_level_nodes[dai][level],
level_nodes[level]))
# prune counts
if self.prune_threshold > 1:
for dai, forms in child_type_counts.items():
self._prune(forms)
if not forms:
del child_type_counts[dai]
self._prune(child_num_counts)
# transform counts
self.child_type_counts = child_type_counts
self.child_num_cdfs = self.cdfs_from_counts(child_num_counts)
self.max_children = {par_id: max(child_num_counts[par_id].keys())
for par_id in child_num_counts.keys()}
self.exp_child_num = self.exp_from_cdfs(self.child_num_cdfs)
if self.node_limits:
self.node_limits = {dai: {'total': max_total}
for dai, max_total in max_total_nodes.iteritems()}
for dai, max_levels in max_level_nodes.iteritems():
self.node_limits[dai].update(max_levels)
else:
self.node_limits = None
# Determine compatible DAIs for given lemmas/nodes (according to the compatibility setting)
if self.compatible_dais_type:
self.compatible_dais = self._compatibility_table(das, ttrees, lambda da: da.dais)
# The same for compatible DA slots
if self.compatible_slots:
self.compatible_slots = self._compatibility_table(das, ttrees,
lambda da: [dai.slot for dai in da.dais])
if self.classif:
self.classif.train(das_file, ttree_file)
def train(self, das_file, ttree_file):
"""``Training'' the generator (collect counts of DAIs and corresponding t-nodes).
@param da_file: file with training DAs
@param t_file: file with training t-trees (YAML or pickle)
"""
# read training data
log_info('Reading ' + ttree_file)
ttrees = ttrees_from_doc(read_ttrees(ttree_file), self.language,
self.selector)
log_info('Reading ' + das_file)
das = read_das(das_file)
# collect counts
log_info('Collecting counts')
child_type_counts = {}
child_num_counts = defaultdict(Counter)
max_total_nodes = defaultdict(int)
max_level_nodes = defaultdict(Counter)
for ttree, da in zip(ttrees, das):
# counts for formeme/lemma given DAI
for dai in da:
for tnode in ttree.get_descendants():
if dai not in child_type_counts:
child_type_counts[dai] = defaultdict(Counter)
parent_id = self._parent_node_id(tnode.parent)
child_id = (tnode.formeme, tnode.t_lemma,
tnode > tnode.parent)
child_type_counts[dai][parent_id][child_id] += 1
# counts for number of children
for tnode in ttree.get_descendants(add_self=1):
child_num_counts[self._parent_node_id(tnode)][len(
tnode.get_children())] += 1
# counts for max. number of nodes
total_nodes = len(ttree.get_descendants(add_self=True))
for dai in da:
max_total_nodes[dai] = max((max_total_nodes[dai], total_nodes))
level_nodes = defaultdict(int)
for tnode in ttree.get_descendants(add_self=True):
level_nodes[tnode.get_depth()] += 1
for dai in da:
for level in level_nodes.iterkeys():
max_level_nodes[dai][level] = max(
(max_level_nodes[dai][level], level_nodes[level]))
# prune counts
if self.prune_threshold > 1:
for dai, forms in child_type_counts.items():
self._prune(forms)
if not forms:
del child_type_counts[dai]
self._prune(child_num_counts)
# transform counts
self.child_type_counts = child_type_counts
self.child_num_cdfs = self.cdfs_from_counts(child_num_counts)
self.max_children = {
par_id: max(child_num_counts[par_id].keys())
for par_id in child_num_counts.keys()
}
self.exp_child_num = self.exp_from_cdfs(self.child_num_cdfs)
if self.node_limits:
self.node_limits = {
dai: {
'total': max_total
}
for dai, max_total in max_total_nodes.iteritems()
}
for dai, max_levels in max_level_nodes.iteritems():
self.node_limits[dai].update(max_levels)
else:
self.node_limits = None
# Determine compatible DAIs for given lemmas/nodes (according to the compatibility setting)
if self.compatible_dais_type:
self.compatible_dais = self._compatibility_table(
das, ttrees, lambda da: da.dais)
# The same for compatible DA slots
if self.compatible_slots:
self.compatible_slots = self._compatibility_table(
das, ttrees, lambda da: [dai.name for dai in da.dais])
if self.classif:
self.classif.train(das_file, ttree_file)
