def predict_elite_status_with_linear_regression():
# Generate graph and user dictionaries
graph = read_graph_from_yelp_JSON_file()
users = read_users_from_yelp_JSON_file(model_type='linear_regression')
# Add PageRank to user dictionaries
pagerank_for_node = networkx.pagerank(graph)
user_pageranks = [{'ID': node_ID, 'pagerank': pagerank} for node_ID, pagerank in pagerank_for_node.iteritems()]
users = join_dictionaries(user_pageranks, users, 'ID')
# Prepare users for learning
users = remove_labels(users, 'ID')
users = normalize_users(users, excluded_attributes=['years_elite'])
users = designate_attribute_as_label(users, 'years_elite')
random.shuffle(users)
# Split data into training and test
user_count = len(users)
training_set_size = int(0.75 * user_count)
test_set_size = user_count - training_set_size
training_set = users[0:training_set_size]
test_set = users[-test_set_size:]
# Fit to hyperplane
model, weights = regression.get_model_and_weights(training_set)
# Show us how important each attribute is
print 'Attribute weights:'
for attribute, weight in weights.items():
print attribute + ': ' + str(weight)
# Test the model by calculating its coefficient of determination (R^2) on test data
test_samples, test_labels, _ = regression.prep_data(test_set)
test_score = model.score(test_samples, test_labels)
print 'Test score: ' + str(test_score)
def predict_pagerank():
# Hyperparameters
MINIMUM_FRIEND_COUNT = 1
# Generate graph and users
graph = read_graph_from_yelp_JSON_file()
remove_low_degree_nodes(graph, MINIMUM_FRIEND_COUNT)
users = read_users_from_yelp_JSON_file()
# Add PageRank to user dictionaries
pagerank_for_node = networkx.pagerank(graph)
user_pageranks = [{'ID': node_ID, 'pagerank': pagerank} for node_ID, pagerank in pagerank_for_node.iteritems()]
users = join_dictionaries(user_pageranks, users, 'ID')
# Prepare users for regression
users = remove_labels(users, label_name='ID')
users = remove_labels(users, label_name='friend_count')
users = normalize_users(users, excluded_attributes=['ID', 'years_elite'])
users = designate_attribute_as_label(users, attribute='pagerank')
# Fit to hyperplane
training_set = users
model, weights = regression.get_model_and_weights(training_set)
# Show us how important each attribute is
print 'Attribute weights:'
for attribute, weight in weights.items():
print attribute + ': ' + str(weight * 100)
