def exploratory_experiment(df, target, target_type='R'):
'''
Func:机器学习探索性实验\n
In:dataframe各指标数据\n
target --> 因变量\n
target_type --> R连续变量；C分类变量
'''
if target_type == 'R' or target_type == 'r':
from pycaret.regression import compare_models, setup
elif target_type == 'C' or target_type == 'c':
from pycaret.classification import compare_models, setup
setup(df, target)
compare_models()
def run_pycaret(name, df_train, df_test, acc_func, target):
pycaret_acc_func_str = 'Accuracy'
for pycaret_metrics in [
'Accuracy', 'AUC', 'Recall', 'Precision', 'F1', 'Kappa', 'MCC',
'MAE', 'MSE', 'RMSE', 'R2', 'RMSLE', 'MAPE'
]:
if pycaret_metrics.lower() in str(acc_func).lower():
pycaret_acc_func_str = pycaret_metrics
import traceback
task_type = 'classification'
if pycaret_acc_func_str in ['MAE', 'MSE', 'RMSE', 'R2', 'RMSLE', 'MAPE']:
task_type = 'regression'
from pycaret.regression import setup, compare_models, predict_model, blend_models, stack_models, automl, create_model
else:
from pycaret.classification import setup, compare_models, predict_model, blend_models, stack_models, automl, create_model
setup_return = setup(data=df_train, target=target)
top_models = compare_models(n_select=3,
verbose=False,
sort=pycaret_acc_func_str,
turbo=True,
blacklist=['catboost', 'xgboost'])
# Ensemble the top models and optimize the resulting model
blender = blend_models(estimator_list=top_models, verbose=False)
stacker = stack_models(estimator_list=top_models,
meta_model=top_models[0],
verbose=False)
best_model = automl(optimize=pycaret_acc_func_str)
df_test_dropped = df_test.drop(columns=[target])
predictions = predict_model(best_model, data=df_test_dropped)
try:
accuracy = acc_func(list(predictions['Label']), list(df_test[target]))
except Exception as e:
traceback.print_exc()
print(f'Exception computing accuracy (1): {e}')
if task_type == 'classification':
accuracy = acc_func([str(x) for x in list(predictions['Label'])],
[str(x) for x in list(df_test[target])])
elif task_type == 'regression':
accuracy = acc_func([float(x) for x in list(predictions['Label'])],
[float(x) for x in list(df_test[target])])
return accuracy
def exec(self):
log.info('[START] {}'.format("exec"))
# h2o.init()
try:
if (platform.system() == 'Windows'):
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': '2019-01-01',
'endDate': '2021-12-31',
'isOverWrite': True
# , 'isOverWrite': False
}
globalVar['inpPath'] = 'E:/DATA'
globalVar['outPath'] = 'E:/DATA'
else:
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': globalVar['srtDate'],
'endDate': globalVar['endDate']
# , 'isOverWrite': True
,
'isOverWrite': False
}
isDlModelInit = False
inpPosFile = '{}/{}'.format(globalVar['cfgPath'],
'stnInfo/GA_STN_INFO.xlsx')
posData = pd.read_excel(inpPosFile, engine='openpyxl')
posDataL1 = posData[['id', 'lat', 'lon']]
modelDirKeyList = ['AI_2Y']
# modelDirKeyList = ['AI_1Y6M']
# modelDirKeyList = ['AI_2Y', 'AI_7D', 'AI_15D', 'AI_1M', 'AI_3M', 'AI_6M']
for k, modelDirKey in enumerate(modelDirKeyList):
log.info("[CHECK] modelDirKey : {}".format(modelDirKey))
for i, posInfo in posDataL1.iterrows():
posId = int(posInfo['id'])
posLat = posInfo['lat']
posLon = posInfo['lon']
if (not re.search('17', str(posId))): continue
# if (re.search('17|50|51|58|60|67|72|81|85|87', str(posId))): continue
log.info('[CHECK] posId : {}'.format(posId))
# break
inpFile = '{}/{}/{}-SRV{:05d}-{}-{}-{}.xlsx'.format(
globalVar['outPath'], 'FOR', serviceName, posId,
'final', 'proc', 'for')
fileList = sorted(glob.glob(inpFile))
# 파일 없을 경우 예외 처리
if fileList is None or len(fileList) < 1:
log.error('[ERROR] inpFile : {} / {}'.format(
inpFile, '입력 자료를 확인해주세요.'))
