From bb6543985489c42e1c8b778ada9a72e306b32152 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 7 Jun 2017 16:20:27 +0530 Subject: [PATCH 01/31] save log params in a dict --- gensim/models/ldamodel.py | 65 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 63 insertions(+), 2 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index 9f41334d47..bdd6f0b8c7 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -35,6 +35,8 @@ import numbers from random import sample import os +import gensim +import copy from gensim import interfaces, utils, matutils from gensim.matutils import dirichlet_expectation @@ -195,7 +197,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, - minimum_phi_value=0.01, per_word_topics=False): + minimum_phi_value=0.01, per_word_topics=False, log_tensorboard=False): """ If given, start training from the iterable `corpus` straight away. If not given, the model is left untrained (presumably because you want to call `update()` manually). @@ -280,6 +282,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.eval_every = eval_every self.minimum_phi_value = minimum_phi_value self.per_word_topics = per_word_topics + self.log_tensorboard = log_tensorboard self.alpha, self.optimize_alpha = self.init_dir_prior(alpha, 'alpha') @@ -531,7 +534,7 @@ def log_perplexity(self, chunk, total_docs=None): def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, eval_every=None, iterations=None, - gamma_threshold=None, chunks_as_numpy=False): + gamma_threshold=None, chunks_as_numpy=False, log_tensorboard=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -578,6 +581,11 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, iterations = self.iterations if gamma_threshold is None: gamma_threshold = self.gamma_threshold + if log_tensorboard is None: + log_tensorboard = self.log_tensorboard + + if log_tensorboard is True: + models = [] try: lencorpus = len(corpus) @@ -686,8 +694,58 @@ def rho(): self.do_mstep(rho(), other, pass_ > 0) del other dirty = False + + # save the current epoch model for tensorboard logging + if log_tensorboard is True: + current_model = copy.deepcopy(self) + models.append(current_model) + + # update finished, pass the models list for tensorboard logging + if log_tensorboard is True: + self.save_tensorboard_dir(models, corpus) + # endfor entire corpus update + def save_tensorboard_dir(self, models, corpus): + """ + Save log parameters from each epoch in a dict. + + """ + # parameters to log for tensorboard visualization + coherence = {} + perplexity = {} + alpha = {} + diff = {} + doc_topic_dist = {} + + for epoch, model in enumerate(models): + # save coherence from each epoch + cm = gensim.models.CoherenceModel(model=model, corpus=corpus, coherence='u_mass') + coherence[epoch] = cm.get_coherence() + # save perplexity from each epoch + perplexity[epoch] = np.exp2(-(model.log_perplexity(corpus))) + # save alpha from each epoch + alpha[epoch] = model.alpha + # save diff from each epoch + if epoch != 0: + diff[epoch] = model.diff(model=models[epoch-1]) + # save document-topic distribution from each epoch + all_topics = model.get_document_topics(corpus, minimum_probability=0) + tensors = [] + for doc_topics in all_topics: + doc_tensor = [] + for topics in doc_topics: + doc_tensor.append(topics[1]) + tensors.append(doc_tensor) + doc_topic_dist[epoch] = tensors + + # viz = TensorboardViz(log_dir = "ldalogs") + # viz.scalar(coherence) + # viz.scalar(perplexity) + # viz.scalar(alpha) + # viz.histogram(diff) + # viz.embedding(doc_topic_dist) + def do_mstep(self, rho, other, extra_pass=False): """ M step: use linear interpolation between the existing topics and @@ -1003,6 +1061,9 @@ def diff(self, other, distance="kulback_leibler", num_words=100, n_ann_terms=10, valid_keys = ", ".join("`{}`".format(x) for x in distances.keys()) raise ValueError("Incorrect distance, valid only {}".format(valid_keys)) + # if other is None: + # d1, + if not isinstance(other, self.__class__): raise ValueError("The parameter `other` must be of type `{}`".format(self.__name__)) From 9d2e78d67e11e9173abced0fdca7a3f06c7af4a5 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 7 Jun 2017 16:23:45 +0530 Subject: [PATCH 02/31] remove redundant line --- gensim/models/ldamodel.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index bdd6f0b8c7..93137fd017 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -1061,9 +1061,6 @@ def diff(self, other, distance="kulback_leibler", num_words=100, n_ann_terms=10, valid_keys = ", ".join("`{}`".format(x) for x in distances.keys()) raise ValueError("Incorrect distance, valid only {}".format(valid_keys)) - # if other is None: - # d1, - if not isinstance(other, self.__class__): raise ValueError("The parameter `other` must be of type `{}`".format(self.__name__)) From 33818ecb87457a094f2852a0e6f5ddfb2227cb7f Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 7 Jun 2017 18:49:17 +0530 Subject: [PATCH 03/31] add diff log --- gensim/models/ldamodel.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index 93137fd017..f8606f97e2 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -532,9 +532,12 @@ def log_perplexity(self, chunk, total_docs=None): (perwordbound, np.exp2(-perwordbound), len(chunk), corpus_words)) return perwordbound + def log_epoch_diff(self, fst_model, scnd_model): + mdiff, annotation = fst_model.diff(scnd_model) + def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, eval_every=None, iterations=None, - gamma_threshold=None, chunks_as_numpy=False, log_tensorboard=None): + gamma_threshold=None, chunks_as_numpy=False, log_diff=None, log_tensorboard=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -583,7 +586,8 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, gamma_threshold = self.gamma_threshold if log_tensorboard is None: log_tensorboard = self.log_tensorboard - + if log_diff is None: + log_diff = self.log_diff if log_tensorboard is True: models = [] @@ -695,6 +699,11 @@ def rho(): del other dirty = False + if log_diff is True: + if pass_ > 0: + self.log_epoch_diff(self, previous) + previous = copy.deepcopy(self) + # save the current epoch model for tensorboard logging if log_tensorboard is True: current_model = copy.deepcopy(self) From 281222cb585422a55b8fa4ca6e2baa05e72493b4 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Thu, 8 Jun 2017 12:58:46 +0530 Subject: [PATCH 04/31] remove diff log --- gensim/models/ldamodel.py | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index f8606f97e2..8a67331a01 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -36,7 +36,6 @@ from random import sample import os import gensim -import copy from gensim import interfaces, utils, matutils from gensim.matutils import dirichlet_expectation @@ -532,12 +531,9 @@ def log_perplexity(self, chunk, total_docs=None): (perwordbound, np.exp2(-perwordbound), len(chunk), corpus_words)) return perwordbound - def log_epoch_diff(self, fst_model, scnd_model): - mdiff, annotation = fst_model.diff(scnd_model) - def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, eval_every=None, iterations=None, - gamma_threshold=None, chunks_as_numpy=False, log_diff=None, log_tensorboard=None): + gamma_threshold=None, chunks_as_numpy=False, log_tensorboard=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -586,8 +582,6 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, gamma_threshold = self.gamma_threshold if log_tensorboard is None: log_tensorboard = self.log_tensorboard - if log_diff is None: - log_diff = self.log_diff if log_tensorboard is True: models = [] @@ -699,11 +693,6 @@ def rho(): del other dirty = False - if log_diff is True: - if pass_ > 0: - self.log_epoch_diff(self, previous) - previous = copy.deepcopy(self) - # save the current epoch model for tensorboard logging if log_tensorboard is True: current_model = copy.deepcopy(self) From c507bbbb5695db24e90e27209057d9de471be733 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Thu, 8 Jun 2017 17:06:48 +0530 Subject: [PATCH 05/31] write params to log directory --- gensim/models/ldamodel.py | 97 +++++++++++++++++++-------------------- 1 file changed, 48 insertions(+), 49 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index 8a67331a01..adeb0b00b4 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -36,6 +36,7 @@ from random import sample import os import gensim +import copy from gensim import interfaces, utils, matutils from gensim.matutils import dirichlet_expectation @@ -196,7 +197,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, - minimum_phi_value=0.01, per_word_topics=False, log_tensorboard=False): + minimum_phi_value=0.01, per_word_topics=False, log_diff=False, log_tensorboard=False): """ If given, start training from the iterable `corpus` straight away. If not given, the model is left untrained (presumably because you want to call `update()` manually). @@ -281,6 +282,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.eval_every = eval_every self.minimum_phi_value = minimum_phi_value self.per_word_topics = per_word_topics + self.log_diff = log_diff self.log_tensorboard = log_tensorboard self.alpha, self.optimize_alpha = self.init_dir_prior(alpha, 'alpha') @@ -531,9 +533,15 @@ def log_perplexity(self, chunk, total_docs=None): (perwordbound, np.exp2(-perwordbound), len(chunk), corpus_words)) return perwordbound + def log_epoch_diff(self, epoch, other_model): + diff_matrix, annotation = self.diff(other_model) + diff_diagonal = diff_matrix.diagonal() + logger.info("Topic difference between %i and %i epoch %s", epoch-1, epoch, diff_diagonal) + return diff_diagonal + def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, eval_every=None, iterations=None, - gamma_threshold=None, chunks_as_numpy=False, log_tensorboard=None): + gamma_threshold=None, chunks_as_numpy=False, log_diff=None, log_tensorboard=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -580,10 +588,10 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, iterations = self.iterations if gamma_threshold is None: gamma_threshold = self.gamma_threshold + if log_diff is None: + log_diff = self.log_diff if log_tensorboard is None: log_tensorboard = self.log_tensorboard - if log_tensorboard is True: - models = [] try: lencorpus = len(corpus) @@ -632,6 +640,15 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, def rho(): return pow(offset + pass_ + (self.num_updates / chunksize), -decay) + if log_tensorboard is True: + + from tensorboard.summary import scalar + from tensorboard import FileWriter + from tensorboard import summary + + logdir = 'LdaLogs' + writer = FileWriter(logdir) + for pass_ in xrange(passes): if self.dispatcher: logger.info('initializing %s workers' % self.numworkers) @@ -693,56 +710,38 @@ def rho(): del other dirty = False - # save the current epoch model for tensorboard logging + # log diff between consecutive epochs + if log_diff is True: + if pass_ > 0: + self.log_epoch_diff(pass_, self, previous) + previous = copy.deepcopy(self) + + # write current epoch parameters to tensorboard log directory if log_tensorboard is True: - current_model = copy.deepcopy(self) - models.append(current_model) + # write coherence log + cm = gensim.models.CoherenceModel(model=self, corpus=corpus, coherence='u_mass') + coherence = scalar('Coherence', cm.get_coherence()) + writer.add_summary(coherence, pass_+1) - # update finished, pass the models list for tensorboard logging - if log_tensorboard is True: - self.save_tensorboard_dir(models, corpus) + # write perplexity log + perplexity = scalar('Perplexity', np.exp2(-(self.log_perplexity(corpus)))) + writer.add_summary(perplexity, pass_+1) - # endfor entire corpus update + # write alpha log + # alpha = scalar('Alpha', self.alpha) + # writer.add_summary(alpha, pass_+1) - def save_tensorboard_dir(self, models, corpus): - """ - Save log parameters from each epoch in a dict. + # write diff log + if pass_ > 0: + diff = self.log_epoch_diff(pass_, previous) + hist = summary.histogram('Diff', diff) + writer.add_summary(hist, pass_+1) + previous = copy.deepcopy(self) - """ - # parameters to log for tensorboard visualization - coherence = {} - perplexity = {} - alpha = {} - diff = {} - doc_topic_dist = {} - - for epoch, model in enumerate(models): - # save coherence from each epoch - cm = gensim.models.CoherenceModel(model=model, corpus=corpus, coherence='u_mass') - coherence[epoch] = cm.get_coherence() - # save perplexity from each epoch - perplexity[epoch] = np.exp2(-(model.log_perplexity(corpus))) - # save alpha from each epoch - alpha[epoch] = model.alpha - # save diff from each epoch - if epoch != 0: - diff[epoch] = model.diff(model=models[epoch-1]) - # save document-topic distribution from each epoch - all_topics = model.get_document_topics(corpus, minimum_probability=0) - tensors = [] - for doc_topics in all_topics: - doc_tensor = [] - for topics in doc_topics: - doc_tensor.append(topics[1]) - tensors.append(doc_tensor) - doc_topic_dist[epoch] = tensors - - # viz = TensorboardViz(log_dir = "ldalogs") - # viz.scalar(coherence) - # viz.scalar(perplexity) - # viz.scalar(alpha) - # viz.histogram(diff) - # viz.embedding(doc_topic_dist) + writer.flush() + writer.close() + + # endfor entire corpus update def do_mstep(self, rho, other, extra_pass=False): """ From 6f75ccc2d9dcefb3dc758c422bd8a56d6061b904 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Fri, 9 Jun 2017 11:49:26 +0530 Subject: [PATCH 06/31] add convergence, remove alpha --- gensim/models/ldamodel.py | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index adeb0b00b4..149a3e42ae 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -535,7 +535,7 @@ def log_perplexity(self, chunk, total_docs=None): def log_epoch_diff(self, epoch, other_model): diff_matrix, annotation = self.diff(other_model) - diff_diagonal = diff_matrix.diagonal() + diff_diagonal = np.flipud(np.diagonal(np.fliplr(diff_matrix))) logger.info("Topic difference between %i and %i epoch %s", epoch-1, epoch, diff_diagonal) return diff_diagonal @@ -641,7 +641,7 @@ def rho(): return pow(offset + pass_ + (self.num_updates / chunksize), -decay) if log_tensorboard is True: - + from tensorboard.summary import scalar from tensorboard import FileWriter from tensorboard import summary @@ -649,6 +649,8 @@ def rho(): logdir = 'LdaLogs' writer = FileWriter(logdir) + previous = copy.deepcopy(self) + for pass_ in xrange(passes): if self.dispatcher: logger.info('initializing %s workers' % self.numworkers) @@ -727,16 +729,17 @@ def rho(): perplexity = scalar('Perplexity', np.exp2(-(self.log_perplexity(corpus)))) writer.add_summary(perplexity, pass_+1) - # write alpha log - # alpha = scalar('Alpha', self.alpha) - # writer.add_summary(alpha, pass_+1) + diff_matrix = self.diff(previous)[0] + diff_diagonal = np.flipud(np.diagonal(np.fliplr(diff_matrix))) + previous = copy.deepcopy(self) + + # write topic convergence log + convergence = scalar('Convergence', np.sum(diff_diagonal)) + writer.add_summary(convergence, pass_+1) # write diff log - if pass_ > 0: - diff = self.log_epoch_diff(pass_, previous) - hist = summary.histogram('Diff', diff) - writer.add_summary(hist, pass_+1) - previous = copy.deepcopy(self) + hist = summary.histogram('Diff', diff_diagonal) + writer.add_summary(hist, pass_+1) writer.flush() writer.close() From d9db4e2efcf68bc8c36bd6db626a8e4810bcbe14 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Fri, 9 Jun 2017 17:04:14 +0530 Subject: [PATCH 07/31] calculate perplexity/diff instead of using log function --- gensim/models/ldamodel.py | 48 +++++++++++++++++++++++---------------- 1 file changed, 28 insertions(+), 20 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index 149a3e42ae..d54bf83caf 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -194,10 +194,10 @@ class LdaModel(interfaces.TransformationABC, basemodel.BaseTopicModel): """ def __init__(self, corpus=None, num_topics=100, id2word=None, distributed=False, chunksize=2000, passes=1, update_every=1, - alpha='symmetric', eta=None, decay=0.5, offset=1.0, - eval_every=10, iterations=50, gamma_threshold=0.001, - minimum_probability=0.01, random_state=None, ns_conf={}, - minimum_phi_value=0.01, per_word_topics=False, log_diff=False, log_tensorboard=False): + alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, + iterations=50, gamma_threshold=0.001, minimum_probability=0.01, + random_state=None, ns_conf={}, minimum_phi_value=0.01, + per_word_topics=False, log_diff=False, log_tensorboard=False, log_dir=None): """ If given, start training from the iterable `corpus` straight away. If not given, the model is left untrained (presumably because you want to call `update()` manually). @@ -284,6 +284,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.per_word_topics = per_word_topics self.log_diff = log_diff self.log_tensorboard = log_tensorboard + self.log_dir = log_dir self.alpha, self.optimize_alpha = self.init_dir_prior(alpha, 'alpha') @@ -539,9 +540,9 @@ def log_epoch_diff(self, epoch, other_model): logger.info("Topic difference between %i and %i epoch %s", epoch-1, epoch, diff_diagonal) return diff_diagonal - def update(self, corpus, chunksize=None, decay=None, offset=None, - passes=None, update_every=None, eval_every=None, iterations=None, - gamma_threshold=None, chunks_as_numpy=False, log_diff=None, log_tensorboard=None): + def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, + update_every=None, eval_every=None, iterations=None, gamma_threshold=None, + chunks_as_numpy=False, log_diff=None, log_tensorboard=None, log_dir=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -592,6 +593,8 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, log_diff = self.log_diff if log_tensorboard is None: log_tensorboard = self.log_tensorboard + if log_dir is None: + log_dir = self.log_dir try: lencorpus = len(corpus) @@ -641,14 +644,12 @@ def rho(): return pow(offset + pass_ + (self.num_updates / chunksize), -decay) if log_tensorboard is True: - from tensorboard.summary import scalar from tensorboard import FileWriter from tensorboard import summary - logdir = 'LdaLogs' - writer = FileWriter(logdir) - + writer = FileWriter(log_dir) + # save first randomly initialized model for diff log previous = copy.deepcopy(self) for pass_ in xrange(passes): @@ -714,8 +715,9 @@ def rho(): # log diff between consecutive epochs if log_diff is True: - if pass_ > 0: - self.log_epoch_diff(pass_, self, previous) + if pass_ == 0: + previous = copy.deepcopy(self) + self.log_epoch_diff(pass_, previous) previous = copy.deepcopy(self) # write current epoch parameters to tensorboard log directory @@ -725,24 +727,30 @@ def rho(): coherence = scalar('Coherence', cm.get_coherence()) writer.add_summary(coherence, pass_+1) + # calculate perplexity + corpus_words = sum(cnt for document in corpus for _, cnt in document) + perwordbound = self.bound(corpus) / corpus_words + # write perplexity log - perplexity = scalar('Perplexity', np.exp2(-(self.log_perplexity(corpus)))) + perplexity = scalar('Perplexity', np.exp2(-perwordbound)) writer.add_summary(perplexity, pass_+1) + # calculate diff diff_matrix = self.diff(previous)[0] diff_diagonal = np.flipud(np.diagonal(np.fliplr(diff_matrix))) previous = copy.deepcopy(self) - # write topic convergence log - convergence = scalar('Convergence', np.sum(diff_diagonal)) - writer.add_summary(convergence, pass_+1) - # write diff log hist = summary.histogram('Diff', diff_diagonal) writer.add_summary(hist, pass_+1) - writer.flush() - writer.close() + # write topic convergence log + convergence = scalar('Convergence', np.sum(diff_diagonal)) + writer.add_summary(convergence, pass_+1) + + if log_tensorboard is True: + writer.flush() + writer.close() # endfor entire corpus update From cd5f8227c2addb82b0def63fb87711ec3ba50fe1 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Fri, 9 Jun 2017 23:14:08 +0530 Subject: [PATCH 08/31] add docstrings and comments --- gensim/models/ldamodel.py | 34 +++++++++++++++++++++++----------- 1 file changed, 23 insertions(+), 11 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index d54bf83caf..acb093d9a9 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -195,8 +195,8 @@ class LdaModel(interfaces.TransformationABC, basemodel.BaseTopicModel): def __init__(self, corpus=None, num_topics=100, id2word=None, distributed=False, chunksize=2000, passes=1, update_every=1, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, - iterations=50, gamma_threshold=0.001, minimum_probability=0.01, - random_state=None, ns_conf={}, minimum_phi_value=0.01, + iterations=50, gamma_threshold=0.001, minimum_probability=0.01, + random_state=None, ns_conf={}, minimum_phi_value=0.01, per_word_topics=False, log_diff=False, log_tensorboard=False, log_dir=None): """ If given, start training from the iterable `corpus` straight away. If not given, @@ -241,6 +241,12 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, `random_state` can be a np.random.RandomState object or the seed for one + `log_diff` set to True to log topic diff between consecutive epochs + + `log_tensorboard` set to True to log training stats for tensorboard visualization + + `log_dir` is the directory name to which Tensorboard log event files should be saved + Example: >>> lda = LdaModel(corpus, num_topics=100) # train model @@ -535,13 +541,16 @@ def log_perplexity(self, chunk, total_docs=None): return perwordbound def log_epoch_diff(self, epoch, other_model): + """ + Log topic diff between consecutive epochs + """ diff_matrix, annotation = self.diff(other_model) - diff_diagonal = np.flipud(np.diagonal(np.fliplr(diff_matrix))) - logger.info("Topic difference between %i and %i epoch %s", epoch-1, epoch, diff_diagonal) + diff_diagonal = np.diagonal(diff_matrix) + logger.info("Topic difference between %i and %i epoch %s", epoch - 1, epoch, diff_diagonal) return diff_diagonal def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, - update_every=None, eval_every=None, iterations=None, gamma_threshold=None, + update_every=None, eval_every=None, iterations=None, gamma_threshold=None, chunks_as_numpy=False, log_diff=None, log_tensorboard=None, log_dir=None): """ Train the model with new documents, by EM-iterating over `corpus` until @@ -644,12 +653,14 @@ def rho(): return pow(offset + pass_ + (self.num_updates / chunksize), -decay) if log_tensorboard is True: + # Install with `pip install tensorboard` from tensorboard.summary import scalar from tensorboard import FileWriter from tensorboard import summary + # run tensorboard --logdir=path/to/log_dir to visualize logs writer = FileWriter(log_dir) - # save first randomly initialized model for diff log + # save randomly initialized model for diff with first pass previous = copy.deepcopy(self) for pass_ in xrange(passes): @@ -716,6 +727,7 @@ def rho(): # log diff between consecutive epochs if log_diff is True: if pass_ == 0: + # save randomly initialized model for diff with first pass previous = copy.deepcopy(self) self.log_epoch_diff(pass_, previous) previous = copy.deepcopy(self) @@ -725,7 +737,7 @@ def rho(): # write coherence log cm = gensim.models.CoherenceModel(model=self, corpus=corpus, coherence='u_mass') coherence = scalar('Coherence', cm.get_coherence()) - writer.add_summary(coherence, pass_+1) + writer.add_summary(coherence, pass_ + 1) # calculate perplexity corpus_words = sum(cnt for document in corpus for _, cnt in document) @@ -733,20 +745,20 @@ def rho(): # write perplexity log perplexity = scalar('Perplexity', np.exp2(-perwordbound)) - writer.add_summary(perplexity, pass_+1) + writer.add_summary(perplexity, pass_ + 1) # calculate diff diff_matrix = self.diff(previous)[0] - diff_diagonal = np.flipud(np.diagonal(np.fliplr(diff_matrix))) + diff_diagonal = np.diagonal(diff_matrix) previous = copy.deepcopy(self) # write diff log hist = summary.histogram('Diff', diff_diagonal) - writer.add_summary(hist, pass_+1) + writer.add_summary(hist, pass_ + 1) # write topic convergence log convergence = scalar('Convergence', np.sum(diff_diagonal)) - writer.add_summary(convergence, pass_+1) + writer.add_summary(convergence, pass_ + 1) if log_tensorboard is True: writer.flush() From f4728e0036fb2939925b01418c56f4ee739adf7b Mon Sep 17 00:00:00 2001 From: parulsethi Date: Tue, 13 Jun 2017 02:21:09 +0530 Subject: [PATCH 09/31] add coherence/diff labels in graphs --- gensim/models/ldamodel.py | 25 ++++++++++++++++++------- 1 file changed, 18 insertions(+), 7 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index acb093d9a9..d6057145ea 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -197,7 +197,8 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, minimum_phi_value=0.01, - per_word_topics=False, log_diff=False, log_tensorboard=False, log_dir=None): + per_word_topics=False, log_diff=False, log_tensorboard=False, + coherence='u_mass', distance="kulback_leibler", log_dir=None): """ If given, start training from the iterable `corpus` straight away. If not given, the model is left untrained (presumably because you want to call `update()` manually). @@ -245,6 +246,10 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, `log_tensorboard` set to True to log training stats for tensorboard visualization + `coherence` is the coherence measure for model coherence to use in tensorboard visualization + + `distance` is the distance measure for `diff` to use in tensorboard visualization + `log_dir` is the directory name to which Tensorboard log event files should be saved Example: @@ -290,6 +295,8 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.per_word_topics = per_word_topics self.log_diff = log_diff self.log_tensorboard = log_tensorboard + self.coherence = coherence + self.distance = distance self.log_dir = log_dir self.alpha, self.optimize_alpha = self.init_dir_prior(alpha, 'alpha') @@ -551,7 +558,7 @@ def log_epoch_diff(self, epoch, other_model): def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, eval_every=None, iterations=None, gamma_threshold=None, - chunks_as_numpy=False, log_diff=None, log_tensorboard=None, log_dir=None): + chunks_as_numpy=False, log_diff=None, log_tensorboard=None, coherence=None, distance=None, log_dir=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -602,6 +609,10 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, log_diff = self.log_diff if log_tensorboard is None: log_tensorboard = self.log_tensorboard + if coherence is None: + coherence = self.coherence + if distance is None: + distance = self.distance if log_dir is None: log_dir = self.log_dir @@ -735,9 +746,9 @@ def rho(): # write current epoch parameters to tensorboard log directory if log_tensorboard is True: # write coherence log - cm = gensim.models.CoherenceModel(model=self, corpus=corpus, coherence='u_mass') - coherence = scalar('Coherence', cm.get_coherence()) - writer.add_summary(coherence, pass_ + 1) + cm = gensim.models.CoherenceModel(model=self, corpus=corpus, coherence=coherence) + coherence_summ = scalar('Coherence ' + coherence, cm.get_coherence()) + writer.add_summary(coherence_summ, pass_ + 1) # calculate perplexity corpus_words = sum(cnt for document in corpus for _, cnt in document) @@ -753,11 +764,11 @@ def rho(): previous = copy.deepcopy(self) # write diff log - hist = summary.histogram('Diff', diff_diagonal) + hist = summary.histogram('Diff ' + distance, diff_diagonal) writer.add_summary(hist, pass_ + 1) # write topic convergence log - convergence = scalar('Convergence', np.sum(diff_diagonal)) + convergence = scalar('Convergence ' + distance, np.sum(diff_diagonal)) writer.add_summary(convergence, pass_ + 1) if log_tensorboard is True: From d4f69f56329b1a7dee22f18e2adde239c3479083 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sat, 17 Jun 2017 00:58:56 +0530 Subject: [PATCH 10/31] optional measures for viz --- gensim/models/ldamodel.py | 143 +++++++++++++++++--------------------- 1 file changed, 63 insertions(+), 80 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index d6057145ea..e39f3857c5 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -42,6 +42,7 @@ from gensim.matutils import dirichlet_expectation from gensim.models import basemodel from gensim.matutils import kullback_leibler, hellinger, jaccard_distance +from visdom import Visdom from itertools import chain from scipy.special import gammaln, psi # gamma function utils @@ -197,8 +198,8 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, minimum_phi_value=0.01, - per_word_topics=False, log_diff=False, log_tensorboard=False, - coherence='u_mass', distance="kulback_leibler", log_dir=None): + per_word_topics=False, viz=False, coherence="u_mass", + distance="kulback_leibler", texts=None): """ If given, start training from the iterable `corpus` straight away. If not given, the model is left untrained (presumably because you want to call `update()` manually). @@ -242,15 +243,19 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, `random_state` can be a np.random.RandomState object or the seed for one - `log_diff` set to True to log topic diff between consecutive epochs + `viz` set True for visualizing LDA training stats in Visdom - `log_tensorboard` set to True to log training stats for tensorboard visualization + `coherence` measure to be used for Coherence plot visualization - `coherence` is the coherence measure for model coherence to use in tensorboard visualization + `distance` measure to be used for Diff plot visualization - `distance` is the distance measure for `diff` to use in tensorboard visualization - - `log_dir` is the directory name to which Tensorboard log event files should be saved + `texts` : Tokenized texts. Needed if sliding_window_based coherence measures (c_v, c_uci, c_npmi) are chosen for visualization. eg:: + texts = [['system', 'human', 'system', 'eps'], + ['user', 'response', 'time'], + ['trees'], + ['graph', 'trees'], + ['graph', 'minors', 'trees'], + ['graph', 'minors', 'survey']] Example: @@ -293,11 +298,11 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.eval_every = eval_every self.minimum_phi_value = minimum_phi_value self.per_word_topics = per_word_topics - self.log_diff = log_diff - self.log_tensorboard = log_tensorboard - self.coherence = coherence - self.distance = distance - self.log_dir = log_dir + self.viz = viz + if self.viz: + self.coherence = coherence + self.distance = distance + self.texts = texts self.alpha, self.optimize_alpha = self.init_dir_prior(alpha, 'alpha') @@ -547,18 +552,9 @@ def log_perplexity(self, chunk, total_docs=None): (perwordbound, np.exp2(-perwordbound), len(chunk), corpus_words)) return perwordbound - def log_epoch_diff(self, epoch, other_model): - """ - Log topic diff between consecutive epochs - """ - diff_matrix, annotation = self.diff(other_model) - diff_diagonal = np.diagonal(diff_matrix) - logger.info("Topic difference between %i and %i epoch %s", epoch - 1, epoch, diff_diagonal) - return diff_diagonal - def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, eval_every=None, iterations=None, gamma_threshold=None, - chunks_as_numpy=False, log_diff=None, log_tensorboard=None, coherence=None, distance=None, log_dir=None): + chunks_as_numpy=False, viz=None, coherence=None, distance=None, texts=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -605,16 +601,6 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, iterations = self.iterations if gamma_threshold is None: gamma_threshold = self.gamma_threshold - if log_diff is None: - log_diff = self.log_diff - if log_tensorboard is None: - log_tensorboard = self.log_tensorboard - if coherence is None: - coherence = self.coherence - if distance is None: - distance = self.distance - if log_dir is None: - log_dir = self.log_dir try: lencorpus = len(corpus) @@ -630,6 +616,16 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, self.state.numdocs += lencorpus + if viz is None: + viz = self.viz + if viz: + if coherence is None: + coherence = self.coherence + if texts is None: + texts = self.texts + if distance is None: + distance = self.distance + if update_every: updatetype = "online" if passes == 1: @@ -663,15 +659,9 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, def rho(): return pow(offset + pass_ + (self.num_updates / chunksize), -decay) - if log_tensorboard is True: - # Install with `pip install tensorboard` - from tensorboard.summary import scalar - from tensorboard import FileWriter - from tensorboard import summary - - # run tensorboard --logdir=path/to/log_dir to visualize logs - writer = FileWriter(log_dir) - # save randomly initialized model for diff with first pass + if self.viz: + viz_window = Visdom() + # save initial random state of model for Diff calculation with first epoch previous = copy.deepcopy(self) for pass_ in xrange(passes): @@ -722,58 +712,51 @@ def rho(): other = LdaState(self.eta, self.state.sstats.shape) dirty = False # endfor single corpus iteration + if reallen != lencorpus: raise RuntimeError("input corpus size changed during training (don't use generators as input)") - if dirty: - # finish any remaining updates - if self.dispatcher: - # distributed mode: wait for all workers to finish - logger.info("reached the end of input; now waiting for all remaining jobs to finish") - other = self.dispatcher.getstate() - self.do_mstep(rho(), other, pass_ > 0) - del other - dirty = False - - # log diff between consecutive epochs - if log_diff is True: - if pass_ == 0: - # save randomly initialized model for diff with first pass - previous = copy.deepcopy(self) - self.log_epoch_diff(pass_, previous) - previous = copy.deepcopy(self) - - # write current epoch parameters to tensorboard log directory - if log_tensorboard is True: - # write coherence log - cm = gensim.models.CoherenceModel(model=self, corpus=corpus, coherence=coherence) - coherence_summ = scalar('Coherence ' + coherence, cm.get_coherence()) - writer.add_summary(coherence_summ, pass_ + 1) + if self.viz: + # calculate coherence + cm = gensim.models.CoherenceModel(model=self, corpus=corpus, texts=texts, coherence=coherence) + Coherence = np.array([cm.get_coherence()]) # calculate perplexity corpus_words = sum(cnt for document in corpus for _, cnt in document) perwordbound = self.bound(corpus) / corpus_words - - # write perplexity log - perplexity = scalar('Perplexity', np.exp2(-perwordbound)) - writer.add_summary(perplexity, pass_ + 1) + Perplexity = np.array([np.exp2(-perwordbound)]) # calculate diff - diff_matrix = self.diff(previous)[0] + diff_matrix = self.diff(previous, distance=distance)[0] diff_diagonal = np.diagonal(diff_matrix) previous = copy.deepcopy(self) + Convergence = np.array([np.sum(diff_diagonal)]) - # write diff log - hist = summary.histogram('Diff ' + distance, diff_diagonal) - writer.add_summary(hist, pass_ + 1) + if pass_ == 0: + # initial plot windows + Diff_mat = np.array([diff_diagonal]) + viz_coherence = viz_window.line(Y=Coherence, X=np.array([pass_]), opts=dict(xlabel='Epochs', ylabel='Coherence', title='Coherence (%s)' % coherence)) + viz_perplexity = viz_window.line(Y=Perplexity, X=np.array([pass_]), opts=dict(xlabel='Epochs', ylabel='Perplexity', title='Perplexity')) + viz_convergence = viz_window.line(Y=Convergence, X=np.array([pass_]), opts=dict(xlabel='Epochs', ylabel='Convergence', title='Convergence (%s)' % distance)) + viz_diff = viz_window.heatmap(X=np.array(Diff_mat).T, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % distance)) - # write topic convergence log - convergence = scalar('Convergence ' + distance, np.sum(diff_diagonal)) - writer.add_summary(convergence, pass_ + 1) + else: + # update the plot with each epoch + Diff_mat = np.concatenate((Diff_mat, np.array([diff_diagonal]))) + viz_window.updateTrace(Y=Coherence, X=np.array([pass_]), win=viz_coherence) + viz_window.updateTrace(Y=Perplexity, X=np.array([pass_]), win=viz_perplexity) + viz_window.updateTrace(Y=Convergence, X=np.array([pass_]), win=viz_convergence) + viz_window.heatmap(X=np.array(Diff_mat).T, win=viz_diff, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % distance)) - if log_tensorboard is True: - writer.flush() - writer.close() + if dirty: + # finish any remaining updates + if self.dispatcher: + # distributed mode: wait for all workers to finish + logger.info("reached the end of input; now waiting for all remaining jobs to finish") + other = self.dispatcher.getstate() + self.do_mstep(rho(), other, pass_ > 0) + del other + dirty = False # endfor entire corpus update From fde7d4d94b4bec201f1492ecfb86ecf05091bafa Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sat, 17 Jun 2017 02:30:31 +0530 Subject: [PATCH 11/31] add coherence params to lda init --- gensim/models/ldamodel.py | 37 ++++++++++++++++++++++++++----------- 1 file changed, 26 insertions(+), 11 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index e39f3857c5..4bcd148fe4 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -198,8 +198,8 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, minimum_phi_value=0.01, - per_word_topics=False, viz=False, coherence="u_mass", - distance="kulback_leibler", texts=None): + per_word_topics=False, viz=False, distance="kulback_leibler", + coherence="u_mass", texts=None, window_size=None, topn=10): """ If given, start training from the iterable `corpus` straight away. If not given, the model is left untrained (presumably because you want to call `update()` manually). @@ -245,10 +245,10 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, `viz` set True for visualizing LDA training stats in Visdom - `coherence` measure to be used for Coherence plot visualization - `distance` measure to be used for Diff plot visualization + `coherence` measure to be used for Coherence plot visualization + `texts` : Tokenized texts. Needed if sliding_window_based coherence measures (c_v, c_uci, c_npmi) are chosen for visualization. eg:: texts = [['system', 'human', 'system', 'eps'], ['user', 'response', 'time'], @@ -257,6 +257,15 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, ['graph', 'minors', 'trees'], ['graph', 'minors', 'survey']] + `window_size` : Is the size of the window to be used for coherence measures using boolean sliding window as their + probability estimator. For 'u_mass' this doesn't matter. + If left 'None' the default window sizes are used which are: + 'c_v' : 110 + 'c_uci' : 10 + 'c_npmi' : 10 + + `topn` Integer corresponding to the number of top words to be extracted from each topic for coherence logging. + Example: >>> lda = LdaModel(corpus, num_topics=100) # train model @@ -300,9 +309,11 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.per_word_topics = per_word_topics self.viz = viz if self.viz: - self.coherence = coherence self.distance = distance self.texts = texts + self.coherence = coherence + self.window_size = window_size + self.topn = topn self.alpha, self.optimize_alpha = self.init_dir_prior(alpha, 'alpha') @@ -552,9 +563,9 @@ def log_perplexity(self, chunk, total_docs=None): (perwordbound, np.exp2(-perwordbound), len(chunk), corpus_words)) return perwordbound - def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, - update_every=None, eval_every=None, iterations=None, gamma_threshold=None, - chunks_as_numpy=False, viz=None, coherence=None, distance=None, texts=None): + def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, + eval_every=None, iterations=None, gamma_threshold=None, chunks_as_numpy=False, + viz=None, distance=None, coherence=None, texts=None, window_size=None, topn=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -619,12 +630,16 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, if viz is None: viz = self.viz if viz: + if distance is None: + distance = self.distance if coherence is None: coherence = self.coherence if texts is None: texts = self.texts - if distance is None: - distance = self.distance + if window_size is None: + window_size = self.window_size + if topn is None: + topn = self.topn if update_every: updatetype = "online" @@ -718,7 +733,7 @@ def rho(): if self.viz: # calculate coherence - cm = gensim.models.CoherenceModel(model=self, corpus=corpus, texts=texts, coherence=coherence) + cm = gensim.models.CoherenceModel(model=self, corpus=corpus, texts=texts, coherence=coherence, window_size=window_size, topn=topn) Coherence = np.array([cm.get_coherence()]) # calculate perplexity From 3f180769f6d39047b4cd663973e359a69057f3a2 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Mon, 26 Jun 2017 17:19:20 +0530 Subject: [PATCH 12/31] added Lda Visom viz notebook --- docs/notebooks/Coherence.gif | Bin 0 -> 83830 bytes docs/notebooks/Convergence.gif | Bin 0 -> 33458 bytes docs/notebooks/Diff.gif | Bin 0 -> 102979 bytes docs/notebooks/Perplexity.gif | Bin 0 -> 102773 bytes docs/notebooks/Training_visualizations.ipynb | 97 +++++++++++++++++++ 5 files changed, 97 insertions(+) create mode 100644 docs/notebooks/Coherence.gif create mode 100644 docs/notebooks/Convergence.gif create mode 100644 docs/notebooks/Diff.gif create mode 100644 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A good model will generate coherent topics, i.e., topics with high topic coherence scores. Good topics are topics that can be described by a short label based on the topic terms they spit out. \n", + "\n", + " \n", + "\n", + " Now, this graph along with the others explained below, can be used to decide if it's time to stop the training. We can see if the value stops changing after some epochs and that we are able to get the highest possible coherence of our model. \n", + "\n", + "\n", + "2. **Perplexity**\n", + "\n", + " Perplexity is a measurement of how well a probability distribution or probability model predicts a sample. In LDA, topics are described by a probability distribution over vocabulary words. So, perplexity can be used to compare probabilistic models like LDA.\n", + " \n", + " \n", + "\n", + " For a good model, perplexity should be as low as possible.\n", + " \n", + " \n", + "3. **Topic Difference**\n", + "\n", + " Topic Diff calculates the distance between two LDA models. This distance is calculated based on the topics, by either using their probability distribution over vocabulary words (kullback_leibler, hellinger) or by simply using the common vocabulary words between the topics from both model.\n", + "\n", + " \n", + " \n", + " In the heatmap, X-axis define the Epoch no. and Y-axis define the distance between the identical topic from consecutive epochs. For ex. a particular cell in the heatmap with values (x=3, y=5, z=0.4) represent the distance(=0.4) between the topic 5 from 3rd epoch and topic 5 from 2nd epoch. With increasing epochs, the distance between the identical topics should decrease.\n", + " \n", + " \n", + "4. **Convergence**\n", + "\n", + " Convergence is the sum of the difference between all the identical topics from two consecutive epochs. It is basically the sum of column values in the heatmap above.\n", + "\n", + " \n", + "\n", + " The model is said to be converged when the convergence value stops descending with increasing epochs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 546908e3dbb3f024fed0c5b3d7d243c7131f91dc Mon Sep 17 00:00:00 2001 From: parulsethi Date: Tue, 27 Jun 2017 04:25:19 +0530 Subject: [PATCH 13/31] add option to specify env --- gensim/models/ldamodel.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index 4bcd148fe4..f95095fb09 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -198,7 +198,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, minimum_phi_value=0.01, - per_word_topics=False, viz=False, distance="kulback_leibler", + per_word_topics=False, viz=False, env=None, distance="kulback_leibler", coherence="u_mass", texts=None, window_size=None, topn=10): """ If given, start training from the iterable `corpus` straight away. If not given, @@ -245,6 +245,8 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, `viz` set True for visualizing LDA training stats in Visdom + `env` defines the environment to use in visdom browser + `distance` measure to be used for Diff plot visualization `coherence` measure to be used for Coherence plot visualization @@ -309,6 +311,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.per_word_topics = per_word_topics self.viz = viz if self.viz: + self.env = env self.distance = distance self.texts = texts self.coherence = coherence @@ -565,7 +568,7 @@ def log_perplexity(self, chunk, total_docs=None): def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, eval_every=None, iterations=None, gamma_threshold=None, chunks_as_numpy=False, - viz=None, distance=None, coherence=None, texts=None, window_size=None, topn=None): + viz=None, env=None, distance=None, coherence=None, texts=None, window_size=None, topn=None): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -630,6 +633,8 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, u if viz is None: viz = self.viz if viz: + if env is None: + env = self.env if distance is None: distance = self.distance if coherence is None: @@ -750,18 +755,18 @@ def rho(): if pass_ == 0: # initial plot windows Diff_mat = np.array([diff_diagonal]) - viz_coherence = viz_window.line(Y=Coherence, X=np.array([pass_]), opts=dict(xlabel='Epochs', ylabel='Coherence', title='Coherence (%s)' % coherence)) - viz_perplexity = viz_window.line(Y=Perplexity, X=np.array([pass_]), opts=dict(xlabel='Epochs', ylabel='Perplexity', title='Perplexity')) - viz_convergence = viz_window.line(Y=Convergence, X=np.array([pass_]), opts=dict(xlabel='Epochs', ylabel='Convergence', title='Convergence (%s)' % distance)) - viz_diff = viz_window.heatmap(X=np.array(Diff_mat).T, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % distance)) + viz_coherence = viz_window.line(Y=Coherence, X=np.array([pass_]), env=env, opts=dict(xlabel='Epochs', ylabel='Coherence', title='Coherence (%s)' % coherence)) + viz_perplexity = viz_window.line(Y=Perplexity, X=np.array([pass_]), env=env, opts=dict(xlabel='Epochs', ylabel='Perplexity', title='Perplexity')) + viz_convergence = viz_window.line(Y=Convergence, X=np.array([pass_]), env=env, opts=dict(xlabel='Epochs', ylabel='Convergence', title='Convergence (%s)' % distance)) + viz_diff = viz_window.heatmap(X=np.array(Diff_mat).T, env=env, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % distance)) else: # update the plot with each epoch Diff_mat = np.concatenate((Diff_mat, np.array([diff_diagonal]))) - viz_window.updateTrace(Y=Coherence, X=np.array([pass_]), win=viz_coherence) - viz_window.updateTrace(Y=Perplexity, X=np.array([pass_]), win=viz_perplexity) - viz_window.updateTrace(Y=Convergence, X=np.array([pass_]), win=viz_convergence) - viz_window.heatmap(X=np.array(Diff_mat).T, win=viz_diff, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % distance)) + viz_window.updateTrace(Y=Coherence, X=np.array([pass_]), env=env, win=viz_coherence) + viz_window.updateTrace(Y=Perplexity, X=np.array([pass_]), env=env, win=viz_perplexity) + viz_window.updateTrace(Y=Convergence, X=np.array([pass_]), env=env, win=viz_convergence) + viz_window.heatmap(X=np.array(Diff_mat).T, env=env, win=viz_diff, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % distance)) if dirty: # finish any remaining updates From 651a61a5e992aa591108e6c77bb98762f1cfc3e9 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Thu, 29 Jun 2017 03:26:22 +0530 Subject: [PATCH 14/31] made requested changes --- docs/notebooks/Diff.gif | Bin 102979 -> 32656 bytes docs/notebooks/Training_visualizations.ipynb | 51 +++--- gensim/models/ldamodel.py | 164 ++++++++++--------- 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zu^C!u#4+puQCNP#o)oR<308p7Q+SdW6Uc)zdT|0QG?Xw?IH)s(F@ZI`XEqyaZ7<>4*t5;-??QBNIwcgcQ`E5&3LE z1=^s|Oe9sQOtmRaJ*l-)m|}7kPPM8hbx?vj@PVFW-Q*NXu+N2-l_y6Pq*K)z(yMkc z4i(UX7WV0cRBUpof;>SRHu+G-s-gpI?Q2P1N)nUObd!~>0b#r7h_!)r4ngfB6M#?> z$Zj$Y87K)GN`TpuJT#(A=qV<7VSy5G;uNP~EG*$F*^@dWp`ESdLs6bSJ tyt>Ju@^b>0l`L*I+0lB|p_OdlClF5C+@iK~yyivcEz7A+^O7VW06Ss!toZ-{ diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 8fba58ad5b..1cd56779f7 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -18,7 +18,7 @@ "\n", "To monitor the training progress of LDA live in Visdom browser, you can set `viz=True` in the LDA function call.\n", "\n", - "model = ldamodel.LdaModel(corpus=corpus, id2word=dictionary, passes=50, **viz=True**)\n", + "`model = ldamodel.LdaModel(corpus=corpus, id2word=dictionary, passes=50, viz=True)`\n", "\n", "When the model is set for training, you can open http://localhost:8097 to see the training progress.\n", "\n", @@ -27,50 +27,41 @@ "\n", "There are four types of graphs which are plotted for LDA:\n", "\n", - "1. **Coherence**\n", + "**Coherence**\n", "\n", - " Coherence is a measure used to evaluate topic models. A good model will generate coherent topics, i.e., topics with high topic coherence scores. Good topics are topics that can be described by a short label based on the topic terms they spit out. \n", + "Coherence is a measure used to evaluate topic models. A good model will generate coherent topics, i.e., topics with high topic coherence scores. Good topics are topics that can be described by a short label based on the topic terms they spit out. \n", "\n", - " \n", + "\n", "\n", - " Now, this graph along with the others explained below, can be used to decide if it's time to stop the training. We can see if the value stops changing after some epochs and that we are able to get the highest possible coherence of our model. \n", + "Now, this graph along with the others explained below, can be used to decide if it's time to stop the training. We can see if the value stops changing after some epochs and that we are able to get the highest possible coherence of our model. \n", "\n", "\n", - "2. **Perplexity**\n", + "**Perplexity**\n", "\n", - " Perplexity is a measurement of how well a probability distribution or probability model predicts a sample. In LDA, topics are described by a probability distribution over vocabulary words. So, perplexity can be used to compare probabilistic models like LDA.\n", - " \n", - " \n", + "Perplexity is a measurement of how well a probability distribution or probability model predicts a sample. In LDA, topics are described by a probability distribution over vocabulary words. So, perplexity can be used to compare probabilistic models like LDA.\n", "\n", - " For a good model, perplexity should be as low as possible.\n", - " \n", - " \n", - "3. **Topic Difference**\n", + "\n", "\n", - " Topic Diff calculates the distance between two LDA models. This distance is calculated based on the topics, by either using their probability distribution over vocabulary words (kullback_leibler, hellinger) or by simply using the common vocabulary words between the topics from both model.\n", + "For a good model, perplexity should be as low as possible.\n", "\n", - " \n", - " \n", - " In the heatmap, X-axis define the Epoch no. and Y-axis define the distance between the identical topic from consecutive epochs. For ex. a particular cell in the heatmap with values (x=3, y=5, z=0.4) represent the distance(=0.4) between the topic 5 from 3rd epoch and topic 5 from 2nd epoch. With increasing epochs, the distance between the identical topics should decrease.\n", + "\n", + "**Topic Difference**\n", + "\n", + "Topic Diff calculates the distance between two LDA models. This distance is calculated based on the topics, by either using their probability distribution over vocabulary words (kullback_leibler, hellinger) or by simply using the common vocabulary words between the topics from both model.\n", + "\n", + "\n", + "\n", + "In the heatmap, X-axis define the Epoch no. and Y-axis define the distance between the identical topic from consecutive epochs. For ex. a particular cell in the heatmap with values (x=3, y=5, z=0.4) represent the distance(=0.4) between the topic 5 from 3rd epoch and topic 5 from 2nd epoch. With increasing epochs, the distance between the identical topics should decrease.\n", " \n", " \n", - "4. **Convergence**\n", + "**Convergence**\n", "\n", - " Convergence is the sum of the difference between all the identical topics from two consecutive epochs. It is basically the sum of column values in the heatmap above.\n", + "Convergence is the sum of the difference between all the identical topics from two consecutive epochs. It is basically the sum of column values in the heatmap above.\n", "\n", - " \n", + "\n", "\n", - " The model is said to be converged when the convergence value stops descending with increasing epochs." + "The model is said to be converged when the convergence value stops descending with increasing epochs." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index f95095fb09..e185209b1c 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -42,7 +42,6 @@ from gensim.matutils import dirichlet_expectation from gensim.models import basemodel from gensim.matutils import kullback_leibler, hellinger, jaccard_distance -from visdom import Visdom from itertools import chain from scipy.special import gammaln, psi # gamma function utils @@ -58,6 +57,12 @@ # maxentropy has been removed in recent releases, logsumexp now in misc from scipy.misc import logsumexp +# Visdom is used for training stats visualization +try: + from visdom import Visdom + VISDOM_INSTALLED = True +except ImportError: + VISDOM_INSTALLED = False logger = logging.getLogger('gensim.models.ldamodel') @@ -198,8 +203,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, minimum_phi_value=0.01, - per_word_topics=False, viz=False, env=None, distance="kulback_leibler", - coherence="u_mass", texts=None, window_size=None, topn=10): + per_word_topics=False, viz=False, metrics={}): """ If given, start training from the iterable `corpus` straight away. If not given, the model is left untrained (presumably because you want to call `update()` manually). @@ -245,28 +249,30 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, `viz` set True for visualizing LDA training stats in Visdom - `env` defines the environment to use in visdom browser + `metrics` : dict containing following parameters for visualization or logging purposes - `distance` measure to be used for Diff plot visualization + `viz_env` defines the environment to use in visdom browser - `coherence` measure to be used for Coherence plot visualization + `coherence` measure to be used for coherence plot visualization. Available values: u_mass, c_v, c_uci, c_npmi - `texts` : Tokenized texts. Needed if sliding_window_based coherence measures (c_v, c_uci, c_npmi) are chosen for visualization. eg:: - texts = [['system', 'human', 'system', 'eps'], - ['user', 'response', 'time'], - ['trees'], - ['graph', 'trees'], - ['graph', 'minors', 'trees'], - ['graph', 'minors', 'survey']] + `coherence_texts` : Tokenized texts. Needed if sliding_window_based coherence measures (c_v, c_uci, c_npmi) are chosen for visualization. eg:: + coherence_texts = [['system', 'human', 'system', 'eps'], + ['user', 'response', 'time'], + ['trees'], + ['graph', 'trees'], + ['graph', 'minors', 'trees'], + ['graph', 'minors', 'survey']] - `window_size` : Is the size of the window to be used for coherence measures using boolean sliding window as their - probability estimator. For 'u_mass' this doesn't matter. - If left 'None' the default window sizes are used which are: - 'c_v' : 110 - 'c_uci' : 10 - 'c_npmi' : 10 + `coherence_window_size` : Is the size of the window to be used for coherence measures using boolean sliding window as their + probability estimator. For 'u_mass' this doesn't matter. + If left 'None' the default window sizes are used which are: + 'c_v' : 110 + 'c_uci' : 10 + 'c_npmi' : 10 - `topn` Integer corresponding to the number of top words to be extracted from each topic for coherence logging. + `coherence_topn` Integer corresponding to the number of top words to be extracted from each topic for coherence visualization. + + `diff_distance` measure to be used for Diff plot visualization. Available values: kullback_leibler, hellinger, jaccard Example: @@ -310,13 +316,19 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.minimum_phi_value = minimum_phi_value self.per_word_topics = per_word_topics self.viz = viz + self.metrics = metrics + if self.viz: - self.env = env - self.distance = distance - self.texts = texts - self.coherence = coherence - self.window_size = window_size - self.topn = topn + if not VISDOM_INSTALLED: + raise ImportError("Please install Visdom for visualization") + # use default values in case viz is True but metrics are not provided + if not self.metrics: + self.metrics = {"viz_env":None, + "coherence":"u_mass", + "coherence_texts":None, + "coherence_window_size":None, + "coherence_topn":10, + "diff_distance":"kullback_leibler"} self.alpha, self.optimize_alpha = self.init_dir_prior(alpha, 'alpha') @@ -567,8 +579,7 @@ def log_perplexity(self, chunk, total_docs=None): return perwordbound def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, - eval_every=None, iterations=None, gamma_threshold=None, chunks_as_numpy=False, - viz=None, env=None, distance=None, coherence=None, texts=None, window_size=None, topn=None): + eval_every=None, iterations=None, gamma_threshold=None, chunks_as_numpy=False): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -630,22 +641,6 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, u self.state.numdocs += lencorpus - if viz is None: - viz = self.viz - if viz: - if env is None: - env = self.env - if distance is None: - distance = self.distance - if coherence is None: - coherence = self.coherence - if texts is None: - texts = self.texts - if window_size is None: - window_size = self.window_size - if topn is None: - topn = self.topn - if update_every: updatetype = "online" if passes == 1: @@ -680,7 +675,7 @@ def rho(): return pow(offset + pass_ + (self.num_updates / chunksize), -decay) if self.viz: - viz_window = Visdom() + self.viz_window = Visdom() # save initial random state of model for Diff calculation with first epoch previous = copy.deepcopy(self) @@ -737,36 +732,9 @@ def rho(): raise RuntimeError("input corpus size changed during training (don't use generators as input)") if self.viz: - # calculate coherence - cm = gensim.models.CoherenceModel(model=self, corpus=corpus, texts=texts, coherence=coherence, window_size=window_size, topn=topn) - Coherence = np.array([cm.get_coherence()]) - - # calculate perplexity - corpus_words = sum(cnt for document in corpus for _, cnt in document) - perwordbound = self.bound(corpus) / corpus_words - Perplexity = np.array([np.exp2(-perwordbound)]) - - # calculate diff - diff_matrix = self.diff(previous, distance=distance)[0] - diff_diagonal = np.diagonal(diff_matrix) + self.visualization(corpus, pass_, previous) + # save the model in previous for next epoch visualization previous = copy.deepcopy(self) - Convergence = np.array([np.sum(diff_diagonal)]) - - if pass_ == 0: - # initial plot windows - Diff_mat = np.array([diff_diagonal]) - viz_coherence = viz_window.line(Y=Coherence, X=np.array([pass_]), env=env, opts=dict(xlabel='Epochs', ylabel='Coherence', title='Coherence (%s)' % coherence)) - viz_perplexity = viz_window.line(Y=Perplexity, X=np.array([pass_]), env=env, opts=dict(xlabel='Epochs', ylabel='Perplexity', title='Perplexity')) - viz_convergence = viz_window.line(Y=Convergence, X=np.array([pass_]), env=env, opts=dict(xlabel='Epochs', ylabel='Convergence', title='Convergence (%s)' % distance)) - viz_diff = viz_window.heatmap(X=np.array(Diff_mat).T, env=env, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % distance)) - - else: - # update the plot with each epoch - Diff_mat = np.concatenate((Diff_mat, np.array([diff_diagonal]))) - viz_window.updateTrace(Y=Coherence, X=np.array([pass_]), env=env, win=viz_coherence) - viz_window.updateTrace(Y=Perplexity, X=np.array([pass_]), env=env, win=viz_perplexity) - viz_window.updateTrace(Y=Convergence, X=np.array([pass_]), env=env, win=viz_convergence) - viz_window.heatmap(X=np.array(Diff_mat).T, env=env, win=viz_diff, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % distance)) if dirty: # finish any remaining updates @@ -805,6 +773,48 @@ def do_mstep(self, rho, other, extra_pass=False): # only update if this isn't an additional pass self.num_updates += other.numdocs + def visualization(self, corpus, pass_, previous): + """ + Visualize training stats (Coherence, Perplexity, Diff, Convergence) in Visdom + + """ + viz_env = self.metrics["viz_env"] + coherence = self.metrics["coherence"] + coherence_texts = self.metrics["coherence_texts"] + coherence_window_size = self.metrics["coherence_window_size"] + coherence_topn = self.metrics["coherence_topn"] + diff_distance = self.metrics["diff_distance"] + viz_window = self.viz_window + + # calculate coherence + cm = gensim.models.CoherenceModel(model=self, corpus=corpus, texts=coherence_texts, coherence=coherence, window_size=coherence_window_size, topn=coherence_topn) + coherence_value = np.array([cm.get_coherence()]) + + # calculate perplexity + corpus_words = sum(cnt for document in corpus for _, cnt in document) + perwordbound = self.bound(corpus) / corpus_words + perplexity = np.array([np.exp2(-perwordbound)]) + + # calculate diff + diff_matrix = self.diff(previous, distance=diff_distance)[0] + diff_diagonal = np.diagonal(diff_matrix) + convergence = np.array([np.sum(diff_diagonal)]) + + if pass_ == 0: + # initial plot windows + self.diff_mat = np.array([diff_diagonal]) + self.viz_coherence = viz_window.line(Y=coherence_value, X=np.array([pass_]), env=viz_env, opts=dict(xlabel='Epochs', ylabel='Coherence', title='Coherence (%s)' % coherence)) + self.viz_perplexity = viz_window.line(Y=perplexity, X=np.array([pass_]), env=viz_env, opts=dict(xlabel='Epochs', ylabel='Perplexity', title='Perplexity')) + self.viz_convergence = viz_window.line(Y=convergence, X=np.array([pass_]), env=viz_env, opts=dict(xlabel='Epochs', ylabel='Convergence', title='Convergence (%s)' % diff_distance)) + self.viz_diff = viz_window.heatmap(X=np.array(self.diff_mat).T, env=viz_env, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % diff_distance)) + else: + # update the plot with each epoch + self.diff_mat = np.concatenate((self.diff_mat, np.array([diff_diagonal]))) + viz_window.updateTrace(Y=coherence_value, X=np.array([pass_]), env=viz_env, win=self.viz_coherence) + viz_window.updateTrace(Y=perplexity, X=np.array([pass_]), env=viz_env, win=self.viz_perplexity) + viz_window.updateTrace(Y=convergence, X=np.array([pass_]), env=viz_env, win=self.viz_convergence) + viz_window.heatmap(X=np.array(self.diff_mat).T, env=viz_env, win=self.viz_diff, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % diff_distance)) + def bound(self, corpus, gamma=None, subsample_ratio=1.0): """ Estimate the variational bound of documents from `corpus`: @@ -1063,12 +1073,12 @@ def get_term_topics(self, word_id, minimum_probability=None): return values - def diff(self, other, distance="kulback_leibler", num_words=100, n_ann_terms=10, normed=True): + def diff(self, other, distance="kullback_leibler", num_words=100, n_ann_terms=10, normed=True): """ Calculate difference topic2topic between two Lda models `other` instances of `LdaMulticore` or `LdaModel` `distance` is function that will be applied to calculate difference between any topic pair. - Available values: `kulback_leibler`, `hellinger` and `jaccard` + Available values: `kullback_leibler`, `hellinger` and `jaccard` `num_words` is quantity of most relevant words that used if distance == `jaccard` (also used for annotation) `n_ann_terms` is max quantity of words in intersection/symmetric difference between topics (used for annotation) Returns a matrix Z with shape (m1.num_topics, m2.num_topics), where Z[i][j] - difference between topic_i and topic_j @@ -1086,7 +1096,7 @@ def diff(self, other, distance="kulback_leibler", num_words=100, n_ann_terms=10, """ distances = { - "kulback_leibler": kullback_leibler, + "kullback_leibler": kullback_leibler, "hellinger": hellinger, "jaccard": jaccard_distance, } From 1376d9091c415ec9d9dd0d6ed7c2de4759a34b46 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sat, 8 Jul 2017 21:08:55 +0530 Subject: [PATCH 15/31] add generic callback API --- gensim/models/callbacks.py | 146 +++++++++++++++++++++++++++++++++++++ gensim/models/ldamodel.py | 113 +++------------------------- 2 files changed, 157 insertions(+), 102 deletions(-) create mode 100644 gensim/models/callbacks.py diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py new file mode 100644 index 0000000000..2686c924d0 --- /dev/null +++ b/gensim/models/callbacks.py @@ -0,0 +1,146 @@ +import gensim +import logging +import copy +import numpy as np +from queue import Queue + +# Visdom is used for training stats visualization +try: + from visdom import Visdom + VISDOM_INSTALLED = True +except ImportError: + VISDOM_INSTALLED = False + + +class Metric(object): + def __init__(self): + pass + + def get_value(self, **parameters): + for parameter, value in parameters.items(): + setattr(self, parameter, value) + + +class CoherenceMetric(Metric): + def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, window_size=None, topn=None, logger=None, viz_env=None, title=None): + self.corpus = corpus + self.dictionary = dictionary + self.coherence = coherence + self.texts = texts + self.window_size = window_size + self.topn = topn + self.logger = logger + self.viz_env = viz_env + self.title = title + + def get_value(self, **kwargs): + super(CoherenceMetric, self).get_value(**kwargs) + cm = gensim.models.CoherenceModel(self.model, self.topics, self.texts, self.corpus, self.dictionary, self.window_size, self.coherence, self.topn) + return cm.get_coherence() + + +class PerplexityMetric(Metric): + def __init__(self, corpus=None, logger=None, viz_env=None, title=None): + self.corpus = corpus + self.logger = logger + self.viz_env = viz_env + self.title = title + + def get_value(self, **kwargs): + super(PerplexityMetric, self).get_value(**kwargs) + corpus_words = sum(cnt for document in self.corpus for _, cnt in document) + perwordbound = self.model.bound(self.corpus) / corpus_words + return np.exp2(-perwordbound) + + +class DiffMetric(Metric): + def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): + self.distance = distance + self.num_words = num_words + self.n_ann_terms = n_ann_terms + self.normed = normed + self.logger = logger + self.viz_env = viz_env + self.title = title + + def get_value(self, **kwargs): + super(DiffMetric, self).get_value(**kwargs) + diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) + return np.diagonal(diff_matrix) + + +class ConvergenceMetric(Metric): + def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): + self.distance = distance + self.num_words = num_words + self.n_ann_terms = n_ann_terms + self.normed = normed + self.logger = logger + self.viz_env = viz_env + self.title = title + + def get_value(self, **kwargs): + super(ConvergenceMetric, self).get_value(**kwargs) + diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) + return np.sum(np.diagonal(diff_matrix)) + + +class Callback(object): + def __init__(self, metrics): + # list of metrics to be plot + self.metrics = metrics + + def set_model(self, model): + self.model = model + self.previous = None + # check for any metric which need model state from previous epoch + if any(isinstance(metric, (DiffMetric, ConvergenceMetric)) for metric in self.metrics): + self.previous = copy.deepcopy(model) + # store diff diagnols of previous epochs + self.diff_mat = Queue() + if any(metric.logger=="visdom" for metric in self.metrics): + if not VISDOM_INSTALLED: + raise ImportError("Please install Visdom for visualization") + self.viz = Visdom() + # store initial plot windows of every metric (same window will be updated with increasing epochs) + self.windows = [] + if any(metric.logger=="shell" for metric in self.metrics): + # set logger for current topic model + model_type = type(self.model).__name__ + self.log_type = logging.getLogger(model_type) + + def on_epoch_end(self, epoch, topics=None): + # plot all metrics in current epoch + for i, metric in enumerate(self.metrics): + value = metric.get_value(topics=topics, model=self.model, other_model=self.previous) + metric_label = type(metric).__name__[:-6] + # check for any metric which need model state from previous epoch + if isinstance(metric, (DiffMetric, ConvergenceMetric)): + self.previous = copy.deepcopy(self.model) + + if metric.logger=="visdom": + if epoch==0: + if value.ndim>0: + diff_mat = np.array([value]) + viz_metric = self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) + # store current epoch's diff diagonal + self.diff_mat.put(diff_mat) + # saving initial plot window + self.windows.append(copy.deepcopy(viz_metric)) + else: + viz_metric = self.viz.line(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) + # saving initial plot window + self.windows.append(copy.deepcopy(viz_metric)) + else: + if value.ndim>0: + # concatenate with previous epoch's diff diagonals + diff_mat = np.concatenate((self.diff_mat.get(), np.array([value]))) + self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, win=self.windows[i], opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) + self.diff_mat.put(diff_mat) + else: + self.viz.updateTrace(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, win=self.windows[i]) + + if metric.logger=="shell": + statement = " ".join(("Epoch:", epoch, metric_label, "estimate:", str(value))) + self.log_type.info(statement) + diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index 6bf5ef7866..f751f5d1ba 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -35,13 +35,12 @@ import numbers from random import sample import os -import gensim -import copy from gensim import interfaces, utils, matutils from gensim.matutils import dirichlet_expectation from gensim.models import basemodel from gensim.matutils import kullback_leibler, hellinger, jaccard_distance +from gensim.models.callbacks import Callback from itertools import chain from scipy.special import gammaln, psi # gamma function utils @@ -57,12 +56,6 @@ # maxentropy has been removed in recent releases, logsumexp now in misc from scipy.misc import logsumexp -# Visdom is used for training stats visualization -try: - from visdom import Visdom - VISDOM_INSTALLED = True -except ImportError: - VISDOM_INSTALLED = False logger = logging.getLogger('gensim.models.ldamodel') @@ -203,7 +196,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, alpha='symmetric', eta=None, decay=0.5, offset=1.0, eval_every=10, iterations=50, gamma_threshold=0.001, minimum_probability=0.01, random_state=None, ns_conf={}, minimum_phi_value=0.01, - per_word_topics=False, viz=False, metrics={}): + per_word_topics=False, callbacks=None): """ If given, start training from the iterable `corpus` straight away. If not given, the model is left untrained (presumably because you want to call `update()` manually). @@ -247,33 +240,6 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, `random_state` can be a np.random.RandomState object or the seed for one - `viz` set True for visualizing LDA training stats in Visdom - - `metrics` : dict containing following parameters for visualization or logging purposes - - `viz_env` defines the environment to use in visdom browser - - `coherence` measure to be used for coherence plot visualization. Available values: u_mass, c_v, c_uci, c_npmi - - `coherence_texts` : Tokenized texts. Needed if sliding_window_based coherence measures (c_v, c_uci, c_npmi) are chosen for visualization. eg:: - coherence_texts = [['system', 'human', 'system', 'eps'], - ['user', 'response', 'time'], - ['trees'], - ['graph', 'trees'], - ['graph', 'minors', 'trees'], - ['graph', 'minors', 'survey']] - - `coherence_window_size` : Is the size of the window to be used for coherence measures using boolean sliding window as their - probability estimator. For 'u_mass' this doesn't matter. - If left 'None' the default window sizes are used which are: - 'c_v' : 110 - 'c_uci' : 10 - 'c_npmi' : 10 - - `coherence_topn` Integer corresponding to the number of top words to be extracted from each topic for coherence visualization. - - `diff_distance` measure to be used for Diff plot visualization. Available values: kullback_leibler, hellinger, jaccard - Example: >>> lda = LdaModel(corpus, num_topics=100) # train model @@ -315,20 +281,7 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, self.eval_every = eval_every self.minimum_phi_value = minimum_phi_value self.per_word_topics = per_word_topics - self.viz = viz - self.metrics = metrics - - if self.viz: - if not VISDOM_INSTALLED: - raise ImportError("Please install Visdom for visualization") - # use default values in case viz is True but metrics are not provided - if not self.metrics: - self.metrics = {"viz_env":None, - "coherence":"u_mass", - "coherence_texts":None, - "coherence_window_size":None, - "coherence_topn":10, - "diff_distance":"kullback_leibler"} + self.callbacks = callbacks self.alpha, self.optimize_alpha = self.init_dir_prior(alpha, 'alpha') @@ -578,8 +531,9 @@ def log_perplexity(self, chunk, total_docs=None): (perwordbound, np.exp2(-perwordbound), len(chunk), corpus_words)) return perwordbound - def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, update_every=None, - eval_every=None, iterations=None, gamma_threshold=None, chunks_as_numpy=False): + def update(self, corpus, chunksize=None, decay=None, offset=None, + passes=None, update_every=None, eval_every=None, iterations=None, + gamma_threshold=None, chunks_as_numpy=False): """ Train the model with new documents, by EM-iterating over `corpus` until the topics converge (or until the maximum number of allowed iterations @@ -674,10 +628,9 @@ def update(self, corpus, chunksize=None, decay=None, offset=None, passes=None, u def rho(): return pow(offset + pass_ + (self.num_updates / chunksize), -decay) - if self.viz: - self.viz_window = Visdom() - # save initial random state of model for Diff calculation with first epoch - previous = copy.deepcopy(self) + if self.callbacks: + callback = Callback(self.callbacks) + callback.set_model(self) for pass_ in xrange(passes): if self.dispatcher: @@ -731,10 +684,8 @@ def rho(): if reallen != lencorpus: raise RuntimeError("input corpus size changed during training (don't use generators as input)") - if self.viz: - self.visualization(corpus, pass_, previous) - # save the model in previous for next epoch visualization - previous = copy.deepcopy(self) + if self.callbacks: + callback.on_epoch_end(pass_) if dirty: # finish any remaining updates @@ -773,48 +724,6 @@ def do_mstep(self, rho, other, extra_pass=False): # only update if this isn't an additional pass self.num_updates += other.numdocs - def visualization(self, corpus, pass_, previous): - """ - Visualize training stats (Coherence, Perplexity, Diff, Convergence) in Visdom - - """ - viz_env = self.metrics["viz_env"] - coherence = self.metrics["coherence"] - coherence_texts = self.metrics["coherence_texts"] - coherence_window_size = self.metrics["coherence_window_size"] - coherence_topn = self.metrics["coherence_topn"] - diff_distance = self.metrics["diff_distance"] - viz_window = self.viz_window - - # calculate coherence - cm = gensim.models.CoherenceModel(model=self, corpus=corpus, texts=coherence_texts, coherence=coherence, window_size=coherence_window_size, topn=coherence_topn) - coherence_value = np.array([cm.get_coherence()]) - - # calculate perplexity - corpus_words = sum(cnt for document in corpus for _, cnt in document) - perwordbound = self.bound(corpus) / corpus_words - perplexity = np.array([np.exp2(-perwordbound)]) - - # calculate diff - diff_matrix = self.diff(previous, distance=diff_distance)[0] - diff_diagonal = np.diagonal(diff_matrix) - convergence = np.array([np.sum(diff_diagonal)]) - - if pass_ == 0: - # initial plot windows - self.diff_mat = np.array([diff_diagonal]) - self.viz_coherence = viz_window.line(Y=coherence_value, X=np.array([pass_]), env=viz_env, opts=dict(xlabel='Epochs', ylabel='Coherence', title='Coherence (%s)' % coherence)) - self.viz_perplexity = viz_window.line(Y=perplexity, X=np.array([pass_]), env=viz_env, opts=dict(xlabel='Epochs', ylabel='Perplexity', title='Perplexity')) - self.viz_convergence = viz_window.line(Y=convergence, X=np.array([pass_]), env=viz_env, opts=dict(xlabel='Epochs', ylabel='Convergence', title='Convergence (%s)' % diff_distance)) - self.viz_diff = viz_window.heatmap(X=np.array(self.diff_mat).T, env=viz_env, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % diff_distance)) - else: - # update the plot with each epoch - self.diff_mat = np.concatenate((self.diff_mat, np.array([diff_diagonal]))) - viz_window.updateTrace(Y=coherence_value, X=np.array([pass_]), env=viz_env, win=self.viz_coherence) - viz_window.updateTrace(Y=perplexity, X=np.array([pass_]), env=viz_env, win=self.viz_perplexity) - viz_window.updateTrace(Y=convergence, X=np.array([pass_]), env=viz_env, win=self.viz_convergence) - viz_window.heatmap(X=np.array(self.diff_mat).T, env=viz_env, win=self.viz_diff, opts=dict(xlabel='Epochs', ylabel='Topic', title='Diff (%s)' % diff_distance)) - def bound(self, corpus, gamma=None, subsample_ratio=1.0): """ Estimate the variational bound of documents from `corpus`: From 44c8e5886d2f70a76f936fd104b727b0608dbebc Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 9 Jul 2017 00:28:03 +0530 Subject: [PATCH 16/31] modified Notebook for new API --- docs/notebooks/Training_visualizations.ipynb | 103 +++++++++++++++++-- gensim/models/callbacks.py | 3 + 2 files changed, 100 insertions(+), 6 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 1cd56779f7..8a0c4ddfc2 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Setup Visdom\n", + "# Setup Visdom\n", "\n", "Install it with:\n", "\n", @@ -16,16 +16,107 @@ "\n", "Visdom now can be accessed at http://localhost:8097 in the browser.\n", "\n", - "To monitor the training progress of LDA live in Visdom browser, you can set `viz=True` in the LDA function call.\n", "\n", - "`model = ldamodel.LdaModel(corpus=corpus, id2word=dictionary, passes=50, viz=True)`\n", + "# LDA Training Visualization\n", "\n", - "When the model is set for training, you can open http://localhost:8097 to see the training progress.\n", + "To monitor the LDA training, a list of Metrics can be passed to the LDA function call for plotting their values live as the training progresses. \n", "\n", + "Let's plot the training stats for an LDA model being trained on Lee corpus. We will use the four evaluation metrics available for topic models in gensim: Coherence, Perplexity, Topic diff and Convergence. (using separate hold_out and test corpus for evaluating the perplexity)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import os\n", + "import re\n", + "import gensim\n", + "from gensim.models import ldamodel\n", + "from gensim.corpora.dictionary import Dictionary\n", + "\n", + "# Set file names for train and test data\n", + "test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])\n", + "lee_train_file = test_data_dir + os.sep + 'lee_background.cor'\n", + "lee_test_file = test_data_dir + os.sep + 'lee.cor'\n", + "\n", + "def read_corpus(fname):\n", + " texts = []\n", + " with open(fname, encoding=\"ISO-8859-1\") as f:\n", + " for line in f:\n", + " # lower case all words\n", + " lowered = line.lower()\n", + " # remove punctuation and split into seperate words\n", + " words = re.compile('\\w+').findall(lowered)\n", + " texts.append(words)\n", + " return texts\n", + "\n", + "training_texts = read_corpus(lee_train_file)\n", + "test_texts = read_corpus(lee_test_file)\n", + "\n", + "# Split test data into hold_out and test corpus\n", + "holdout_texts = test_texts[:150]\n", + "test_texts = test_texts[150:]\n", + "\n", + "dictionary = Dictionary(training_texts)\n", + "\n", + "training_corpus = [dictionary.doc2bow(text) for text in training_texts]\n", + "holdout_corpus = [dictionary.doc2bow(text) for text in holdout_texts]\n", + "test_corpus = [dictionary.doc2bow(text) for text in test_texts]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", + "\n", + "# define perplexity callback for hold_out and test corpus\n", + "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity\")\n", + "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity\")\n", + "\n", + "# define other remaining metrics available\n", + "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Coherence (u_mass)\")\n", + "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Diff (kullback_leibler)\")\n", + "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Convergence (jaccard)\")\n", "\n", - "## LDA Training Visualization\n", + "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", "\n", - "There are four types of graphs which are plotted for LDA:\n", + "# training LDA model\n", + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, passes=5, num_topics=5, callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When the model is set for training, you can open http://localhost:8097 to see the training progress." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# to get a metric value on a trained model\n", + "print(CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\").get_value(model=model))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The four types of graphs which are plotted for LDA:\n", "\n", "**Coherence**\n", "\n", diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index 2686c924d0..d7b7ec82a9 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -34,6 +34,9 @@ def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, win self.title = title def get_value(self, **kwargs): + # only one of the model or topic would be defined + self.model = None + self.topics = None super(CoherenceMetric, self).get_value(**kwargs) cm = gensim.models.CoherenceModel(self.model, self.topics, self.texts, self.corpus, self.dictionary, self.window_size, self.coherence, self.topn) return cm.get_coherence() From 92949a3d988883a1ee6c5e1ad22811a0d24ed8c7 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Sun, 9 Jul 2017 01:12:21 +0530 Subject: [PATCH 17/31] fix flake8 --- docs/notebooks/Training_visualizations.ipynb | 4 ++-- gensim/models/callbacks.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 8a0c4ddfc2..760eb276ed 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -79,8 +79,8 @@ "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", "\n", "# define perplexity callback for hold_out and test corpus\n", - "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity\")\n", - "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity\")\n", + "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (hold_out)\")\n", + "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (test)\")\n", "\n", "# define other remaining metrics available\n", "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Coherence (u_mass)\")\n", diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index d7b7ec82a9..ccb9977799 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -2,7 +2,7 @@ import logging import copy import numpy as np -from queue import Queue +from queue import Queue # Visdom is used for training stats visualization try: @@ -55,7 +55,7 @@ def get_value(self, **kwargs): perwordbound = self.model.bound(self.corpus) / corpus_words return np.exp2(-perwordbound) - + class DiffMetric(Metric): def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): self.distance = distance @@ -133,7 +133,7 @@ def on_epoch_end(self, epoch, topics=None): else: viz_metric = self.viz.line(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) # saving initial plot window - self.windows.append(copy.deepcopy(viz_metric)) + self.windows.append(copy.deepcopy(viz_metric)) else: if value.ndim>0: # concatenate with previous epoch's diff diagonals @@ -142,7 +142,7 @@ def on_epoch_end(self, epoch, topics=None): self.diff_mat.put(diff_mat) else: self.viz.updateTrace(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, win=self.windows[i]) - + if metric.logger=="shell": statement = " ".join(("Epoch:", epoch, metric_label, "estimate:", str(value))) self.log_type.info(statement) From 5b22e4d07da8c1cf9fecd1fb164f39a2b39359e6 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 12 Jul 2017 20:13:06 +0530 Subject: [PATCH 18/31] correct lee corpus division --- docs/notebooks/Training_visualizations.ipynb | 42 ++++++++++++-------- 1 file changed, 26 insertions(+), 16 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 760eb276ed..d92f83c048 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -26,11 +26,17 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], "source": [ "import os\n", "import re\n", @@ -58,8 +64,8 @@ "test_texts = read_corpus(lee_test_file)\n", "\n", "# Split test data into hold_out and test corpus\n", - "holdout_texts = test_texts[:150]\n", - "test_texts = test_texts[150:]\n", + "holdout_texts = test_texts[:25]\n", + "test_texts = test_texts[25:]\n", "\n", "dictionary = Dictionary(training_texts)\n", "\n", @@ -70,10 +76,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", @@ -102,11 +106,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-21.8411539992\n" + ] + } + ], "source": [ "# to get a metric value on a trained model\n", "print(CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\").get_value(model=model))" From c369fc57a87aa95e578ccfd6328b0ef4e2822cff Mon Sep 17 00:00:00 2001 From: parulsethi Date: Mon, 17 Jul 2017 16:18:26 +0530 Subject: [PATCH 19/31] added docstrings --- docs/notebooks/Training_visualizations.ipynb | 36 ++++- gensim/models/callbacks.py | 149 ++++++++++++++++++- gensim/models/ldamodel.py | 2 + 3 files changed, 181 insertions(+), 6 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index d92f83c048..5733017f82 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -77,7 +77,9 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", @@ -163,6 +165,38 @@ "\n", "The model is said to be converged when the convergence value stops descending with increasing epochs." