def test_tagging(): """Test the tagging functionality of this extension.""" try: # TODO: serial.save should be able to take an open file-like object so # we can direct its output to a StringIO or something and not need to # screw around like this in tests that don't actually need to touch # the filesystem. /dev/null would work but the test would fail on # Windows. fd, fn = tempfile.mkstemp(suffix='.pkl') os.close(fd) # Test that the default key gets created. def_model = MockModel() def_model.monitor = MockMonitor() def_ext = MonitorBasedSaveBest(channel_name='foobar', save_path=fn) def_ext.setup(def_model, None, None) assert 'MonitorBasedSaveBest' in def_model.tag # Test with a custom key. model = MockModel() model.monitor = MockMonitor() model.monitor.channels['foobar'] = MockChannel() ext = MonitorBasedSaveBest(channel_name='foobar', tag_key='test123', save_path=fn) # Best cost is initially infinity. ext.setup(model, None, None) assert model.tag['test123']['best_cost'] == float("inf") # Best cost after one iteration. model.monitor.channels['foobar'].val_record.append(5.0) ext.on_monitor(model, None, None) assert model.tag['test123']['best_cost'] == 5.0 # Best cost after a second, worse iteration. model.monitor.channels['foobar'].val_record.append(7.0) ext.on_monitor(model, None, None) assert model.tag['test123']['best_cost'] == 5.0 # Best cost after a third iteration better than 2 but worse than 1. model.monitor.channels['foobar'].val_record.append(6.0) ext.on_monitor(model, None, None) assert model.tag['test123']['best_cost'] == 5.0 # Best cost after a fourth, better iteration. model.monitor.channels['foobar'].val_record.append(3.0) ext.on_monitor(model, None, None) assert model.tag['test123']['best_cost'] == 3.0 finally: os.remove(fn)
class SequenceTaggerNetwork(Model): def __init__(self, dataset, w2i, t2i, featurizer, edim=None, hdims=None, fedim=None, max_epochs=100, use_momentum=False, lr=.01, lr_lin_decay=None, lr_scale=False, lr_monitor_decay=False, valid_stop=False, reg_factors=None, dropout=False, dropout_params=None, embedding_init=None, embedded_model=None, monitor_train=True, plot_monitor=None, num=False): super(SequenceTaggerNetwork, self).__init__() self.vocab_size = dataset.vocab_size self.window_size = dataset.window_size self.total_feats = dataset.total_feats self.feat_num = dataset.feat_num self.n_classes = dataset.n_classes self.max_epochs = max_epochs if edim is None: edim = 50 if hdims is None: hdims = [100] if fedim is None: fedim = 5 self.edim = edim self.fedim = fedim self.hdims = hdims self.w2i = w2i self.t2i = t2i self.featurizer = featurizer self._create_tagger() A_value = numpy.random.uniform(low=-.1, high=.1, size=(self.n_classes + 2, self.n_classes)) self.A = sharedX(A_value, name='A') self.use_momentum = use_momentum self.lr = lr self.lr_lin_decay = lr_lin_decay self.lr_monitor_decay = lr_monitor_decay self.lr_scale = lr_scale self.valid_stop = valid_stop self.reg_factors = reg_factors self.close_cache = {} self.dropout_params = dropout_params self.dropout = dropout or self.dropout_params is not None self.hdims = hdims self.monitor_train = monitor_train self.num = num self.plot_monitor = plot_monitor if embedding_init is not None: self.set_embedding_weights(embedding_init) def _create_tagger(self): self.tagger = WordTaggerNetwork(self.vocab_size, self.window_size, self.total_feats, self.feat_num, self.hdims, self.edim, self.fedim, self.n_classes) def _create_data_specs(self, dataset): self.input_space = CompositeSpace([ dataset.data_specs[0].components[i] for i in xrange(len(dataset.data_specs[0].components) - 1) ]) self.output_space = dataset.data_specs[0].components[-1] self.input_source = dataset.data_specs[1][:-1] self.target_source = dataset.data_specs[1][-1] def __getstate__(self): d = {} d['vocab_size'] = self.vocab_size d['window_size'] = self.window_size d['feat_num'] = self.feat_num d['total_feats'] = self.total_feats d['n_classes'] = self.n_classes d['input_space'] = self.input_space d['output_space'] = self.output_space d['input_source'] = self.input_source d['target_source'] = self.target_source d['A'] = self.A d['tagger'] = self.tagger d['w2i'] = self.w2i d['t2i'] = self.t2i d['featurizer'] = self.featurizer d['max_epochs'] = self.max_epochs d['use_momentum'] = self.use_momentum d['lr'] = self.lr d['lr_lin_decay'] = self.lr_lin_decay d['lr_monitor_decay'] = self.lr_monitor_decay d['lr_scale'] = self.lr_scale d['valid_stop'] = self.valid_stop d['reg_factors'] = self.reg_factors d['dropout'] = self.dropout d['dropout_params'] = self.dropout_params d['monitor_train'] = self.monitor_train d['num'] = self.num d['plot_monitor'] = self.plot_monitor return d def fprop(self, data): tagger_out = self.tagger.fprop(data) probs = T.concatenate([self.A, tagger_out]) return probs def dropout_fprop(self, data, default_input_include_prob=0.5, input_include_probs=None, default_input_scale=2.0, input_scales=None, per_example=True): if input_scales is None: input_scales = {'input': 1.0} if input_include_probs is None: input_include_probs = {'input': 1.0} if self.dropout_params is not None: if len(self.dropout_params) == len(self.tagger.layers) - 1: input_include_probs['tagger_out'] = self.dropout_params[-1] input_scales['tagger_out'] = 1.0 / self.dropout_params[-1] for i, p in enumerate(self.dropout_params[:-1]): input_include_probs['h{0}'.format(i)] = p input_scales['h{0}'.format(i)] = 1.0 / p tagger_out = self.tagger.dropout_fprop(data, default_input_include_prob, input_include_probs, default_input_scale, input_scales, per_example) probs = T.concatenate([self.A, tagger_out]) return probs @functools.wraps(Model.get_lr_scalers) def get_lr_scalers(self): if not self.lr_scale: return {} d = self.tagger.get_lr_scalers() d[self.A] = 1. / self.n_classes return d @functools.wraps(Model.get_params) def get_params(self): return self.tagger.get_params() + [self.A] def create_adjustors(self): initial_momentum = .5 final_momentum = .99 start = 1 saturate = self.max_epochs self.momentum_adjustor = learning_rule.MomentumAdjustor( final_momentum, start, saturate) self.momentum_rule = learning_rule.Momentum(initial_momentum, nesterov_momentum=True) if self.lr_monitor_decay: self.learning_rate_adjustor = MonitorBasedLRAdjuster( high_trigger=1., shrink_amt=0.9, low_trigger=.95, grow_amt=1.1, channel_name='train_objective') elif self.lr_lin_decay: self.learning_rate_adjustor = LinearDecayOverEpoch( start, saturate, self.lr_lin_decay) def compute_used_inputs(self): seen = {'words': set(), 'feats': set()} for sen_w in self.dataset['train'].X1: seen['words'] |= reduce(lambda x, y: set(x) | set(y), sen_w, set()) for sen_f in self.dataset['train'].X2: seen['feats'] |= reduce(lambda x, y: set(x) | set(y), sen_f, set()) words = set(xrange(len(self.w2i))) feats = set(xrange(self.total_feats)) self.notseen = { 'words': numpy.array(sorted(words - seen['words'])), 'feats': numpy.array(sorted(feats - seen['feats'])) } def set_dataset(self, data): self._create_data_specs(data['train']) self.dataset = data self.compute_used_inputs() self.tagger.notseen = self.notseen def create_algorithm(self, data, save_best_path=None): self.set_dataset(data) self.create_adjustors() term = EpochCounter(max_epochs=self.max_epochs) if self.valid_stop: cost_crit = MonitorBased(channel_name='valid_objective', prop_decrease=.0, N=3) term = And(criteria=[cost_crit, term]) #(layers, A_weight_decay) coeffs = None if self.reg_factors: rf = self.reg_factors lhdims = len(self.tagger.hdims) l_inputlayer = len(self.tagger.layers[0].layers) coeffs = ([[rf] * l_inputlayer] + ([rf] * lhdims) + [rf], rf) cost = SeqTaggerCost(coeffs, self.dropout) self.cost = cost self.mbsb = MonitorBasedSaveBest(channel_name='valid_objective', save_path=save_best_path) mon_dataset = dict(self.dataset) if not self.monitor_train: del mon_dataset['train'] _learning_rule = (self.momentum_rule if self.use_momentum else None) self.algorithm = SGD( batch_size=1, learning_rate=self.lr, termination_criterion=term, monitoring_dataset=mon_dataset, cost=cost, learning_rule=_learning_rule, ) self.algorithm.setup(self, self.dataset['train']) if self.plot_monitor: cn = ["valid_objective", "test_objective"] if self.monitor_train: cn.append("train_objective") plots = Plots(channel_names=cn, save_path=self.plot_monitor) self.pm = PlotManager([plots], freq=1) self.pm.setup(self, None, self.algorithm) def train(self): while True: if not self.algorithm.continue_learning(self): break self.algorithm.train(dataset=self.dataset['train']) self.monitor.report_epoch() self.monitor() self.mbsb.on_monitor(self, self.dataset['valid'], self.algorithm) if self.use_momentum: self.momentum_adjustor.on_monitor(self, self.dataset['valid'], self.algorithm) if hasattr(self, 'learning_rate_adjustor'): self.learning_rate_adjustor.on_monitor(self, self.dataset['valid'], self.algorithm) if hasattr(self, 'pm'): self.pm.on_monitor(self, self.dataset['valid'], self.algorithm) def prepare_tagging(self): X = self.get_input_space().make_theano_batch(batch_size=1) Y = self.fprop(X) self.f = theano.function([X[0], X[1]], Y) self.start = self.A.get_value()[0] self.end = self.A.get_value()[1] self.A_value = self.A.get_value()[2:] def process_input(self, words, feats): return self.f(words, feats) def tag_sen(self, words, feats, debug=False, return_probs=False): if not hasattr(self, 'f'): self.prepare_tagging() y = self.process_input(words, feats) tagger_out = y[2 + self.n_classes:] res = viterbi(self.start, self.A_value, self.end, tagger_out, self.n_classes, return_probs) if return_probs: return res / res.sum(axis=1)[:, numpy.newaxis] #return res.reshape((1, len(res))) if debug: return numpy.array([[e] for e in res[1]]), tagger_out return numpy.array([[e] for e in res[1]]) def get_score(self, dataset, mode='pwp'): self.prepare_tagging() tagged = (self.tag_sen(w, f) for w, f in izip(dataset.X1, dataset.X2)) gold = dataset.y good, bad = 0., 0. if mode == 'pwp': for t, g in izip(tagged, gold): g = g.argmax(axis=1) t = t.flatten() good += sum(t == g) bad += sum(t != g) return [good / (good + bad)] elif mode == 'f1': i2t = [t for t, i in sorted(self.t2i.items(), key=lambda x: x[1])] f1c = FScCounter(i2t, binary_input=False) gold = map(lambda x: x.argmax(axis=1), gold) tagged = map(lambda x: x.flatten(), tagged) return f1c.count_score(gold, tagged) def set_embedding_weights(self, embedding_init): # load embedding with gensim from gensim.models import Word2Vec try: m = Word2Vec.load_word2vec_format(embedding_init, binary=False) edim = m.layer1_size except UnicodeDecodeError: try: m = Word2Vec.load_word2vec_format(embedding_init, binary=True) edim = m.layer1_size except UnicodeDecodeError: # not in word2vec format m = Word2Vec.load(embedding_init) edim = m.layer1_size except ValueError: # glove model m = {} if embedding_init.endswith('gz'): fp = gzip.open(embedding_init) else: fp = open(embedding_init) for l in fp: le = l.split() m[le[0].decode('utf-8')] = numpy.array( [float(e) for