continue
fileInfo = fileList[0]
inpData = pd.read_excel(fileInfo, engine='openpyxl')
# inpData['CA_TOT'].where(inpData['CA_TOT'] < 0, np.nan)
inpData['CA_TOT'][inpData['CA_TOT'] < 0] = np.nan
inpData['WS'][inpData['WS'] < 0] = np.nan
inpData['WD'][inpData['WD'] < 0] = np.nan
inpData['SWR'][inpData['SWR'] < 0] = np.nan
inpData['pv'][inpData['pv'] < 0] = np.nan
inpDataL1 = inpData.dropna().reset_index(drop=True)
inpDataL1 = inpDataL1.sort_values(by=['dtDateKst'], axis=0)
# idxInfo = inpDataL1.loc[inpDataL1['dtDateKst'] >= pd.to_datetime('2021-01-01', format='%Y-%m-%d')].index.to_numpy()[0]
# idxInfo = inpDataL1.loc[inpDataL1['dtDateKst'] >= pd.to_datetime('2021-11-30', format='%Y-%m-%d')].index.to_numpy()
idxInfo = inpDataL1.loc[
inpDataL1['dtDateKst'] >= pd.to_datetime(
'2021-06-01', format='%Y-%m-%d')].index.to_numpy()
# idxInfo = inpDataL1.loc[inpDataL1['dtDateKst'] >= pd.to_datetime('2022-01-01', format='%Y-%m-%d')].index.to_numpy()
if (len(idxInfo) < 1): continue
idx = idxInfo[0]
# 7일, 15일, 1달, 3달, 6달, 2년
if (modelDirKey == 'AI_2Y'):
# 전체 데이터
# trainData = inpDataL1
# 2021년 기준으로 데이터 분할
trainData, testData = inpDataL1[0:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_7D'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
timedelta(days=7)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_15D'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
timedelta(days=15)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_1M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=1)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_3M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=3)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_6M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=6)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
log.info('[CHECK] len(trainData) : {}'.format(
len(trainData)))
log.info('[CHECK] len(testData) : {}'.format(
len(testData)))
log.info('[CHECK] trainData : {} - {}'.format(
trainData['dtDateKst'].min(),
trainData['dtDateKst'].max()))
# log.info('[CHECK] testData : {} - {}'.format(trainData['testData'].min(), trainData['testData'].max()))
# trainData['year'] = trainData['dtDateKst'].dt.strftime('%Y').astype('int64')
# trainData['month'] = trainData['dtDateKst'].dt.strftime('%m').astype('int64')
# trainData['day'] = trainData['dtDateKst'].dt.strftime('%d').astype('int64')
# trainData['hour'] = trainData['dtDateKst'].dt.strftime('%H').astype('int64')
trainDataL1 = trainData[[
'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR',
'pv', 'sza', 'aza', 'et'
]]
# trainDataL1 = trainData[['year', 'month', 'day', 'hour', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'pv', 'sza', 'aza', 'et']]
# trainDataL1.describe()
# trainDataL1 = trainDataL1.loc[(trainDataL1['CA_TOT'] == 0)]
# CA_TOT = 0 (전운량)
# plt.scatter(trainData['dtDateKst'], trainData['CA_TOT'])
# plt.scatter(trainData['dtDateKst'], trainData['SWR'])
# plt.scatter(trainData['pv'], trainData['SWR'])
# plt.scatter(trainDataL1['CA_TOT'], trainDataL1['SWR'])
# plt.show()
# trainDataL1 = trainData[['dtDateKst', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'pv']]
# # )[['dtDate', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'PM10', 'DSR', 'CLD', 'CF', 'SWR', 'pv']]
# # )[['dtDate', 'dtDateKst', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'PM10', 'DSR', 'CF', 'CLD', 'SWR', 'pv']].dropna()
# # )[['dtDate', 'dtDateKst', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'PM10', 'CF', 'CLD', 'SWR', 'pv']]
#
# import pandas as pd
# from autofeat import AutoFeatClassifier
# from sklearn.model_selection import train_test_split
# from sklearn.datasets import load_breast_cancer
# from sklearn.linear_model import LogisticRegression
# from sklearn.metrics import accuracy_score, confusion_matrix
# #
# # load_breast_cancer = load_breast_cancer(as_frame=True)
# # X = load_breast_cancer.data
# # y = load_breast_cancer.target
# # trainData, testData
# model = AutoFeatClassifier(verbose=1)
# X_train_feature_creation = model.fit_transform(trainData, testData)
#
# import pandas as pd # 기본라이브러리
# # from prophet import Prophet # Prophet
# from neuralprophet import NeuralProphet # NeuralProphet
# from sklearn.metrics import mean_absolute_error # 평가 지표 MAE
# from statistics import mean # 평균값 계산
# import matplotlib.pyplot as plt # 그래프묘사
#
# df1_nprophet_model = NeuralProphet(seasonality_mode='multiplicative')
# df1_nprophet_model_result = df1_nprophet_model.fit(trainData, freq="H")
# trainData['ds'] = trainData['dtDateKst']
# **********************************************************************************************************
# TEST
# ***************
# trainData
plt.scatter(trainData['dtDateKst'], trainData['pv'])
