def process_query_results(query_results, base_map):
in_shape_ids = []
incoming_flow = {}
out_shape_ids = []
outgoing_flow = {}
for itinerary in query_results:
origin_id = Utils.convert_id(itinerary[0])
destination_id = Utils.convert_id(itinerary[1])
weight = itinerary[2]
origin_ids = []
destination_ids = []
# we look only at shapes that are to be rendered
if origin_id in base_map.shape_dict or destination_id in base_map.shape_dict:
shape_origin = base_map.shape_dict[origin_id]
origin_ids.append(origin_id)
shape_destination = base_map.shape_dict[destination_id]
destination_ids.append(destination_id)
# We build a dictionary of outgoing traffic
for origin_id in origin_ids:
if origin_id not in out_shape_ids:
out_shape_ids.append(origin_id)
outgoing_flow[shape_origin] = []
outgoing_flow[shape_origin].append((shape_destination, weight))
# We build a dictionary of incoming traffic
for destination_id in destination_ids:
if destination_id not in in_shape_ids:
in_shape_ids.append(destination_id)
incoming_flow[shape_destination] = []
incoming_flow[shape_destination].append((shape_origin, weight))
return outgoing_flow, incoming_flow
def get_shape_coords(self):
shape_zone = self.shapefile.shape(self.shape_id)
points = [(i[0], i[1]) for i in shape_zone.points]
x_center, y_center = Utils.calculate_centroid(points)
center = (x_center, y_center)
max_bound, min_bound = Utils.calculate_boundaries(points)
return (points, center, max_bound, min_bound)
def find_max_coords(self):
all_max_bound = []
all_min_bound = []
shape_dict = self.shape_dict
for zone_id in shape_dict:
zone_shape = shape_dict[zone_id]
max_bound_zone = zone_shape.max_bound
min_bound_zone = zone_shape.min_bound
all_max_bound.append(max_bound_zone)
all_min_bound.append(min_bound_zone)
map_max_bound, unused_max = Utils.calculate_boundaries(all_max_bound)
unused_min, map_min_bound = Utils.calculate_boundaries(all_min_bound)
return (map_max_bound, map_min_bound)
def get_user_articles(self, user_id):
'''
INPUT:
user_id - (int) a user id
user_item - (pandas dataframe) matrix of users by articles:
1's when a user has interacted with an article, 0 otherwise
OUTPUT:
article_ids - (list) a list of the article ids seen by the user
article_names - (list) a list of article names associated with the list of article ids
(this is identified by the doc_full_name column in df_content)
Description:
Provides a list of the article_ids and article titles that have been seen by a user
'''
user_row = np.where(self.user_item.index == user_id)[0][0]
user_articles = np.where(self.user_item.iloc[user_row] == 1)[0]
article_ids = []
for article in user_articles:
article_id = self.user_item.iloc[:, article].name
article_ids.append(str(article_id)) # to match the expected str type as output
article_names = Utils.get_article_names(article_ids, self.interactions_df, 'title')
return article_ids, article_names # return the ids and names
def project_shape_coords(self, projection):
shape_zone = self.shapefile.shape(self.shape_id)
points = [
projection.apply_projection([i[0], i[1]])
for i in shape_zone.points
]
points = [projection.apply_translation([i[0], i[1]]) for i in points]
self.points = points
x_center, y_center = Utils.calculate_centroid(points)
self.center = (x_center, y_center)
max_bound, min_bound = Utils.calculate_boundaries(points)
self.max_bound = max_bound
self.min_bound = min_bound
def make_user_user_recommendations(self, user_id, num_recommendations=10):
'''
INPUT:
user_id - (int) a user id
num_recommendations - (int) the number of recommendations you want for the user
OUTPUT:
recs - (list) a list of recommendations for the user by article id
rec_names - (list) a list of recommendations for the user by article title
Description:
Loops through the users based on closeness to the input user_id
For each user - finds articles the user hasn't seen before and provides them as recs
Does this until m recommendations are found
Notes:
* Choose the users that have the most total article interactions
before choosing those with fewer article interactions.
* Choose articles with the articles with the most total interactions
before choosing those with fewer total interactions.
