main.py

"""
    # main.py

    - Load된 데이터들에 대해 Linear Regression을 진행합니다.
    - 진행된 후의 Weights를 파일로 저장합니다.
"""

import datetime
from os import getcwd
import sys
import pymysql
import numpy as np
from preprocessing import preprocessingData
from linear_regression import LinearRegression


def storeParameters(link, filename, data):
    today = datetime.datetime.today()
    year = str(today.year)
    month = str(today.month) if today.month >= 10 else '0'+str(today.month)
    day = str(today.day) if today.day >= 10 else '0'+str(today.day)

    time_dir = '/' + year + '/' + year+month + '/' + year + month + day

    file_dir = getcwd() + '/server' + link + time_dir + filename

    file = open(file_dir, "w")

    file.write(data)

    file.close()


dbconfig = {"host": sys.argv[1], "user": sys.argv[2],
            "password": sys.argv[3], "database": sys.argv[4]}

eue_db = pymysql.connect(user=dbconfig["user"], password=dbconfig["password"],
                         host=dbconfig["host"], db=dbconfig["database"], charset='utf8')
cursor = eue_db.cursor(pymysql.cursors.DictCursor)

query = "SELECT ID,DATALINK FROM USER;"
cursor.execute(query)
result = cursor.fetchall()

for userdata in result:

    print("User ID : ", userdata["ID"])
    print("Data Processing Start...")

    # Get Data links
    # ./data/DO/SGG/EMD/Users/ID
    user_datalink = userdata["DATALINK"]
    dir_ls = user_datalink.split("/")
    # ./data/DO/SGG/EMD/Outside
    outside_datalink = ("/").join(dir_ls[:-2]) + "/Outside"

    # data load
    train_x, train_t, weights, bias, mean, std_d = preprocessingData(
        user_datalink, outside_datalink)

    # linear regression
    model = LinearRegression(train_x, train_t, weights,
                             bias, learning_rate=0.05)
    model.gradientDescent()


'''
    # Test Codes Start.
'''

print("After Linear Regression -\n")
test_data = np.array([[5], [20], [0], [16.87], [40], [
    1011], [0.72], [26.70], [47.00], [64]])
print(test_data.shape, mean.shape, std_d.shape)
test_data = (test_data - mean) / std_d
y_hat = model.predict(test_data, model.weights, model.bias)
print(y_hat.shape)

print("Test Data.\n", test_data, "\n")
print("Predict - standard deviation : ", y_hat)
print("Predict - temperature : ", y_hat*std_d[7][0] + mean[7][0], "\n")
print("Cost.")
print(model.cost_MSE(model.train_x, model.train_t,
                     model.weights, model.bias), "\n")
print("Weights.")
print(model.weights, "\n")
print("Bias.")
print(model.bias)


'''
    # Test Codes End.
'''

# Save the Parameters.

# - analysis_parameters
analysis_data = ""

for i in range(len(model.weights[0])):
    analysis_data += str(model.weights[0][i]) + ','
analysis_data += str(model.bias)

storeParameters(user_datalink, "/analysis_parameters.csv", analysis_data)

# - prediction_parameters
prediction_data = ""

for i in range(len(mean)):
    prediction_data += str(mean[i][0]) + ','
prediction_data = prediction_data[:-1]
prediction_data += '\n'

for i in range(len(std_d)):
    prediction_data += str(std_d[i][0]) + ','
prediction_data = prediction_data[:-1]

storeParameters(
    user_datalink, "/prediction_parameters.csv", prediction_data)