def full_cycle(self):
Constants.print_properties()
print('%s: full cycle' % time.strftime("%Y/%m/%d-%H:%M:%S"))
utilities.plant_seeds()
if self.use_cache and \
os.path.exists(Constants.PROCESSED_RECORDS_FILE):
print('Records have already been processed')
self.records = \
ETLUtils.load_json_file(Constants.PROCESSED_RECORDS_FILE)
else:
self.load_records()
if 'yelp' in Constants.ITEM_TYPE:
self.transform_yelp_records()
elif 'fourcity' in Constants.ITEM_TYPE:
self.transform_fourcity_records()
self.add_integer_ids()
self.clean_reviews()
self.remove_duplicate_reviews()
self.tag_reviews_language()
self.remove_foreign_reviews()
self.lemmatize_records()
self.remove_users_with_low_reviews()
self.remove_items_with_low_reviews()
self.count_frequencies()
self.shuffle_records()
print('total_records: %d' % len(self.records))
self.classify_reviews()
self.build_bag_of_words()
self.tag_contextual_reviews()
# self.load_full_records()
self.build_dictionary()
self.build_corpus()
self.label_review_targets()
self.export_records()
self.count_specific_generic_ratio()
# self.export_to_triplet()
rda = ReviewsDatasetAnalyzer(self.records)
print('density: %f' % rda.calculate_density_approx())
print('sparsity: %f' % rda.calculate_sparsity_approx())
print('total_records: %d' % len(self.records))
user_ids = \
extractor.get_groupby_list(self.records, Constants.USER_ID_FIELD)
item_ids = \
extractor.get_groupby_list(self.records, Constants.ITEM_ID_FIELD)
print('total users', len(user_ids))
print('total items', len(item_ids))
if Constants.SEPARATE_TOPIC_MODEL_RECSYS_REVIEWS:
self.separate_recsys_topic_model_records()
def preprocess(self):
self.load_records()
if 'yelp' in Constants.ITEM_TYPE:
self.transform_yelp_records()
elif 'fourcity' in Constants.ITEM_TYPE:
self.transform_fourcity_records()
self.add_integer_ids()
self.clean_reviews()
self.remove_duplicate_reviews()
self.tag_reviews_language()
self.remove_foreign_reviews()
self.remove_reviews_from_classifier_training_set()
self.lemmatize_records()
self.remove_users_with_low_reviews()
self.remove_items_with_low_reviews()
self.count_frequencies()
self.shuffle_records()
print('total_records: %d' % len(self.records))
self.classify_reviews()
self.build_bag_of_words()
self.tag_contextual_reviews()
# self.load_full_records()
self.build_dictionary()
self.build_corpus()
self.label_review_targets()
self.export_records()
self.count_specific_generic_ratio()
self.export_to_triplet()
rda = ReviewsDatasetAnalyzer(self.records)
print('density: %f' % rda.calculate_density_approx())
print('sparsity: %f' % rda.calculate_sparsity_approx())
print('total_records: %d' % len(self.records))
user_ids = \
extractor.get_groupby_list(self.records, Constants.USER_ID_FIELD)
item_ids = \
extractor.get_groupby_list(self.records, Constants.ITEM_ID_FIELD)
print('total users', len(user_ids))
print('total items', len(item_ids))
def test_calculate_sparsity_approx(self):
expected_value = 0.