'''
recs = []
neighbors_df = self.get_top_sorted_users(user_id)
user_articles_id, user_articles_names = self.get_user_articles(user_id)
for neighbor in neighbors_df.index:
neighbor_articles_id, neighbor_articles_names = self.get_user_articles(neighbor)
sorted_neighbor_article_ids = Utils.get_top_articles_df(neighbor_articles_id, self.interactions_df)
sorted_neighbor_article_ids = sorted_neighbor_article_ids.index.values
article_not_read = np.setdiff1d(sorted_neighbor_article_ids, user_articles_id, assume_unique=True)
article_not_read = [str(i) for i in article_not_read]
recs = np.unique(np.concatenate([article_not_read, recs], axis=0))
if len(recs) >= num_recommendations:
break
if len(recs) >= num_recommendations:
recs = recs[:num_recommendations]
recommended_articles = Utils.get_article_names(recs, self.interactions_df, 'title')
return recommended_articles
def make_SVD_recommendations(self, user_id, num_recommendations=10):
preds = np.around(np.dot(np.dot(self.u_matrix, self.s_matrix), self.vt_matrix))
articles_idx = preds.argsort()[-num_recommendations:][::-1]
rec_ids = self.user_item.columns[articles_idx]
recommended_articles = Utils.get_article_names(rec_ids, self.interactions_df, 'title')
recommended_articles = recommended_articles[:num_recommendations]
return recommended_articles
def process_query_results(query_results_dict, map_item):
processed_query_results_dict = {}
# we find the min and max passengers for the whole year
min_passenger = 999999999
max_passenger = 0
for query_date in query_results_dict:
temp_min, temp_max = Utils.compute_min_max_passengers(query_results_dict[query_date], 2)
if temp_min < min_passenger:
min_passenger = temp_min
if temp_max > max_passenger:
max_passenger = temp_max
# we transform the query_results_dict to use instances of the PointOnMap class
for query_date in query_results_dict:
query_result = query_results_dict[query_date]
processed_query_results_dict[query_date] = []
for itinerary in query_result:
processed_itinerary = []
zone_id_origin = Utils.convert_id(itinerary[0])
zone_id_destination = Utils.convert_id(itinerary[1])
if zone_id_origin == zone_id_destination:
color = (141, 91, 67)
else:
color = (135, 162, 34)
weight = compute_weight(map_item[0], itinerary[2], max_passenger)
shape_origin = map_item[1].shape_dict[zone_id_origin]
coords = shape_origin.center
point_to_render = classfile.PointOnMap(coords, weight, color)
processed_itinerary.append(point_to_render)
shape_dest = map_item[1].shape_dict[zone_id_destination]
target_coords = shape_dest.center
processed_itinerary.append(target_coords)
processed_itinerary.append(weight)
processed_query_results_dict[query_date].append(processed_itinerary)
return processed_query_results_dict, min_passenger, max_passenger
def render_single_map(flow_dict, flow_dir, base_map, file_name, zone_shape):
map_rendered = base_map.map_file.copy()
zone_name = find_names(zone_shape, base_map)
zone_id = Utils.convert_id(zone_shape.shape_id, inverse=True)
map_title = '{}_{}_{}_{}_{}'.format(file_name[0], zone_id, zone_name,
flow_dir, file_name[1])
trips_list = flow_dict[zone_shape]
min_passenger, max_passenger = Utils.compute_min_max_passengers(
trips_list, 1)
colors = []
for linked_zone in trips_list:
shape_to_color = linked_zone[0]
if shape_to_color.shape_id != zone_shape.shape_id:
weight = linked_zone[1]
render_color = compute_color(weight, min_passenger, max_passenger)
shape_to_color.color_fill = render_color
if render_color not in colors:
colors.append(render_color)
shape_to_color.fill_in_shape(map_rendered)
# we draw again the boundaries of the shape after filling it in
pts = np.array(shape_to_color.points, np.int32)
cv2.polylines(map_rendered, [pts], True, (255, 255, 255), 1,
cv2.LINE_AA)
# outline the focused shape
zone_shape.color_line = [95, 240, 255]
zone_shape.line_thick = 3
pts = np.array(zone_shape.points, np.int32)
cv2.polylines(map_rendered, [pts], True, zone_shape.color_line,
zone_shape.line_thick, cv2.LINE_AA)
# display the legend
display_specific_text(map_rendered, zone_id, zone_name, flow_dir,
min_passenger, max_passenger, colors)