e in le[1:]], dtype=theano.config.floatX) edim = len(le) - 1 if edim != self.edim: raise Exception("Embedding dim and edim doesn't match") m_lower = {} vocab = (m.vocab if hasattr(m, 'vocab') else m) for k in vocab: if k in ['UNKNOWN', 'PADDING']: continue if self.num: m_lower[replace_numerals(k.lower())] = m[k] else: m_lower[k.lower()] = m[k] # transform weight matrix with using self.w2i params = numpy.zeros( self.tagger.layers[0].layers[0].get_param_vector().shape, dtype=theano.config.floatX) e = self.edim for w in self.w2i: if w in m_lower: v = m_lower[w] i = self.w2i[w] params[i * e:(i + 1) * e] = v if 'UNKNOWN' in vocab: params[-1 * e:] = vocab['UNKNOWN'] if 'PADDING' in vocab: params[-2 * e:-1 * e] = vocab['PADDING'] self.tagger.layers[0].layers[0].set_param_vector(params)
def on_monitor(self, model, dataset, algorithm): if self.epoch > self.k: return MonitorBasedSaveBest.on_monitor(self, model, dataset, algorithm) self.epoch += 1
class SequenceTaggerNetwork(Model): def __init__(self, dataset, w2i, t2i, featurizer, edim=None, hdims=None, fedim=None, max_epochs=100, use_momentum=False, lr=.01, lr_lin_decay=None, lr_scale=False, lr_monitor_decay=False, valid_stop=False, reg_factors=None, dropout=False, dropout_params=None, embedding_init=None, embedded_model=None, monitor_train=True, plot_monitor=None, num=False): super(SequenceTaggerNetwork, self).__init__() self.vocab_size = dataset.vocab_size self.window_size = dataset.window_size self.total_feats = dataset.total_feats self.feat_num = dataset.feat_num self.n_classes = dataset.n_classes self.max_epochs = max_epochs if edim is None: edim = 50 if hdims is None: hdims = [100] if fedim is None: fedim = 5 self.edim = edim self.fedim = fedim self.hdims = hdims self.w2i = w2i self.t2i = t2i self.featurizer = featurizer self._create_tagger() A_value = numpy.random.uniform(low=-.1, high=.1, size=(self.n_classes + 2, self.n_classes)) self.A = sharedX(A_value, name='A') self.use_momentum = use_momentum self.lr = lr self.lr_lin_decay = lr_lin_decay self.lr_monitor_decay = lr_monitor_decay self.lr_scale = lr_scale self.valid_stop = valid_stop self.reg_factors = reg_factors self.close_cache = {} self.dropout_params = dropout_params self.dropout = dropout or self.dropout_params is not None self.hdims = hdims self.monitor_train = monitor_train self.num = num self.plot_monitor = plot_monitor if embedding_init is not None: self.set_embedding_weights(embedding_init) def _create_tagger(self): self.tagger = WordTaggerNetwork( self.vocab_size, self.window_size, self.total_feats, self.feat_num, self.hdims, self.edim, self.fedim, self.n_classes) def _create_data_specs(self, dataset): self.input_space = CompositeSpace([ dataset.data_specs[0].components[i] for i in xrange(len(dataset.data_specs[0].components) - 1)]) self.output_space = dataset.data_specs[0].components[-1] self.input_source = dataset.data_specs[1][:-1] self.target_source = dataset.data_specs[1][-1] def __getstate__(self): d = {} d['vocab_size'] = self.vocab_size d['window_size'] = self.window_size d['feat_num'] = self.feat_num d['total_feats'] = self.total_feats d['n_classes'] = self.n_classes d['input_space'] = self.input_space d['output_space'] = self.output_space d['input_source'] = self.input_source d['target_source'] = self.target_source d['A'] = self.A d['tagger'] = self.tagger d['w2i'] = self.w2i d['t2i'] = self.t2i d['featurizer'] = self.featurizer d['max_epochs'] = self.max_epochs d['use_momentum'] = self.use_momentum d['lr'] = self.lr d['lr_lin_decay'] = self.lr_lin_decay d['lr_monitor_decay'] = self.lr_monitor_decay d['lr_scale'] = self.lr_scale