def __calculate_top_n_dice_neighbours_for_index(temporary_keyword_cache_id,
n, index_id, index_directory,
posting_lists, trainingdata_handler,
training_data_filters):
top_dice_neighbours = {}
for category in posting_lists:
top_dice_neighbours[category] = {}
for id_reference_word_group in posting_lists[category]:
top_dice_neighbours[category][id_reference_word_group] = []
index = cache.load(index_directory, index_id)
print("Index id "+ index_id)
n_categories_left = len(posting_lists)
for category in posting_lists:
posting_lists_category = posting_lists[category]
#TODO remove
print("Calculate keyword for category "+category)
n_posts = sum([len(posting_lists_category[ref_group_id]) for ref_group_id in posting_lists_category])
print("Number of posts for category:" + str(n_posts))
print("Number of categories left "+ str(n_categories_left))
n_categories_left = n_categories_left -1
for id_reference_word_group in posting_lists_category:
posting_list_reference_word_group = posting_lists_category[id_reference_word_group]
all_dice_neighbours = __get_all_dice_neighbours(posting_list_reference_word_group, index, trainingdata_handler, training_data_filters)
top_dice_neighbours[category][id_reference_word_group] = all_dice_neighbours[:n]
cache.write(__RAW_KEYWORD_BY_INDEX_CACHE,temporary_keyword_cache_id, top_dice_neighbours)
def get_dice_keywords(keyword_specification):
training_dataset_spec = keyword_specification["training_dataset"]
given_reference_words = keyword_specification["given_reference_words"]
#Try load Keyword from cache else calculate
keyword_seed_id = keyword_specification["seed_id"]
raw_keywords_id = keyword_setup_id_generator.get_no_filtered_keywords_id(keyword_seed_id,training_dataset_spec)
if cache.in_cache(__RAW_KEYWORD_CACHE, raw_keywords_id):
raw_keywords = cache.load(__RAW_KEYWORD_CACHE,raw_keywords_id)
else:
raw_keywords = __calculate_raw_dice_keywords(keyword_specification)
cache.write(__RAW_KEYWORD_CACHE, raw_keywords_id, raw_keywords)
raw_reference_words = raw_keywords["raw_reference_words"]
raw_context_words = raw_keywords["raw_context_words"]
# Apply filter and clean from scores
index_directory = training_dataset_spec["index_directory"]
indices_id = training_dataset_spec["index_id"]
index_spec = {"index_directory": index_directory, "index_id": indices_id}
reference_words_filter = keyword_specification["reference_word_filter"]
new_reference_words = keyword_filters.apply_reference_word_filters(reference_words_filter,given_reference_words,raw_reference_words,index_spec)
new_reference_words = __remove_dice_coefficients(new_reference_words)
new_context_words = __remove_dice_coefficients(raw_context_words)
reference_words = __merge_new_and_given_reference_words(given_reference_words,new_reference_words)
return {"reference_words":reference_words,"context_words":new_context_words}
def prepare_gold_standard_categorization(experiment_spec, gold_standard_categorization_directory):
test_data_id = dataset_id_handler.get_test_data_id(experiment_spec)
if cache.in_cache(gold_standard_categorization_directory,test_data_id):
print("Gold standard categorization in cache: "+ test_data_id)
return
test_dataset_handler = TestDatasetHandler(experiment_spec["test_dataset"]["id"])
gold_standard_categorization = test_dataset_handler.get_gold_standard_categorization()
pprint.pprint(gold_standard_categorization)
cache.write(gold_standard_categorization_directory,test_data_id,gold_standard_categorization)
def prepare_index(experiment_spec, index_cache_directory):
# Check that all indxes are created or init creation
training_data_spec = experiment_spec["training_dataset"]
index_specs = __get_all_index_specs(training_data_spec)