# save the image
cv2.imwrite(('{}.png').format(map_title), map_rendered)
def create_test_and_train_user_item(self):
'''
INPUT:
df_train - training dataframe
df_test - test dataframe
OUTPUT:
user_item_train - a user-item matrix of the training dataframe
(unique users for each row and unique articles for each column)
user_item_test - a user-item matrix of the testing dataframe
(unique users for each row and unique articles for each column)
'''
num_interactions = len(self.interactions_df)
len_train = int(70*num_interactions/100) # 70% of the df for train
len_test = num_interactions - len_train # 30% of the df for test
df_train = self.interactions_df.head(len_train)
df_test = self.interactions_df.tail(len_test)
# we reuse the create_user_item_matrix we defined earlier
user_item_train = Utils.create_user_item_matrix(df_train)
user_item_test = Utils.create_user_item_matrix(df_test)
return (user_item_train, user_item_test)
def NLP_processing(self, text):
# remove NaN from text
text = Utils.remove_NaN(text, 0)
# initialize count vectorizer object
vect = CountVectorizer(lowercase=False, tokenizer=self.tokenize)
# get counts of each token (word) in text data
X = vect.fit_transform(self.content_analysis_target)
# initialize tf-idf transformer object
transformer = TfidfTransformer(smooth_idf=False)
# use counts from count vectorizer results to compute tf-idf values
tfidf = transformer.fit_transform(X)
return tfidf
def make_content_user_recommendations(self, _id, num_recommendations=10):
'''
INPUT:
_id, the id of the user we want recommended articles for
self.content_similarity_matrix, the similarity matrix of the articles, by default cosine matrix computed separately
self.interactions_df, the dataframe with the interactions of users with articles
self.article_content_df - the df containing details about the articles
num_recommendations, the number of recommendations expected as an output, by default 10
OUTPUT:
recommended_articles, a list of recommended articles, given by name
'''
# get the articles a user read
user_articles_id, user_articles_names = self.get_user_articles(_id)
# filter out the articles that are not in the df of article details
user_articles_id = [float(i) for i in user_articles_id]
user_articles = self.article_content_df[self.article_content_df['article_id'].isin(user_articles_id)]['article_id'].values
# sort the articles_id per number of interactions
user_article_inter_dict = {}
for article in user_articles:
interact = len(self.interactions_df[(self.interactions_df['user_id'] == _id) & (self.interactions_df['article_id'] == article)])
article_title = self.interactions_df[self.interactions_df['article_id'] == article]['title'].values[0]
user_article_inter_dict[article] = {'num_interactions': interact, 'title': article_title}
top_user_articles_df = pd.DataFrame.from_dict(user_article_inter_dict, orient='index')
top_user_articles_df = top_user_articles_df.sort_values(by='num_interactions', ascending=False)
# find similar articles in order
recommended_articles = []
for article in top_user_articles_df.index:
articles_sim = self.find_similar_articles(article)
unread_articles = np.setdiff1d(articles_sim, top_user_articles_df.index, assume_unique=True)
for unread_article in unread_articles:
if unread_article not in recommended_articles:
recommended_articles.append(unread_article)
if len(recommended_articles) > num_recommendations:
break
recommended_articles = recommended_articles[:num_recommendations]
recommended_articles = Utils.get_article_names(recommended_articles, self.article_content_df, 'doc_full_name')
return recommended_articles
def make_content_article_recommendations(self, _id, num_recommendations=10):
'''
INPUT:
_id, the id of the article we want similar articles for
self.content_similarity_matrix, the similarity matrix of the articles, by default cosine matrix computed separately
self.interactions_df, the dataframe with the interactions of users with articles
self.article_content_df - the df containing details about the articles
num_recommendations, the number of recommendations expected as an output, by default 10