d['valid_stop'] = self.valid_stop d['reg_factors'] = self.reg_factors d['dropout'] = self.dropout d['dropout_params'] = self.dropout_params d['monitor_train'] = self.monitor_train d['num'] = self.num d['plot_monitor'] = self.plot_monitor return d def fprop(self, data): tagger_out = self.tagger.fprop(data) probs = T.concatenate([self.A, tagger_out]) return probs def dropout_fprop(self, data, default_input_include_prob=0.5, input_include_probs=None, default_input_scale=2.0, input_scales=None, per_example=True): if input_scales is None: input_scales = {'input': 1.0} if input_include_probs is None: input_include_probs = {'input': 1.0} if self.dropout_params is not None: if len(self.dropout_params) == len(self.tagger.layers) - 1: input_include_probs['tagger_out'] = self.dropout_params[-1] input_scales['tagger_out'] = 1.0/self.dropout_params[-1] for i, p in enumerate(self.dropout_params[:-1]): input_include_probs['h{0}'.format(i)] = p input_scales['h{0}'.format(i)] = 1.0/p tagger_out = self.tagger.dropout_fprop( data, default_input_include_prob, input_include_probs, default_input_scale, input_scales, per_example) probs = T.concatenate([self.A, tagger_out]) return probs @functools.wraps(Model.get_lr_scalers) def get_lr_scalers(self): if not self.lr_scale: return {} d = self.tagger.get_lr_scalers() d[self.A] = 1. / self.n_classes return d @functools.wraps(Model.get_params) def get_params(self): return self.tagger.get_params() + [self.A] def create_adjustors(self): initial_momentum = .5 final_momentum = .99 start = 1 saturate = self.max_epochs self.momentum_adjustor = learning_rule.MomentumAdjustor( final_momentum, start, saturate) self.momentum_rule = learning_rule.Momentum(initial_momentum, nesterov_momentum=True) if self.lr_monitor_decay: self.learning_rate_adjustor = MonitorBasedLRAdjuster( high_trigger=1., shrink_amt=0.9, low_trigger=.95, grow_amt=1.1, channel_name='train_objective') elif self.lr_lin_decay: self.learning_rate_adjustor = LinearDecayOverEpoch( start, saturate, self.lr_lin_decay) def compute_used_inputs(self): seen = {'words': set(), 'feats': set()} for sen_w in self.dataset['train'].X1: seen['words'] |= reduce( lambda x, y: set(x) | set(y), sen_w, set()) for sen_f in self.dataset['train'].X2: seen['feats'] |= reduce( lambda x, y: set(x) | set(y), sen_f, set()) words = set(xrange(len(self.w2i))) feats = set(xrange(self.total_feats)) self.notseen = { 'words': numpy.array(sorted(words - seen['words'])), 'feats': numpy.array(sorted(feats - seen['feats'])) } def set_dataset(self, data): self._create_data_specs(data['train']) self.dataset = data self.compute_used_inputs() self.tagger.notseen = self.notseen def create_algorithm(self, data, save_best_path=None): self.set_dataset(data) self.create_adjustors() term = EpochCounter(max_epochs=self.max_epochs) if self.valid_stop: cost_crit = MonitorBased(channel_name='valid_objective', prop_decrease=.0, N=3) term = And(criteria=[cost_crit, term]) #(layers, A_weight_decay) coeffs = None if self.reg_factors: rf = self.reg_factors lhdims = len(self.tagger.hdims) l_inputlayer = len(self.tagger.layers[0].layers) coeffs = ([[rf] * l_inputlayer] + ([rf] * lhdims) + [rf], rf) cost = SeqTaggerCost(coeffs, self.dropout) self.cost = cost self.mbsb = MonitorBasedSaveBest(channel_name='valid_objective', save_path=save_best_path) mon_dataset = dict(self.dataset) if not self.monitor_train: del mon_dataset['train'] _learning_rule = (self.momentum_rule if self.use_momentum else None) self.algorithm = SGD(batch_size=1, learning_rate=self.lr, termination_criterion=term, monitoring_dataset=mon_dataset, cost=cost, learning_rule=_learning_rule, ) self.algorithm.setup(self, self.dataset['train']) if