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training Logs\n", + "\n", + "We can also log the metric values to the shell apart from visualizing them in Visdom. The only change is to define `logger=\"shell\"` instead of `\"visdom\"` in the input callbacks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# define perplexity callback for hold_out and test corpus\n", + "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"shell\")\n", + "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"shell\")\n", + "\n", + "# define other remaining metrics available\n", + "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"shell\")\n", + "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"shell\")\n", + "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"shell\")\n", + "\n", + "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", + "\n", + "# training LDA model\n", + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, passes=5, num_topics=5, callbacks=callbacks)" + ] } ], "metadata": { diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index ccb9977799..ff3ca28a54 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -13,16 +13,62 @@ class Metric(object): + """ + Base Metric class for topic model evaluation metrics + """ def __init__(self): pass def get_value(self, **parameters): + """ + Set the parameters + """ for parameter, value in parameters.items(): setattr(self, parameter, value) class CoherenceMetric(Metric): - def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, window_size=None, topn=None, logger=None, viz_env=None, title=None): + """ + Metric class for coherence evaluation + """ + def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, window_size=None, topn=None, logger="shell", viz_env=None, title=None): + """ + Args: + corpus : Gensim document corpus. + texts : Tokenized texts. Needed for coherence models that use sliding window based probability estimator, + eg:: + texts = [['system', 'human', 'system', 'eps'], + ['user', 'response', 'time'], + ['trees'], + ['graph', 'trees'], + ['graph', 'minors', 'trees'], + ['graph', 'minors', 'survey']] + + dictionary : Gensim dictionary mapping of id word to create corpus. If model.id2word is present, + this is not needed. If both are provided, dictionary will be used. + window_size : Is the size of the window to be used for coherence measures using boolean sliding window as their + probability estimator. For 'u_mass' this doesn't matter. + If left 'None' the default window sizes are used which are: + + 'c_v' : 110 + 'c_uci' : 10 + 'c_npmi' : 10 + + coherence : Coherence measure to be used. Supported values are: + 'u_mass' + 'c_v' + 'c_uci' also popularly known as c_pmi + 'c_npmi' + For 'u_mass' corpus should be provided. If texts is provided, it will be converted + to corpus using the dictionary. For 'c_v', 'c_uci' and 'c_npmi' texts should be provided. + Corpus is not needed. + topn : Integer corresponding to the number of top words to be extracted from each topic. + logger : Monitor training process using: + "shell" : print coherence value in shell + "visdom" : visualize coherence value with increasing epochs in Visdom visualization framework + viz_env : Visdom environment to use for plotting the graph + title : title of the graph plot + """ self.corpus = corpus self.dictionary = dictionary self.coherence = coherence @@ -34,6 +80,16 @@ def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, win self.title = title def get_value(self, **kwargs): + """ + Args: + model : Pre-trained topic model. Should be provided if topics is not provided. + Currently supports LdaModel, LdaMallet wrapper and LdaVowpalWabbit wrapper. Use 'topics' + parameter to plug in an as yet unsupported model. + topics : List of tokenized topics. + eg:: + topics = [['human', 'machine', 'computer', 'interface'], + ['graph', 'trees', 'binary', 'widths']] + """ # only one of the model or topic would be defined self.model = None self.topics = None @@ -43,13 +99,29 @@ def get_value(self, **kwargs): class PerplexityMetric(Metric): - def __init__(self, corpus=None, logger=None, viz_env=None, title=None): + """ + Metric class for perplexity evaluation + """ + def __init__(self, corpus=None, logger="shell", viz_env=None, title=None): + """ + Args: + corpus : Gensim document corpus + logger : Monitor training process using: + "shell" : print coherence value in shell + "visdom" : visualize coherence value with increasing epochs in Visdom visualization framework + viz_env : Visdom environment to use for plotting the graph + title : title of the graph plot + """ self.corpus = corpus self.logger = logger self.viz_env = viz_env self.title = title def get_value(self, **kwargs): + """ + Args: + model : Trained topic model + """ super(PerplexityMetric, self).get_value(**kwargs) corpus_words = sum(cnt for document in self.corpus for _, cnt in document) perwordbound = self.model.bound(self.corpus) / corpus_words @@ -57,7 +129,25 @@ def get_value(self, **kwargs): class DiffMetric(Metric): - def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): + """ + Metric class for topic difference evaluation + """ + def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger="shell", viz_env=None, title=None): + """ + Args: + distance : measure used to calculate difference between any topic pair. Available values: + `kullback_leibler` + `hellinger` + `jaccard` + num_words : is quantity of most relevant words that used if distance == `jaccard` (also used for annotation) + n_ann_terms : max quantity of words in intersection/symmetric difference between topics (used for annotation) + normed (bool) : If `true`, matrix/array Z will be normalized + logger : Monitor training process using: + "shell" : print coherence value in shell + "visdom" : visualize coherence value with increasing epochs in Visdom visualization framework + viz_env : Visdom environment to use for plotting the graph + title : title of the graph plot + """ self.distance = distance self.num_words = num_words self.n_ann_terms = n_ann_terms @@ -67,13 +157,36 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=Tru self.title = title def get_value(self, **kwargs): + """ + Args: + model : Trained topic model + other_model : second topic model instance to calculate the difference from + """ super(DiffMetric, self).get_value(**kwargs) diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) return np.diagonal(diff_matrix) class ConvergenceMetric(Metric): - def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): + """ + Metric class for convergence evaluation + """ + def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger="shell", viz_env=None, title=None): + """ + Args: + distance : measure used to calculate difference between any topic pair. Available values: + `kullback_leibler` + `hellinger` + `jaccard` + num_words : is quantity of most relevant words that used if distance == `jaccard` (also used for annotation) + n_ann_terms : max quantity of words in intersection/symmetric difference between topics (used for annotation) + normed (bool) : If `true`, matrix/array Z will be normalized + logger : Monitor training process using: + "shell" : print coherence value in shell + "visdom" : visualize coherence value with increasing epochs in Visdom visualization framework + viz_env : Visdom environment to use for plotting the graph + title : title of the graph plot + """ self.distance = distance self.num_words = num_words self.n_ann_terms = n_ann_terms @@ -83,17 +196,36 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=Tru self.title = title def get_value(self, **kwargs): + """ + Args: + model : Trained topic model + other_model : second topic model instance to calculate the difference from + """ super(ConvergenceMetric, self).get_value(**kwargs) diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) return np.sum(np.diagonal(diff_matrix)) class Callback(object): + """ + Used to log/visualize the evaluation metrics during training. The values are stored at the end of each epoch. + """ def __init__(self, metrics): + """ + Args: + metrics : a list of callbacks. Possible values: + "CoherenceMetric" + "PerplexityMetric" + "DiffMetric" + "ConvergenceMetric" + """ # list of metrics to be plot self.metrics = metrics def set_model(self, model): + """ + Save the model instance and initialize any required variables which would be updated throughout training + """ self.model = model self.previous = None # check for any metric which need model state from previous epoch @@ -113,6 +245,13 @@ def set_model(self, model): self.log_type = logging.getLogger(model_type) def on_epoch_end(self, epoch, topics=None): + """ + Log or visualize current epoch's metric value + + Args: + epoch : current epoch no. + topics : topic distribution from current epoch (required for coherence of unsupported topic models) + """ # plot all metrics in current epoch for i, metric in enumerate(self.metrics): value = metric.get_value(topics=topics, model=self.model, other_model=self.previous) @@ -144,6 +283,6 @@ def on_epoch_end(self, epoch, topics=None): self.viz.updateTrace(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, win=self.windows[i]) if metric.logger=="shell": - statement = " ".join(("Epoch:", epoch, metric_label, "estimate:", str(value))) + statement = " ".join(("Epoch:", str(epoch), metric_label, "estimate:", str(value))) self.log_type.info(statement) diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index f751f5d1ba..7504268337 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -240,6 +240,8 @@ def __init__(self, corpus=None, num_topics=100, id2word=None, `random_state` can be a np.random.RandomState object or the seed for one + `callbacks` a list of metric callbacks to log/visualize evaluation metrics of topic model during training + Example: >>> lda = LdaModel(corpus, num_topics=100) # train model From a32960d0266bb13d8be8bc4cf384eef0ec2d9091 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 19 Jul 2017 03:13:47 +0530 Subject: [PATCH 20/31] fix flake8 --- gensim/models/callbacks.py | 37 ++++++++++++++++++------------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index ff3ca28a54..d4f8dbeb35 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -135,7 +135,7 @@ class DiffMetric(Metric): def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger="shell", viz_env=None, title=None): """ Args: - distance : measure used to calculate difference between any topic pair. Available values: + distance : measure used to calculate difference between any topic pair. Available values: `kullback_leibler` `hellinger` `jaccard` @@ -174,7 +174,7 @@ class ConvergenceMetric(Metric): def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger="shell", viz_env=None, title=None): """ Args: - distance : measure used to calculate difference between any topic pair. Available values: + distance : measure used to calculate difference between any topic pair. Available values: `kullback_leibler` `hellinger` `jaccard` @@ -204,7 +204,7 @@ def get_value(self, **kwargs): super(ConvergenceMetric, self).get_value(**kwargs) diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) return np.sum(np.diagonal(diff_matrix)) - + class Callback(object): """ @@ -233,25 +233,25 @@ def set_model(self, model): self.previous = copy.deepcopy(model) # store diff diagnols of previous epochs self.diff_mat = Queue() - if any(metric.logger=="visdom" for metric in self.metrics): + if any(metric.logger == "visdom" for metric in self.metrics): if not VISDOM_INSTALLED: raise ImportError("Please install Visdom for visualization") self.viz = Visdom() - # store initial plot windows of every metric (same window will be updated with increasing epochs) + # store initial plot windows of every metric (same window will be updated with increasing epochs) self.windows = [] - if any(metric.logger=="shell" for metric in self.metrics): + if any(metric.logger == "shell" for metric in self.metrics): # set logger for current topic model model_type = type(self.model).__name__ self.log_type = logging.getLogger(model_type) def on_epoch_end(self, epoch, topics=None): - """ - Log or visualize current epoch's metric value + """ + Log or visualize current epoch's metric value - Args: - epoch : current epoch no. - topics : topic distribution from current epoch (required for coherence of unsupported topic models) - """ + Args: + epoch : current epoch no. + topics : topic distribution from current epoch (required for coherence of unsupported topic models) + """ # plot all metrics in current epoch for i, metric in enumerate(self.metrics): value = metric.get_value(topics=topics, model=self.model, other_model=self.previous) @@ -260,21 +260,21 @@ def on_epoch_end(self, epoch, topics=None): if isinstance(metric, (DiffMetric, ConvergenceMetric)): self.previous = copy.deepcopy(self.model) - if metric.logger=="visdom": - if epoch==0: - if value.ndim>0: + if metric.logger == "visdom": + if epoch == 0: + if value.ndim > 0: diff_mat = np.array([value]) viz_metric = self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) # store current epoch's diff diagonal self.diff_mat.put(diff_mat) # saving initial plot window - self.windows.append(copy.deepcopy(viz_metric)) + self.windows.append(copy.deepcopy(viz_metric)) else: viz_metric = self.viz.line(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) # saving initial plot window self.windows.append(copy.deepcopy(viz_metric)) else: - if value.ndim>0: + if value.ndim > 0: # concatenate with previous epoch's diff diagonals diff_mat = np.concatenate((self.diff_mat.get(), np.array([value]))) self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, win=self.windows[i], opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) @@ -282,7 +282,6 @@ def on_epoch_end(self, epoch, topics=None): else: self.viz.updateTrace(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, win=self.windows[i]) - if metric.logger=="shell": + if metric.logger == "shell": statement = " ".join(("Epoch:", str(epoch), metric_label, "estimate:", str(value))) self.log_type.info(statement) - From 48526d9e7f6470ed059a2b2f9ed14ae0cd58b1ce Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 19 Jul 2017 04:01:08 +0530 Subject: [PATCH 21/31] add shell example --- docs/notebooks/Training_visualizations.ipynb | 311 ++++++++++++++++++- gensim/models/callbacks.py | 2 +- 2 files changed, 298 insertions(+), 15 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 5733017f82..6e67b1e6dc 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -26,24 +26,32 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Using TensorFlow backend.\n" + "INFO:gensim.corpora.dictionary:adding document #0 to Dictionary(0 unique tokens: [])\n", + "INFO:gensim.corpora.dictionary:built Dictionary(7194 unique tokens: ['payment', 'protesters', 'responded', 'scrapper', 'daunting']...) from 300 documents (total 61260 corpus positions)\n", + "INFO:gensim.corpora.dictionary:adding document #0 to Dictionary(0 unique tokens: [])\n", + "INFO:gensim.corpora.dictionary:built Dictionary(10 unique tokens: ['water', 'fall', 'river', 'rain', 'fast']...) from 5 documents (total 22 corpus positions)\n" ] } ], "source": [ "import os\n", "import re\n", + "import logging\n", "import gensim\n", "from gensim.models import ldamodel\n", "from gensim.corpora.dictionary import Dictionary\n", "\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "logger.setLevel(logging.DEBUG)\n", + "\n", "# Set file names for train and test data\n", "test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])\n", "lee_train_file = test_data_dir + os.sep + 'lee_background.cor'\n", @@ -71,21 +79,180 @@ "\n", "training_corpus = [dictionary.doc2bow(text) for text in training_texts]\n", "holdout_corpus = [dictionary.doc2bow(text) for text in holdout_texts]\n", + "test_corpus = [dictionary.doc2bow(text) for text in test_texts]\n", + "\n", + "\n", + "\n", + "\n", + "texts = [['bank','river','shore','water'],\n", + " ['river','water','flow','fast','tree'],\n", + " ['bank','water','fall','flow'],\n", + " ['bank','bank','water','rain','river'],\n", + " ['river','water','mud','tree'],\n", + " ['money','transaction','bank','finance'],\n", + " ['bank','borrow','money'], \n", + " ['bank','finance'],\n", + " ['finance','money','sell','bank'],\n", + " ['borrow','sell'],\n", + " ['bank','loan','sell']]\n", + "\n", + "training_texts = texts[:5]\n", + "test_texts = texts[5:]\n", + "\n", + "dictionary = Dictionary(training_texts)\n", + "\n", + "training_corpus = [dictionary.doc2bow(text) for text in training_texts]\n", "test_corpus = [dictionary.doc2bow(text) for text in test_texts]" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.ldamodel:using symmetric alpha at 0.2\n", + "INFO:gensim.models.ldamodel:using symmetric eta at 0.1\n", + "INFO:gensim.models.ldamodel:using serial LDA version on this node\n", + "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 5 passes over the supplied corpus of 5 documents, updating model once every 5 documents, evaluating perplexity every 5 documents, iterating 50x with a convergence threshold of 0.001000\n", + "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-4.740 per-word bound, 26.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.314*\"bank\" + 0.172*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fall\" + 0.029*\"flow\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"bank\" + 0.200*\"flow\" + 0.199*\"fall\" + 0.034*\"river\" + 0.034*\"tree\" + 0.034*\"rain\" + 0.034*\"shore\" + 0.034*\"fast\" + 0.034*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.199*\"tree\" + 0.199*\"mud\" + 0.034*\"bank\" + 0.034*\"flow\" + 0.034*\"fall\" + 0.034*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.199*\"river\" + 0.199*\"shore\" + 0.199*\"bank\" + 0.034*\"tree\" + 0.034*\"flow\" + 0.034*\"fall\" + 0.034*\"rain\" + 0.034*\"mud\" + 0.034*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"water\" + 0.171*\"river\" + 0.171*\"tree\" + 0.171*\"flow\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic diff=2.719831, rho=1.000000\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-3.395 per-word bound, 10.5 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.314*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"flow\" + 0.029*\"fall\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"flow\" + 0.200*\"bank\" + 0.200*\"fall\" + 0.034*\"river\" + 0.034*\"tree\" + 0.033*\"rain\" + 0.033*\"shore\" + 0.033*\"fast\" + 0.033*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"tree\" + 0.200*\"mud\" + 0.034*\"bank\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"shore\" + 0.200*\"bank\" + 0.034*\"tree\" + 0.034*\"flow\" + 0.033*\"fall\" + 0.033*\"rain\" + 0.033*\"mud\" + 0.033*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"water\" + 0.171*\"river\" + 0.171*\"tree\" + 0.171*\"flow\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.017857, rho=0.577350\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-3.395 per-word bound, 10.5 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.314*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"flow\" + 0.029*\"fall\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"flow\" + 0.200*\"fall\" + 0.200*\"bank\" + 0.034*\"river\" + 0.033*\"tree\" + 0.033*\"rain\" + 0.033*\"shore\" + 0.033*\"fast\" + 0.033*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"tree\" + 0.200*\"mud\" + 0.034*\"bank\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"shore\" + 0.200*\"bank\" + 0.033*\"tree\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"rain\" + 0.033*\"mud\" + 0.033*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"water\" + 0.171*\"river\" + 0.171*\"tree\" + 0.171*\"flow\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.006606, rho=0.500000\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-3.395 per-word bound, 10.5 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 3, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.315*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"flow\" + 0.029*\"fall\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"flow\" + 0.200*\"fall\" + 0.200*\"bank\" + 0.033*\"river\" + 0.033*\"tree\" + 0.033*\"rain\" + 0.033*\"shore\" + 0.033*\"fast\" + 0.033*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"tree\" + 0.200*\"mud\" + 0.033*\"bank\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"shore\" + 0.200*\"bank\" + 0.033*\"tree\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"rain\" + 0.033*\"mud\" + 0.033*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"river\" + 0.171*\"water\" + 0.171*\"tree\" + 0.171*\"fast\" + 0.171*\"flow\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.002968, rho=0.447214\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-3.395 per-word bound, 10.5 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 4, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.315*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"flow\" + 0.029*\"fall\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"flow\" + 0.200*\"fall\" + 0.200*\"bank\" + 0.033*\"river\" + 0.033*\"tree\" + 0.033*\"rain\" + 0.033*\"shore\" + 0.033*\"fast\" + 0.033*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"tree\" + 0.200*\"mud\" + 0.033*\"bank\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"shore\" + 0.200*\"bank\" + 0.033*\"tree\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"rain\" + 0.033*\"mud\" + 0.033*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"river\" + 0.171*\"fast\" + 0.171*\"water\" + 0.171*\"tree\" + 0.171*\"flow\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.001501, rho=0.408248\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", + "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", + "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n" + ] + } + ], "source": [ "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", "\n", "# define perplexity callback for hold_out and test corpus\n", - "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (hold_out)\")\n", + "# pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (hold_out)\")\n", "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (test)\")\n", "\n", "# define other remaining metrics available\n", @@ -93,7 +260,7 @@ "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Diff (kullback_leibler)\")\n", "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Convergence (jaccard)\")\n", "\n", - "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", + "callbacks = [pl_test, ch_umass, diff_kl, convergence_jc]\n", "\n", "# training LDA model\n", "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, passes=5, num_topics=5, callbacks=callbacks)" @@ -177,14 +344,121 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { - "collapsed": true + "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.ldamodel:using symmetric alpha at 0.2\n", + "INFO:gensim.models.ldamodel:using symmetric eta at 0.1\n", + "INFO:gensim.models.ldamodel:using serial LDA version on this node\n", + "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 5 passes over the supplied corpus of 5 documents, updating model once every 5 documents, evaluating perplexity every 5 documents, iterating 50x with a convergence threshold of 0.001000\n", + "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-4.742 per-word bound, 26.8 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:4/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.171*\"river\" + 0.171*\"flow\" + 0.171*\"water\" + 0.171*\"tree\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.102*\"water\" + 0.101*\"bank\" + 0.101*\"river\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.099*\"shore\" + 0.099*\"fall\" + 0.099*\"mud\" + 0.099*\"fast\" + 0.099*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.312*\"bank\" + 0.172*\"water\" + 0.171*\"river\" + 0.170*\"rain\" + 0.030*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.228*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"flow\" + 0.086*\"shore\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.102*\"water\" + 0.101*\"bank\" + 0.101*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.099*\"fall\" + 0.099*\"shore\" + 0.099*\"mud\" + 0.099*\"rain\" + 0.099*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic diff=2.427522, rho=1.000000\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:LdaModel:Epoch: 0 Perplexity estimate: 53.7531451081\n", + "INFO:LdaModel:Epoch: 0 Coherence estimate: -12.3096030034\n", + "INFO:LdaModel:Epoch: 0 Diff estimate: [ 0.56577408 0.00186987 0.8476325 0.42813478 0.00592724]\n", + "INFO:LdaModel:Epoch: 0 Convergence estimate: 0.0\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-3.415 per-word bound, 10.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.172*\"flow\" + 0.171*\"river\" + 0.171*\"tree\" + 0.171*\"water\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.101*\"water\" + 0.101*\"bank\" + 0.101*\"river\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.100*\"shore\" + 0.100*\"fall\" + 0.100*\"mud\" + 0.100*\"fast\" + 0.100*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.314*\"bank\" + 0.172*\"water\" + 0.171*\"river\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.229*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"shore\" + 0.086*\"flow\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.101*\"water\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.100*\"fall\" + 0.100*\"shore\" + 0.100*\"mud\" + 0.100*\"rain\" + 0.100*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.025640, rho=0.577350\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:LdaModel:Epoch: 1 Perplexity estimate: 53.8189543573\n", + "INFO:LdaModel:Epoch: 1 Coherence estimate: -12.2993692919\n", + "INFO:LdaModel:Epoch: 1 Diff estimate: [ 8.26921207e-06 2.09745049e-05 2.55995368e-05 8.01051743e-07\n", + " 1.84826416e-05]\n", + "INFO:LdaModel:Epoch: 1 Convergence estimate: 0.0\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-3.415 per-word bound, 10.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.172*\"flow\" + 0.171*\"tree\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.100*\"water\" + 0.100*\"bank\" + 0.100*\"river\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.100*\"shore\" + 0.100*\"fall\" + 0.100*\"mud\" + 0.100*\"fast\" + 0.100*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.314*\"bank\" + 0.171*\"water\" + 0.171*\"river\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.229*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"shore\" + 0.086*\"flow\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.101*\"water\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.100*\"fall\" + 0.100*\"shore\" + 0.100*\"mud\" + 0.100*\"rain\" + 0.100*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.009586, rho=0.500000\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:LdaModel:Epoch: 2 Perplexity estimate: 53.8493708819\n", + "INFO:LdaModel:Epoch: 2 Coherence estimate: -12.2962886378\n", + "INFO:LdaModel:Epoch: 2 Diff estimate: [ 1.10093639e-06 2.89059096e-06 3.47294813e-06 1.06855942e-07\n", + " 2.54900217e-06]\n", + "INFO:LdaModel:Epoch: 2 Convergence estimate: 0.0\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-3.415 per-word bound, 10.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 3, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.172*\"flow\" + 0.172*\"tree\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.100*\"water\" + 0.100*\"river\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.100*\"shore\" + 0.100*\"fall\" + 0.100*\"mud\" + 0.100*\"fast\" + 0.100*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.314*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.229*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"shore\" + 0.086*\"flow\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.100*\"water\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.100*\"fall\" + 0.100*\"shore\" + 0.100*\"mud\" + 0.100*\"rain\" + 0.100*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.004327, rho=0.447214\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:LdaModel:Epoch: 3 Perplexity estimate: 53.8641110442\n", + "INFO:LdaModel:Epoch: 3 Coherence estimate: -12.2965754756\n", + "INFO:LdaModel:Epoch: 3 Diff estimate: [ 2.20614278e-07 5.86332873e-07 6.98894080e-07 2.13793303e-08\n", + " 5.17215648e-07]\n", + "INFO:LdaModel:Epoch: 3 Convergence estimate: 0.0\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:gensim.models.ldamodel:-3.415 per-word bound, 10.