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from sqlalchemy import *
from threading import Timer
import signal, sys, time, threading
import configparser
from datetime import datetime
import pandas
import pickle
from sklearn import datasets, cross_validation, metrics
from sklearn import preprocessing
from sklearn.linear_model import PassiveAggressiveRegressor
from sklearn.svm import SVR
class PredictData:
def __init__(self, partial_fit_flag=True):
self.partial_fit_flag = partial_fit_flag
self.regressor = None
def fit(self, X_train, y_train):
if self.partial_fit_flag:
self.regressor.partial_fit(X_train, y_train)
else:
self.regressor.fit(X_train, y_train)
def train(self, X_train, y_train):
if self.regressor == None:
if self.partial_fit_flag:
self.regressor = PassiveAggressiveRegressor(random_state=42)
else:
self.regressor = SVR(kernel='rbf', C=1000, gamma=0.1)
self.fit(X_train, y_train)
else:
if self.partial_fit_flag:
self.fit(X_train, y_train)
def predict(self, X_test):
return self.regressor.predict(X_test)
def metrics_errror(self, y_predict, y_test):
return metrics.mean_squared_error(y_predict,y_test)
def save_model(self, name):
if self.regressor != None:
with open(name, 'wb') as m:
pickle.dump(self.regressor, m)
def load_model(self, name):
with open(name, 'rb') as m:
self.regressor = pickle.load(m)
class ProcessData:
def __init__(self, scaler_partialfit_flag=True):
self.scaler_partialfit_flag = scaler_partialfit_flag
self.scaler = None
def fit(self, X_train):
self.scaler.partial_fit(X_train)
def train_test_split_scale(self, data, target, testSize=0):
X_train, X_test, y_train, y_test = cross_validation.train_test_split(data, target,
test_size=testSize, random_state=42)
if self.scaler == None:
self.scaler = preprocessing.MinMaxScaler(feature_range=(-1, 1))
self.fit(X_train)
if len(X_test) == 0:
X_train = self.scaler.transform(X_train)
return X_train, y_train
else:
X_train = self.scaler.transform(X_train)
X_test = self.scaler.transform(X_test)
return X_train, y_train, X_test, y_test
def validate_date(self,datetime_string):
try:
return datetime.strptime(datetime_string,"%Y-%m-%d %H:%M:%S")
except ValueError:
return False
def set_dateTime(self,datetime_string):
if self.validate_date(datetime_string) != False:
configW = configparser.RawConfigParser()
configW.optionxform = str
configW.add_section('DB')
configW.set('DB', 'LatestTime', datetime_string)
with open('DB.ini', 'w') as configfile:
configW.write(configfile)
def get_dateTime(self):
configR = configparser.RawConfigParser()
configR.read("DB.ini")
db_time=configR.get("DB","LatestTime")
if self.validate_date(db_time) == False:
return 0
else:
return db_time
def load_data_from_db(self, alwaysLatestFlag=False):
pass
def load_data_from_file(self, path):
try:
data = pandas.read_csv(path)
X = data[['Date','Time','Global_reactive_power','Voltage','Global_intensity','Sub_metering_1','Sub_metering_2','Sub_metering_3']]
y = data['Global_active_power']
D = pandas.to_datetime(X["Date"], format="%d/%m/%Y")
X["Month"] = D.apply(lambda x: x.month)
X = X.drop(["Date"], axis=1)
T = pandas.to_datetime(X["Time"], format="%H:%M:%S")
X["Hour"] = T.apply(lambda x: x.hour)
X = X.drop(["Time"], axis=1)
return X,y
except:
print('ERROR load_data_from_file!!!' )
return [],[]
def save_scaler(self, scalerName):
if self.scaler != None:
with open(scalerName, 'wb') as s:
pickle.dump(self.scaler, s)
def load_scaler(self, scalerName):
with open(scalerName, 'rb') as s:
self.scaler = pickle.load(s)
def Online_Learning_Train():
pd = ProcessData()
X,y = pd.load_data_from_file('pw3.csv')
Xt,Xyt = pd.train_test_split_scale(X,y)
pdd = PredictData()
pdd.train(Xt, Xyt)
Xy_predict = pdd.predict(Xt)
scoreX = pdd.metrics_errror(Xy_predict,Xyt)
pd.save_scaler('scaler.pkl')
pdd.save_model('model.pkl')
print('MSE: {0:f}'.format(scoreX))
def Online_Learning_Test():
pd = ProcessData()
pd.load_scaler('scaler.pkl')
X,y = pd.load_data_from_file('pw5.csv')
Xt,Xyt = pd.train_test_split_scale(X,y)
pdd = PredictData()
pdd.load_model('model.pkl')
pdd.train(Xt, Xyt)
Xy_predict = pdd.predict(Xt)
scoreX = pdd.metrics_errror(Xy_predict,Xyt)
print('MSE: {0:f}'.format(scoreX))
def Offline_Learning_Train():
pd = ProcessData()
X,y = pd.load_data_from_file('pw.csv')
Xt,Xyt = pd.train_test_split_scale(X,y)
pdd = PredictData(False)
pdd.train(Xt, Xyt)
Xy_predict = pdd.predict(Xt)
scoreX = pdd.metrics_errror(Xy_predict,Xyt)
pd.save_scaler('scaler.pkl')
pdd.save_model('model.pkl')
print('MSE: {0:f}'.format(scoreX))
def Offline_Learning_Test():
pd = ProcessData()
pd.load_scaler('scaler.pkl')
X,y = pd.load_data_from_file('pw5.csv')
Xt,Xyt = pd.train_test_split_scale(X,y)
pdd = PredictData(False)
pdd.load_model('model.pkl')
Xy_predict = pdd.predict(Xt)
scoreX = pdd.metrics_errror(Xy_predict,Xyt)
print('MSE: {0:f}'.format(scoreX))
def Main():
Online_Learning_Train()
Online_Learning_Test()
'''
Offline_Learning_Train()
Offline_Learning_Test()
'''
if __name__ == '__main__':
Main()
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