def test_score(self):
u_words = {'pancakes': 3, 'syrup': 12, 'communism': -10}
a_words = {'communism': 0.2, 'syrup': 0.1}
sticky_marxism = score(a_words, u_words)
a_words2 = {'pancakes': 0.2, 'syrup': 0.1}
trip_to_canada = score(a_words2, u_words)
self.assertTrue(sticky_marxism < trip_to_canada)
a_words = {'marxism': 0.2, 'sugar': 0.1}
sticky_marxism = score(a_words, u_words)
a_words2 = {'cake': 0.2, 'honey': 0.1}
trip_to_canada = score(a_words2, u_words)
self.assertTrue(sticky_marxism < trip_to_canada)
def main():
gearman_client = gearman.GearmanClient(['localhost:4730'])
if len(sys.argv) < 2:
print('Please specify a user to get the error of. See README.md')
return
username = get_username_from_input(sys.argv)
print 'Getting model error of {}'.format(username)
print 'Loading user\'s votes from database'
# get the user's votes on articles
db_result = db_get('vote', {'username': username}, {
'article_url': 1,
'feed_url': 1,
'positive_opinion': 1,
'vote_datetime': 1
})
if db_result['status'] != 'ok':
print 'Error'
print 'Could not get user data from vote collection'
print db_result['description']
return
articles = db_result['docs']
print len(articles), 'article opinions found in vote db for given user'
# map each article url to 1 or -1, if the user liked or disliked it
article_opinions = {}
vote_datetimes = {}
for article in articles:
# make sure all the required fields are there
req_fields = ['article_url', 'positive_opinion', 'vote_datetime']
if not all([s in article for s in req_fields]):
print 'Error'
print 'Vote is missing some fields: {}'.format(article)
continue
url = article['article_url']
# set the classes for the votes to 1 for positive and -1 for negative
vote = 1 if article['positive_opinion'] else -1
article_opinions[url] = vote
vote_datetimes[url] = article['vote_datetime']
# split the articles into the feeds they belong to, to minimise db lookups
# the dict maps feed urls to a list of article urls fromt that feed
feeds = {}
for article in articles:
if article['feed_url'] in feeds:
feeds[article['feed_url']].append(article['article_url'])
else:
feeds[article['feed_url']] = [article['article_url']]
# get a set of the unique article urls
article_url_set = set(article_opinions.keys())
print len(article_url_set), 'unique articles in set'
if len(article_url_set) < 0:
print 'Error'
print 'Not enough articles in data set'
return
# get the words the user is interested in
db_result = db_get('user', {'username': username}, {'words': 1})
if db_result['status'] != 'ok':
print 'Error'
print 'Could not load data from user collection'
print db_result['description']
return
if len(db_result['docs']) < 1:
print 'Error'
print 'No such user in user collection'
return
user_data = db_result['docs'][0]
user_words = user_data['words']
# it is required to have at least classes, so create two
# inputs with extreme values to train the model
data_x = [[10.0, 1], [0.0, 10000000]]
data_y = [1, -1]
# get the data from the db for each feed a user voted on an article in
for feed in feeds:
db_result = db_get('feed', {'url': feed}, {'items': 1})
if db_result['status'] != 'ok':
print 'Error'
print 'Could not get data from feed collection'
print db_result['description']
return
if 'docs' not in db_result or len(db_result['docs']) < 1:
print 'Error'
print 'No feed returned for url', feed
return
items = db_result['docs'][0]['items']
# check the items in that feed for one the user voted on
for item in items:
if item['link'] not in article_url_set:
continue
print 'adding', item['link']
if 'topics' not in item:
print 'Error'
print 'No topics for given item, skipping'
continue
words = item['topics']
topic_crossover = kw_score.score(words, user_words)
if 'pub_date' not in item:
print 'Error'
print 'No pub_date for given item, skipping'
continue
time_diff = vote_datetimes[item['link']] - item['pub_date']
x = [topic_crossover, time_diff.total_seconds()]
y = article_opinions[item['link']]
data_x.append(x)
data_y.append(y)
print 'Articles from feed', feed, 'added to data'
print data_x
print data_y
if len(data_x) < 3:
print 'Error'
print 'Not enough data points'
return
data_points = [(data_x[i], data_y[i]) for i in xrange(len(data_x))]
n = 0
score = 0
# start the 2-fold cross-validation, doing up to 10 folds of the data
repetitions = min(len(data_points), 10)
for k in xrange(repetitions):
print 'Iteration {} out of {} ({}% finished)'.format(
k, len(data_points), 100 * (float(k) / repetitions))
shuffle(data_points)
training = data_points[:len(data_points) / 2]
validation = data_points[len(data_points) / 2:]
if has_enough_classes(training):
curr_score = get_model_score(training, validation)
print '- Score 1 this fold: {}'.format(curr_score)
score += curr_score
n += 1
else:
print '- Not enough training classes, skipping'
continue
#swap the training and validation data
training, validation = validation, training
if has_enough_classes(training):
curr_score = get_model_score(training, validation)
print '- Score 2 this fold: {}'.format(curr_score)
