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Separating feature-related stuff to support EmbNNRanker
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@tuetschek
tuetschek committed Apr 9, 2015
1 parent 2aaa4b5 commit afbe0c1
Showing 1 changed file with 120 additions and 106 deletions.
226 changes: 120 additions & 106 deletions tgen/rank.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,21 +44,18 @@ def save_to_file(self, model_fname):


class BasePerceptronRanker(Ranker):
"""Base class for global ranker for whole trees, based on features."""

def __init__(self, cfg):
super(BasePerceptronRanker, self).__init__()
if not cfg:
cfg = {}
self.feats = ['bias: bias']
self.vectorizer = None
self.normalizer = None
self.alpha = cfg.get('alpha', 1)
self.passes = cfg.get('passes', 5)
self.prune_feats = cfg.get('prune_feats', 1)
self.rival_number = cfg.get('rival_number', 10)
self.language = cfg.get('language', 'en')
self.selector = cfg.get('selector', '')
# initialize diagnostics
self.lists_analyzer = None
self.evaluator = None
self.prune_feats = cfg.get('prune_feats', 1)
self.rival_number = cfg.get('rival_number', 10)
self.averaging = cfg.get('averaging', False)
self.randomize = cfg.get('randomize', False)
self.future_promise_weight = cfg.get('future_promise_weight', 1.0)
Expand All @@ -69,14 +66,6 @@ def __init__(self, cfg):
self.rival_gen_beam_size = cfg.get('rival_gen_beam_size')
self.candgen_model = cfg.get('candgen_model')
self.diffing_trees = cfg.get('diffing_trees', False)
self.binarize = cfg.get('binarize', False)
# initialize feature functions
if 'features' in cfg:
self.feats.extend(cfg['features'])
self.feats = Features(self.feats, cfg.get('intermediate_features', []))
# initialize diagnostics
self.lists_analyzer = None
self.evaluator = None

def score(self, cand_tree, da):
"""Score the given tree in the context of the given dialogue act.
Expand All @@ -86,27 +75,7 @@ def score(self, cand_tree, da):
return self._score(self._extract_feats(cand_tree, da))

def _extract_feats(self, tree, da):
feats = self.vectorizer.transform([self.feats.get_features(tree, {'da': da})])
if self.normalizer:
feats = self.normalizer.transform(feats)
return feats[0]

def get_future_promise(self, cand_tree):
"""Compute expected future promise for a tree."""
w_sum = self.get_weights_sum()
if self.future_promise_type == 'num_nodes':
return w_sum * self.future_promise_weight * max(0, 10 - len(cand_tree))
elif self.future_promise_type == 'norm_exp_children':
return (self.candgen.get_future_promise(cand_tree) / len(cand_tree)) * w_sum * self.future_promise_weight
elif self.future_promise_type == 'ands':
prom = 0
for idx, node in enumerate(cand_tree.nodes):
if node.t_lemma == 'and':
num_kids = cand_tree.children_num(idx)
prom += max(0, 2 - num_kids)
return prom * w_sum * self.future_promise_weight
else: # expected children (default)
return self.candgen.get_future_promise(cand_tree) * w_sum * self.future_promise_weight
raise NotImplementedError

def train(self, das_file, ttree_file, data_portion=1.0):
"""Run training on the given training data."""
Expand All @@ -124,8 +93,8 @@ def train(self, das_file, ttree_file, data_portion=1.0):
self.set_weights_iter_average()

def _init_training(self, das_file, ttree_file, data_portion):
"""Initialize training (read input files, reset weights, reset
training data features.)"""
"""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)
Expand All @@ -142,23 +111,6 @@ def _init_training(self, das_file, ttree_file, data_portion):
self.train_order = range(len(self.train_trees))
log_info('Using %d training instances.' % train_size)

# precompute training data features
X = []
for da, tree in zip(self.train_das, self.train_trees):
X.append(self.feats.get_features(tree, {'da': da}))
if self.prune_feats > 1:
self._prune_features(X)
# vectorize and binarize or normalize (+train vectorizer/normalizer)
if self.binarize:
self.vectorizer = DictVectorizer(sparse=False, binarize_numeric=True)
self.train_feats = self.vectorizer.fit_transform(X)
else:
self.vectorizer = DictVectorizer(sparse=False)
self.normalizer = StandardScaler(copy=False)
self.train_feats = self.normalizer.fit_transform(self.vectorizer.fit_transform(X))

log_info('Features matrix shape: %s' % str(self.train_feats.shape))

# initialize candidate generator + planner if needed
if self.candgen_model is not None:
self.candgen = RandomCandidateGenerator.load_from_file(self.candgen_model)
Expand Down Expand Up @@ -238,55 +190,6 @@ def diffing_trees_with_scores(self, da, good_tree, bad_tree):
ret += "%.3f" % self.score(bad_st, da) + "\t" + unicode(bad_st) + "\n"
return ret

def get_weights(self):
"""Return the current ranker weights (parameters). To be overridden by derived classes."""
raise NotImplementedError

def set_weights(self, w):
"""Set new ranker weights. To be overridden by derived classes."""
raise NotImplementedError

def set_weights_average(self, ws):
"""Set the weights as the average of the given array of weights (used in parallel training).
To be overridden by derived classes."""
raise NotImplementedError

def store_iter_weights(self):
"""Remember the current weights to be used for averaging.
To be overridden by derived classes."""
raise NotImplementedError

def set_weights_iter_average(self):
"""Set new weights as the average of all remembered weights. To be overridden by
derived classes."""
raise NotImplementedError

def get_weights_sum(self):
"""Return weights size in order to weigh future promise against them."""
raise NotImplementedError

