dynamicLda = DynamicLda(lda_model, lda_model.bows)
biased_topic_distribution = dynamicLda.get_biased_topic_distribution(
query_terms, good_doc_ids, bad_doc_ids, good_terms, bad_terms
)
sims = lda_model.calc_sims_for_topic_distribution(biased_topic_distribution, sim_method)
topn = sims[:N]
json_list = build_json_list(topn)
# topn_json = json.dumps(topn)
# print str(topn_json)
return json_list
if __name__ == "__main__":
print("Loading BoW Vectors from cache...\n")
vocab = Vocabulary(vocab_file)
vocab.load_from_cache(reuters_cache_path)
text_corpus = Text(text_file, vocab)
text_corpus.load_from_cache(reuters_cache_path)
print("Loading LDA model...\n")
lda_model = LdaCalc(
bows=text_corpus.bow_vectors,
sims_cache_dir=sims_cache_path,
lda_cache_dir=reuters_cache_path,
num_topics=num_topics,
)
lda_model.load()
lda_model.print_topics()
# print("Indexing Model for similarities...")
__author__ = 'thomas'
import os
import gensim
from reuters.vocabulary import Vocabulary
from reuters.text import Text
from reuters.vectors import Vectors
from gensim.similarities import SparseMatrixSimilarity
reuters_cache_path = "/home/thomas/projects/clms/internship/lda/cache/reuters"
reuters_path = "/home/thomas/projects/clms/internship/corpora/reuters/"
vocab_file = os.path.join(reuters_path, "stem.termid.idf.map.txt")
text_file = os.path.join(reuters_path, "lyrl2004_tokens_train.dat")
vector_file = os.path.join(reuters_path, "lyrl2004_vectors_train.dat")
print("Loading Vocabulary...")
vocab = Vocabulary(vocab_file)
vocab.load_from_text()
print("Saving Vocabulary to Cache...")
vocab.save_to_cache(reuters_cache_path)
#vectors = Vectors(vector_file, 100)
#vectors.load_from_text()
print("Loading Corpus...")
text = Text(text_file, vocab)
text.load_from_text()
print("Saving Corpus to Cache...")
text.save_to_cache(reuters_cache_path)
num_topics = 100
print("Making Dictionary from Corpus (" + str(len(text.bow_vectors)) + " documents)...")
# get an lda-compatible dictionary