self.plot_monitor: cn = ["valid_objective", "test_objective"] if self.monitor_train: cn.append("train_objective") plots = Plots(channel_names=cn, save_path=self.plot_monitor) self.pm = PlotManager([plots], freq=1) self.pm.setup(self, None, self.algorithm) def train(self): while True: if not self.algorithm.continue_learning(self): break self.algorithm.train(dataset=self.dataset['train']) self.monitor.report_epoch() self.monitor() self.mbsb.on_monitor(self, self.dataset['valid'], self.algorithm) if self.use_momentum: self.momentum_adjustor.on_monitor(self, self.dataset['valid'], self.algorithm) if hasattr(self, 'learning_rate_adjustor'): self.learning_rate_adjustor.on_monitor( self, self.dataset['valid'], self.algorithm) if hasattr(self, 'pm'): self.pm.on_monitor( self, self.dataset['valid'], self.algorithm) def prepare_tagging(self): X = self.get_input_space().make_theano_batch(batch_size=1) Y = self.fprop(X) self.f = theano.function([X[0], X[1]], Y) self.start = self.A.get_value()[0] self.end = self.A.get_value()[1] self.A_value = self.A.get_value()[2:] def process_input(self, words, feats): return self.f(words, feats) def tag_sen(self, words, feats, debug=False, return_probs=False): if not hasattr(self, 'f'): self.prepare_tagging() y = self.process_input(words, feats) tagger_out = y[2 + self.n_classes:] res = viterbi(self.start, self.A_value, self.end, tagger_out, self.n_classes, return_probs) if return_probs: return res / res.sum(axis=1)[:,numpy.newaxis] #return res.reshape((1, len(res))) if debug: return numpy.array([[e] for e in res[1]]), tagger_out return numpy.array([[e] for e in res[1]]) def get_score(self, dataset, mode='pwp'): self.prepare_tagging() tagged = (self.tag_sen(w, f) for w, f in izip(dataset.X1, dataset.X2)) gold = dataset.y good, bad = 0., 0. if mode == 'pwp': for t, g in izip(tagged, gold): g = g.argmax(axis=1) t = t.flatten() good += sum(t == g) bad += sum(t != g) return [good / (good + bad)] elif mode == 'f1': i2t = [t for t, i in sorted(self.t2i.items(), key=lambda x: x[1])] f1c = FScCounter(i2t, binary_input=False) gold = map(lambda x:x.argmax(axis=1), gold) tagged = map(lambda x:x.flatten(), tagged) return f1c.count_score(gold, tagged) def set_embedding_weights(self, embedding_init): # load embedding with gensim from gensim.models import Word2Vec try: m = Word2Vec.load_word2vec_format(embedding_init, binary=False) edim = m.layer1_size except UnicodeDecodeError: try: m = Word2Vec.load_word2vec_format(embedding_init, binary=True) edim = m.layer1_size except UnicodeDecodeError: # not in word2vec format m = Word2Vec.load(embedding_init) edim = m.layer1_size except ValueError: # glove model m = {} if embedding_init.endswith('gz'): fp = gzip.open(embedding_init) else: fp = open(embedding_init) for l in fp: le = l.split() m[le[0].decode('utf-8')] = numpy.array( [float(e) for e in le[1:]], dtype=theano.config.floatX) edim = len(le) - 1 if edim != self.edim: raise Exception("Embedding dim and edim doesn't match") m_lower = {} vocab = (m.vocab if hasattr(m, 'vocab') else m) for k in vocab: if k in ['UNKNOWN', 'PADDING']: continue if self.num: m_lower[replace_numerals(k.lower())] = m[k] else: m_lower[k.lower()] = m[k] # transform weight matrix with using self.w2i params = numpy.zeros( self.tagger.layers[0].layers[0].get_param_vector().shape, dtype=theano.config.floatX) e = self.edim for w in self.w2i: if w in m_lower: v = m_lower[w] i = self.w2i[w] params[i*e:(i+1)*e] = v if 'UNKNOWN' in vocab: params[-1*e:] = vocab['UNKNOWN'] if 'PADDING' in vocab: params[-2*e:-1*e] = vocab['PADDING'] self.tagger.layers[0].layers[0].set_param_vector(params)