def eval_ranking_methods(file_prefix, config_file,
test_directory,
tm_query,
limit = 1000,
img_extension = '.eps'):
lucene_query = 'all:(%s)' % tm_query # search in all fields
print 'Lucene query:', lucene_query
print 'TM query:', tm_query
positive_dir = os.path.join(test_directory, "1") # TRUE positive documents
TOP_K_TOPICS = 5 # the number topics used for Topic-LDA
rocs_file_name = '%s-ROCs' % file_prefix + img_extension
rocs_img_title = '' # %s: ROC curves' % file_prefix
roc_labels = ['Lucene ranking',
'Keyword-LDA ranking' ,
'Keyword-LDA * Lucene ranking',
'Topic-LDA ranking' ,
'Topic-LDA * Lucene Ranking',
'Keyword-LSI ranking']
line_styles = ['ro-','kx-','b+-','c^-','yv-.','gd-']
#---------------------------------------------- Reads the configuration file
mdl_cfg = read_config(config_file)
#------------ Checks whether the keywords are there in the corpus dictionary
dictionary = load_dictionary(mdl_cfg['CORPUS']['dict_file'])
valid_tokens = 0
for token in tm_query.split():
if token.strip() not in dictionary.values():
print token, "is not in the corpus vocabulary. Hence, this word will be ignored from the topic search."
else:
valid_tokens += 1
if valid_tokens == 0:
print 'None of the tokens exist in the dictionary. Exiting topic search!'
exit()
#------------------------------------------------------------- Lucene search
print 'Lucene ranking'
lu_docs = search_li(lucene_query, limit, mdl_cfg)
lu_docs_dict, lu_docs_list = lu_append_nonresp(lu_docs, test_directory)
lu_res = convert_to_roc_format(lu_docs_list, positive_dir)
print
#---------------------------------------------------------------- LDA search
# Loads the LDA model
lda_dictionary, lda_mdl, lda_index, lda_file_path_index, lda_theta, lda_beta = load_lda_parameters(mdl_cfg)
# To display the LDA model topics based on the
# increasing order of entropy
# print_lda_topics_on_entropy(lda_mdl, file_name='%s-topic-words.csv' % file_prefix, topn=50)
# Gets the dominant topics from the LDA model
dominant_topics = get_dominant_query_topics(tm_query, lda_dictionary, lda_mdl, TOP_K_TOPICS)
dominant_topics_idx = [idx for (idx, _) in dominant_topics] # get the topic indices
print 'LDA (w/ keywords) ranking'
lda_docs = search_tm(tm_query, limit, lda_dictionary, lda_mdl, lda_index, lda_file_path_index)
lda_res = convert_to_roc_format(lda_docs, positive_dir)
# plot_doc_class_predictions(lda_res, '%s-Keyword-LDA' % file_prefix, img_extension)
print 'LDA (w/ keywords) * Lucene ranking'
lu_tm_docs = fuse_lucene_tm_scores(lu_docs_dict, lda_docs)
lda_lu_res = convert_to_roc_format(lu_tm_docs, positive_dir)
# plot_doc_class_predictions(lda_lu_res, '%s-Keyword-LDA-Lucene' % file_prefix, img_extension)
print 'LDA (w/ query topics) ranking'
lda_tts_docs = search_tm_topics(dominant_topics_idx, limit, lda_file_path_index, lda_theta)
lda_tts_res = convert_to_roc_format(lda_tts_docs, positive_dir)
# plot_doc_class_predictions(lda_tts_res, '%s-Topic-LDA' % file_prefix, img_extension)
print 'LDA (w/ query topics) * Lucene Ranking'
final_docs_tts = fuse_lucene_tm_scores(lu_docs_dict, lda_tts_docs)
lda_tts_lu_res = convert_to_roc_format(final_docs_tts, positive_dir)
# plot_doc_class_predictions(lda_tts_lu_res, '%s-Topic-LDA-Lucene' % file_prefix, img_extension)
#---------------------------------------------------------------- LSI search
print 'LSI (w/ keywords) ranking'
lsi_docs = search_lsi(tm_query, limit, mdl_cfg)
lsi_res = convert_to_roc_format(lsi_docs, positive_dir)
## Plot ROC curves
results_list = [lu_res,
