def exportGephi(self):
e = Etl()
from tkFileDialog import asksaveasfilename
import tkMessageBox
filename = asksaveasfilename()
if e.exportGephi(filename):
tkMessageBox.showinfo("Info", "File salvato con successo.")
def addRows():
"""
only populates db if there is no data in it
"""
if Virus.query.first() == None:
df = Etl().data
df.to_sql('viruses', engine, if_exists="replace", index=False)
print("done adding rows")
def predict(self):
data = Data(self.training_url, self.testing_url)
data.fetch_data()
etl = Etl(data)
etl.do_etl()
model = Model(etl)
model.train()
#model.test(data.testing_data)
self.today_data = data.testing_data[-1]
self.today_suggestion = model.predict_one(self.today_data)
def cluster_data(data):
etl = Etl()
df = etl.process_data(data)
df = etl.generate_rfm(df)
df = etl.normalize_df(df)
clustering = Clustering()
[metrics, clusters] = clustering.generate_cluster(df)
headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
try:
requests.post(server_url + '/metrics', headers=headers, json=metrics)
requests.post(server_url + '/clusters', headers=headers, json=clusters)
except Exception as e:
print('Error', e)
def main():
if len(sys.argv) == 2:
data = Data(sys.argv[1])
elif len(sys.argv) == 3:
data = Data(sys.argv[1], sys.argv[2])
else:
data = Data()
data.fetch_data()
etl = Etl(data)
etl.do_etl()
model = Model(etl)
model.train()
#model.test(data.testing_data)
result = model.predict_one(data.testing_data[-1])
print (result)
def lambda_connect(event, context):
etl = Etl()
etl.retrieve_all_stats()
return 'pickle rick'
def __init__(self):
t = Politician()
self.politicianList = t.getList()
self.classifier = NBClassifier()
self.e = Etl()
def __init__(self, categories):
self.list_categories = categories
self.json_products = Etl(self.list_categories)
self.all_prod = []
def __init__(self):
self.db = Database()
self.etl = Etl()
self.last_session = self.db.get_count(coll='brainwriting_sessions') - 1