for index_id in index_specs:
index_spec = index_specs[index_id]
if not cache.in_cache(index_cache_directory, index_id):
index = index_factory.create_index(index_spec)
cache.write(index_cache_directory, index_id, index)
print("Created index " + index_id)
else:
print("Index present in cache " + index_id)
def prepare_freq_dists(experiment_spec, freq_dists_cache_directory):
freq_dist_map_id = document_vectorization.get_freq_dist_map_id(experiment_spec)
if cache.in_cache(freq_dists_cache_directory, freq_dist_map_id):
print( "FREQDISTS stored in cache: " + freq_dist_map_id)
return
test_data_id = dataset_id_handler.get_test_data_id(experiment_spec)
preprocessing_filter_names = experiment_spec["training_dataset"]["filters"]
test_document_term_map = document_vectorization.get_test_document_term_map(test_data_id,preprocessing_filter_names)
index_types = ["word", "bigram", "trigram"]
freq_dist_map = document_vectorization.get_freq_dists_map(test_document_term_map,index_types)
pprint.pprint(freq_dist_map)
cache.write(freq_dists_cache_directory,freq_dist_map_id,freq_dist_map)
def fix_olympic_games_2(keywords_no_filter_id):
if cache.in_cache(__RAW_KEYWORD_CACHE, keywords_no_filter_id):
keywords= cache.load(__RAW_KEYWORD_CACHE, keywords_no_filter_id)
keyword_types = list(keywords.keys())
for keyword_type in keyword_types:
if "olympic games" in keywords[keyword_type]:
print("Fix olympic game in keyword directory " + keywords_no_filter_id)
olympic_keywords = keywords[keyword_type].pop("olympic games")
keywords[keyword_type]["olympic_games"] = olympic_keywords
for category in keywords[keyword_type]:
keywords[keyword_type][category]['0'] = [keyword for keyword in keywords[keyword_type][category]['0'] if not " " in keyword]
cache.write(__RAW_KEYWORD_CACHE, keywords_no_filter_id, keywords)
return keywords
def fix_olympic_games_1(keywords_id):
if cache.in_cache("keywords", keywords_id):
keywords = cache.load("keywords", keywords_id)
keyword_types = ["reference_words","context_words"]
for keyword_type in keyword_types:
if "olympic games" in keywords[keyword_type]:
print("Fix olympic game in keyword directory " + keywords_id)
olympic_keywords = keywords[keyword_type].pop("olympic games")
keywords[keyword_type]["olympic_games"] = olympic_keywords
for category in keywords[keyword_type]:
keywords[keyword_type][category]['0'] = [keyword for keyword in keywords[keyword_type][category]['0'] if not " " in keyword]
cache.write("keywords", keywords_id, keywords)
return keywords
def prepare_tf_idf_vectors(experiment_spec,tf_idf_cache_dirctory,index_cache_directory):
# Create test data handler
tf_idf_vector_map_id = document_vectorization.get_tf_idf_map_id(experiment_spec)
if cache.in_cache(tf_idf_cache_dirctory, tf_idf_vector_map_id):
print( "TF_IDF_VECTORS stored in cache: " + tf_idf_vector_map_id)
return
test_data_id = dataset_id_handler.get_test_data_id(experiment_spec)
preprocessing_filter_names = experiment_spec["training_dataset"]["filters"]
test_docuement_term_map = document_vectorization.get_test_document_term_map(test_data_id,preprocessing_filter_names)
print("test data preprocessed")
index_id_index_type_map = __get_index_id_index_type(experiment_spec["training_dataset"])
index_types = ["word", "bigram", "trigram"]
max_freq_map = index_factory.create_max_freq_term_by_index_types(test_docuement_term_map, index_types)
print("max_freq_map_calculated")
tf_idf_vector_map = document_vectorization.get_docs_id_tf_idf_map(test_docuement_term_map, index_id_index_type_map, index_cache_directory,max_freq_map)
pprint.pprint(tf_idf_vector_map)
cache.write(tf_idf_cache_dirctory, tf_idf_vector_map_id, tf_idf_vector_map)