lda_res, lda_lu_res,
lda_tts_res, lda_tts_lu_res,
lsi_res]
roc_data_list = [ROCData(result, linestyle=line_styles[idx])
for idx, result in enumerate(results_list)]
plot_multiple_roc(roc_data_list, title=rocs_img_title,
labels=roc_labels, include_baseline=True,
file_name=rocs_file_name)
def eval_ranking_varying_topics(query_id, dir_path,
keywords,
limit = 1000,
img_extension = '.eps'):
tm_query = ' '.join( lemmatize_tokens( regex_tokenizer(keywords) ) ) # Lemmatization
lucene_query = 'all:(%s)' % tm_query # search in all fields
print 'Lucene query:', lucene_query
print 'TM query:', tm_query
test_directory = "%s%d" % (dir_path, query_id)
positive_dir = os.path.join(test_directory, "1") # TRUE positive documents
TOP_K_TOPICS = 5 # the number topics used for Topic-LDA
topiclda_rocs_file_name = '%d-LT-Topic-LDA-VaryingTopics-ROCs' % query_id + img_extension
topiclda_rocs_img_title = 'Q%d Topic-LDA with Varying Number of Topics' % query_id
keywordlda_rocs_file_name = '%d-LT-Keyword-LDA-VaryingTopics-ROCs' % query_id + img_extension
keywordlda_rocs_img_title = 'Q%d Keyword-LDA with Varying Number of Topics' % query_id
topics = [5, 10, 15, 20, 30, 40, 50, 60, 70, 80]
roc_labels = []
roc_topiclda_list = []
roc_keywordlda_list = []
for idx, num_topics in enumerate(topics):
#---------------------------------------------- Reads the configuration file
config_file = "%sQ%d-LT-%dT.cfg" % (dir_path, query_id, num_topics) # configuration file, created using the SMARTeR GUI
mdl_cfg = read_config(config_file)
# Loads the LDA model
lda_dictionary, lda_mdl, _, _ = load_tm(mdl_cfg)
#------------ Checks whether the keywords are there in the corpus dictionary
valid_tokens = 0
for token in tm_query.split():
if token.strip() not in lda_dictionary.values():
print token, "is not in the corpus vocabulary. Hence, this word will be ignored from the topic search."
else:
valid_tokens += 1
if valid_tokens == 0:
print 'None of the tokens exist in the dictionary. Exiting topic search!'
exit()
#------------------------------------------------------------- Lucene search
if idx == 0: # the first Lucene ranking is added as a reference
print 'Lucene ranking'
lu_docs = search_li(lucene_query, limit, mdl_cfg)
_, lu_docs_list = lu_append_nonresp(lu_docs, test_directory)
lu_res = convert_to_roc_format(lu_docs_list, positive_dir)
roc_topiclda_list.append(ROCData(lu_res))
roc_keywordlda_list.append(ROCData(lu_res))
roc_labels.append('Lucene')
#---------------------------------------------------------------- LDA search
# Gets the dominant topics from the LDA model
dominant_topics = get_dominant_query_topics(tm_query, lda_dictionary, lda_mdl, TOP_K_TOPICS)
dominant_topics_idx = [idx for (idx, _) in dominant_topics] # get the topic indices
print 'LDA (w/ keywords) ranking'
lda_docs = search_tm(tm_query, limit, mdl_cfg)
lda_res = convert_to_roc_format(lda_docs, positive_dir)
print 'LDA (w/ query topics) ranking'
lda_tts_docs = search_tm_topics(dominant_topics_idx, limit, mdl_cfg)
lda_tts_res = convert_to_roc_format(lda_tts_docs, positive_dir)
roc_topiclda_list.append(ROCData(lda_tts_res))
roc_keywordlda_list.append(ROCData(lda_res))
roc_labels.append('%d topics' % num_topics)
## Plot ROC curves
plot_multiple_roc(roc_topiclda_list, title=topiclda_rocs_img_title,
labels=roc_labels, include_baseline=True,
file_name=topiclda_rocs_file_name)
plot_multiple_roc(roc_keywordlda_list, title=keywordlda_rocs_img_title,
labels=roc_labels, include_baseline=True,
file_name=keywordlda_rocs_file_name)