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 4, at document #5/5\n", + "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", + "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:gensim.models.ldamodel:updating topics\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.172*\"flow\" + 0.172*\"tree\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.100*\"water\" + 0.100*\"river\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.100*\"shore\" + 0.100*\"fall\" + 0.100*\"mud\" + 0.100*\"fast\" + 0.100*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.315*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.229*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"shore\" + 0.086*\"flow\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.100*\"water\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.100*\"fall\" + 0.100*\"shore\" + 0.100*\"mud\" + 0.100*\"rain\" + 0.100*\"fast\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.002194, rho=0.408248\n", + "DEBUG:gensim.models.ldamodel:bound: at document #0\n", + "INFO:LdaModel:Epoch: 4 Perplexity estimate: 53.8717756756\n", + "INFO:LdaModel:Epoch: 4 Coherence estimate: -12.2965754756\n", + "INFO:LdaModel:Epoch: 4 Diff estimate: [ 5.59594760e-08 1.50634130e-07 1.78680797e-07 5.46795809e-09\n", + " 1.32903255e-07]\n", + "INFO:LdaModel:Epoch: 4 Convergence estimate: 0.0\n" + ] + } + ], "source": [ "# define perplexity callback for hold_out and test corpus\n", - "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"shell\")\n", + "# pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"shell\")\n", "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"shell\")\n", "\n", "# define other remaining metrics available\n", @@ -192,11 +466,20 @@ "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"shell\")\n", "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"shell\")\n", "\n", - "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", + "callbacks = [pl_test, ch_umass, diff_kl, convergence_jc]\n", "\n", "# training LDA model\n", "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, passes=5, num_topics=5, callbacks=callbacks)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index d4f8dbeb35..2038850f01 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -283,5 +283,5 @@ def on_epoch_end(self, epoch, topics=None): self.viz.updateTrace(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, win=self.windows[i]) if metric.logger == "shell": - statement = " ".join(("Epoch:", str(epoch), metric_label, "estimate:", str(value))) + statement = "".join(("Epoch ", str(epoch), ": ", metric_label, " estimate: ", str(value))) self.log_type.info(statement) From adf2a606021fdfead66566b4c0c02435d33ec153 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 19 Jul 2017 22:32:42 +0530 Subject: [PATCH 22/31] fix queue import for both py2/py3 --- docs/notebooks/Training_visualizations.ipynb | 377 ++++--------------- gensim/models/callbacks.py | 7 +- 2 files changed, 82 insertions(+), 302 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 6e67b1e6dc..4134929283 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -26,36 +26,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:gensim.corpora.dictionary:adding document #0 to Dictionary(0 unique tokens: [])\n", - "INFO:gensim.corpora.dictionary:built Dictionary(7194 unique tokens: ['payment', 'protesters', 'responded', 'scrapper', 'daunting']...) from 300 documents (total 61260 corpus positions)\n", - "INFO:gensim.corpora.dictionary:adding document #0 to Dictionary(0 unique tokens: [])\n", - "INFO:gensim.corpora.dictionary:built Dictionary(10 unique tokens: ['water', 'fall', 'river', 'rain', 'fast']...) from 5 documents (total 22 corpus positions)\n" + "Using TensorFlow backend.\n" ] } ], "source": [ "import os\n", "import re\n", - "import logging\n", "import gensim\n", "from gensim.models import ldamodel\n", "from gensim.corpora.dictionary import Dictionary\n", "\n", - "logging.basicConfig(level=logging.INFO)\n", - "logger = logging.getLogger(__name__)\n", - "logger.setLevel(logging.DEBUG)\n", - "\n", - "# Set file names for train and test data\n", + "# Set file names for train data\n", "test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])\n", - "lee_train_file = test_data_dir + os.sep + 'lee_background.cor'\n", - "lee_test_file = test_data_dir + os.sep + 'lee.cor'\n", + "lee_corpus = test_data_dir + os.sep + 'lee.cor'\n", "\n", "def read_corpus(fname):\n", " texts = []\n", @@ -68,202 +59,45 @@ " texts.append(words)\n", " return texts\n", "\n", - "training_texts = read_corpus(lee_train_file)\n", - "test_texts = read_corpus(lee_test_file)\n", + "texts = read_corpus(lee_corpus)\n", "\n", "# Split test data into hold_out and test corpus\n", - "holdout_texts = test_texts[:25]\n", - "test_texts = test_texts[25:]\n", - "\n", - "dictionary = Dictionary(training_texts)\n", - "\n", - "training_corpus = [dictionary.doc2bow(text) for text in training_texts]\n", - "holdout_corpus = [dictionary.doc2bow(text) for text in holdout_texts]\n", - "test_corpus = [dictionary.doc2bow(text) for text in test_texts]\n", + "training_texts = texts[:25]\n", + "holdout_texts = texts[25:40]\n", + "test_texts = texts[40:50]\n", "\n", + "training_dictionary = Dictionary(training_texts)\n", + "holdout_dictionary = Dictionary(holdout_texts)\n", + "test_dictionary = Dictionary(test_texts)\n", "\n", - "\n", - "\n", - "texts = [['bank','river','shore','water'],\n", - " ['river','water','flow','fast','tree'],\n", - " ['bank','water','fall','flow'],\n", - " ['bank','bank','water','rain','river'],\n", - " ['river','water','mud','tree'],\n", - " ['money','transaction','bank','finance'],\n", - " ['bank','borrow','money'], \n", - " ['bank','finance'],\n", - " ['finance','money','sell','bank'],\n", - " ['borrow','sell'],\n", - " ['bank','loan','sell']]\n", - "\n", - "training_texts = texts[:5]\n", - "test_texts = texts[5:]\n", - "\n", - "dictionary = Dictionary(training_texts)\n", - "\n", - "training_corpus = [dictionary.doc2bow(text) for text in training_texts]\n", - "test_corpus = [dictionary.doc2bow(text) for text in test_texts]" + "training_corpus = [training_dictionary.doc2bow(text) for text in training_texts]\n", + "holdout_corpus = [holdout_dictionary.doc2bow(text) for text in holdout_texts]\n", + "test_corpus = [test_dictionary.doc2bow(text) for text in test_texts]" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:gensim.models.ldamodel:using symmetric alpha at 0.2\n", - "INFO:gensim.models.ldamodel:using symmetric eta at 0.1\n", - "INFO:gensim.models.ldamodel:using serial LDA version on this node\n", - "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 5 passes over the supplied corpus of 5 documents, updating model once every 5 documents, evaluating perplexity every 5 documents, iterating 50x with a convergence threshold of 0.001000\n", - "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-4.740 per-word bound, 26.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.314*\"bank\" + 0.172*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fall\" + 0.029*\"flow\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"bank\" + 0.200*\"flow\" + 0.199*\"fall\" + 0.034*\"river\" + 0.034*\"tree\" + 0.034*\"rain\" + 0.034*\"shore\" + 0.034*\"fast\" + 0.034*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.199*\"tree\" + 0.199*\"mud\" + 0.034*\"bank\" + 0.034*\"flow\" + 0.034*\"fall\" + 0.034*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.199*\"river\" + 0.199*\"shore\" + 0.199*\"bank\" + 0.034*\"tree\" + 0.034*\"flow\" + 0.034*\"fall\" + 0.034*\"rain\" + 0.034*\"mud\" + 0.034*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"water\" + 0.171*\"river\" + 0.171*\"tree\" + 0.171*\"flow\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic diff=2.719831, rho=1.000000\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-3.395 per-word bound, 10.5 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.314*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"flow\" + 0.029*\"fall\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"flow\" + 0.200*\"bank\" + 0.200*\"fall\" + 0.034*\"river\" + 0.034*\"tree\" + 0.033*\"rain\" + 0.033*\"shore\" + 0.033*\"fast\" + 0.033*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"tree\" + 0.200*\"mud\" + 0.034*\"bank\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"shore\" + 0.200*\"bank\" + 0.034*\"tree\" + 0.034*\"flow\" + 0.033*\"fall\" + 0.033*\"rain\" + 0.033*\"mud\" + 0.033*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"water\" + 0.171*\"river\" + 0.171*\"tree\" + 0.171*\"flow\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.017857, rho=0.577350\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-3.395 per-word bound, 10.5 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.314*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"flow\" + 0.029*\"fall\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"flow\" + 0.200*\"fall\" + 0.200*\"bank\" + 0.034*\"river\" + 0.033*\"tree\" + 0.033*\"rain\" + 0.033*\"shore\" + 0.033*\"fast\" + 0.033*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"tree\" + 0.200*\"mud\" + 0.034*\"bank\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"shore\" + 0.200*\"bank\" + 0.033*\"tree\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"rain\" + 0.033*\"mud\" + 0.033*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"water\" + 0.171*\"river\" + 0.171*\"tree\" + 0.171*\"flow\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.006606, rho=0.500000\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-3.395 per-word bound, 10.5 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 3, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.315*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"flow\" + 0.029*\"fall\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"flow\" + 0.200*\"fall\" + 0.200*\"bank\" + 0.033*\"river\" + 0.033*\"tree\" + 0.033*\"rain\" + 0.033*\"shore\" + 0.033*\"fast\" + 0.033*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"tree\" + 0.200*\"mud\" + 0.033*\"bank\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"shore\" + 0.200*\"bank\" + 0.033*\"tree\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"rain\" + 0.033*\"mud\" + 0.033*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"river\" + 0.171*\"water\" + 0.171*\"tree\" + 0.171*\"fast\" + 0.171*\"flow\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.002968, rho=0.447214\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-3.395 per-word bound, 10.5 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 4, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.315*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"flow\" + 0.029*\"fall\" + 0.029*\"mud\" + 0.029*\"fast\" + 0.029*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.200*\"water\" + 0.200*\"flow\" + 0.200*\"fall\" + 0.200*\"bank\" + 0.033*\"river\" + 0.033*\"tree\" + 0.033*\"rain\" + 0.033*\"shore\" + 0.033*\"fast\" + 0.033*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"tree\" + 0.200*\"mud\" + 0.033*\"bank\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"fast\" + 0.033*\"rain\" + 0.033*\"shore\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.200*\"water\" + 0.200*\"river\" + 0.200*\"shore\" + 0.200*\"bank\" + 0.033*\"tree\" + 0.033*\"flow\" + 0.033*\"fall\" + 0.033*\"rain\" + 0.033*\"mud\" + 0.033*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.171*\"river\" + 0.171*\"fast\" + 0.171*\"water\" + 0.171*\"tree\" + 0.171*\"flow\" + 0.029*\"bank\" + 0.029*\"fall\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.001501, rho=0.408248\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /events HTTP/1.1\" 200 19\n", - "DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): localhost\n", - "DEBUG:urllib3.connectionpool:http://localhost:8097 \"POST /update HTTP/1.1\" 200 19\n" - ] - } - ], + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", "\n", "# define perplexity callback for hold_out and test corpus\n", - "# pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (hold_out)\")\n", + "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (hold_out)\")\n", "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (test)\")\n", "\n", "# define other remaining metrics available\n", "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Coherence (u_mass)\")\n", "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Diff (kullback_leibler)\")\n", - "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Convergence (jaccard)\")\n", + "convergence_jc = ConvergenceMetric(distance=\"hellinger\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Convergence (jaccard)\")\n", "\n", - "callbacks = [pl_test, ch_umass, diff_kl, convergence_jc]\n", + "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", "\n", "# training LDA model\n", - "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, passes=5, num_topics=5, callbacks=callbacks)" + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=5, num_topics=5, callbacks=callbacks)" ] }, { @@ -282,7 +116,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "-21.8411539992\n" + "-22.4298221364\n" ] } ], @@ -339,12 +173,12 @@ "source": [ "# Training Logs\n", "\n", - "We can also log the metric values to the shell apart from visualizing them in Visdom. The only change is to define `logger=\"shell\"` instead of `\"visdom\"` in the input callbacks." + "We can also log the metric values after every epoch to the shell apart from visualizing them in Visdom. The only change is to define `logger=\"shell\"` instead of `\"visdom\"` in the input callbacks." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -354,111 +188,61 @@ "output_type": "stream", "text": [ "INFO:gensim.models.ldamodel:using symmetric alpha at 0.2\n", - "INFO:gensim.models.ldamodel:using symmetric eta at 0.1\n", + "INFO:gensim.models.ldamodel:using symmetric eta at 0.0009950248756218905\n", "INFO:gensim.models.ldamodel:using serial LDA version on this node\n", - "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 5 passes over the supplied corpus of 5 documents, updating model once every 5 documents, evaluating perplexity every 5 documents, iterating 50x with a convergence threshold of 0.001000\n", + "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 3 passes over the supplied corpus of 25 documents, updating model once every 25 documents, evaluating perplexity every 25 documents, iterating 50x with a convergence threshold of 0.001000\n", "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-4.742 per-word bound, 26.8 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:4/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.171*\"river\" + 0.171*\"flow\" + 0.171*\"water\" + 0.171*\"tree\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.102*\"water\" + 0.101*\"bank\" + 0.101*\"river\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.099*\"shore\" + 0.099*\"fall\" + 0.099*\"mud\" + 0.099*\"fast\" + 0.099*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.312*\"bank\" + 0.172*\"water\" + 0.171*\"river\" + 0.170*\"rain\" + 0.030*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.228*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"flow\" + 0.086*\"shore\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.102*\"water\" + 0.101*\"bank\" + 0.101*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.099*\"fall\" + 0.099*\"shore\" + 0.099*\"mud\" + 0.099*\"rain\" + 0.099*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic diff=2.427522, rho=1.000000\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:LdaModel:Epoch: 0 Perplexity estimate: 53.7531451081\n", - "INFO:LdaModel:Epoch: 0 Coherence estimate: -12.3096030034\n", - "INFO:LdaModel:Epoch: 0 Diff estimate: [ 0.56577408 0.00186987 0.8476325 0.42813478 0.00592724]\n", - "INFO:LdaModel:Epoch: 0 Convergence estimate: 0.0\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-3.415 per-word bound, 10.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.172*\"flow\" + 0.171*\"river\" + 0.171*\"tree\" + 0.171*\"water\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.101*\"water\" + 0.101*\"bank\" + 0.101*\"river\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.100*\"shore\" + 0.100*\"fall\" + 0.100*\"mud\" + 0.100*\"fast\" + 0.100*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.314*\"bank\" + 0.172*\"water\" + 0.171*\"river\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.229*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"shore\" + 0.086*\"flow\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.101*\"water\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.100*\"fall\" + 0.100*\"shore\" + 0.100*\"mud\" + 0.100*\"rain\" + 0.100*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.025640, rho=0.577350\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:LdaModel:Epoch: 1 Perplexity estimate: 53.8189543573\n", - "INFO:LdaModel:Epoch: 1 Coherence estimate: -12.2993692919\n", - "INFO:LdaModel:Epoch: 1 Diff estimate: [ 8.26921207e-06 2.09745049e-05 2.55995368e-05 8.01051743e-07\n", - " 1.84826416e-05]\n", - "INFO:LdaModel:Epoch: 1 Convergence estimate: 0.0\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-3.415 per-word bound, 10.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.172*\"flow\" + 0.171*\"tree\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.100*\"water\" + 0.100*\"bank\" + 0.100*\"river\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.100*\"shore\" + 0.100*\"fall\" + 0.100*\"mud\" + 0.100*\"fast\" + 0.100*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.314*\"bank\" + 0.171*\"water\" + 0.171*\"river\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.229*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"shore\" + 0.086*\"flow\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.101*\"water\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.100*\"fall\" + 0.100*\"shore\" + 0.100*\"mud\" + 0.100*\"rain\" + 0.100*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.009586, rho=0.500000\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:LdaModel:Epoch: 2 Perplexity estimate: 53.8493708819\n", - "INFO:LdaModel:Epoch: 2 Coherence estimate: -12.2962886378\n", - "INFO:LdaModel:Epoch: 2 Diff estimate: [ 1.10093639e-06 2.89059096e-06 3.47294813e-06 1.06855942e-07\n", - " 2.54900217e-06]\n", - "INFO:LdaModel:Epoch: 2 Convergence estimate: 0.0\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-3.415 per-word bound, 10.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 3, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n", - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.172*\"flow\" + 0.172*\"tree\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.100*\"water\" + 0.100*\"river\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.100*\"shore\" + 0.100*\"fall\" + 0.100*\"mud\" + 0.100*\"fast\" + 0.100*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.314*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.229*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"shore\" + 0.086*\"flow\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.100*\"water\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.100*\"fall\" + 0.100*\"shore\" + 0.100*\"mud\" + 0.100*\"rain\" + 0.100*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.004327, rho=0.447214\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:LdaModel:Epoch: 3 Perplexity estimate: 53.8641110442\n", - "INFO:LdaModel:Epoch: 3 Coherence estimate: -12.2965754756\n", - "INFO:LdaModel:Epoch: 3 Diff estimate: [ 2.20614278e-07 5.86332873e-07 6.98894080e-07 2.13793303e-08\n", - " 5.17215648e-07]\n", - "INFO:LdaModel:Epoch: 3 Convergence estimate: 0.0\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:gensim.models.ldamodel:-3.415 per-word bound, 10.7 perplexity estimate based on a held-out corpus of 5 documents with 22 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 4, at document #5/5\n", - "DEBUG:gensim.models.ldamodel:performing inference on a chunk of 5 documents\n", - "DEBUG:gensim.models.ldamodel:5/5 documents converged within 50 iterations\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:gensim.models.ldamodel:updating topics\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.172*\"flow\" + 0.172*\"tree\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"fast\" + 0.029*\"bank\" + 0.029*\"shore\" + 0.029*\"mud\" + 0.029*\"fall\" + 0.029*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.100*\"water\" + 0.100*\"river\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"flow\" + 0.100*\"shore\" + 0.100*\"fall\" + 0.100*\"mud\" + 0.100*\"fast\" + 0.100*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.315*\"bank\" + 0.171*\"river\" + 0.171*\"water\" + 0.171*\"rain\" + 0.029*\"tree\" + 0.029*\"fast\" + 0.029*\"flow\" + 0.029*\"shore\" + 0.029*\"fall\" + 0.029*\"mud\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.229*\"water\" + 0.157*\"river\" + 0.157*\"bank\" + 0.086*\"mud\" + 0.086*\"fall\" + 0.086*\"shore\" + 0.086*\"flow\" + 0.086*\"tree\" + 0.014*\"fast\" + 0.014*\"rain\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.100*\"water\" + 0.100*\"bank\" + 0.100*\"tree\" + 0.100*\"river\" + 0.100*\"flow\" + 0.100*\"fall\" + 0.100*\"shore\" + 0.100*\"mud\" + 0.100*\"rain\" + 0.100*\"fast\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.002194, rho=0.408248\n", - "DEBUG:gensim.models.ldamodel:bound: at document #0\n", - "INFO:LdaModel:Epoch: 4 Perplexity estimate: 53.8717756756\n", - "INFO:LdaModel:Epoch: 4 Coherence estimate: -12.2965754756\n", - "INFO:LdaModel:Epoch: 4 Diff estimate: [ 5.59594760e-08 1.50634130e-07 1.78680797e-07 5.46795809e-09\n", - " 1.32903255e-07]\n", - "INFO:LdaModel:Epoch: 4 Convergence estimate: 0.0\n" + "INFO:gensim.models.ldamodel:-9.032 per-word bound, 523.4 perplexity estimate based on a held-out corpus of 25 documents with 2182 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #25/25\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.025*\"the\" + 0.022*\"to\" + 0.013*\"in\" + 0.011*\"of\" + 0.010*\"and\" + 0.007*\"s\" + 0.007*\"a\" + 0.005*\"said\" + 0.004*\"he\" + 0.004*\"iraqi\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.054*\"the\" + 0.028*\"to\" + 0.023*\"of\" + 0.019*\"and\" + 0.017*\"in\" + 0.016*\"a\" + 0.013*\"s\" + 0.009*\"that\" + 0.008*\"has\" + 0.007*\"on\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.049*\"the\" + 0.027*\"in\" + 0.024*\"to\" + 0.016*\"of\" + 0.012*\"and\" + 0.007*\"a\" + 0.007*\"that\" + 0.006*\"is\" + 0.006*\"for\" + 0.006*\"work\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.041*\"the\" + 0.024*\"of\" + 0.021*\"to\" + 0.016*\"a\" + 0.016*\"and\" + 0.012*\"s\" + 0.010*\"in\" + 0.007*\"as\" + 0.006*\"with\" + 0.006*\"party\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.034*\"the\" + 0.023*\"of\" + 0.016*\"a\" + 0.015*\"and\" + 0.012*\"in\" + 0.011*\"to\" + 0.008*\"as\" + 0.007*\"is\" + 0.007*\"with\" + 0.007*\"that\"\n", + "INFO:gensim.models.ldamodel:topic diff=1.878616, rho=1.000000\n", + "INFO:LdaModel:Epoch 0: Perplexity estimate: 495.948922721\n", + "INFO:LdaModel:Epoch 0: Perplexity estimate: 609.481073631\n", + "INFO:LdaModel:Epoch 0: Coherence estimate: -22.4407925538\n", + "INFO:LdaModel:Epoch 0: Diff estimate: [ 0.44194712 0.96670853 0.8036907 0.72372737 0.63141336]\n", + "INFO:LdaModel:Epoch 0: Convergence estimate: 0.0\n", + "INFO:gensim.models.ldamodel:-7.121 per-word bound, 139.2 perplexity estimate based on a held-out corpus of 25 documents with 2182 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #25/25\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.024*\"the\" + 0.015*\"to\" + 0.014*\"in\" + 0.008*\"iraqi\" + 0.008*\"of\" + 0.008*\"s\" + 0.007*\"and\" + 0.007*\"said\" + 0.005*\"u\" + 0.005*\"air\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.056*\"the\" + 0.029*\"to\" + 0.025*\"of\" + 0.022*\"and\" + 0.017*\"in\" + 0.015*\"a\" + 0.013*\"s\" + 0.008*\"that\" + 0.008*\"has\" + 0.008*\"on\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.050*\"the\" + 0.027*\"in\" + 0.025*\"to\" + 0.017*\"of\" + 0.010*\"and\" + 0.008*\"is\" + 0.007*\"be\" + 0.007*\"work\" + 0.007*\"a\" + 0.007*\"that\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.031*\"the\" + 0.020*\"of\" + 0.019*\"to\" + 0.018*\"a\" + 0.014*\"and\" + 0.012*\"s\" + 0.009*\"in\" + 0.008*\"as\" + 0.007*\"party\" + 0.007*\"by\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.033*\"the\" + 0.022*\"of\" + 0.014*\"and\" + 0.014*\"a\" + 0.012*\"in\" + 0.010*\"to\" + 0.010*\"iraq\" + 0.008*\"with\" + 0.008*\"that\" + 0.008*\"as\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.570396, rho=0.577350\n", + "INFO:LdaModel:Epoch 1: Perplexity estimate: 439.092043455\n", + "INFO:LdaModel:Epoch 1: Perplexity estimate: 534.74704957\n", + "INFO:LdaModel:Epoch 1: Coherence estimate: -22.4302876151\n", + "INFO:LdaModel:Epoch 1: Diff estimate: [ 0.08300085 0.0312854 0.06168011 0.06740617 0.08645297]\n", + "INFO:LdaModel:Epoch 1: Convergence estimate: 0.0\n", + "INFO:gensim.models.ldamodel:-6.842 per-word bound, 114.7 perplexity estimate based on a held-out corpus of 25 documents with 2182 words\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #25/25\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.023*\"the\" + 0.014*\"in\" + 0.011*\"to\" + 0.009*\"iraqi\" + 0.008*\"s\" + 0.007*\"said\" + 0.007*\"u\" + 0.007*\"air\" + 0.007*\"coalition\" + 0.007*\"military\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.056*\"the\" + 0.030*\"to\" + 0.026*\"of\" + 0.022*\"and\" + 0.017*\"in\" + 0.014*\"a\" + 0.014*\"s\" + 0.008*\"has\" + 0.008*\"on\" + 0.008*\"that\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.050*\"the\" + 0.026*\"in\" + 0.025*\"to\" + 0.017*\"of\" + 0.008*\"and\" + 0.008*\"is\" + 0.008*\"be\" + 0.008*\"work\" + 0.007*\"a\" + 0.007*\"that\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.027*\"the\" + 0.021*\"a\" + 0.019*\"of\" + 0.019*\"to\" + 0.013*\"s\" + 0.013*\"and\" + 0.009*\"in\" + 0.009*\"as\" + 0.009*\"party\" + 0.008*\"by\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.036*\"the\" + 0.022*\"of\" + 0.015*\"and\" + 0.013*\"a\" + 0.012*\"in\" + 0.011*\"iraq\" + 0.011*\"to\" + 0.010*\"with\" + 0.009*\"that\" + 0.008*\"are\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.363037, rho=0.500000\n", + "INFO:LdaModel:Epoch 2: Perplexity estimate: 413.6047717\n", + "INFO:LdaModel:Epoch 2: Perplexity estimate: 515.596865513\n", + "INFO:LdaModel:Epoch 2: Coherence estimate: -22.4273737487\n", + "INFO:LdaModel:Epoch 2: Diff estimate: [ 0.01187035 0.01098017 0.00861298 0.02232568 0.01953778]\n", + "INFO:LdaModel:Epoch 2: Convergence estimate: 0.0\n" ] } ], "source": [ + "import logging\n", + "\n", + "logging.basicConfig(level=logging.INFO)\n", + "logger = logging.getLogger(__name__)\n", + "logger.setLevel(logging.DEBUG)\n", + "\n", "# define perplexity callback for hold_out and test corpus\n", - "# pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"shell\")\n", + "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"shell\")\n", "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"shell\")\n", "\n", "# define other remaining metrics available\n", @@ -466,20 +250,11 @@ "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"shell\")\n", "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"shell\")\n", "\n", - "callbacks = [pl_test, ch_umass, diff_kl, convergence_jc]\n", + "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", "\n", "# training LDA model\n", - "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, passes=5, num_topics=5, callbacks=callbacks)" + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, callbacks=callbacks)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index 2038850f01..f51137a085 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -1,8 +1,13 @@ import gensim import logging import copy +import sys import numpy as np -from queue import Queue + +if sys.version_info[0] >= 3: + from queue import Queue +else: + from Queue import Queue # Visdom is used for training stats visualization try: From a2720902f4dc22c4b51060daa3a28494c837f9df Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 2 Aug 2017 23:56:01 +0530 Subject: [PATCH 23/31] store metrics in model instance --- docs/notebooks/Training_visualizations.ipynb | 2 +- gensim/models/callbacks.py | 40 +++++++++++++------- gensim/models/ldamodel.py | 10 ++++- 3 files changed, 36 insertions(+), 16 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 4134929283..0b9ad914af 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -92,7 +92,7 @@ "# define other remaining metrics available\n", "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Coherence (u_mass)\")\n", "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Diff (kullback_leibler)\")\n", - "convergence_jc = ConvergenceMetric(distance=\"hellinger\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Convergence (jaccard)\")\n", + "convergence_jc = ConvergenceMetric(distance=\"hellinger\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Convergence (hellinger)\")\n", "\n", "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", "\n", diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index f51137a085..707cbafc90 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -24,19 +24,22 @@ class Metric(object): def __init__(self): pass - def get_value(self, **parameters): + def set_parameters(self, **parameters): """ Set the parameters """ for parameter, value in parameters.items(): setattr(self, parameter, value) + def get_value(self): + pass + class CoherenceMetric(Metric): """ Metric class for coherence evaluation """ - def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, window_size=None, topn=None, logger="shell", viz_env=None, title=None): + def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, window_size=None, topn=10, logger=None, viz_env=None, title=None): """ Args: corpus : Gensim document corpus. @@ -98,7 +101,7 @@ def get_value(self, **kwargs): # only one of the model or topic would be defined self.model = None self.topics = None - super(CoherenceMetric, self).get_value(**kwargs) + super(CoherenceMetric, self).set_parameters(**kwargs) cm = gensim.models.CoherenceModel(self.model, self.topics, self.texts, self.corpus, self.dictionary, self.window_size, self.coherence, self.topn) return cm.get_coherence() @@ -107,7 +110,7 @@ class PerplexityMetric(Metric): """ Metric class for perplexity evaluation """ - def __init__(self, corpus=None, logger="shell", viz_env=None, title=None): + def __init__(self, corpus=None, logger=None, viz_env=None, title=None): """ Args: corpus : Gensim document corpus @@ -127,7 +130,7 @@ def get_value(self, **kwargs): Args: model : Trained topic model """ - super(PerplexityMetric, self).get_value(**kwargs) + super(PerplexityMetric, self).set_parameters(**kwargs) corpus_words = sum(cnt for document in self.corpus for _, cnt in document) perwordbound = self.model.bound(self.corpus) / corpus_words return np.exp2(-perwordbound) @@ -137,7 +140,7 @@ class DiffMetric(Metric): """ Metric class for topic difference evaluation """ - def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger="shell", viz_env=None, title=None): + def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): """ Args: distance : measure used to calculate difference between any topic pair. Available values: @@ -167,7 +170,7 @@ def get_value(self, **kwargs): model : Trained topic model other_model : second topic model instance to calculate the difference from """ - super(DiffMetric, self).get_value(**kwargs) + super(DiffMetric, self).set_parameters(**kwargs) diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) return np.diagonal(diff_matrix) @@ -176,7 +179,7 @@ class ConvergenceMetric(Metric): """ Metric class for convergence evaluation """ - def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger="shell", viz_env=None, title=None): + def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): """ Args: distance : measure used to calculate difference between any topic pair. Available values: @@ -206,7 +209,7 @@ def get_value(self, **kwargs): model : Trained topic model other_model : second topic model instance to calculate the difference from """ - super(ConvergenceMetric, self).get_value(**kwargs) + super(ConvergenceMetric, self).set_parameters(**kwargs) diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) return np.sum(np.diagonal(diff_matrix)) @@ -257,10 +260,16 @@ def on_epoch_end(self, epoch, topics=None): epoch : current epoch no. topics : topic distribution from current epoch (required for coherence of unsupported topic models) """ + # stores current epoch's metric values + current_metrics = {} + # plot all metrics in current epoch for i, metric in enumerate(self.metrics): value = metric.get_value(topics=topics, model=self.model, other_model=self.previous) - metric_label = type(metric).__name__[:-6] + metric_label = type(metric).__name__ + + current_metrics[metric_label] = value + # check for any metric which need model state from previous epoch if isinstance(metric, (DiffMetric, ConvergenceMetric)): self.previous = copy.deepcopy(self.model) @@ -269,24 +278,27 @@ def on_epoch_end(self, epoch, topics=None): if epoch == 0: if value.ndim > 0: diff_mat = np.array([value]) - viz_metric = self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) + viz_metric = self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label[:-6], title=metric.title)) # store current epoch's diff diagonal self.diff_mat.put(diff_mat) # saving initial plot window self.windows.append(copy.deepcopy(viz_metric)) else: - viz_metric = self.viz.line(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) + viz_metric = self.viz.line(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label[:-6], title=metric.title)) # saving initial plot window self.windows.append(copy.deepcopy(viz_metric)) else: if value.ndim > 0: # concatenate with previous epoch's diff diagonals diff_mat = np.concatenate((self.diff_mat.get(), np.array([value]))) - self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, win=self.windows[i], opts=dict(xlabel='Epochs', ylabel=metric_label, title=metric.title)) + self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, win=self.windows[i], opts=dict(xlabel='Epochs', ylabel=metric_label[:-6], title=metric.title)) self.diff_mat.put(diff_mat) else: self.viz.updateTrace(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, win=self.windows[i]) if metric.logger == "shell": - statement = "".join(("Epoch ", str(epoch), ": ", metric_label, " estimate: ", str(value))) + statement = "".join(("Epoch ", str(epoch), ": ", metric_label[:-6], " estimate: ", str(value))) self.log_type.info(statement) + + return current_metrics + diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index 7504268337..b345cae088 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -631,8 +631,13 @@ def rho(): return pow(offset + pass_ + (self.num_updates / chunksize), -decay) if self.callbacks: + # pass the list of input callbacks to Callback class callback = Callback(self.callbacks) callback.set_model(self) + # initialize metrics dict to store metric values after every epoch + self.metrics = {} + for metric in self.callbacks: + self.metrics[type(metric).__name__] = [] for pass_ in xrange(passes): if self.dispatcher: @@ -686,8 +691,11 @@ def rho(): if reallen != lencorpus: raise RuntimeError("input corpus size changed during training (don't use generators as input)") + # append current epoch's metric values if self.callbacks: - callback.on_epoch_end(pass_) + current_metrics = callback.on_epoch_end(pass_) + for metric, value in current_metrics.items(): + self.metrics[metric].append(value) if dirty: # finish any remaining updates From d3389bb3c637177287e0bbe12e3d7485a5a2bcdf Mon Sep 17 00:00:00 2001 From: parulsethi Date: Thu, 3 Aug 2017 05:58:53 +0530 Subject: [PATCH 24/31] add nb example for getting metrics after train --- docs/notebooks/Training_visualizations.ipynb | 71 +++++++++++++++----- 1 file changed, 54 insertions(+), 17 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 0b9ad914af..f0eb038d1a 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -44,9 +44,11 @@ "from gensim.models import ldamodel\n", "from gensim.corpora.dictionary import Dictionary\n", "\n", - "# Set file names for train data\n", + "\n", + "# Set file names for train and test data\n", "test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])\n", - "lee_corpus = test_data_dir + os.sep + 'lee.cor'\n", + "lee_train_file = test_data_dir + os.sep + 'lee_background.cor'\n", + "lee_test_file = test_data_dir + os.sep + 'lee.cor'\n", "\n", "def read_corpus(fname):\n", " texts = []\n", @@ -59,12 +61,12 @@ " texts.append(words)\n", " return texts\n", "\n", - "texts = read_corpus(lee_corpus)\n", + "training_texts = read_corpus(lee_train_file)\n", + "eval_texts = read_corpus(lee_test_file)\n", "\n", "# Split test data into hold_out and test corpus\n", - "training_texts = texts[:25]\n", - "holdout_texts = texts[25:40]\n", - "test_texts = texts[40:50]\n", + "holdout_texts = eval_texts[:25]\n", + "test_texts = eval_texts[25:]\n", "\n", "training_dictionary = Dictionary(training_texts)\n", "holdout_dictionary = Dictionary(holdout_texts)\n", @@ -78,26 +80,25 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", "\n", "# define perplexity callback for hold_out and test corpus\n", - "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (hold_out)\")\n", - "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"visdom\", viz_env=\"LdaModel\", title=\"Perplexity (test)\")\n", + "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"visdom\", title=\"Perplexity (hold_out)\")\n", + "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"visdom\", title=\"Perplexity (test)\")\n", "\n", "# define other remaining metrics available\n", - "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Coherence (u_mass)\")\n", - "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Diff (kullback_leibler)\")\n", - "convergence_jc = ConvergenceMetric(distance=\"hellinger\", logger=\"visdom\", viz_env=\"LdaModel\", title=\"Convergence (hellinger)\")\n", + "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", title=\"Coherence (u_mass)\")\n", + "ch_cv = CoherenceMetric(corpus=training_corpus, texts=training_texts, coherence=\"c_v\", logger=\"visdom\", title=\"Coherence (c_v)\")\n", + "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", title=\"Diff (kullback_leibler)\")\n", + "convergence_hl = ConvergenceMetric(distance=\"hellinger\", logger=\"visdom\", title=\"Convergence (hellinger)\")\n", "\n", - "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", + "callbacks = [pl_holdout, pl_test, ch_umass, ch_cv, diff_kl, convergence_hl]\n", "\n", "# training LDA model\n", - "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=5, num_topics=5, callbacks=callbacks)" + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, callbacks=callbacks)" ] }, { @@ -116,7 +117,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "-22.4298221364\n" + "-0.259766196856\n" ] } ], @@ -255,6 +256,42 @@ "# training LDA model\n", "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, callbacks=callbacks)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The metric values can also be accessed from the model instance for custom uses." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'CoherenceMetric': [0.33266605793626819,\n", + " 0.3316839843742313,\n", + " 0.33237246830927009],\n", + " 'ConvergenceMetric': [0.0, 0.0, 0.0],\n", + " 'DiffMetric': [array([ 0.92795546, 0.83166895, 0.8926528 , 0.96382424, 0.98886188]),\n", + " array([ 0.1486518 , 0.16031907, 0.18798994, 0.13619778, 0.11326997]),\n", + " array([ 0.02155673, 0.03477041, 0.03180156, 0.02133546, 0.01840971])],\n", + " 'PerplexityMetric': [2374469.2517599338,\n", + " 1708181.2721127137,\n", + " 1485456.3900059697]}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.metrics" + ] } ], "metadata": { From 7d0f0ec27923d98ed87dddfb37181a2d4a6862b6 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Tue, 8 Aug 2017 17:02:35 +0530 Subject: [PATCH 25/31] made rquested changes --- docs/notebooks/Training_visualizations.ipynb | 136 ++++++++++--------- gensim/models/callbacks.py | 29 ++-- gensim/models/ldamodel.py | 11 +- 3 files changed, 96 insertions(+), 80 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index f0eb038d1a..74c64c971f 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -46,9 +46,9 @@ "\n", "\n", "# Set file names for train and test data\n", - "test_data_dir = '{}'.format(os.sep).join([gensim.__path__[0], 'test', 'test_data'])\n", - "lee_train_file = test_data_dir + os.sep + 'lee_background.cor'\n", - "lee_test_file = test_data_dir + os.sep + 'lee.cor'\n", + "test_data_dir = os.path.join(gensim.__path__[0], 'test', 'test_data')\n", + "lee_train_file = os.path.join(test_data_dir, 'lee_background.cor')\n", + "lee_test_file = os.path.join(test_data_dir, 'lee.cor')\n", "\n", "def read_corpus(fname):\n", " texts = []\n", @@ -79,8 +79,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", @@ -93,12 +95,12 @@ "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", title=\"Coherence (u_mass)\")\n", "ch_cv = CoherenceMetric(corpus=training_corpus, texts=training_texts, coherence=\"c_v\", logger=\"visdom\", title=\"Coherence (c_v)\")\n", "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", title=\"Diff (kullback_leibler)\")\n", - "convergence_hl = ConvergenceMetric(distance=\"hellinger\", logger=\"visdom\", title=\"Convergence (hellinger)\")\n", + "convergence_kl = ConvergenceMetric(distance=\"kullback_leibler\", logger=\"visdom\", title=\"Convergence (kullback_leibler)\")\n", "\n", - "callbacks = [pl_holdout, pl_test, ch_umass, ch_cv, diff_kl, convergence_hl]\n", + "callbacks = [pl_holdout, pl_test, ch_umass, ch_cv, diff_kl, convergence_kl]\n", "\n", "# training LDA model\n", - "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, callbacks=callbacks)" + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, eval_every=None, callbacks=callbacks)" ] }, { @@ -179,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "scrolled": false }, @@ -189,54 +191,52 @@ "output_type": "stream", "text": [ "INFO:gensim.models.ldamodel:using symmetric alpha at 0.2\n", - "INFO:gensim.models.ldamodel:using symmetric eta at 0.0009950248756218905\n", + "INFO:gensim.models.ldamodel:using symmetric eta at 0.00013900472616068947\n", "INFO:gensim.models.ldamodel:using serial LDA version on this node\n", - "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 3 passes over the supplied corpus of 25 documents, updating model once every 25 documents, evaluating perplexity every 25 documents, iterating 50x with a convergence threshold of 0.001000\n", + "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 3 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n", - "INFO:gensim.models.ldamodel:-9.032 per-word bound, 523.4 perplexity estimate based on a held-out corpus of 25 documents with 2182 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #25/25\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.025*\"the\" + 0.022*\"to\" + 0.013*\"in\" + 0.011*\"of\" + 0.010*\"and\" + 0.007*\"s\" + 0.007*\"a\" + 0.005*\"said\" + 0.004*\"he\" + 0.004*\"iraqi\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.054*\"the\" + 0.028*\"to\" + 0.023*\"of\" + 0.019*\"and\" + 0.017*\"in\" + 0.016*\"a\" + 0.013*\"s\" + 0.009*\"that\" + 0.008*\"has\" + 0.007*\"on\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.049*\"the\" + 0.027*\"in\" + 0.024*\"to\" + 0.016*\"of\" + 0.012*\"and\" + 0.007*\"a\" + 0.007*\"that\" + 0.006*\"is\" + 0.006*\"for\" + 0.006*\"work\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.041*\"the\" + 0.024*\"of\" + 0.021*\"to\" + 0.016*\"a\" + 0.016*\"and\" + 0.012*\"s\" + 0.010*\"in\" + 0.007*\"as\" + 0.006*\"with\" + 0.006*\"party\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.034*\"the\" + 0.023*\"of\" + 0.016*\"a\" + 0.015*\"and\" + 0.012*\"in\" + 0.011*\"to\" + 0.008*\"as\" + 0.007*\"is\" + 0.007*\"with\" + 0.007*\"that\"\n", - "INFO:gensim.models.ldamodel:topic diff=1.878616, rho=1.000000\n", - "INFO:LdaModel:Epoch 0: Perplexity estimate: 495.948922721\n", - "INFO:LdaModel:Epoch 0: Perplexity estimate: 609.481073631\n", - "INFO:LdaModel:Epoch 0: Coherence estimate: -22.4407925538\n", - "INFO:LdaModel:Epoch 0: Diff estimate: [ 0.44194712 0.96670853 0.8036907 0.72372737 0.63141336]\n", - "INFO:LdaModel:Epoch 0: Convergence estimate: 0.0\n", - "INFO:gensim.models.ldamodel:-7.121 per-word bound, 139.2 perplexity estimate based on a held-out corpus of 25 documents with 2182 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #25/25\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.024*\"the\" + 0.015*\"to\" + 0.014*\"in\" + 0.008*\"iraqi\" + 0.008*\"of\" + 0.008*\"s\" + 0.007*\"and\" + 0.007*\"said\" + 0.005*\"u\" + 0.005*\"air\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.056*\"the\" + 0.029*\"to\" + 0.025*\"of\" + 0.022*\"and\" + 0.017*\"in\" + 0.015*\"a\" + 0.013*\"s\" + 0.008*\"that\" + 0.008*\"has\" + 0.008*\"on\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.050*\"the\" + 0.027*\"in\" + 0.025*\"to\" + 0.017*\"of\" + 0.010*\"and\" + 0.008*\"is\" + 0.007*\"be\" + 0.007*\"work\" + 0.007*\"a\" + 0.007*\"that\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.031*\"the\" + 0.020*\"of\" + 0.019*\"to\" + 0.018*\"a\" + 0.014*\"and\" + 0.012*\"s\" + 0.009*\"in\" + 0.008*\"as\" + 0.007*\"party\" + 0.007*\"by\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.033*\"the\" + 0.022*\"of\" + 0.014*\"and\" + 0.014*\"a\" + 0.012*\"in\" + 0.010*\"to\" + 0.010*\"iraq\" + 0.008*\"with\" + 0.008*\"that\" + 0.008*\"as\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.570396, rho=0.577350\n", - "INFO:LdaModel:Epoch 1: Perplexity estimate: 439.092043455\n", - "INFO:LdaModel:Epoch 1: Perplexity estimate: 534.74704957\n", - "INFO:LdaModel:Epoch 1: Coherence estimate: -22.4302876151\n", - "INFO:LdaModel:Epoch 1: Diff estimate: [ 0.08300085 0.0312854 0.06168011 0.06740617 0.08645297]\n", - "INFO:LdaModel:Epoch 1: Convergence estimate: 0.0\n", - "INFO:gensim.models.ldamodel:-6.842 per-word bound, 114.7 perplexity estimate based on a held-out corpus of 25 documents with 2182 words\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #25/25\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.023*\"the\" + 0.014*\"in\" + 0.011*\"to\" + 0.009*\"iraqi\" + 0.008*\"s\" + 0.007*\"said\" + 0.007*\"u\" + 0.007*\"air\" + 0.007*\"coalition\" + 0.007*\"military\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.056*\"the\" + 0.030*\"to\" + 0.026*\"of\" + 0.022*\"and\" + 0.017*\"in\" + 0.014*\"a\" + 0.014*\"s\" + 0.008*\"has\" + 0.008*\"on\" + 0.008*\"that\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.050*\"the\" + 0.026*\"in\" + 0.025*\"to\" + 0.017*\"of\" + 0.008*\"and\" + 0.008*\"is\" + 0.008*\"be\" + 0.008*\"work\" + 0.007*\"a\" + 0.007*\"that\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.027*\"the\" + 0.021*\"a\" + 0.019*\"of\" + 0.019*\"to\" + 0.013*\"s\" + 0.013*\"and\" + 0.009*\"in\" + 0.009*\"as\" + 0.009*\"party\" + 0.008*\"by\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.036*\"the\" + 0.022*\"of\" + 0.015*\"and\" + 0.013*\"a\" + 0.012*\"in\" + 0.011*\"iraq\" + 0.011*\"to\" + 0.010*\"with\" + 0.009*\"that\" + 0.008*\"are\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.363037, rho=0.500000\n", - "INFO:LdaModel:Epoch 2: Perplexity estimate: 413.6047717\n", - "INFO:LdaModel:Epoch 2: Perplexity estimate: 515.596865513\n", - "INFO:LdaModel:Epoch 2: Coherence estimate: -22.4273737487\n", - "INFO:LdaModel:Epoch 2: Diff estimate: [ 0.01187035 0.01098017 0.00861298 0.02232568 0.01953778]\n", - "INFO:LdaModel:Epoch 2: Convergence estimate: 0.0\n" + "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #300/300\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.059*\"the\" + 0.031*\"in\" + 0.019*\"and\" + 0.018*\"a\" + 0.017*\"of\" + 0.016*\"to\" + 0.011*\"for\" + 0.008*\"is\" + 0.007*\"on\" + 0.007*\"s\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.056*\"the\" + 0.030*\"to\" + 0.023*\"a\" + 0.020*\"and\" + 0.016*\"in\" + 0.015*\"of\" + 0.009*\"said\" + 0.008*\"for\" + 0.008*\"is\" + 0.008*\"on\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.069*\"the\" + 0.031*\"of\" + 0.026*\"to\" + 0.018*\"a\" + 0.018*\"and\" + 0.017*\"in\" + 0.009*\"he\" + 0.009*\"s\" + 0.009*\"is\" + 0.009*\"for\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.058*\"the\" + 0.026*\"to\" + 0.023*\"of\" + 0.021*\"in\" + 0.016*\"and\" + 0.014*\"a\" + 0.011*\"he\" + 0.008*\"is\" + 0.007*\"are\" + 0.007*\"has\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.046*\"the\" + 0.022*\"to\" + 0.019*\"a\" + 0.018*\"in\" + 0.017*\"and\" + 0.017*\"of\" + 0.009*\"on\" + 0.009*\"s\" + 0.008*\"he\" + 0.007*\"for\"\n", + "INFO:gensim.models.ldamodel:topic diff=1.998099, rho=1.000000\n", + "INFO:gensim.models.ldamodel:Epoch 0: Perplexity estimate: 557760.366254\n", + "INFO:gensim.models.ldamodel:Epoch 0: Perplexity estimate: 1398892.0238\n", + "INFO:gensim.models.ldamodel:Epoch 0: Coherence estimate: -0.239114528508\n", + "INFO:gensim.models.ldamodel:Epoch 0: Diff estimate: [ 0.87066886 0.89404796 1. 0.86438108 0.80259906]\n", + "INFO:gensim.models.ldamodel:Epoch 0: Convergence estimate: 0.0\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #300/300\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.056*\"the\" + 0.029*\"in\" + 0.018*\"and\" + 0.018*\"a\" + 0.017*\"to\" + 0.017*\"of\" + 0.011*\"for\" + 0.008*\"s\" + 0.008*\"he\" + 0.008*\"was\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.055*\"the\" + 0.029*\"to\" + 0.022*\"a\" + 0.019*\"and\" + 0.015*\"of\" + 0.015*\"in\" + 0.008*\"is\" + 0.008*\"for\" + 0.008*\"said\" + 0.007*\"on\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.070*\"the\" + 0.029*\"of\" + 0.026*\"to\" + 0.019*\"a\" + 0.018*\"and\" + 0.018*\"in\" + 0.009*\"he\" + 0.009*\"is\" + 0.009*\"s\" + 0.009*\"for\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.054*\"the\" + 0.025*\"to\" + 0.022*\"in\" + 0.020*\"of\" + 0.017*\"and\" + 0.014*\"a\" + 0.009*\"he\" + 0.008*\"is\" + 0.007*\"has\" + 0.007*\"mr\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.043*\"the\" + 0.021*\"to\" + 0.018*\"a\" + 0.017*\"in\" + 0.015*\"and\" + 0.015*\"of\" + 0.009*\"on\" + 0.009*\"he\" + 0.008*\"s\" + 0.008*\"for\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.639297, rho=0.577350\n", + "INFO:gensim.models.ldamodel:Epoch 1: Perplexity estimate: 364255.929481\n", + "INFO:gensim.models.ldamodel:Epoch 1: Perplexity estimate: 870027.350434\n", + "INFO:gensim.models.ldamodel:Epoch 1: Coherence estimate: -0.263564259646\n", + "INFO:gensim.models.ldamodel:Epoch 1: Diff estimate: [ 1. 0.65859849 0.27993545 0.86568637 0.70939543]\n", + "INFO:gensim.models.ldamodel:Epoch 1: Convergence estimate: 0.0\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #300/300\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.055*\"the\" + 0.027*\"in\" + 0.018*\"to\" + 0.018*\"and\" + 0.018*\"a\" + 0.017*\"of\" + 0.011*\"for\" + 0.009*\"s\" + 0.008*\"he\" + 0.008*\"was\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.055*\"the\" + 0.028*\"to\" + 0.021*\"a\" + 0.019*\"and\" + 0.016*\"of\" + 0.015*\"in\" + 0.009*\"is\" + 0.008*\"for\" + 0.008*\"said\" + 0.007*\"at\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.070*\"the\" + 0.029*\"of\" + 0.026*\"to\" + 0.019*\"a\" + 0.019*\"in\" + 0.019*\"and\" + 0.010*\"is\" + 0.009*\"he\" + 0.009*\"s\" + 0.009*\"for\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.053*\"the\" + 0.025*\"to\" + 0.023*\"in\" + 0.020*\"of\" + 0.018*\"and\" + 0.016*\"a\" + 0.009*\"palestinian\" + 0.008*\"he\" + 0.008*\"mr\" + 0.008*\"said\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.041*\"the\" + 0.020*\"to\" + 0.017*\"a\" + 0.016*\"in\" + 0.014*\"and\" + 0.013*\"of\" + 0.009*\"he\" + 0.008*\"on\" + 0.008*\"for\" + 0.007*\"s\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.440013, rho=0.500000\n", + "INFO:gensim.models.ldamodel:Epoch 2: Perplexity estimate: 307382.596558\n", + "INFO:gensim.models.ldamodel:Epoch 2: Perplexity estimate: 724828.178637\n", + "INFO:gensim.models.ldamodel:Epoch 2: Coherence estimate: -0.327008857571\n", + "INFO:gensim.models.ldamodel:Epoch 2: Diff estimate: [ 0.68276338 0.59768978 0.20701571 1. 0.78838212]\n", + "INFO:gensim.models.ldamodel:Epoch 2: Convergence estimate: 0.0\n" ] } ], "source": [ "import logging\n", + "from gensim.models.callbacks import CoherenceMetric, DiffMetric, PerplexityMetric, ConvergenceMetric\n", "\n", "logging.basicConfig(level=logging.INFO)\n", "logger = logging.getLogger(__name__)\n", @@ -249,12 +249,12 @@ "# define other remaining metrics available\n", "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"shell\")\n", "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"shell\")\n", - "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"shell\")\n", + "convergence_kl = ConvergenceMetric(distance=\"kullback_leibler\", logger=\"shell\")\n", "\n", - "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", + "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_kl]\n", "\n", "# training LDA model\n", - "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, callbacks=callbacks)" + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, eval_every=None, callbacks=callbacks)" ] }, { @@ -272,16 +272,17 @@ { "data": { "text/plain": [ - "{'CoherenceMetric': [0.33266605793626819,\n", - " 0.3316839843742313,\n", - " 0.33237246830927009],\n", - " 'ConvergenceMetric': [0.0, 0.0, 0.0],\n", - " 'DiffMetric': [array([ 0.92795546, 0.83166895, 0.8926528 , 0.96382424, 0.98886188]),\n", - " array([ 0.1486518 , 0.16031907, 0.18798994, 0.13619778, 0.11326997]),\n", - " array([ 0.02155673, 0.03477041, 0.03180156, 0.02133546, 0.01840971])],\n", - " 'PerplexityMetric': [2374469.2517599338,\n", - " 1708181.2721127137,\n", - " 1485456.3900059697]}" + "[('PerplexityMetric',\n", + " [557760.36625438754, 364255.92948058416, 307382.59655802854]),\n", + " ('PerplexityMetric',\n", + " [1398892.0237993822, 870027.3504341701, 724828.17863728071]),\n", + " ('CoherenceMetric',\n", + " [-0.23911452850779141, -0.26356425964553953, -0.32700885757127918]),\n", + " ('DiffMetric',\n", + " [array([ 0.87066886, 0.89404796, 1. , 0.86438108, 0.80259906]),\n", + " array([ 1. , 0.65859849, 0.27993545, 0.86568637, 0.70939543]),\n", + " array([ 0.68276338, 0.59768978, 0.20701571, 1. , 0.78838212])]),\n", + " ('ConvergenceMetric', [0.0, 0.0, 0.0])]" ] }, "execution_count": 4, @@ -292,6 +293,15 @@ "source": [ "model.metrics" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index 707cbafc90..d1802b5994 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -140,7 +140,7 @@ class DiffMetric(Metric): """ Metric class for topic difference evaluation """ - def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): + def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, diagonal=True, annotation=False, normed=True, logger=None, viz_env=None, title=None): """ Args: distance : measure used to calculate difference between any topic pair. Available values: @@ -149,6 +149,8 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=Tru `jaccard` num_words : is quantity of most relevant words that used if distance == `jaccard` (also used for annotation) n_ann_terms : max quantity of words in intersection/symmetric difference between topics (used for annotation) + diagonal : difference between identical topic no.s + annotation : intersection or difference of words between topics normed (bool) : If `true`, matrix/array Z will be normalized logger : Monitor training process using: "shell" : print coherence value in shell @@ -159,6 +161,8 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=Tru self.distance = distance self.num_words = num_words self.n_ann_terms = n_ann_terms + self.diagonal = diagonal + self.annotation = annotation self.normed = normed self.logger = logger self.viz_env = viz_env @@ -171,15 +175,15 @@ def get_value(self, **kwargs): other_model : second topic model instance to calculate the difference from """ super(DiffMetric, self).set_parameters(**kwargs) - diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) - return np.diagonal(diff_matrix) + diff_diagonal, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.diagonal, self.annotation, self.normed) + return diff_diagonal class ConvergenceMetric(Metric): """ Metric class for convergence evaluation """ - def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=True, logger=None, viz_env=None, title=None): + def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, diagonal=True, annotation=False, normed=True, logger=None, viz_env=None, title=None): """ Args: distance : measure used to calculate difference between any topic pair. Available values: @@ -188,6 +192,8 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=Tru `jaccard` num_words : is quantity of most relevant words that used if distance == `jaccard` (also used for annotation) n_ann_terms : max quantity of words in intersection/symmetric difference between topics (used for annotation) + diagonal : difference between identical topic no.s + annotation : intersection or difference of words between topics normed (bool) : If `true`, matrix/array Z will be normalized logger : Monitor training process using: "shell" : print coherence value in shell @@ -198,6 +204,8 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, normed=Tru self.distance = distance self.num_words = num_words self.n_ann_terms = n_ann_terms + self.diagonal = diagonal + self.annotation = annotation self.normed = normed self.logger = logger self.viz_env = viz_env @@ -210,8 +218,8 @@ def get_value(self, **kwargs): other_model : second topic model instance to calculate the difference from """ super(ConvergenceMetric, self).set_parameters(**kwargs) - diff_matrix, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.normed) - return np.sum(np.diagonal(diff_matrix)) + diff_diagonal, _ = self.model.diff(self.other_model, self.distance, self.num_words, self.n_ann_terms, self.diagonal, self.annotation, self.normed) + return np.sum(diff_diagonal) class Callback(object): @@ -239,7 +247,7 @@ def set_model(self, model): # check for any metric which need model state from previous epoch if any(isinstance(metric, (DiffMetric, ConvergenceMetric)) for metric in self.metrics): self.previous = copy.deepcopy(model) - # store diff diagnols of previous epochs + # store diff diagonals of previous epochs self.diff_mat = Queue() if any(metric.logger == "visdom" for metric in self.metrics): if not VISDOM_INSTALLED: @@ -249,8 +257,7 @@ def set_model(self, model): self.windows = [] if any(metric.logger == "shell" for metric in self.metrics): # set logger for current topic model - model_type = type(self.model).