def check_users_with_top1000_repo(users, repos=None):
users = find_all('users')
if repos is None:
top1000_repo_count = {
repo['name']: 0
for repo in find_all('top1000_repos')
}
repos_count = top1000_repo_count
else:
repos_count = {repo: 0 for repo in repos}
for i, user in enumerate(users):
print(f"{i}/{len(users)} checking user {user['name']}")
if len(user) == 1:
# ignore users that not crawl repo yet
print("not crwal yet, ignore")
continue
for repo in user['repos']:
if repo in repos_count:
repos_count[repo] += 1
repos_count = {
k: v
for k, v in sorted(
repos_count.items(), key=lambda item: item[1], reverse=True)
}
for repo in repos_count:
print(f"{repo}: {repos_count[repo]}")
#print(repos_count)
return repos_count
def predict(username):
'''
return top 10 predict repos to user
'''
repos = get_user_starred_repo(username)
positive_set, negative_set = DirectParseData(repo2idx(repos), 838)
predict_repos = idx2repo(
GetTopKRecommend(
path='./predict/weight/model_MAP_0.10040_np_20_epoch_32_d_64',
all_u=1168,
all_i=838,
dim=64,
epoch=256,
ratio=20,
top_k=10,
positive_set=positive_set,
negative_set=negative_set))
predict_detail = find_all(col_name='top1000_repos_detail',
target={"full_name": {
"$in": predict_repos
}},
field_filter={
'_id': 0,
'full_name': 1,
'description': 1,
'language': 1,
'stargazers_count': 1
})
return predict_detail
def concurrent_crawl_repo_of_users(max_workers, mode='token'):
users = find_all('users')
users = [user for user in users if len(user) == 1]
print(f'crawl user num: {len(users)}......')
with ThreadPoolExecutor(max_workers=max_workers) as executor:
for i, user in enumerate(users):
#print(f'crawling {i+1}/{len(users)}...')
executor.submit(insert_user_starred_repo, user, mode)
def crawl_top_users():
users = find_all('users')
users = [user for user in users if len(user) == 1]
for i, user in enumerate(users):
print(f'crawling {i+1}/{len(users)}...')
repos = get_user_starred_repo(user['name'])
print(f"finish {user['name']} total: {len(repos)} update to db")
update_one('users', {'name': user['name']}, {'repos': repos})
def init_users_to_db(users):
#datas = [{'name': user} for user in users]
users_in_db = find_all('users', field_filter={'_id': 0, 'repos': 0})
users_in_db = [user['name'] for user in users_in_db]
new_users = [user for user in users if user not in users_in_db]
print(
f"{len(users) - len(new_users)} users already in db, insert {len(new_users)} users"
)
if len(new_users) > 0:
insert_many('users', [{'name': user} for user in new_users])
def concurrent_crawl_top1000_repo_details(max_workers, mode='token'):
repos = find_all('top1000_repos')
exist_repos = find_all('top1000_repos_detail',
field_filter={
'_id': 0,
'full_name': 1
})
exist_repo_names = [
exist_repo['full_name'].lower() for exist_repo in exist_repos
]
repo_names = [
repo['name'] for repo in repos
if repo['name'].lower() not in exist_repo_names
]
print(len(repo_names))
#print(repo_names)
#exit()
with ThreadPoolExecutor(max_workers=max_workers) as executor:
for i, repo_name in enumerate(repo_names):
#print(f'crawling {i+1}/{len(users)}...')