OUTPUT:
recommended_articles, a list of similar articles, given by name
'''
recommended_articles = self.find_similar_articles(_id)
recommended_articles = recommended_articles[:num_recommendations]
recommended_articles = Utils.get_article_names(recommended_articles, self.article_content_df, 'doc_full_name')
return recommended_articles
def __init__(self, interactions_df):
self.interactions_df = interactions_df
self.article_ids = self.interactions_df['article_id'].unique()
self.top_articles_df = Utils.get_top_articles_df(self.article_ids, self.interactions_df)
def process_query_arg(render_animation_dict):
period = render_animation_dict['period']
query_dict = render_animation_dict['query_dict']
database = render_animation_dict['database']
specific_weekdays = query_dict['specific_weekdays']
date = query_dict['date']
aggregate_period = render_animation_dict['aggregate_period']
weekdays = render_animation_dict['weekdays']
query_results_dict = {}
if aggregate_period is False and query_dict['date'] == 'loop_through_period':
# in this case we want the result for each day of the period provided
# if we have the flag loop_through_period in the query dict, it means the period
# set for the query is multiple dates
daterange = pd.date_range(period[0], period[1])
# we run queries for each date in the daterange specified
for single_date in daterange:
date = pd.to_datetime(single_date)
if specific_weekdays == 'on_specific_weekdays':
# we check if the date of the daterange matches the weekday(s) we target
if date.dayofweek in weekdays:
single_date = date.date().strftime('%Y-%m-%d')
query_dict['date'] = single_date
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date']] = query_results
else:
# if a date in the range is not among the weekdays we want, we skip it
continue
else:
single_date = date.date().strftime('%Y-%m-%d')
query_dict['date'] = single_date
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date']] = query_results
elif aggregate_period is True and query_dict['date'] == 'loop_through_period':
# in this case, we want to aggregate the results (sum) per week
daterange = pd.date_range(period[0], period[1])
start_date = pd.to_datetime(period[0])
end_date = pd.to_datetime(period[1])
# let's build a list of all intervals we will want to aggregate the data for
all_aggr_init = []
start = start_date
end = end_date
# we add one list of dates per week to the list of all intervals
i = 0
for date in daterange:
# we handle separately the first date of the period
if i == 0:
curr_week = [start.date().strftime('%Y-%m-%d')]
if date != start_date and date != end_date:
start_week_number = start.isocalendar()[1]
date_week_number = date.isocalendar()[1]
if date_week_number == start_week_number:
curr_week.append(date.date().strftime('%Y-%m-%d'))
i += 1
else:
start = date
all_aggr_init.append(curr_week)
i = 0
# we handle separately the last date of the period
if curr_week not in all_aggr_init:
curr_week.append(end_date.date().strftime('%Y-%m-%d'))
all_aggr_init.append(curr_week)
else:
curr_week = [end_date.date().strftime('%Y-%m-%d')]
all_aggr_init.append(curr_week)
# now we keep only the first and last item of each interval
all_aggr = []
for interval in all_aggr_init:
interval_new = [interval[0], interval[-1]]
all_aggr.append(interval_new)
# we now query for each interval
for interval in all_aggr:
query_dict['date'] = interval
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date'][0]] = query_results
else:
# we have a single date to render for, so nothing to aggregate!
# just in case we check that there is no mismatch between the single day and the
# argument containing specific weekdays restrictions if any
if specific_weekdays == 'on_specific_weekdays':
# we check if the date of the daterange matches the weekday(s) we target
date = pd.to_datetime(query_dict['date'])
if date.dayofweek in weekdays:
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date']] = query_results
else:
print("The date selected does not match the weekday(s) indicated. Please select either an interval ('time_granularity': 'period') or a valid weekday(s) list.")