plt.show()
from pmdarima import auto_arima
import statsmodels.tsa.api as tsa
import statsmodels.api as sm
# arima_model = auto_arima(y_to_train, seasonal=True, m=7)
sxmodel = auto_arima(trainData[['pv']],
exogenous=trainData[['SWR']],
start_p=1,
start_q=1,
test='adf',
max_p=3,
max_q=3,
m=12,
start_P=0,
seasonal=True,
d=None,
D=1,
trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
# Fit model
# arima_exog_model = auto_arima(y=trainData['pv'], exogenous=trainData['SWR'], seasonal=True, m=7)
# Forecast
# y_arima_exog_forecast = arima_exog_model.predict(n_periods=365, exogenous=exog_to_test)
trainData.index = trainData['dtDateKst']
# import pmdarima as pm
# y = pm.datasets.load_wineind()
#
# from pmdarima.model_selection import train_test_split
# import numpy as np
# train, test = train_test_split(y, train_size=150)
#
#
# auto_arima = auto_arima(
# y=trainData['pv'].values,
# # X=trainData['dtDatekst', 'law']],
# X=trainData['dtDateKst'].values,
# # stepwise=False,
# seasonal=True,
# max_order=5,
# m=12,
# approximation=False,
# information_criterion='aic')
#
# from darts.models import (
# NaiveSeasonal,
# NaiveDrift,
# Prophet,
# ExponentialSmoothing,
# ARIMA,
# AutoARIMA,
# StandardRegressionModel,
# Theta,
# FFT
# )
#
# from darts import TimeSeries
# series = TimeSeries.from_dataframe(trainData, time_col='dtDateKst', value_cols='pv', fill_missing_dates=True, freq='H')
#
#
# # for model in (
# # NaiveSeasonal,
# # NaiveDrift,
# # Prophet,
# # ExponentialSmoothing,
# # ARIMA,
# # AutoARIMA,
# # # StandardRegressionModel, -> 初期化時にtrain_n_points が必要
# # Theta,
# # FFT
# # )
# m = model()
# m.fit(trainData)
# pred = m.predict(len(val))
# `
# **********************************************************************************************************
# 머신러닝
# **********************************************************************************************************
# 시게열
# https://towardsdatascience.com/time-series-forecasting-with-pycaret-regression-module-237b703a0c63
#
# saveCsvFile = '{}/{}_{}.csv'.format(globalVar['outPath'], serviceName, "trainDataL4")
# # trainDataL4.to_csv(saveCsvFile, index=False)
# log.info('[CHECK] saveCsvFile : {}'.format(saveCsvFile))
#
# trainDataL4 = pd.read_csv(saveCsvFile)
# trainDataL4.describe()
saveMlModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model.pkl'.format(
globalVar['outPath'], modelDirKey, serviceName, posId,
'final', 'pycaret', 'for', '*')
saveMlModelList = sorted(glob.glob(saveMlModel),
reverse=True)
# 학습 모델이 없을 경우
# if (sysOpt['isOverWrite']) or (len(saveMlModelList) < 1):
if (len(saveMlModelList) < 1):
pyModel = setup(data=trainDataL1,
session_id=123,
silent=True,
target='pv')
# 각 모형에 따른 자동 머신러닝
modelList = compare_models(sort='RMSE', n_select=3)
# 앙상블 모형
blendModel = blend_models(estimator_list=modelList,
fold=10)
# 앙상블 튜닝
tuneModel = tune_model(blendModel,
fold=10,
choose_better=True)
# 학습 모델
fnlModel = finalize_model(tuneModel)
# 학습 모델 저장
saveModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName,
posId, 'final', 'pycaret', 'for',
datetime.now().strftime("%Y%m%d"))
os.makedirs(os.path.dirname(saveModel), exist_ok=True)
save_model(fnlModel, saveModel)
# **********************************************************************************************************
# 딥러닝
# **********************************************************************************************************
saveDlModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName, posId,
'final', 'h2o', 'for', '*')
saveDlModelList = sorted(glob.glob(saveDlModel),
reverse=True)
# 학습 모델이 없을 경우
if (sysOpt['isOverWrite']) or (len(saveDlModelList) < 1):
if (isDlModelInit == False):
h2o.init()
