def write_topics_for_status(status):
"""
One of my analysis questions is how different do the topics
look for each of the different status that I have. To help
with this, I wrote this little macro like procedure to make
it easier to pull all the topics for a particular status.
"""
data = cu.get_sample_data_frame_by_status(status)
lda = OnlineLDA(vocab, K, D, 1.0 / K, 1.0 / K, 1024.0, 0.7)
make_topic_columns(lda, data, K, D, batchsize)
write_topics_csv(vocab, lda._lambda, "topics.csv", 10)
def write_topics_for_status(status):
'''
One of my analysis questions is how different do the topics
look for each of the different status that I have. To help
with this, I wrote this little macro like procedure to make
it easier to pull all the topics for a particular status.
'''
data = cu.get_sample_data_frame_by_status(status)
lda = OnlineLDA(vocab, K, D, 1./K, 1./K, 1024., 0.7)
make_topic_columns(lda, data, K, D, batchsize)
write_topics_csv(vocab, lda._lambda, 'topics.csv', 10)
def topic_summary_by_status(vocab, K, D, n, limit, batchsize):
"""
The purpose of this procedure is to write out a CSV that compares
all the different topics for all the different categories in the
main sample.
"""
n = 10
pieces = []
for status in cu.labels:
data = cu.get_sample_data_frame_by_status(status, limit)
lda = OnlineLDA(vocab, K, D, 1.0 / K, 1.0 / K, 1024.0, 0.7)
make_topic_columns(lda, data, K, D, batchsize)
summary = make_topic_summary(vocab, lda._lambda, n)
pieces.append(summary)
return pd.concat(pieces, keys=cu.labels)
def topic_summary_by_status(vocab, K, D, n, limit, batchsize):
'''
The purpose of this procedure is to write out a CSV that compares
all the different topics for all the different categories in the
main sample.
'''
n = 10
pieces = []
for status in cu.labels:
data = cu.get_sample_data_frame_by_status(status, limit)
lda = OnlineLDA(vocab, K, D, 1./K, 1./K, 1024., 0.7)
make_topic_columns(lda, data, K, D, batchsize)
summary = make_topic_summary(vocab, lda._lambda, n)
pieces.append(summary)
return pd.concat(pieces, keys=cu.labels)