executor.submit(insert_repo_detail, repo_name, mode)
def check_user_star_in_repos(users, repos=None):
if repos is None:
top1000_repos = [repo['name'] for repo in find_all('top1000_repos')]
repos = top1000_repos
users = [user for user in users if len(user) == 2]
for user in users:
user['repos'] = [
repo for repo in user['repos'] if repo in top1000_repos
]
users.sort(key=lambda item: len(item['repos']), reverse=True)
for user in users:
print(f"{user['name']} : {len(user['repos'])}")
def generate_dataset(user_treshold=20, repos_threshold=50):
users = find_all('users')
top1000_repos = [repo['name'] for repo in find_all('top1000_repos')]
users = filter_users(users, top1000_repos, user_treshold)
repos = filter_repos(users, top1000_repos, repos_threshold)
users = filter_users(users, repos, user_treshold)
# reverse user click star repos to [early:late]
for user in users:
user['repos'].reverse()
print(
f'user > {user_treshold} : {len(users)}, repos > {repos_threshold} : {len(repos)}'
)
item_index = list(range(len(repos)))
user_index = list(range(len(users)))
item2id = {pair[0]: pair[1] for pair in zip(repos, item_index)}
user2id = {pair[0]['name']: pair[1] for pair in zip(users, user_index)}
dataset = []
for user in users:
dataset.append({
"UserId":
user2id[user['name']],
"ItemId":
str([item2id[repo]
for repo in user['repos']])[1:-1].replace(',', '')
})
df = pd.DataFrame(dataset)
df.to_csv('dataset.csv', index=False)
df = pd.DataFrame([{
'repo': pair[0],
'idx': pair[1]
} for pair in zip(repos, item_index)])
df.to_csv('repo2idx.csv', index=False)
df = pd.DataFrame([{
'user': pair[0]['name'],
'idx': pair[1]
} for pair in zip(users, user_index)])
df.to_csv('user2idx.csv', index=False)
def random_get_repos(n=10):
df = pd.read_csv('repo2idx.csv')
items = list(df['repo'])
return find_all(col_name='top1000_repos_detail',
target={"full_name": {
"$in": sample(items, n)
}},
field_filter={
'_id': 0,
'full_name': 1,
'description': 1,
'language': 1,
'stargazers_count': 1
})
def k_means(): dataList = list(database.find_all()) vectors = [] uuids = [] for data in dataList: counter = Counter() uuids.append(data['uuid']) for event in data['events']: counter[event['name']] += 1 vector = [] for typ in counter: vector.append(counter[typ]) vectors.append(vector) result = vectors labels, error, nfound = Pycluster.kcluster(vectors, 3) classes = [] for label in labels: classes.append(numpy.asscalar(label)) result = dict(zip(uuids,classes)) return result
def main():
# Call AI module to get prediction of BP1, BP2, Oxy Pulse
p = predictor()
p.train()
predictedData = p.predict(5000)
# get new data of BP1, BP2, Oxy, Pulse from DB
# latestData = database.find()
# call Display module to get format the html
#parse data into graphable format
timeData = []
pulseData = []
bpSysData = []
bpDiaData = []
oxygenData = []
for i in database.find_all():
p.appendData(i)
x_val = i['createAt']
pulse_val = i['pulse']
bp_sys_val = i['bloodPreSys']
bp_dia_val = i['bloodPreDia']
oxy_val = i['bloodOx']
timeData.append(x_val)
pulseData.append(pulse_val )
bpSysData.append(bp_sys_val)
bpDiaData.append(bp_dia_val)
oxygenData.append(oxy_val)
p.train()
predictedData = p.predict(5000) #5000 is time in the future
alert = {'bloodPressureSys': globals.bp_sys_flag, 'bloodPressureDia': globals.bp_dia_flag,'pulse': globals.pulse_flag, 'oxygenContent': globals.oxygen_flag}
data = {'time': timeData,'pulse': pulseData, 'bloodPressureSys': bpSysData, 'bloodPressureDia': bpDiaData,'oxygenContent': oxygenData}
AIdata = {'AIpulse': predictedData[0], 'AIbloodPressure': predictedData[1], 'AIoxygenContent': predictedData[2]}
return render_template('display.html', title='Display', alert=alert, data=data, AIdata=AIdata)
def get(self): dataList = list(database.find_all()) return json_dump(dataList)
def get(self):
rows = find_all()
response = HTTPResponse()
response.write(rows)
return response
def __handle_get_all(reply):
reply["type"] = "SUCCESS"
reply["data"] = database.find_all()
def on_get(self, req, resp):
resp.status = falcon.HTTP_200
rows = find_all()
resp.body = rows
def find():
rows = find_all()
resp = Response(None, status=200, mimetype='application/json')
resp.data = rows
return resp