else:
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
query_results_dict[query_dict['date']] = query_results
return query_results_dict
import os
import argparse
from utility import Utils
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Downloading ncodes for imagenet dataset')
dir_path = os.path.realpath(os.path.dirname(__file__))
path_to_save = os.path.realpath(os.path.dirname(__file__))
optional = parser.add_argument_group('optional arguments')
optional.add_argument('-nc_dir',
'--ncodes_dir',
default=path_to_save,
help='path to saved ncodes data')
optional.add_argument('-url_data',
'--with_url_data',
default=False,
help='tag whether to download url data')
args = parser.parse_args()
util_obj = Utils()
# download ncodes data csv
util_obj.download_ncodes_image_net(path_to_save=args.ncodes_dir)
# download imagenet urls csv
args.url_data = eval(str(args.with_url_data))
util_obj.download_image_net_urls(path_to_save=args.ncodes_dir)
import pandas as pd
from classfile import RecommendationEngine
from utility import Utils
# import and cleandata sources
interactions_df = pd.read_csv('data/user-item-interactions.csv')
article_content_df = pd.read_csv('data/articles_community.csv')
del interactions_df['Unnamed: 0']
del article_content_df['Unnamed: 0']
email_encoded = Utils.email_mapper(interactions_df['email'])
del interactions_df['email']
interactions_df['user_id'] = email_encoded
# create a matrix of user-article interactions
user_item = Utils.create_user_item_matrix(interactions_df)
# create an instance of the Recommendation Engine that can be used for multiple situations
rec_engine = RecommendationEngine(interactions_df, article_content_df,
user_item)
# test the code for a few situations (expected returned output of 10 article titles)
# recommendations for an article
_id_type = 'article'
_id = 10
recommended_articles = rec_engine.make_recommendations(_id, _id_type)
print('Test article')
print(
'The following articles are recommended based on your query for {} id {}:'.
format(_id_type, _id))
'weekdays': [],
'aggregate_period': False
}
query_dict = build_query_dict(render_heat_map_dict)
if query_dict['date'] == 'loop_through_period':
# if we have the flag loop_through_period in the query dict, it means the period
# set for the query is multiple dates, therefore we want the query to return an
# average on a time interval, and not on a single date
period = render_heat_map_dict['period']
daterange = pd.date_range(period[0], period[1])
query_dict['date'] = period
query = prepare_sql_query(query_dict)
query_results = Utils.make_sql_query(query, database)
for single_map, base_map, projection in base_maps:
# we process the query results
outgoing_flow, incoming_flow = process_query_results(
query_results, base_map)
print('Rendering {}...'.format(single_map))
if single_map == 'total':
if time_granularity == 'weekdays_vs_weekends':
file_name = ['NYC', '2018_diff_WD_WE']
else:
file_name = ['NYC', '2018']
else:
if time_granularity == 'weekdays_vs_weekends':
file_name = ['{}'.format(single_map), '2018_diff_WD_WE']
else:
default=True,
help='downloaded parallelly or sequentially')
optional.add_argument(
'-v',
'--verbose',
default=True,
help='bool represent whether to display ncode level download stats')
optional.add_argument(
'-b',
'--batch_size',
default=None,
help='number of images to download paralelly, if parallel is TRUE')
args = parser.parse_args()
args.parallel = eval(str(args.parallel))
args.with_annotation = eval(str(args.with_annotation))
args.verbose = eval(str(args.verbose))
args.batch_size = eval(str(args.batch_size))
# starting the download process
util_obj = Utils()
util_obj.subset_ncodes_to_download(ncodes_data_path)
util_obj.download_partial_imagenet_dataset(
path_to_url_dataset=args.url_data,
path_to_annotations=args.annotations_dir,
path_to_save_dataset=args.save_dir,
only_annotations=args.with_annotation,
parallel=args.parallel,
verbose=args.verbose,
batch_size_=args.batch_size)
username, fname, lname = '', '', ''
email = ''
def __init__(self, username, fname, lname, *args, **kwargs):
self.username = username
self.fname, self.lname = fname, lname
@classmethod
def setEmail(cls, email):
cls.email = f'{email}@g.net'
return cls.email
def setFullName(self):
return f'{self.fname} {self.lname}'
def __str__(self):
return f'Hi {self.username}\nEmail:{self.setEmail(self.username)}\nFull Name:{self.setFullName()}'
usr = User(username='******', fname='John', lname='@Doe')
setmail = usr.setEmail(email='John')
assert Utils.remove_punctuation(usr.setEmail('john')) == 'johngnet'
assert Utils.remove_dollar_white_space(",12") == 12.0
assert Utils.remove_dollar_white_space("$123") == 123.0
assert Utils.remove_comma_and_spaces(" , ,1234") == 1234.0
my_str = "Hello!!!, he said ---and went."
assert Utils.remove_punctuation(my_str) == "Hello he said and went"
print('Assertion Test Complete')