rda = ReviewsDatasetAnalyzer(reviews_matrix_3)
actual_value = rda.calculate_sparsity()
self.assertEqual(expected_value, actual_value)
expected_value = 1 - 6./9
rda = ReviewsDatasetAnalyzer(reviews_matrix_3_1)
actual_value = rda.calculate_sparsity()
self.assertEqual(expected_value, actual_value)
expected_value = 1 - 3./9
rda = ReviewsDatasetAnalyzer(reviews_matrix_3_2)
actual_value = rda.calculate_sparsity()
self.assertEqual(expected_value, actual_value)
expected_value = 1 - 15./24
rda = ReviewsDatasetAnalyzer(reviews)
actual_value = rda.calculate_sparsity_approx()
self.assertEqual(expected_value, actual_value)
def generate_report(reviews, dataset_name, file_name, load_reviews_code):
nb = nbf.new_notebook()
title = '# ' + dataset_name + ' Dataset Analysis'
title_cell = nbf.new_text_cell(u'markdown', title)
rda = ReviewsDatasetAnalyzer(reviews)
num_reviews = len(rda.reviews)
num_users = len(rda.user_ids)
num_items = len(rda.item_ids)
user_avg_reviews = float(num_reviews) / num_users
item_avg_reviews = float(num_reviews) / num_items
sparsity = rda.calculate_sparsity_approx()
fact_sheet_text =\
'## Fact Sheet\n' +\
'The ' + dataset_name + ' contains:\n' +\
'* ' + str(num_reviews) + ' reviews\n' +\
'* Made by ' + str(num_users) + ' users\n' +\
'* About ' + str(num_items) + ' items\n' +\
'* It has an approximated sparsity of ' + str(sparsity) + '\n' +\
'\nNow we are going to analyze the number of reviews per user and ' \
'per item'
fact_sheet_cell = nbf.new_text_cell(u'markdown', fact_sheet_text)
reviews_analysis_code =\
'import sys\n' +\
'sys.path.append(\'/Users/fpena/UCC/Thesis/projects/yelp/source/python\')\n' +\
'from etl import ETLUtils\n\n' +\
'from etl.reviews_dataset_analyzer import ReviewsDatasetAnalyzer\n' +\
'\n# Load reviews\n' + load_reviews_code + '\n' +\
'rda = ReviewsDatasetAnalyzer(reviews)\n'
reviews_analysis_cell = nbf.new_code_cell(reviews_analysis_code)
user_analysis_text =\
'## Users Reviews Analysis\n' +\
'* The average number of reviews per user is ' + str(user_avg_reviews) + '\n' +\
'* The minimum number of reviews a user has is ' + str(min(rda.users_count)) + '\n' +\
'* The maximum number of reviews a user has is ' + str(max(rda.users_count))
user_analysis_cell = nbf.new_text_cell(u'markdown', user_analysis_text)
counts_per_user_code =\
'# Number of reviews per user\n' +\
'users_summary = rda.summarize_reviews_by_field(\'user_id\')\n' +\
'print(\'Average number of reviews per user\', float(rda.num_reviews)/rda.num_users)\n' +\
'users_summary.plot(kind=\'line\', rot=0)'
counts_per_user_cell = nbf.new_code_cell(counts_per_user_code)
item_analysis_text =\
'## Items Reviews Analysis\n' +\
'* The average number of reviews per item is ' + str(item_avg_reviews) + '\n' +\
'* The minimum number of reviews an item has is ' + str(min(rda.items_count)) + '\n' +\
'* The maximum number of reviews an item has is ' + str(max(rda.items_count))
item_analysis_cell = nbf.new_text_cell(u'markdown', item_analysis_text)
counts_per_item_code =\
'# Number of reviews per item\n' +\
'items_summary = rda.summarize_reviews_by_field(\'offering_id\')\n' +\
'print(\'Average number of reviews per item\', float(rda.num_reviews)/rda.num_items)\n' +\
'items_summary.plot(kind=\'line\', rot=0)'
counts_per_item_cell = nbf.new_code_cell(counts_per_item_code)
common_items_text =\
'## Number of items 2 users have in common\n' +\
'In this section we are going to count the number of items two ' \
'users have in common'
common_items_text_cell = nbf.new_text_cell(u'markdown', common_items_text)
common_items_code =\
'# Number of items 2 users have in common\n' +\
'common_item_counts = rda.count_items_in_common()\n' +\
'plt.plot(common_item_counts.keys(), common_item_counts.values())\n'
common_items_code_cell = nbf.new_code_cell(common_items_code)
common_items_box_code =\
'from pylab import boxplot\n' +\
'my_data = [key for key, value in common_item_counts.iteritems() for i in xrange(value)]\n' +\
'mean_common_items = float(sum(my_data))/len(my_data)\n' +\
'print(\'Average number of common items between two users:\', mean_common_items)\n' +\
'boxplot(my_data)'
common_items_box_cell = nbf.new_code_cell(common_items_box_code)
cells = []
cells.append(title_cell)
cells.append(fact_sheet_cell)
cells.append(reviews_analysis_cell)
cells.append(user_analysis_cell)
cells.append(counts_per_user_cell)
cells.append(item_analysis_cell)
cells.append(counts_per_item_cell)
cells.append(common_items_text_cell)
cells.append(common_items_code_cell)
cells.append(common_items_box_cell)
nb['worksheets'].append(nbf.new_worksheet(cells=cells))
with open(file_name, 'w') as f:
nbf.write(nb, f, 'ipynb')