__name__ - self.log_type = logging.getLogger(model_type) + self.log_type = logging.getLogger('gensim.models.ldamodel') def on_epoch_end(self, epoch, topics=None): """ @@ -261,14 +268,14 @@ def on_epoch_end(self, epoch, topics=None): topics : topic distribution from current epoch (required for coherence of unsupported topic models) """ # stores current epoch's metric values - current_metrics = {} + current_metrics = [] # plot all metrics in current epoch for i, metric in enumerate(self.metrics): value = metric.get_value(topics=topics, model=self.model, other_model=self.previous) metric_label = type(metric).__name__ - current_metrics[metric_label] = value + current_metrics.append(value) # check for any metric which need model state from previous epoch if isinstance(metric, (DiffMetric, ConvergenceMetric)): diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index 9421948afd..c42a6d6a26 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -46,6 +46,7 @@ from scipy.special import gammaln, psi # gamma function utils from scipy.special import polygamma from six.moves import xrange +from collections import defaultdict import six # log(sum(exp(x))) that tries to avoid overflow @@ -634,10 +635,8 @@ def rho(): # pass the list of input callbacks to Callback class callback = Callback(self.callbacks) callback.set_model(self) - # initialize metrics dict to store metric values after every epoch - self.metrics = {} - for metric in self.callbacks: - self.metrics[type(metric).__name__] = [] + # initialize metrics list to store metric values after every epoch + self.metrics = [(type(metric).__name__, []) for metric in self.callbacks] for pass_ in xrange(passes): if self.dispatcher: @@ -694,8 +693,8 @@ def rho(): # append current epoch's metric values if self.callbacks: current_metrics = callback.on_epoch_end(pass_) - for metric, value in current_metrics.items(): - self.metrics[metric].append(value) + for i, metric in enumerate(current_metrics): + self.metrics[i][1].append(metric) if dirty: # finish any remaining updates From dcc64a103189f88c9efd3ca57a9225142f82aec4 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Wed, 9 Aug 2017 15:45:41 +0530 Subject: [PATCH 26/31] use dict for saving metrics --- docs/notebooks/Coherence.gif | Bin 83830 -> 72908 bytes docs/notebooks/Convergence.gif | Bin 33458 -> 53742 bytes docs/notebooks/Diff.gif | Bin 32656 -> 136587 bytes docs/notebooks/Perplexity.gif | Bin 102773 -> 71102 bytes docs/notebooks/Training_visualizations.ipynb | 126 +++++++++---------- gensim/models/callbacks.py | 25 ++-- gensim/models/ldamodel.py | 6 +- 7 files changed, 80 insertions(+), 77 deletions(-) diff --git a/docs/notebooks/Coherence.gif b/docs/notebooks/Coherence.gif index 26a3ded00312194a2ebd11c51838908f310b3a99..eb1dd57f985345e341a8202a91c0aa8eaddfa00c 100644 GIT binary patch literal 72908 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zI41$@Zo&AU{$DB(cLbm+lY$iijKb%HlVC425V1j_s11;tLKz|LrJz{4W}>XSgCNut}5 z#KNk?K)A5om+Z=`vD$ zkb(mW^Ibf16_-G@}3mvQmfUHJG&fwz? zme9tn7RbARx{ZJw41_IC20v5{%$rZRfFy>)0JO-k&HR80K0qBLAeRrAUl{rvgUU4k zaN;Q3wS&8wBfXG;xxNY3{AiF{f?F-%Cr02NFK>)5odSbipE(hA7Mm;jfh!_80zWmC F`Ct9Em%IP~ diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 74c64c971f..0f346a9d7a 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -26,17 +26,11 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ "import os\n", "import re\n", @@ -79,9 +73,10 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { - "collapsed": true + "collapsed": true, + "scrolled": false }, "outputs": [], "source": [ @@ -100,7 +95,7 @@ "callbacks = [pl_holdout, pl_test, ch_umass, ch_cv, diff_kl, convergence_kl]\n", "\n", "# training LDA model\n", - "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, eval_every=None, callbacks=callbacks)" + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=20, num_topics=5, eval_every=None, callbacks=callbacks)" ] }, { @@ -181,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": { "scrolled": false }, @@ -196,41 +191,41 @@ "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 3 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n", "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #300/300\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.059*\"the\" + 0.031*\"in\" + 0.019*\"and\" + 0.018*\"a\" + 0.017*\"of\" + 0.016*\"to\" + 0.011*\"for\" + 0.008*\"is\" + 0.007*\"on\" + 0.007*\"s\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.056*\"the\" + 0.030*\"to\" + 0.023*\"a\" + 0.020*\"and\" + 0.016*\"in\" + 0.015*\"of\" + 0.009*\"said\" + 0.008*\"for\" + 0.008*\"is\" + 0.008*\"on\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.069*\"the\" + 0.031*\"of\" + 0.026*\"to\" + 0.018*\"a\" + 0.018*\"and\" + 0.017*\"in\" + 0.009*\"he\" + 0.009*\"s\" + 0.009*\"is\" + 0.009*\"for\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.058*\"the\" + 0.026*\"to\" + 0.023*\"of\" + 0.021*\"in\" + 0.016*\"and\" + 0.014*\"a\" + 0.011*\"he\" + 0.008*\"is\" + 0.007*\"are\" + 0.007*\"has\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.046*\"the\" + 0.022*\"to\" + 0.019*\"a\" + 0.018*\"in\" + 0.017*\"and\" + 0.017*\"of\" + 0.009*\"on\" + 0.009*\"s\" + 0.008*\"he\" + 0.007*\"for\"\n", - "INFO:gensim.models.ldamodel:topic diff=1.998099, rho=1.000000\n", - "INFO:gensim.models.ldamodel:Epoch 0: Perplexity estimate: 557760.366254\n", - "INFO:gensim.models.ldamodel:Epoch 0: Perplexity estimate: 1398892.0238\n", - "INFO:gensim.models.ldamodel:Epoch 0: Coherence estimate: -0.239114528508\n", - "INFO:gensim.models.ldamodel:Epoch 0: Diff estimate: [ 0.87066886 0.89404796 1. 0.86438108 0.80259906]\n", - "INFO:gensim.models.ldamodel:Epoch 0: Convergence estimate: 0.0\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.051*\"the\" + 0.020*\"in\" + 0.019*\"to\" + 0.018*\"of\" + 0.015*\"a\" + 0.013*\"and\" + 0.011*\"is\" + 0.011*\"for\" + 0.009*\"he\" + 0.008*\"says\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.048*\"the\" + 0.030*\"to\" + 0.022*\"in\" + 0.021*\"and\" + 0.020*\"a\" + 0.019*\"of\" + 0.010*\"s\" + 0.009*\"for\" + 0.008*\"that\" + 0.008*\"have\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.023*\"the\" + 0.022*\"to\" + 0.018*\"of\" + 0.015*\"in\" + 0.013*\"and\" + 0.013*\"a\" + 0.007*\"is\" + 0.007*\"on\" + 0.006*\"that\" + 0.006*\"says\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.072*\"the\" + 0.028*\"of\" + 0.024*\"to\" + 0.021*\"in\" + 0.021*\"a\" + 0.020*\"and\" + 0.010*\"he\" + 0.009*\"for\" + 0.009*\"is\" + 0.008*\"on\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.066*\"the\" + 0.024*\"to\" + 0.018*\"of\" + 0.017*\"and\" + 0.015*\"in\" + 0.014*\"a\" + 0.010*\"has\" + 0.008*\"it\" + 0.008*\"s\" + 0.008*\"is\"\n", + "INFO:gensim.models.ldamodel:topic diff=2.058826, rho=1.000000\n", + "INFO:gensim.models.ldamodel:Epoch 0: Perplexity (hold_out) estimate: 400318.441165\n", + "INFO:gensim.models.ldamodel:Epoch 0: Perplexity (test) estimate: 1242745.72103\n", + "INFO:gensim.models.ldamodel:Epoch 0: Coherence estimate: -0.254109275924\n", + "INFO:gensim.models.ldamodel:Epoch 0: Diff estimate: [ 0.79628357 0.90363194 0.55469714 1. 0.86688377]\n", + "INFO:gensim.models.ldamodel:Epoch 0: Convergence estimate: 4.12149642523\n", "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #300/300\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.056*\"the\" + 0.029*\"in\" + 0.018*\"and\" + 0.018*\"a\" + 0.017*\"to\" + 0.017*\"of\" + 0.011*\"for\" + 0.008*\"s\" + 0.008*\"he\" + 0.008*\"was\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.055*\"the\" + 0.029*\"to\" + 0.022*\"a\" + 0.019*\"and\" + 0.015*\"of\" + 0.015*\"in\" + 0.008*\"is\" + 0.008*\"for\" + 0.008*\"said\" + 0.007*\"on\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.070*\"the\" + 0.029*\"of\" + 0.026*\"to\" + 0.019*\"a\" + 0.018*\"and\" + 0.018*\"in\" + 0.009*\"he\" + 0.009*\"is\" + 0.009*\"s\" + 0.009*\"for\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.054*\"the\" + 0.025*\"to\" + 0.022*\"in\" + 0.020*\"of\" + 0.017*\"and\" + 0.014*\"a\" + 0.009*\"he\" + 0.008*\"is\" + 0.007*\"has\" + 0.007*\"mr\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.043*\"the\" + 0.021*\"to\" + 0.018*\"a\" + 0.017*\"in\" + 0.015*\"and\" + 0.015*\"of\" + 0.009*\"on\" + 0.009*\"he\" + 0.008*\"s\" + 0.008*\"for\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.639297, rho=0.577350\n", - "INFO:gensim.models.ldamodel:Epoch 1: Perplexity estimate: 364255.929481\n", - "INFO:gensim.models.ldamodel:Epoch 1: Perplexity estimate: 870027.350434\n", - "INFO:gensim.models.ldamodel:Epoch 1: Coherence estimate: -0.263564259646\n", - "INFO:gensim.models.ldamodel:Epoch 1: Diff estimate: [ 1. 0.65859849 0.27993545 0.86568637 0.70939543]\n", - "INFO:gensim.models.ldamodel:Epoch 1: Convergence estimate: 0.0\n", + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.046*\"the\" + 0.019*\"in\" + 0.017*\"to\" + 0.017*\"of\" + 0.014*\"a\" + 0.012*\"and\" + 0.012*\"is\" + 0.010*\"for\" + 0.008*\"he\" + 0.008*\"says\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.048*\"the\" + 0.030*\"to\" + 0.022*\"in\" + 0.021*\"and\" + 0.019*\"a\" + 0.018*\"of\" + 0.010*\"s\" + 0.009*\"for\" + 0.008*\"have\" + 0.008*\"on\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.016*\"to\" + 0.016*\"the\" + 0.013*\"of\" + 0.010*\"in\" + 0.010*\"and\" + 0.009*\"a\" + 0.005*\"says\" + 0.005*\"that\" + 0.005*\"on\" + 0.005*\"is\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.071*\"the\" + 0.028*\"of\" + 0.025*\"to\" + 0.021*\"in\" + 0.021*\"a\" + 0.019*\"and\" + 0.010*\"he\" + 0.009*\"for\" + 0.008*\"is\" + 0.008*\"s\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.062*\"the\" + 0.025*\"to\" + 0.017*\"of\" + 0.016*\"and\" + 0.014*\"a\" + 0.014*\"in\" + 0.010*\"has\" + 0.009*\"is\" + 0.008*\"it\" + 0.007*\"s\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.567364, rho=0.577350\n", + "INFO:gensim.models.ldamodel:Epoch 1: Perplexity (hold_out) estimate: 231516.22057\n", + "INFO:gensim.models.ldamodel:Epoch 1: Perplexity (test) estimate: 666335.540876\n", + "INFO:gensim.models.ldamodel:Epoch 1: Coherence estimate: -0.248792041182\n", + "INFO:gensim.models.ldamodel:Epoch 1: Diff estimate: [ 0.83029118 0.72960219 1. 0.22719304 0.75709049]\n", + "INFO:gensim.models.ldamodel:Epoch 1: Convergence estimate: 3.54417690778\n", "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #300/300\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.055*\"the\" + 0.027*\"in\" + 0.018*\"to\" + 0.018*\"and\" + 0.018*\"a\" + 0.017*\"of\" + 0.011*\"for\" + 0.009*\"s\" + 0.008*\"he\" + 0.008*\"was\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.055*\"the\" + 0.028*\"to\" + 0.021*\"a\" + 0.019*\"and\" + 0.016*\"of\" + 0.015*\"in\" + 0.009*\"is\" + 0.008*\"for\" + 0.008*\"said\" + 0.007*\"at\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.070*\"the\" + 0.029*\"of\" + 0.026*\"to\" + 0.019*\"a\" + 0.019*\"in\" + 0.019*\"and\" + 0.010*\"is\" + 0.009*\"he\" + 0.009*\"s\" + 0.009*\"for\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.053*\"the\" + 0.025*\"to\" + 0.023*\"in\" + 0.020*\"of\" + 0.018*\"and\" + 0.016*\"a\" + 0.009*\"palestinian\" + 0.008*\"he\" + 0.008*\"mr\" + 0.008*\"said\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.041*\"the\" + 0.020*\"to\" + 0.017*\"a\" + 0.016*\"in\" + 0.014*\"and\" + 0.013*\"of\" + 0.009*\"he\" + 0.008*\"on\" + 0.008*\"for\" + 0.007*\"s\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.440013, rho=0.500000\n", - "INFO:gensim.models.ldamodel:Epoch 2: Perplexity estimate: 307382.596558\n", - "INFO:gensim.models.ldamodel:Epoch 2: Perplexity estimate: 724828.178637\n", - "INFO:gensim.models.ldamodel:Epoch 2: Coherence estimate: -0.327008857571\n", - "INFO:gensim.models.ldamodel:Epoch 2: Diff estimate: [ 0.68276338 0.59768978 0.20701571 1. 0.78838212]\n", - "INFO:gensim.models.ldamodel:Epoch 2: Convergence estimate: 0.0\n" + "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.043*\"the\" + 0.019*\"in\" + 0.018*\"to\" + 0.016*\"of\" + 0.012*\"a\" + 0.012*\"is\" + 0.012*\"and\" + 0.009*\"for\" + 0.008*\"says\" + 0.007*\"he\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.050*\"the\" + 0.029*\"to\" + 0.023*\"in\" + 0.021*\"and\" + 0.019*\"of\" + 0.018*\"a\" + 0.009*\"s\" + 0.009*\"for\" + 0.009*\"on\" + 0.008*\"he\"\n", + "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.012*\"to\" + 0.012*\"the\" + 0.009*\"of\" + 0.008*\"and\" + 0.007*\"in\" + 0.007*\"a\" + 0.004*\"says\" + 0.004*\"have\" + 0.004*\"that\" + 0.003*\"on\"\n", + "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.071*\"the\" + 0.027*\"of\" + 0.025*\"to\" + 0.021*\"in\" + 0.021*\"a\" + 0.019*\"and\" + 0.010*\"he\" + 0.009*\"for\" + 0.008*\"is\" + 0.008*\"s\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.061*\"the\" + 0.025*\"to\" + 0.018*\"of\" + 0.015*\"and\" + 0.015*\"a\" + 0.014*\"in\" + 0.010*\"has\" + 0.009*\"is\" + 0.008*\"says\" + 0.007*\"it\"\n", + "INFO:gensim.models.ldamodel:topic diff=0.393123, rho=0.500000\n", + "INFO:gensim.models.ldamodel:Epoch 2: Perplexity (hold_out) estimate: 185972.72653\n", + "INFO:gensim.models.ldamodel:Epoch 2: Perplexity (test) estimate: 516819.885154\n", + "INFO:gensim.models.ldamodel:Epoch 2: Coherence estimate: -0.257564279899\n", + "INFO:gensim.models.ldamodel:Epoch 2: Diff estimate: [ 0.82668066 0.50774819 1. 0.19109239 0.50630086]\n", + "INFO:gensim.models.ldamodel:Epoch 2: Convergence estimate: 3.03182210104\n" ] } ], @@ -243,8 +238,8 @@ "logger.setLevel(logging.DEBUG)\n", "\n", "# define perplexity callback for hold_out and test corpus\n", - "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"shell\")\n", - "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"shell\")\n", + "pl_holdout = PerplexityMetric(corpus=holdout_corpus, logger=\"shell\", title=\"Perplexity (hold_out)\")\n", + "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"shell\", title=\"Perplexity (test)\")\n", "\n", "# define other remaining metrics available\n", "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"shell\")\n", @@ -266,26 +261,31 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[('PerplexityMetric',\n", - " [557760.36625438754, 364255.92948058416, 307382.59655802854]),\n", - " ('PerplexityMetric',\n", - " [1398892.0237993822, 870027.3504341701, 724828.17863728071]),\n", - " ('CoherenceMetric',\n", - " [-0.23911452850779141, -0.26356425964553953, -0.32700885757127918]),\n", - " ('DiffMetric',\n", - " [array([ 0.87066886, 0.89404796, 1. , 0.86438108, 0.80259906]),\n", - " array([ 1. , 0.65859849, 0.27993545, 0.86568637, 0.70939543]),\n", - " array([ 0.68276338, 0.59768978, 0.20701571, 1. , 0.78838212])]),\n", - " ('ConvergenceMetric', [0.0, 0.0, 0.0])]" + "defaultdict(list,\n", + " {'Coherence': [-0.25410927592387839,\n", + " -0.24879204118159887,\n", + " -0.25756427989868341],\n", + " 'Convergence': [4.1214964252266926,\n", + " 3.5441769077766914,\n", + " 3.031822101038804],\n", + " 'Diff': [array([ 0.79628357, 0.90363194, 0.55469714, 1. , 0.86688377]),\n", + " array([ 0.83029118, 0.72960219, 1. , 0.22719304, 0.75709049]),\n", + " array([ 0.82668066, 0.50774819, 1. , 0.19109239, 0.50630086])],\n", + " 'Perplexity (hold_out)': [400318.44116470998,\n", + " 231516.22056950352,\n", + " 185972.72652968348],\n", + " 'Perplexity (test)': [1242745.7210251174,\n", + " 666335.54087631544,\n", + " 516819.88515415508]})" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index d1802b5994..dc4c7b27aa 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -268,44 +268,47 @@ def on_epoch_end(self, epoch, topics=None): topics : topic distribution from current epoch (required for coherence of unsupported topic models) """ # stores current epoch's metric values - current_metrics = [] + current_metrics = {} # plot all metrics in current epoch for i, metric in enumerate(self.metrics): value = metric.get_value(topics=topics, model=self.model, other_model=self.previous) - metric_label = type(metric).__name__ + if metric.title is not None: + label = metric.title + else: + label = type(metric).__name__[:-6] - current_metrics.append(value) - - # check for any metric which need model state from previous epoch - if isinstance(metric, (DiffMetric, ConvergenceMetric)): - self.previous = copy.deepcopy(self.model) + current_metrics[label] = value if metric.logger == "visdom": if epoch == 0: if value.ndim > 0: diff_mat = np.array([value]) - viz_metric = self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label[:-6], title=metric.title)) + viz_metric = self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=label, title=label)) # store current epoch's diff diagonal self.diff_mat.put(diff_mat) # saving initial plot window self.windows.append(copy.deepcopy(viz_metric)) else: - viz_metric = self.viz.line(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=metric_label[:-6], title=metric.title)) + viz_metric = self.viz.line(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, opts=dict(xlabel='Epochs', ylabel=label, title=label)) # saving initial plot window self.windows.append(copy.deepcopy(viz_metric)) else: if value.ndim > 0: # concatenate with previous epoch's diff diagonals diff_mat = np.concatenate((self.diff_mat.get(), np.array([value]))) - self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, win=self.windows[i], opts=dict(xlabel='Epochs', ylabel=metric_label[:-6], title=metric.title)) + self.viz.heatmap(X=diff_mat.T, env=metric.viz_env, win=self.windows[i], opts=dict(xlabel='Epochs', ylabel=label, title=label)) self.diff_mat.put(diff_mat) else: self.viz.updateTrace(Y=np.array([value]), X=np.array([epoch]), env=metric.viz_env, win=self.windows[i]) if metric.logger == "shell": - statement = "".join(("Epoch ", str(epoch), ": ", metric_label[:-6], " estimate: ", str(value))) + statement = "".join(("Epoch ", str(epoch), ": ", label, " estimate: ", str(value))) self.log_type.info(statement) + # check for any metric which need model state from previous epoch + if isinstance(metric, (DiffMetric, ConvergenceMetric)): + self.previous = copy.deepcopy(self.model) + return current_metrics diff --git a/gensim/models/ldamodel.py b/gensim/models/ldamodel.py index c42a6d6a26..6cfc8f572f 100755 --- a/gensim/models/ldamodel.py +++ b/gensim/models/ldamodel.py @@ -636,7 +636,7 @@ def rho(): callback = Callback(self.callbacks) callback.set_model(self) # initialize metrics list to store metric values after every epoch - self.metrics = [(type(metric).__name__, []) for metric in self.callbacks] + self.metrics = defaultdict(list) for pass_ in xrange(passes): if self.dispatcher: @@ -693,8 +693,8 @@ def rho(): # append current epoch's metric values if self.callbacks: current_metrics = callback.on_epoch_end(pass_) - for i, metric in enumerate(current_metrics): - self.metrics[i][1].append(metric) + for metric, value in current_metrics.items(): + self.metrics[metric].append(value) if dirty: # finish any remaining updates From 47434f9f5b41b5d7807493577115274e2abc7b93 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Thu, 10 Aug 2017 22:57:56 +0530 Subject: [PATCH 27/31] use str method for metric classes --- docs/notebooks/Training_visualizations.ipynb | 17 +++--------- gensim/models/callbacks.py | 28 +++++++++++++++----- 2 files changed, 25 insertions(+), 20 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 0f346a9d7a..9efbefc2f3 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -131,7 +131,7 @@ "\n", "**Coherence**\n", "\n", - "Coherence is a measure used to evaluate topic models. A good model will generate coherent topics, i.e., topics with high topic coherence scores. Good topics are topics that can be described by a short label based on the topic terms they spit out. \n", + "Coherence measures are generally based on the idea of computing the sum of pairwise scores of top *n* top words w1, ...,wn used to describe the topic. There are four coherence measure available in gensim: `u_mass, c_v, c_uci, c_npmi`. A good model will generate coherent topics, i.e., topics with high topic coherence scores. Good topics can be described by a short label based on the topic terms they spit out. \n", "\n", "\n", "\n", @@ -140,11 +140,11 @@ "\n", "**Perplexity**\n", "\n", - "Perplexity is a measurement of how well a probability distribution or probability model predicts a sample. In LDA, topics are described by a probability distribution over vocabulary words. So, perplexity can be used to compare probabilistic models like LDA.\n", + "Perplexity is a measurement of how well a probability distribution or probability model predicts a sample. In LDA, topics are described by a probability distribution over vocabulary words. So, perplexity can be used to evaluate the topic-term distribution output by LDA.\n", "\n", "\n", "\n", - "For a good model, perplexity should be as low as possible.\n", + "For a good model, perplexity should be low.\n", "\n", "\n", "**Topic Difference**\n", @@ -153,7 +153,7 @@ "\n", "\n", "\n", - "In the heatmap, X-axis define the Epoch no. and Y-axis define the distance between the identical topic from consecutive epochs. For ex. a particular cell in the heatmap with values (x=3, y=5, z=0.4) represent the distance(=0.4) between the topic 5 from 3rd epoch and topic 5 from 2nd epoch. With increasing epochs, the distance between the identical topics should decrease.\n", + "In the heatmap, X-axis define the Epoch no. and Y-axis define the distance between identical topics from consecutive epochs. For ex. a particular cell in the heatmap with values (x=3, y=5, z=0.4) represent the distance(=0.4) between the topic 5 from 3rd epoch and topic 5 from 2nd epoch. With increasing epochs, the distance between the identical topics should decrease.\n", " \n", " \n", "**Convergence**\n", @@ -293,15 +293,6 @@ "source": [ "model.metrics" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index dc4c7b27aa..bb8d2563fb 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -21,8 +21,14 @@ class Metric(object): """ Base Metric class for topic model evaluation metrics """ - def __init__(self): - pass + def __str__(self): + """ + Return a string representation of Metric class + """ + if self.title is not None: + return self.title + else: + return type(self).__name__[:-6] def set_parameters(self, **parameters): """ @@ -87,6 +93,9 @@ def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, win self.viz_env = viz_env self.title = title + def __str__(self): + return super(CoherenceMetric, self).__str__() + def get_value(self, **kwargs): """ Args: @@ -125,6 +134,9 @@ def __init__(self, corpus=None, logger=None, viz_env=None, title=None): self.viz_env = viz_env self.title = title + def __str__(self): + return super(PerplexityMetric, self).__str__() + def get_value(self, **kwargs): """ Args: @@ -168,6 +180,9 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, diagonal=T self.viz_env = viz_env self.title = title + def __str__(self): + return super(DiffMetric, self).__str__() + def get_value(self, **kwargs): """ Args: @@ -211,6 +226,9 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, diagonal=T self.viz_env = viz_env self.title = title + def __str__(self): + return super(ConvergenceMetric, self).__str__() + def get_value(self, **kwargs): """ Args: @@ -272,11 +290,8 @@ def on_epoch_end(self, epoch, topics=None): # plot all metrics in current epoch for i, metric in enumerate(self.metrics): + label = str(metric) value = metric.get_value(topics=topics, model=self.model, other_model=self.previous) - if metric.title is not None: - label = metric.title - else: - label = type(metric).__name__[:-6] current_metrics[label] = value @@ -311,4 +326,3 @@ def on_epoch_end(self, epoch, topics=None): self.previous = copy.deepcopy(self.model) return current_metrics - From 30c9b643fe983330250012111ff27fae27deca66 Mon Sep 17 00:00:00 2001 From: Parul Sethi Date: Thu, 10 Aug 2017 23:26:01 +0530 Subject: [PATCH 28/31] correct a notebook description --- docs/notebooks/Training_visualizations.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 9efbefc2f3..6beffe61bd 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -131,7 +131,7 @@ "\n", "**Coherence**\n", "\n", - "Coherence measures are generally based on the idea of computing the sum of pairwise scores of top *n* top words w1, ...,wn used to describe the topic. There are four coherence measure available in gensim: `u_mass, c_v, c_uci, c_npmi`. A good model will generate coherent topics, i.e., topics with high topic coherence scores. Good topics can be described by a short label based on the topic terms they spit out. \n", + "Coherence measures are generally based on the idea of computing the sum of pairwise scores of top *n* words w1, ...,wn used to describe the topic. There are four coherence measures available in gensim: `u_mass, c_v, c_uci, c_npmi`. A good model will generate coherent topics, i.e., topics with high topic coherence scores. Good topics can be described by a short label based on the topic terms they spit out. \n", "\n", "\n", "\n", From e55af471066e0ba0cfa08393c219542956722885 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Fri, 11 Aug 2017 01:34:24 +0530 Subject: [PATCH 29/31] remove child-classes str method --- gensim/models/callbacks.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/gensim/models/callbacks.py b/gensim/models/callbacks.py index bb8d2563fb..d4598cca1d 100644 --- a/gensim/models/callbacks.py +++ b/gensim/models/callbacks.py @@ -93,9 +93,6 @@ def __init__(self, corpus=None, texts=None, dictionary=None, coherence=None, win self.viz_env = viz_env self.title = title - def __str__(self): - return super(CoherenceMetric, self).__str__() - def get_value(self, **kwargs): """ Args: @@ -134,9 +131,6 @@ def __init__(self, corpus=None, logger=None, viz_env=None, title=None): self.viz_env = viz_env self.title = title - def __str__(self): - return super(PerplexityMetric, self).