score += curr_score
n += 1
else:
print '- Not enough training classes, skipping'
continue
if n == 0:
print 'Error'
print 'Not enough valid data points'
return
print 'Score: {:.6f}, based on {} divisions of the data.'.format(
float(score) / n, n)
return
def main():
gearman_client = gearman.GearmanClient(['localhost:4730'])
if len(sys.argv) < 2:
print('Please specify a user to get the error of. See README.md')
return
username = get_username_from_input(sys.argv)
print 'Getting model error of {}'.format(username)
print 'Loading user\'s votes from database'
# get the user's votes on articles
db_result = db_get('vote', {
'username': username
},{
'article_url': 1,
'feed_url': 1,
'positive_opinion': 1,
'vote_datetime': 1
})
if db_result['status'] != 'ok':
print 'Error'
print 'Could not get user data from vote collection'
print db_result['description']
return
articles = db_result['docs']
print len(articles), 'article opinions found in vote db for given user'
# map each article url to 1 or -1, if the user liked or disliked it
article_opinions = {}
vote_datetimes = {}
for article in articles:
# make sure all the required fields are there
req_fields = ['article_url', 'positive_opinion', 'vote_datetime']
if not all([s in article for s in req_fields]):
print 'Error'
print 'Vote is missing some fields: {}'.format(article)
continue
url = article['article_url']
# set the classes for the votes to 1 for positive and -1 for negative
vote = 1 if article['positive_opinion'] else -1
article_opinions[url] = vote
vote_datetimes[url] = article['vote_datetime']
# split the articles into the feeds they belong to, to minimise db lookups
# the dict maps feed urls to a list of article urls fromt that feed
feeds = {}
for article in articles:
if article['feed_url'] in feeds:
feeds[article['feed_url']].append(article['article_url'])
else:
feeds[article['feed_url']] = [article['article_url']]
# get a set of the unique article urls
article_url_set = set(article_opinions.keys())
print len(article_url_set), 'unique articles in set'
if len(article_url_set) < 0:
print 'Error'
print 'Not enough articles in data set'
return
# get the words the user is interested in
db_result = db_get('user', {
'username': username
}, {
'words': 1
})
if db_result['status'] != 'ok':
print 'Error'
print 'Could not load data from user collection'
print db_result['description']
return
if len(db_result['docs']) < 1:
print 'Error'
print 'No such user in user collection'
return
user_data = db_result['docs'][0]
user_words = user_data['words']
# it is required to have at least classes, so create two
# inputs with extreme values to train the model
data_x = [[10.0, 1], [0.0, 10000000]]
data_y = [1, -1]
# get the data from the db for each feed a user voted on an article in
for feed in feeds:
db_result = db_get('feed', {
'url': feed
}, {
'items': 1
})
if db_result['status'] != 'ok':
print 'Error'
print 'Could not get data from feed collection'
print db_result['description']
return
if 'docs' not in db_result or len(db_result['docs']) < 1:
print 'Error'
print 'No feed returned for url', feed
return
items = db_result['docs'][0]['items']
# check the items in that feed for one the user voted on
for item in items:
if item['link'] not in article_url_set:
continue
print 'adding', item['link']
if 'topics' not in item:
print 'Error'
print 'No topics for given item, skipping'
continue
words = item['topics']
topic_crossover = kw_score.score(words, user_words)
if 'pub_date' not in item:
print 'Error'
print 'No pub_date for given item, skipping'
continue
time_diff = vote_datetimes[item['link']] - item['pub_date']
x = [topic_crossover, time_diff.total_seconds()]
y = article_opinions[item['link']]
data_x.append(x)
data_y.append(y)
print 'Articles from feed', feed, 'added to data'
print data_x
print data_y
if len(data_x) < 3:
print 'Error'
print 'Not enough data points'
return
data_points = [(data_x[i], data_y[i]) for i in xrange(len(data_x))]
n = 0
score = 0
# start the 2-fold cross-validation, doing up to 10 folds of the data
repetitions = min(len(data_points), 10)
for k in xrange(repetitions):
print 'Iteration {} out of {} ({}% finished)'.format(k, len(data_points), 100*(float(k)/repetitions))
shuffle(data_points)
training = data_points[:len(data_points)/2]
validation = data_points[len(data_points)/2:]
if has_enough_classes(training):
curr_score = get_model_score(training, validation)
print '- Score 1 this fold: {}'.format(curr_score)
score += curr_score
n += 1
else:
print '- Not enough training classes, skipping'
continue
#swap the training and validation data
training, validation = validation, training
if has_enough_classes(training):
curr_score = get_model_score(training, validation)
print '- Score 2 this fold: {}'.format(curr_score)
score += curr_score
n += 1
else:
print '- Not enough training classes, skipping'
continue
if n == 0:
print 'Error'
print 'Not enough valid data points'
return
print 'Score: {:.6f}, based on {} divisions of the data.'.format(float(score)/n, n)
return
