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Select the most likely topics for institutions #32

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Select the most likely topics for institutions #32

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38 changes: 35 additions & 3 deletions topic-model/topic-model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@
from util import DocumentItem, MongoDB_loader
import models
import reduction
import numpy as np

# From http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf_lda.html#example-applications-topics-extraction-with-nmf-lda-py

Expand All @@ -40,7 +41,7 @@ def write_to_file(filename, uc):


class TopicModel():
n_topics = 3
n_topics = 6
n_top_words = 10

def __init__(self, max_degree=3, fn=models.poisson_law, alpha=1.0, ratio=0.5):
Expand All @@ -51,9 +52,14 @@ def __init__(self, max_degree=3, fn=models.poisson_law, alpha=1.0, ratio=0.5):
print("Importing data...")
document_items = MongoDB_loader().get_corpus()
reduction_obj = reduction.Reduction()
self.inst_urls = []
self.inst_names = []
self.data_samples = []
for item in document_items:
# TODO: process to save into inst_names
self.inst_urls.append(item.get_base_url())
# removes numbers
print("Filtering: {}".format(item.get_base_url()))
document = self.filter_document(item.get_document())
#document = item.get_document()

Expand Down Expand Up @@ -117,29 +123,55 @@ def nmf_analysis(self, ngram_low, ngram_high):
stop_words='english')
t0 = time()
tfidf = tfidf_vectorizer.fit_transform(self.data_samples)
tfidf_dtm = tfidf.toarray()
print("done in %0.3fs." % (time() - t0))

# AFTER FITTING VOCAB
tfidf_feature_names = tfidf_vectorizer.get_feature_names()
tfidf_vocab = np.array(tfidf_feature_names)

# Fit the NMF model
print("Fitting the NMF model with tf-idf features,")
# "n_samples=%d and n_features=%d..."
# % (n_samples, n_features))
t0 = time()
nmf = NMF(n_components=self.n_topics, random_state=1, alpha=.1, l1_ratio=.5).fit(tfidf)
nmf_clf = NMF(n_components=self.n_topics, random_state=1, alpha=.1, l1_ratio=.5)
doctopic = nmf_clf.fit_transform(tfidf_dtm)

print("done in %0.3fs." % (time() - t0))

print("\nTopics in NMF model:")
tfidf_feature_names = tfidf_vectorizer.get_feature_names()
topic_words = []
for topic in nmf_clf.components_:
word_idx = np.argsort(topic)[::-1][0:self.n_top_words]
topic_words.append([tfidf_vocab[i] for i in word_idx])
doctopic_prop = doctopic / np.sum(doctopic, axis=1, keepdims=True)
self.print_top_words(nmf, tfidf_feature_names, self.n_top_words)

inst_urls = np.asarray(self.inst_urls)
doctopic_orig = doctopic_prop.copy()

n_groups = len(set(inst_urls))
doctopic_grouped = np.zeros((n_groups, self.n_topics))
for i, name in enumerate(sorted(set(inst_urls))):
doctopic_grouped[i, :] = np.mean(doctopic_prop[inst_urls == name, :], axis=0)

insts = sorted(set(inst_urls))
for i in range(len(doctopic_grouped)):
top_topics = np.argsort(doctopic_grouped[i,:])[::-1][0:3]
top_topics_str = ' '.join(str(t) for t in top_topics)
print("{}: {}".format(insts[i], top_topics_str))


if __name__ == "__main__":
arguments = docopt(__doc__)
max_degree = int(arguments['--tiers'])
fn = getattr(models, arguments['--fn'])
alpha = float(arguments['--alpha'])
ratio = float(arguments['--ratio'])
tm = TopicModel(max_degree, fn, alpha, ratio)
tm.lda_analysis(2,3)
#tm.lda_analysis(2,3)
tm.nmf_analysis(2,3)

##### PLAYING WITH TF-IDF #####
Expand Down