def search_tm_topics(topics_list, limit, mdl_cfg):
'''
Performs search on the topic model using relevant
topic indices
'''
EPS = 1e-24 # a constant
lda_theta_file = mdl_cfg['LDA']['lda_theta_file']
index_dir = mdl_cfg['LUCENE']['lucene_index_dir']
path_index_file = mdl_cfg['CORPUS']['path_index_file']
lda_file_path_index = load_file_paths_index(path_index_file) # loads the file paths
lda_theta = np.loadtxt(lda_theta_file, dtype=np.longdouble) # loads the LDA theta from the model theta file
num_docs, num_topics = lda_theta.shape
print 'LDA-theta is loaded: number of documents: ', num_docs, ' number of topics: ', num_topics
unsel_topic_idx = [idx for idx in range(0, num_topics) if idx not in topics_list]
sel = np.log(lda_theta[:, topics_list] + EPS)
unsel = np.log(1.0 - lda_theta[:, unsel_topic_idx] + EPS)
ln_score = sel.sum(axis=1) + unsel.sum(axis=1)
sorted_idx = ln_score.argsort(axis=0)[::-1]
# score = np.exp(ln_score)
# Normalize the topic index search score
# TODO: this is an adhoc method right now. May come back later...
min_ln_score = min(ln_score)
n_ln_score = (1.0 - ln_score / min_ln_score)
ts_results = []
for i in range(0, min(limit, num_docs)):
ts_results.append([lda_file_path_index[sorted_idx[i]][0], # document id
lda_file_path_index[sorted_idx[i]][1], # document directory path
lda_file_path_index[sorted_idx[i]][2], # document name
n_ln_score[sorted_idx[i]]]) # similarity score
# print lda_file_path_index[sorted_idx[i]], ln_score[sorted_idx[i]], n_ln_score[sorted_idx[i]], score[sorted_idx[i]]
# grabs the files details from the index
ts_results = get_indexed_file_details(ts_results, index_dir)
results = [[row[0], float(row[10])] for row in ts_results] # Note: we need a float conversion because it's retrieving as string
return results
def search_tm(query_text, limit, mdl_cfg):
lda_dictionary, lda_mdl, lda_index, lda_file_path_index = load_tm(mdl_cfg)
ts_results = search_lda_model(query_text, lda_dictionary, lda_mdl, lda_index, lda_file_path_index, limit)
## ts_results are in this format [doc_id, doc_dir_path, doc_name, score]
# grabs the files details from the index
index_dir = mdl_cfg['LUCENE']['lucene_index_dir']
ts_results = get_indexed_file_details(ts_results, index_dir)
if len(ts_results) == 0:
print 'No documents found.'
return
# Normalize the similarity scores
results = [[row[0], ((float(row[10]) + 1.0) / 2.0)] for row in ts_results]
return results
def search_lsi(query_text, limit, mdl_cfg):
lsi_dictionary, lsi_mdl, lsi_index, lsi_file_path_index = load_lsi(mdl_cfg)
ts_results = search_lsi_model(query_text, lsi_dictionary, lsi_mdl, lsi_index, lsi_file_path_index, limit)
## ts_results are in this format [doc_id, doc_dir_path, doc_name, score]
# grabs the files details from the index
index_dir = mdl_cfg['LUCENE']['lucene_index_dir']
ts_results = get_indexed_file_details(ts_results, index_dir)
if len(ts_results) == 0:
print 'No documents found.'
return
'''
Sahil
Considering documents that satisfy a certain condition
'''
results = [[row[0], ((float(row[10]) + 1.0) / 2.0)] for row in ts_results]
return results
def search_tm_sel_topics_cos(topics_list, topics_prob, limit, mdl_cfg):
lda_theta_file = mdl_cfg['LDA']['lda_theta_file']
index_dir = mdl_cfg['LUCENE']['lucene_index_dir']
path_index_file = mdl_cfg['CORPUS']['path_index_file']
lda_file_path_index = load_file_paths_index(path_index_file)
lda_theta = np.loadtxt(lda_theta_file, dtype=np.longdouble)
num_docs, num_topics = lda_theta.shape
print 'Number of documents: ', num_docs, ' number of topics: ', num_topics
from scipy.spatial.distance import cosine
topics_prob = np.array(topics_prob)
sel = lda_theta[:, topics_list]
cos_scores = np.zeros(num_docs)
for i in range(0, num_docs):
cos_scores[i] = cosine(topics_prob, sel[i, :])
sorted_idx = cos_scores.argsort(axis=0)[::-1]
ts_results = []
for i in range(0, min(limit, num_docs)):
ts_results.append([lda_file_path_index[sorted_idx[i]][0],
lda_file_path_index[sorted_idx[i]][1],
lda_file_path_index[sorted_idx[i]][2],
cos_scores[sorted_idx[i]]])
print lda_file_path_index[sorted_idx[i]], cos_scores[sorted_idx[i]]
# grabs the files details from the index
ts_results = get_indexed_file_details(ts_results, index_dir)
results = [[row[0], row[10]] for row in ts_results]
return results