__str__() - def get_value(self, **kwargs): """ Args: @@ -180,9 +174,6 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, diagonal=T self.viz_env = viz_env self.title = title - def __str__(self): - return super(DiffMetric, self).__str__() - def get_value(self, **kwargs): """ Args: @@ -226,9 +217,6 @@ def __init__(self, distance="jaccard", num_words=100, n_ann_terms=10, diagonal=T self.viz_env = viz_env self.title = title - def __str__(self): - return super(ConvergenceMetric, self).__str__() - def get_value(self, **kwargs): """ Args: From df5e01f3f720e14a800cb02cd2feba4fc4b5f7eb Mon Sep 17 00:00:00 2001 From: parulsethi Date: Thu, 24 Aug 2017 02:23:16 +0530 Subject: [PATCH 30/31] made requested changes --- docs/notebooks/Training_visualizations.ipynb | 274 +++++++++++-------- 1 file changed, 166 insertions(+), 108 deletions(-) diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 6beffe61bd..77c3c43870 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -19,61 +19,68 @@ "\n", "# LDA Training Visualization\n", "\n", - "To monitor the LDA training, a list of Metrics can be passed to the LDA function call for plotting their values live as the training progresses. \n", + "Knowing about the progress and performance of a model, as we train them, could be very helpful in understanding it’s learning process and makes it easier to debug and optimize them. In this notebook, we will learn how to visualize training statistics for LDA topic model in gensim. To monitor the training, a list of Metrics is passed to the LDA function call for plotting their values live as the training progresses. \n", "\n", - "Let's plot the training stats for an LDA model being trained on Lee corpus. We will use the four evaluation metrics available for topic models in gensim: Coherence, Perplexity, Topic diff and Convergence. (using separate hold_out and test corpus for evaluating the perplexity)" + "\n", + "\n", + "\n", + "\n", + "Let's plot the training stats for an LDA model being trained on kaggle's [fake news dataset](https://www.kaggle.com/mrisdal/fake-news). We will use the four evaluation metrics available for topic models in gensim: Coherence, Perplexity, Topic diff and Convergence. (using separate hold_out and test corpus for evaluating the perplexity)" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], "source": [ - "import os\n", - "import re\n", - "import gensim\n", "from gensim.models import ldamodel\n", - "from gensim.corpora.dictionary import Dictionary\n", - "\n", - "\n", - "# Set file names for train and test data\n", - "test_data_dir = os.path.join(gensim.__path__[0], 'test', 'test_data')\n", - "lee_train_file = os.path.join(test_data_dir, 'lee_background.cor')\n", - "lee_test_file = os.path.join(test_data_dir, 'lee.cor')\n", - "\n", - "def read_corpus(fname):\n", - " texts = []\n", - " with open(fname, encoding=\"ISO-8859-1\") as f:\n", - " for line in f:\n", - " # lower case all words\n", - " lowered = line.lower()\n", - " # remove punctuation and split into seperate words\n", - " words = re.compile('\\w+').findall(lowered)\n", - " texts.append(words)\n", - " return texts\n", - "\n", - "training_texts = read_corpus(lee_train_file)\n", - "eval_texts = read_corpus(lee_test_file)\n", - "\n", - "# Split test data into hold_out and test corpus\n", - "holdout_texts = eval_texts[:25]\n", - "test_texts = eval_texts[25:]\n", - "\n", - "training_dictionary = Dictionary(training_texts)\n", - "holdout_dictionary = Dictionary(holdout_texts)\n", - "test_dictionary = Dictionary(test_texts)\n", - "\n", - "training_corpus = [training_dictionary.doc2bow(text) for text in training_texts]\n", - "holdout_corpus = [holdout_dictionary.doc2bow(text) for text in holdout_texts]\n", - "test_corpus = [test_dictionary.doc2bow(text) for text in test_texts]" + "from gensim.corpora import Dictionary\n", + "import pandas as pd\n", + "import re\n", + "from gensim.parsing.preprocessing import remove_stopwords, strip_punctuation\n", + "\n", + "import numpy as np\n", + "\n", + "df_fake = pd.read_csv('fake.csv')\n", + "df_fake[['title', 'text', 'language']].head()\n", + "df_fake = df_fake.loc[(pd.notnull(df_fake.text)) & (df_fake.language == 'english')]\n", + "\n", + "# remove stopwords and punctuations\n", + "def preprocess(row):\n", + " return strip_punctuation(remove_stopwords(row.lower()))\n", + " \n", + "df_fake['text'] = df_fake['text'].apply(preprocess)\n", + "\n", + "# Convert data to required input format by LDA\n", + "texts = []\n", + "for line in df_fake.text:\n", + " lowered = line.lower()\n", + " words = re.findall(r'\\w+', lowered, flags = re.UNICODE | re.LOCALE)\n", + " texts.append(words)\n", + "\n", + "dictionary = Dictionary(texts)\n", + "\n", + "training_texts = texts[:5000]\n", + "holdout_texts = texts[5000:7500]\n", + "test_texts = texts[7500:10000]\n", + "\n", + "training_corpus = [dictionary.doc2bow(text) for text in training_texts]\n", + "holdout_corpus = [dictionary.doc2bow(text) for text in holdout_texts]\n", + "test_corpus = [dictionary.doc2bow(text) for text in test_texts]" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "metadata": { "collapsed": true, "scrolled": false @@ -90,12 +97,12 @@ "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"visdom\", title=\"Coherence (u_mass)\")\n", "ch_cv = CoherenceMetric(corpus=training_corpus, texts=training_texts, coherence=\"c_v\", logger=\"visdom\", title=\"Coherence (c_v)\")\n", "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"visdom\", title=\"Diff (kullback_leibler)\")\n", - "convergence_kl = ConvergenceMetric(distance=\"kullback_leibler\", logger=\"visdom\", title=\"Convergence (kullback_leibler)\")\n", + "convergence_kl = ConvergenceMetric(distance=\"jaccard\", logger=\"visdom\", title=\"Convergence (jaccard)\")\n", "\n", "callbacks = [pl_holdout, pl_test, ch_umass, ch_cv, diff_kl, convergence_kl]\n", "\n", "# training LDA model\n", - "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=20, num_topics=5, eval_every=None, callbacks=callbacks)" + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, num_topics=35, passes=50, chunksize=1500, iterations=200, alpha='auto', callbacks=callbacks)" ] }, { @@ -176,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -185,47 +192,95 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:gensim.models.ldamodel:using symmetric alpha at 0.2\n", - "INFO:gensim.models.ldamodel:using symmetric eta at 0.00013900472616068947\n", + "INFO:gensim.models.ldamodel:using symmetric alpha at 0.02857142857142857\n", + "INFO:gensim.models.ldamodel:using symmetric eta at 1.0996744963490807e-05\n", "INFO:gensim.models.ldamodel:using serial LDA version on this node\n", - "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 5 topics, 3 passes over the supplied corpus of 300 documents, updating model once every 300 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", + "INFO:gensim.models.ldamodel:running online (multi-pass) LDA training, 35 topics, 2 passes over the supplied corpus of 5000 documents, updating model once every 2000 documents, evaluating perplexity every 0 documents, iterating 50x with a convergence threshold of 0.001000\n", "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #300/300\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.051*\"the\" + 0.020*\"in\" + 0.019*\"to\" + 0.018*\"of\" + 0.015*\"a\" + 0.013*\"and\" + 0.011*\"is\" + 0.011*\"for\" + 0.009*\"he\" + 0.008*\"says\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.048*\"the\" + 0.030*\"to\" + 0.022*\"in\" + 0.021*\"and\" + 0.020*\"a\" + 0.019*\"of\" + 0.010*\"s\" + 0.009*\"for\" + 0.008*\"that\" + 0.008*\"have\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.023*\"the\" + 0.022*\"to\" + 0.018*\"of\" + 0.015*\"in\" + 0.013*\"and\" + 0.013*\"a\" + 0.007*\"is\" + 0.007*\"on\" + 0.006*\"that\" + 0.006*\"says\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.072*\"the\" + 0.028*\"of\" + 0.024*\"to\" + 0.021*\"in\" + 0.021*\"a\" + 0.020*\"and\" + 0.010*\"he\" + 0.009*\"for\" + 0.009*\"is\" + 0.008*\"on\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.066*\"the\" + 0.024*\"to\" + 0.018*\"of\" + 0.017*\"and\" + 0.015*\"in\" + 0.014*\"a\" + 0.010*\"has\" + 0.008*\"it\" + 0.008*\"s\" + 0.008*\"is\"\n", - "INFO:gensim.models.ldamodel:topic diff=2.058826, rho=1.000000\n", - "INFO:gensim.models.ldamodel:Epoch 0: Perplexity (hold_out) estimate: 400318.441165\n", - "INFO:gensim.models.ldamodel:Epoch 0: Perplexity (test) estimate: 1242745.72103\n", - "INFO:gensim.models.ldamodel:Epoch 0: Coherence estimate: -0.254109275924\n", - "INFO:gensim.models.ldamodel:Epoch 0: Diff estimate: [ 0.79628357 0.90363194 0.55469714 1. 0.86688377]\n", - "INFO:gensim.models.ldamodel:Epoch 0: Convergence estimate: 4.12149642523\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #300/300\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.046*\"the\" + 0.019*\"in\" + 0.017*\"to\" + 0.017*\"of\" + 0.014*\"a\" + 0.012*\"and\" + 0.012*\"is\" + 0.010*\"for\" + 0.008*\"he\" + 0.008*\"says\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.048*\"the\" + 0.030*\"to\" + 0.022*\"in\" + 0.021*\"and\" + 0.019*\"a\" + 0.018*\"of\" + 0.010*\"s\" + 0.009*\"for\" + 0.008*\"have\" + 0.008*\"on\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.016*\"to\" + 0.016*\"the\" + 0.013*\"of\" + 0.010*\"in\" + 0.010*\"and\" + 0.009*\"a\" + 0.005*\"says\" + 0.005*\"that\" + 0.005*\"on\" + 0.005*\"is\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.071*\"the\" + 0.028*\"of\" + 0.025*\"to\" + 0.021*\"in\" + 0.021*\"a\" + 0.019*\"and\" + 0.010*\"he\" + 0.009*\"for\" + 0.008*\"is\" + 0.008*\"s\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.062*\"the\" + 0.025*\"to\" + 0.017*\"of\" + 0.016*\"and\" + 0.014*\"a\" + 0.014*\"in\" + 0.010*\"has\" + 0.009*\"is\" + 0.008*\"it\" + 0.007*\"s\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.567364, rho=0.577350\n", - "INFO:gensim.models.ldamodel:Epoch 1: Perplexity (hold_out) estimate: 231516.22057\n", - "INFO:gensim.models.ldamodel:Epoch 1: Perplexity (test) estimate: 666335.540876\n", - "INFO:gensim.models.ldamodel:Epoch 1: Coherence estimate: -0.248792041182\n", - "INFO:gensim.models.ldamodel:Epoch 1: Diff estimate: [ 0.83029118 0.72960219 1. 0.22719304 0.75709049]\n", - "INFO:gensim.models.ldamodel:Epoch 1: Convergence estimate: 3.54417690778\n", - "INFO:gensim.models.ldamodel:PROGRESS: pass 2, at document #300/300\n", - "INFO:gensim.models.ldamodel:topic #0 (0.200): 0.043*\"the\" + 0.019*\"in\" + 0.018*\"to\" + 0.016*\"of\" + 0.012*\"a\" + 0.012*\"is\" + 0.012*\"and\" + 0.009*\"for\" + 0.008*\"says\" + 0.007*\"he\"\n", - "INFO:gensim.models.ldamodel:topic #1 (0.200): 0.050*\"the\" + 0.029*\"to\" + 0.023*\"in\" + 0.021*\"and\" + 0.019*\"of\" + 0.018*\"a\" + 0.009*\"s\" + 0.009*\"for\" + 0.009*\"on\" + 0.008*\"he\"\n", - "INFO:gensim.models.ldamodel:topic #2 (0.200): 0.012*\"to\" + 0.012*\"the\" + 0.009*\"of\" + 0.008*\"and\" + 0.007*\"in\" + 0.007*\"a\" + 0.004*\"says\" + 0.004*\"have\" + 0.004*\"that\" + 0.003*\"on\"\n", - "INFO:gensim.models.ldamodel:topic #3 (0.200): 0.071*\"the\" + 0.027*\"of\" + 0.025*\"to\" + 0.021*\"in\" + 0.021*\"a\" + 0.019*\"and\" + 0.010*\"he\" + 0.009*\"for\" + 0.008*\"is\" + 0.008*\"s\"\n", - "INFO:gensim.models.ldamodel:topic #4 (0.200): 0.061*\"the\" + 0.025*\"to\" + 0.018*\"of\" + 0.015*\"and\" + 0.015*\"a\" + 0.014*\"in\" + 0.010*\"has\" + 0.009*\"is\" + 0.008*\"says\" + 0.007*\"it\"\n", - "INFO:gensim.models.ldamodel:topic diff=0.393123, rho=0.500000\n", - "INFO:gensim.models.ldamodel:Epoch 2: Perplexity (hold_out) estimate: 185972.72653\n", - "INFO:gensim.models.ldamodel:Epoch 2: Perplexity (test) estimate: 516819.885154\n", - "INFO:gensim.models.ldamodel:Epoch 2: Coherence estimate: -0.257564279899\n", - "INFO:gensim.models.ldamodel:Epoch 2: Diff estimate: [ 0.82668066 0.50774819 1. 0.19109239 0.50630086]\n", - "INFO:gensim.models.ldamodel:Epoch 2: Convergence estimate: 3.03182210104\n" + "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #2000/5000\n", + "INFO:gensim.models.ldamodel:merging changes from 2000 documents into a model of 5000 documents\n", + "INFO:gensim.models.ldamodel:topic #5 (0.029): 0.019*\"s\" + 0.007*\"clinton\" + 0.006*\"hillary\" + 0.005*\"t\" + 0.005*\"it\" + 0.005*\"people\" + 0.004*\"trump\" + 0.003*\"2016\" + 0.003*\"state\" + 0.003*\"new\"\n", + "INFO:gensim.models.ldamodel:topic #24 (0.029): 0.024*\"s\" + 0.006*\"it\" + 0.005*\"trump\" + 0.005*\"t\" + 0.004*\"u\" + 0.004*\"clinton\" + 0.003*\"people\" + 0.003*\"world\" + 0.003*\"obama\" + 0.003*\"new\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.029): 0.012*\"s\" + 0.005*\"people\" + 0.004*\"time\" + 0.003*\"trump\" + 0.003*\"t\" + 0.003*\"email\" + 0.003*\"new\" + 0.003*\"it\" + 0.003*\"war\" + 0.002*\"state\"\n", + "INFO:gensim.models.ldamodel:topic #34 (0.029): 0.011*\"s\" + 0.007*\"trump\" + 0.005*\"people\" + 0.004*\"t\" + 0.004*\"clinton\" + 0.003*\"new\" + 0.003*\"government\" + 0.003*\"said\" + 0.003*\"it\" + 0.003*\"time\"\n", + "INFO:gensim.models.ldamodel:topic #25 (0.029): 0.020*\"s\" + 0.004*\"u\" + 0.004*\"trump\" + 0.004*\"people\" + 0.004*\"government\" + 0.004*\"it\" + 0.003*\"american\" + 0.003*\"t\" + 0.003*\"state\" + 0.003*\"saudi\"\n", + "INFO:gensim.models.ldamodel:topic diff=24.655166, rho=1.000000\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #4000/5000\n", + "INFO:gensim.models.ldamodel:merging changes from 2000 documents into a model of 5000 documents\n", + "INFO:gensim.models.ldamodel:topic #25 (0.029): 0.016*\"s\" + 0.004*\"people\" + 0.003*\"u\" + 0.003*\"saudi\" + 0.003*\"government\" + 0.003*\"daily\" + 0.003*\"trump\" + 0.003*\"stormer\" + 0.003*\"it\" + 0.003*\"2016\"\n", + "INFO:gensim.models.ldamodel:topic #32 (0.029): 0.010*\"s\" + 0.005*\"world\" + 0.005*\"it\" + 0.004*\"horowitz\" + 0.004*\"ice\" + 0.003*\"people\" + 0.003*\"t\" + 0.003*\"like\" + 0.002*\"white\" + 0.002*\"fukushima\"\n", + "INFO:gensim.models.ldamodel:topic #6 (0.029): 0.018*\"s\" + 0.009*\"clinton\" + 0.009*\"trump\" + 0.009*\"t\" + 0.007*\"people\" + 0.007*\"hillary\" + 0.006*\"election\" + 0.005*\"like\" + 0.004*\"new\" + 0.004*\"it\"\n", + "INFO:gensim.models.ldamodel:topic #1 (0.029): 0.009*\"s\" + 0.005*\"comment\" + 0.004*\"facebook\" + 0.004*\"article\" + 0.004*\"account\" + 0.004*\"people\" + 0.003*\"war\" + 0.003*\"goat\" + 0.003*\"t\" + 0.003*\"disqus\"\n", + "INFO:gensim.models.ldamodel:topic #33 (0.029): 0.009*\"s\" + 0.008*\"people\" + 0.005*\"said\" + 0.004*\"t\" + 0.004*\"it\" + 0.004*\"trump\" + 0.003*\"government\" + 0.003*\"clinton\" + 0.003*\"fbi\" + 0.003*\"police\"\n", + "INFO:gensim.models.ldamodel:topic diff=3.855081, rho=0.707107\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 0, at document #5000/5000\n", + "INFO:gensim.models.ldamodel:merging changes from 1000 documents into a model of 5000 documents\n", + "INFO:gensim.models.ldamodel:topic #7 (0.029): 0.013*\"s\" + 0.007*\"it\" + 0.006*\"nuclear\" + 0.006*\"t\" + 0.006*\"activation\" + 0.005*\"placement\" + 0.004*\"clinton\" + 0.003*\"people\" + 0.003*\"hillary\" + 0.003*\"time\"\n", + "INFO:gensim.models.ldamodel:topic #23 (0.029): 0.008*\"s\" + 0.004*\"people\" + 0.004*\"water\" + 0.003*\"halloween\" + 0.003*\"t\" + 0.003*\"it\" + 0.003*\"silver\" + 0.003*\"jesus\" + 0.003*\"world\" + 0.003*\"flickr\"\n", + "INFO:gensim.models.ldamodel:topic #31 (0.029): 0.014*\"s\" + 0.006*\"t\" + 0.004*\"it\" + 0.004*\"people\" + 0.004*\"new\" + 0.003*\"girl\" + 0.003*\"child\" + 0.003*\"ellison\" + 0.003*\"know\" + 0.003*\"trump\"\n", + "INFO:gensim.models.ldamodel:topic #13 (0.029): 0.014*\"s\" + 0.010*\"t\" + 0.005*\"government\" + 0.004*\"school\" + 0.004*\"i\" + 0.004*\"president\" + 0.003*\"people\" + 0.003*\"trump\" + 0.003*\"like\" + 0.003*\"time\"\n", + "INFO:gensim.models.ldamodel:topic #25 (0.029): 0.111*\"utm\" + 0.015*\"force\" + 0.012*\"s\" + 0.007*\"tzrwu\" + 0.007*\"ims\" + 0.007*\"infowarsstore\" + 0.006*\"dr\" + 0.006*\"25\" + 0.006*\"and\" + 0.005*\"vitamin\"\n", + "INFO:gensim.models.ldamodel:topic diff=2.676513, rho=0.577350\n", + "INFO:gensim.models.ldamodel:Epoch 0: Perplexity (hold_out) estimate: 2891.88630978\n", + "INFO:gensim.models.ldamodel:Epoch 0: Perplexity (test) estimate: 1874.8109115\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 1000 documents\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 2000 documents\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 3000 documents\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 4000 documents\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 5000 documents\n", + "INFO:gensim.models.ldamodel:Epoch 0: Coherence (u_mass) estimate: -2.03953260715\n", + "INFO:gensim.models.ldamodel:Epoch 0: Diff (kullback_leibler) estimate: [ 0.93640518 0.95952878 0.64519803 0.78364027 0.50865099 0.90607399\n", + " 0.87627853 0.77941292 0.52529268 0.5848095 0.78475031 0.86041719\n", + " 0.98346917 0.79422948 0.82464972 0.53735652 0.79245303 0.64592471\n", + " 0.89265179 0.78584557 0.66037967 0.613174 0.8009025 0.68748952\n", + " 0.77344932 0.96950451 0.80075146 0.83959967 0.83689681 0.70191726\n", + " 1. 0.69856365 0.60616776 0.81112339 0.54781754]\n", + "INFO:gensim.models.ldamodel:Epoch 0: Convergence (jaccard) estimate: 34.9295467235\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #2000/5000\n", + "INFO:gensim.models.ldamodel:merging changes from 2000 documents into a model of 5000 documents\n", + "INFO:gensim.models.ldamodel:topic #13 (0.029): 0.013*\"s\" + 0.009*\"t\" + 0.006*\"black\" + 0.005*\"school\" + 0.004*\"government\" + 0.004*\"i\" + 0.003*\"people\" + 0.003*\"president\" + 0.003*\"police\" + 0.003*\"like\"\n", + "INFO:gensim.models.ldamodel:topic #12 (0.029): 0.030*\"infowars\" + 0.023*\"brain\" + 0.013*\"force\" + 0.010*\"s\" + 0.009*\"com\" + 0.007*\"life\" + 0.006*\"www\" + 0.005*\"http\" + 0.005*\"content\" + 0.005*\"source\"\n", + "INFO:gensim.models.ldamodel:topic #17 (0.029): 0.012*\"obamacare\" + 0.010*\"people\" + 0.009*\"s\" + 0.007*\"care\" + 0.006*\"health\" + 0.006*\"insurance\" + 0.005*\"t\" + 0.005*\"premiums\" + 0.005*\"brock\" + 0.004*\"it\"\n", + "INFO:gensim.models.ldamodel:topic #10 (0.029): 0.025*\"s\" + 0.009*\"israel\" + 0.005*\"said\" + 0.005*\"jewish\" + 0.004*\"american\" + 0.004*\"muslim\" + 0.004*\"t\" + 0.003*\"it\" + 0.003*\"anti\" + 0.003*\"israeli\"\n", + "INFO:gensim.models.ldamodel:topic #33 (0.029): 0.009*\"s\" + 0.008*\"vaccine\" + 0.007*\"people\" + 0.006*\"flu\" + 0.005*\"vaccines\" + 0.005*\"virus\" + 0.005*\"marijuana\" + 0.005*\"court\" + 0.005*\"medical\" + 0.004*\"zika\"\n", + "INFO:gensim.models.ldamodel:topic diff=1.496938, rho=0.471405\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #4000/5000\n", + "INFO:gensim.models.ldamodel:merging changes from 2000 documents into a model of 5000 documents\n", + "INFO:gensim.models.ldamodel:topic #17 (0.029): 0.017*\"obamacare\" + 0.009*\"people\" + 0.008*\"s\" + 0.008*\"insurance\" + 0.007*\"care\" + 0.007*\"premiums\" + 0.006*\"health\" + 0.005*\"t\" + 0.005*\"godlike\" + 0.004*\"brock\"\n", + "INFO:gensim.models.ldamodel:topic #28 (0.029): 0.018*\"retired\" + 0.016*\"syria\" + 0.016*\"s\" + 0.013*\"general\" + 0.011*\"syrian\" + 0.010*\"russian\" + 0.010*\"air\" + 0.009*\"russia\" + 0.009*\"force\" + 0.009*\"army\"\n", + "INFO:gensim.models.ldamodel:topic #24 (0.029): 0.021*\"s\" + 0.008*\"t\" + 0.006*\"it\" + 0.004*\"people\" + 0.004*\"world\" + 0.004*\"u\" + 0.003*\"that\" + 0.003*\"don\" + 0.003*\"like\" + 0.003*\"i\"\n", + "INFO:gensim.models.ldamodel:topic #4 (0.029): 0.019*\"email\" + 0.015*\"posts\" + 0.011*\"new\" + 0.010*\"subscribe\" + 0.009*\"notify\" + 0.009*\"follow\" + 0.007*\"donate\" + 0.006*\"radioactive\" + 0.005*\"up\" + 0.005*\"receive\"\n", + "INFO:gensim.models.ldamodel:topic #19 (0.029): 0.013*\"s\" + 0.007*\"said\" + 0.006*\"water\" + 0.005*\"pipeline\" + 0.005*\"police\" + 0.004*\"dakota\" + 0.004*\"year\" + 0.004*\"t\" + 0.003*\"2016\" + 0.003*\"north\"\n", + "INFO:gensim.models.ldamodel:topic diff=1.335664, rho=0.471405\n", + "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #5000/5000\n", + "INFO:gensim.models.ldamodel:merging changes from 1000 documents into a model of 5000 documents\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.ldamodel:topic #31 (0.029): 0.011*\"s\" + 0.008*\"child\" + 0.008*\"girl\" + 0.007*\"parents\" + 0.006*\"family\" + 0.005*\"old\" + 0.005*\"t\" + 0.005*\"baby\" + 0.005*\"ellison\" + 0.005*\"hospital\"\n", + "INFO:gensim.models.ldamodel:topic #16 (0.029): 0.036*\"trump\" + 0.013*\"white\" + 0.011*\"obama\" + 0.009*\"people\" + 0.008*\"s\" + 0.008*\"president\" + 0.007*\"vote\" + 0.006*\"america\" + 0.006*\"black\" + 0.006*\"right\"\n", + "INFO:gensim.models.ldamodel:topic #12 (0.029): 0.050*\"infowars\" + 0.042*\"brain\" + 0.026*\"force\" + 0.023*\"com\" + 0.015*\"life\" + 0.013*\"wellness\" + 0.011*\"content\" + 0.011*\"www\" + 0.010*\"source\" + 0.010*\"medium\"\n", + "INFO:gensim.models.ldamodel:topic #18 (0.029): 0.015*\"vaccine\" + 0.013*\"s\" + 0.011*\"medical\" + 0.010*\"doctors\" + 0.009*\"dr\" + 0.008*\"vaccines\" + 0.008*\"cdc\" + 0.007*\"children\" + 0.006*\"cancer\" + 0.006*\"autism\"\n", + "INFO:gensim.models.ldamodel:topic #13 (0.029): 0.013*\"s\" + 0.010*\"t\" + 0.007*\"government\" + 0.007*\"school\" + 0.004*\"police\" + 0.004*\"black\" + 0.003*\"law\" + 0.003*\"data\" + 0.003*\"at\" + 0.003*\"youtube\"\n", + "INFO:gensim.models.ldamodel:topic diff=1.217662, rho=0.471405\n", + "INFO:gensim.models.ldamodel:Epoch 1: Perplexity (hold_out) estimate: 1875.59367127\n", + "INFO:gensim.models.ldamodel:Epoch 1: Perplexity (test) estimate: 1369.87404449\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 1000 documents\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 2000 documents\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 3000 documents\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 4000 documents\n", + "INFO:gensim.topic_coherence.text_analysis:CorpusAccumulator accumulated stats from 5000 documents\n", + "INFO:gensim.models.ldamodel:Epoch 1: Coherence (u_mass) estimate: -3.33917142783\n", + "INFO:gensim.models.ldamodel:Epoch 1: Diff (kullback_leibler) estimate: [ 0.09301981 0.71566666 0.67676685 0.35730119 1. 0.54864275\n", + " 0.24120049 0.45830206 0.96709113 0.78362169 0.33318101 0.38479912\n", + " 0.62038738 0.43474664 0.31502652 0.87644947 0.5223633 0.76557247\n", + " 0.41316022 0.40029654 0.84127697 0.76384605 0.13714947 0.64728141\n", + " 0.3992811 0.70301974 0.17202488 0.33375843 0.55556689 0.68845743\n", + " 0.23326017 0.6709471 0.75061125 0.48405455 0.81424992]\n", + "INFO:gensim.models.ldamodel:Epoch 1: Convergence (jaccard) estimate: 22.293262955\n" ] } ], @@ -242,14 +297,14 @@ "pl_test = PerplexityMetric(corpus=test_corpus, logger=\"shell\", title=\"Perplexity (test)\")\n", "\n", "# define other remaining metrics available\n", - "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"shell\")\n", - "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"shell\")\n", - "convergence_kl = ConvergenceMetric(distance=\"kullback_leibler\", logger=\"shell\")\n", + "ch_umass = CoherenceMetric(corpus=training_corpus, coherence=\"u_mass\", logger=\"shell\", title=\"Coherence (u_mass)\")\n", + "diff_kl = DiffMetric(distance=\"kullback_leibler\", logger=\"shell\", title=\"Diff (kullback_leibler)\")\n", + "convergence_jc = ConvergenceMetric(distance=\"jaccard\", logger=\"shell\", title=\"Convergence (jaccard)\")\n", "\n", - "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_kl]\n", + "callbacks = [pl_holdout, pl_test, ch_umass, diff_kl, convergence_jc]\n", "\n", "# training LDA model\n", - "model = ldamodel.LdaModel(corpus=training_corpus, id2word=training_dictionary, passes=3, num_topics=5, eval_every=None, callbacks=callbacks)" + "model = ldamodel.LdaModel(corpus=training_corpus, id2word=dictionary, num_topics=35, passes=2, eval_every=None, callbacks=callbacks)" ] }, { @@ -261,31 +316,34 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "defaultdict(list,\n", - " {'Coherence': [-0.25410927592387839,\n", - " -0.24879204118159887,\n", - " -0.25756427989868341],\n", - " 'Convergence': [4.1214964252266926,\n", - " 3.5441769077766914,\n", - " 3.031822101038804],\n", - " 'Diff': [array([ 0.79628357, 0.90363194, 0.55469714, 1. , 0.86688377]),\n", - " array([ 0.83029118, 0.72960219, 1. , 0.22719304, 0.75709049]),\n", - " array([ 0.82668066, 0.50774819, 1. , 0.19109239, 0.50630086])],\n", - " 'Perplexity (hold_out)': [400318.44116470998,\n", - " 231516.22056950352,\n", - " 185972.72652968348],\n", - " 'Perplexity (test)': [1242745.7210251174,\n", - " 666335.54087631544,\n", - " 516819.88515415508]})" + " {'Coherence (u_mass)': [-2.0395326071549267, -3.339171427828973],\n", + " 'Convergence (jaccard)': [34.929546723516573, 22.293262954969034],\n", + " 'Diff (kullback_leibler)': [array([ 0.93640518, 0.95952878, 0.64519803, 0.78364027, 0.50865099,\n", + " 0.90607399, 0.87627853, 0.77941292, 0.52529268, 0.5848095 ,\n", + " 0.78475031, 0.86041719, 0.98346917, 0.79422948, 0.82464972,\n", + " 0.53735652, 0.79245303, 0.64592471, 0.89265179, 0.78584557,\n", + " 0.66037967, 0.613174 , 0.8009025 , 0.68748952, 0.77344932,\n", + " 0.96950451, 0.80075146, 0.83959967, 0.83689681, 0.70191726,\n", + " 1. , 0.69856365, 0.60616776, 0.81112339, 0.54781754]),\n", + " array([ 0.09301981, 0.71566666, 0.67676685, 0.35730119, 1. ,\n", + " 0.54864275, 0.24120049, 0.45830206, 0.96709113, 0.78362169,\n", + " 0.33318101, 0.38479912, 0.62038738, 0.43474664, 0.31502652,\n", + " 0.87644947, 0.5223633 , 0.76557247, 0.41316022, 0.40029654,\n", + " 0.84127697, 0.76384605, 0.13714947, 0.64728141, 0.3992811 ,\n", + " 0.70301974, 0.17202488, 0.33375843, 0.55556689, 0.68845743,\n", + " 0.23326017, 0.6709471 , 0.75061125, 0.48405455, 0.81424992])],\n", + " 'Perplexity (hold_out)': [2891.8863097795142, 1875.5936712709661],\n", + " 'Perplexity (test)': [1874.8109114962135, 1369.8740444934508]})" ] }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } From c54e6bfa45d229183387473114ef9df76693b7e2 Mon Sep 17 00:00:00 2001 From: parulsethi Date: Thu, 24 Aug 2017 07:55:04 +0530 Subject: [PATCH 31/31] add visdom screenshot --- docs/notebooks/Training_visualizations.ipynb | 31 ++++++++++--------- docs/notebooks/visdom_graph.png | Bin 0 -> 292783 bytes 2 files changed, 16 insertions(+), 15 deletions(-) create mode 100644 docs/notebooks/visdom_graph.png diff --git a/docs/notebooks/Training_visualizations.ipynb b/docs/notebooks/Training_visualizations.ipynb index 77c3c43870..dfd0d8bb81 100644 --- a/docs/notebooks/Training_visualizations.ipynb +++ b/docs/notebooks/Training_visualizations.ipynb @@ -31,7 +31,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stderr", @@ -115,7 +117,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -185,6 +189,7 @@ "cell_type": "code", "execution_count": 5, "metadata": { + "collapsed": false, "scrolled": false }, "outputs": [ @@ -253,13 +258,7 @@ "INFO:gensim.models.ldamodel:topic #19 (0.029): 0.013*\"s\" + 0.007*\"said\" + 0.006*\"water\" + 0.005*\"pipeline\" + 0.005*\"police\" + 0.004*\"dakota\" + 0.004*\"year\" + 0.004*\"t\" + 0.003*\"2016\" + 0.003*\"north\"\n", "INFO:gensim.models.ldamodel:topic diff=1.335664, rho=0.471405\n", "INFO:gensim.models.ldamodel:PROGRESS: pass 1, at document #5000/5000\n", - "INFO:gensim.models.ldamodel:merging changes from 1000 documents into a model of 5000 documents\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "INFO:gensim.models.ldamodel:merging changes from 1000 documents into a model of 5000 documents\n", "INFO:gensim.models.ldamodel:topic #31 (0.029): 0.011*\"s\" + 0.008*\"child\" + 0.008*\"girl\" + 0.007*\"parents\" + 0.006*\"family\" + 0.005*\"old\" + 0.005*\"t\" + 0.005*\"baby\" + 0.005*\"ellison\" + 0.005*\"hospital\"\n", "INFO:gensim.models.ldamodel:topic #16 (0.029): 0.036*\"trump\" + 0.013*\"white\" + 0.011*\"obama\" + 0.009*\"people\" + 0.008*\"s\" + 0.008*\"president\" + 0.007*\"vote\" + 0.006*\"america\" + 0.006*\"black\" + 0.006*\"right\"\n", "INFO:gensim.models.ldamodel:topic #12 (0.029): 0.050*\"infowars\" + 0.042*\"brain\" + 0.026*\"force\" + 0.023*\"com\" + 0.015*\"life\" + 0.013*\"wellness\" + 0.011*\"content\" + 0.011*\"www\" + 0.010*\"source\" + 0.010*\"medium\"\n", @@ -317,7 +316,9 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { @@ -355,21 +356,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 2", "language": "python", - "name": "python3" + "name": "python2" }, "language_info": { 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