def prepare_keywords(experiment_spec,keyword_cache_directory, index_cache_directory):
keyword_spec = experiment_spec["keywords"]
#if manual keywords
keyword_method = keyword_spec["keyword_generate_algorithm"]
if keyword_method == "manual":
keywords = specification_handler.get_specification("keyword_setups",keyword_spec["setup_id"])
keyword_setup_id = keyword_spec["setup_id"]
keyword_id = keyword_setup_id_generator.get_keyword_setup_id(keyword_setup_id,experiment_spec["training_dataset"])
cache.write(keyword_cache_directory,keyword_id,keywords)
print("Manual keyword now stored in cache")
return
keyword_setup_id = keyword_spec["setup_id"]
keyword_seed_id =keyword_spec["seed_id"]
keyword_id = keyword_setup_id_generator.get_keyword_setup_id(keyword_setup_id,experiment_spec["training_dataset"])
if cache.in_cache(keyword_cache_directory, keyword_id):
print("Keyword stored in cache: "+keyword_id)
keywords = cache.load(keyword_cache_directory,keyword_id)
keyword_factory.print_keyword_setup_to_json(keyword_id,keywords)
# keywords = keyword_factory.check_for_constructed_keyword_setup(keyword_id)
# print(keywords)
return
crete_new_keywords_spec = {}
training_data_spec = experiment_spec["training_dataset"]
crete_new_keywords_spec["seed_id"] = keyword_spec["seed_id"]
crete_new_keywords_spec["training_dataset"] = training_data_spec
crete_new_keywords_spec["training_dataset"]["index_directory"] = index_cache_directory
crete_new_keywords_spec["training_dataset"]["index_id"]= get_all_index_indices(training_data_spec)
given_reference_words = specification_handler.get_specification(__KEYWORD_SEEDS_DIRECTORY_SPECIFICATION, keyword_seed_id)
given_reference_words = seed_words_to_index_terms(given_reference_words)
crete_new_keywords_spec["given_reference_words"] = given_reference_words
crete_new_keywords_spec["keyword_generate_algorithm"] = keyword_spec["keyword_generate_algorithm"]
crete_new_keywords_spec["parameters"] = keyword_spec["parameters"]
crete_new_keywords_spec["reference_word_filter"] = keyword_spec["reference_word_filter"]
keywords = keyword_factory.get_keywords(crete_new_keywords_spec,keyword_id)
cache.write(keyword_cache_directory,keyword_id, keywords)
def __calculate_raw_dice_keywords(keyword_specification):
# load paramters
training_dataset_spec = keyword_specification["training_dataset"]
index_directory = training_dataset_spec["index_directory"]
indices_id = training_dataset_spec["index_id"]
training_data_filters = training_dataset_spec["filters"]
trainingdata_handler = TrainingDatasetHandler(training_dataset_spec["id"])
given_reference_words = keyword_specification["given_reference_words"]
weight_limit = float(keyword_specification["parameters"]["weight_limit"])
max_number_of_key_words = int(keyword_specification["parameters"]["max_number_of_key_words"])
keyword_seed_id = keyword_specification["seed_id"]
raw_keywords_id = keyword_setup_id_generator.get_no_filtered_keywords_id(keyword_seed_id,training_dataset_spec)
# Calculate keywords
# calculate posting lists for reference word groups
posting_lists = posting_list_handler.get_seed_words_posting_lists(given_reference_words,index_directory,indices_id)
posting_lists_id = raw_keywords_id
cache.write("posting_lists",posting_lists_id,posting_lists)
print("Merged posting lists calculated")
top_dice_neighbours = __get_top_n_dice_neighbours(max_number_of_key_words,
posting_lists,
index_directory,
indices_id,
trainingdata_handler,
training_data_filters,
raw_keywords_id)
new_reference_words, new_context_words = __seperate_reference_and_context_words(top_dice_neighbours, weight_limit)
return {"raw_reference_words": new_reference_words, "raw_context_words":new_context_words}