def update_weights_sum(self):
"""Update the current weights size for future promise weighining."""
raise NotImplementedError

def reset_diagnostics(self):
"""Reset the evaluation statistics (Evaluator and ASearchListsAnalyzer objects)."""
self.evaluator = Evaluator()
self.lists_analyzer = ASearchListsAnalyzer()

def get_diagnostics(self):
"""Return the current evaluation statistics (a tuple of Evaluator and ASearchListsAnalyzer
objects."""
return self.evaluator, self.lists_analyzer

def set_diagnostics_average(self, diags):
"""Given an array of evaluation statistics objects, average them an store in this ranker
instance."""
self.reset_diagnostics()
for evaluator, lists_analyzer in diags:
self.evaluator.merge(evaluator)
self.lists_analyzer.merge(lists_analyzer)

def _get_num_iters(self, cur_pass_no, iter_setting):
"""Return the maximum number of iterations (total/deficit) given the current pass.
Used to keep track of variable iteration number setting in configuration."""
Expand Down Expand Up @@ -375,6 +278,117 @@ def _get_rival_candidates(self, tree_no, max_iter, max_defic_iter, beam_size):
# return all resulting candidates
return rival_trees, rival_feats

def get_weights(self):
"""Return the current ranker weights (parameters). To be overridden by derived classes."""
raise NotImplementedError

def set_weights(self, w):
"""Set new ranker weights. To be overridden by derived classes."""
raise NotImplementedError

def set_weights_average(self, ws):
"""Set the weights as the average of the given array of weights (used in parallel training).
To be overridden by derived classes."""
raise NotImplementedError

def store_iter_weights(self):
"""Remember the current weights to be used for averaging.
To be overridden by derived classes."""
raise NotImplementedError

def set_weights_iter_average(self):
"""Set new weights as the average of all remembered weights. To be overridden by
derived classes."""
raise NotImplementedError

def get_weights_sum(self):
"""Return weights size in order to weigh future promise against them."""
raise NotImplementedError

def update_weights_sum(self):
"""Update the current weights size for future promise weighining."""
raise NotImplementedError

def reset_diagnostics(self):
"""Reset the evaluation statistics (Evaluator and ASearchListsAnalyzer objects)."""
self.evaluator = Evaluator()
self.lists_analyzer = ASearchListsAnalyzer()

def get_diagnostics(self):
"""Return the current evaluation statistics (a tuple of Evaluator and ASearchListsAnalyzer
objects."""
return self.evaluator, self.lists_analyzer

def set_diagnostics_average(self, diags):
"""Given an array of evaluation statistics objects, average them an store in this ranker
instance."""
self.reset_diagnostics()
for evaluator, lists_analyzer in diags:
self.evaluator.merge(evaluator)
self.lists_analyzer.merge(lists_analyzer)

def get_future_promise(self, cand_tree):
"""Compute expected future promise for a tree."""
w_sum = self.get_weights_sum()
if self.future_promise_type == 'num_nodes':
return w_sum * self.future_promise_weight * max(0, 10 - len(cand_tree))
elif self.future_promise_type == 'norm_exp_children':
return (self.candgen.get_future_promise(cand_tree) / len(cand_tree)) * w_sum * self.future_promise_weight
elif self.future_promise_type == 'ands':
prom = 0
for idx, node in enumerate(cand_tree.nodes):
if node.t_lemma == 'and':
num_kids = cand_tree.children_num(idx)
prom += max(0, 2 - num_kids)
return prom * w_sum * self.future_promise_weight
else: # expected children (default)
return self.candgen.get_future_promise(cand_tree) * w_sum * self.future_promise_weight


class FeaturesPerceptronRanker(BasePerceptronRanker):
"""Base class for global ranker for whole trees, based on features."""

def __init__(self, cfg):
super(FeaturesPerceptronRanker, self).__init__(cfg)
if not cfg:
cfg = {}
self.feats = ['bias: bias']
self.vectorizer = None
self.normalizer = None
self.alpha = cfg.get('alpha', 1)
self.binarize = cfg.get('binarize', False)
# initialize feature functions
if 'features' in cfg:
self.feats.extend(cfg['features'])
self.feats = Features(self.feats, cfg.get('intermediate_features', []))

def _extract_feats(self, tree, da):
feats = self.vectorizer.transform([self.feats.get_features(tree, {'da': da})])
if self.normalizer:
feats = self.normalizer.transform(feats)
return feats[0]

def _init_training(self, das_file, ttree_file, data_portion):

super(FeaturesPerceptronRanker, self)._init_training(das_file, ttree_file, data_portion)

# precompute training data features
X = []
for da, tree in zip(self.train_das, self.train_trees):
X.append(self.feats.get_features(tree, {'da': da}))
if self.prune_feats > 1:
self._prune_features(X)
# vectorize and binarize or normalize (+train vectorizer/normalizer)
if self.binarize:
self.vectorizer = DictVectorizer(sparse=False, binarize_numeric=True)
self.train_feats = self.vectorizer.fit_transform(X)
else:
self.vectorizer = DictVectorizer(sparse=False)
self.normalizer = StandardScaler(copy=False)
self.train_feats = self.normalizer.fit_transform(self.vectorizer.fit_transform(X))

log_info('Features matrix shape: %s' % str(self.train_feats.shape))

def _prune_features(self, X):
"""Prune features – remove all entries from X that involve features not having a
specified minimum occurrence count.
Expand All @@ -389,7 +403,7 @@ def _prune_features(self, X):
del inst[key]


class PerceptronRanker(BasePerceptronRanker):
class PerceptronRanker(FeaturesPerceptronRanker):
"""Peceptron ranker based on linear Perceptron by Collins & Duffy (2002)."""

def __init__(self, cfg):
Expand Down

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