Esempio n. 1
0
 def retrieve_known_pods_list(self):
     self.known_pods = []
     db = DBConnection()
     db.cursor.execute("select host from pods")
     rows = db.cursor.fetchall()
     for row in rows:
         self.known_pods.append(row["host"])
     db.close()
 def retrieve_known_pods_list(self):
     self.known_pods = []
     db = DBConnection()
     db.cursor.execute("select host from pods")
     rows = db.cursor.fetchall()
     for row in rows:
         self.known_pods.append(row["host"])
     db.close()
Esempio n. 3
0
"""
Get data from SQL database, split and export to csv for training and testing
"""
from sklearn.model_selection import train_test_split
from src.utils import DBConnection

# Get data from database
db = DBConnection(source_file='./login_details.json')
all_data = db.query('select * from dbo.patients')

# Split data
LABEL_COL = 'DEATH_EVENT'
features = all_data.drop([LABEL_COL], axis=1)
labels = all_data[LABEL_COL]

features_train, features_test, labels_train, labels_test = train_test_split(
    features, labels,
    train_size=0.8, random_state=24, stratify=labels
)

# Export
features_train.to_csv('./data/train/features.csv', index=False)
features_test.to_csv('./data/test/features.csv', index=False)
labels_train.to_csv('./data/train/labels.csv', index=False)
labels_test.to_csv('./data/test/labels.csv', index=False)
#!/usr/env python
# -*- coding: utf-8 -*-
from src.utils import DBConnection

db = DBConnection()

db.cursor.execute("select date, count(1) from stats group by date")

rows = db.cursor.fetchall()
for row in rows:
    db.cursor.execute("update global_stats set pod_count = %s where date = '%s'" % (row["count(1)"], row["date"]))

db.close()