isDlModelInit = True
# dnModel = H2OAutoML(max_models=20, max_runtime_secs=10000, balance_classes=True, seed=123)
# 2022-03-29
# dnModel = H2OAutoML(max_models=2, max_runtime_secs=20000, balance_classes=True, seed=123)
dnModel = H2OAutoML(max_models=20,
max_runtime_secs=99999,
balance_classes=True,
seed=123)
# java.lang.OutOfMemoryError: Java heap space
# dnModel = H2OAutoML(max_models=None, max_runtime_secs=99999, balance_classes=True, seed=123)
# dnModel = H2OAutoML(max_models=40, max_runtime_secs=99999, balance_classes=True, seed=123)
# dnModel = H2OAutoML(max_models=30, max_runtime_secs=99999, balance_classes=True, seed=123)
# dnModel = H2OAutoML(max_models=40, max_runtime_secs=20000, balance_classes=True, seed=123)
dnModel.train(x=[
'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS',
'SWR', 'sza', 'aza', 'et'
],
y='pv',
training_frame=h2o.H2OFrame(trainDataL1))
# dnModel.train(x=['year', 'month', 'day', 'hour', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL1))
# dnModel.train(x=['hour', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL1))
# trainSet, validSet = np.split(trainDataL1, [int(0.70 * len(trainDataL1))])
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainSetL1), validation_frame=h2o.H2OFrame(validSetL1))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL2), validation_frame=h2o.H2OFrame(testData))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainSet), validation_frame=h2o.H2OFrame(validSet))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL2))
# 학습 모델 저장
saveModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName,
posId, 'final', 'h2o', 'for',
datetime.now().strftime("%Y%m%d"))
os.makedirs(os.path.dirname(saveModel), exist_ok=True)
# h2o.save_model(model=dnModel.get_best_model(), path=os.path.dirname(saveModel), filename=os.path.basename(saveModel), force=True)
dnModel.get_best_model().save_mojo(
path=os.path.dirname(saveModel),
filename=os.path.basename(saveModel),
force=True)
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
def exec(self):
log.info('[START] {}'.format("exec"))
# import pandas as pd
# import numpy as np
h2o.init()
try:
if (platform.system() == 'Windows'):
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': '2021-10-01',
'endDate': '2021-11-01',
'isOverWrite': True
# , 'isOverWrite': False
}
globalVar['inpPath'] = 'E:/DATA'
globalVar['outPath'] = 'E:/DATA'
else:
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': globalVar['srtDate'],
'endDate': globalVar['endDate']
# , 'isOverWrite': True
,
'isOverWrite': False
}
inpPosFile = '{}/{}'.format(globalVar['cfgPath'],
'stnInfo/GA_STN_INFO.xlsx')
posData = pd.read_excel(inpPosFile, engine='openpyxl')
posDataL1 = posData[['id', 'lat', 'lon']]
modelDirKeyList = ['AI_2Y']
# modelDirKeyList = ['AI_2Y', 'AI_7D', 'AI_15D', 'AI_1M', 'AI_3M', 'AI_6M']
for k, modelDirKey in enumerate(modelDirKeyList):
log.info("[CHECK] modelDirKey : {}".format(modelDirKey))
for i, posInfo in posDataL1.iterrows():
posId = int(posInfo['id'])
posLat = posInfo['lat']
posLon = posInfo['lon']
log.info(
"[CHECK] posId (posLon, posLat) : {} ({}. {})".format(
posId, posLon, posLat))
# break
inpFile = '{}/{}/{}-SRV{:05d}-{}-{}-{}.xlsx'.format(
globalVar['outPath'], 'ACT', serviceName, posId,
'final', 'proc', 'act')
fileList = sorted(glob.glob(inpFile))
# 파일 없을 경우 예외 처리
if fileList is None or len(fileList) < 1:
log.error('[ERROR] inpFile : {} / {}'.format(
inpFile, '입력 자료를 확인해주세요.'))
continue
fileInfo = fileList[0]
inpData = pd.read_excel(fileInfo, engine='openpyxl')
inpData['CA_TOT'][inpData['CA_TOT'] < 0] = np.nan
inpData['WS'][inpData['WS'] < 0] = np.nan
inpData['WD'][inpData['WD'] < 0] = np.nan
inpData['SWR'][inpData['SWR'] < 0] = np.nan
inpData['pv'][inpData['pv'] < 0] = np.nan
inpDataL1 = inpData.dropna().reset_index(drop=True)
# idxInfo = inpDataL1.loc[inpDataL1['dtDateKst'] >= pd.to_datetime('2021-01-01', format='%Y-%m-%d')].index.to_numpy()[0]
idxInfo = inpDataL1.loc[
inpDataL1['dtDateKst'] >= pd.to_datetime(
'2021-10-30', format='%Y-%m-%d')].index.to_numpy()
if (len(idxInfo) < 1): continue
idx = idxInfo[0]
# 7일, 15일, 1달, 3달, 6달, 2년
if (modelDirKey == 'AI_2Y'):
# 2021년 기준으로 데이터 분할
# trainData, testData = inpDataL1[0:idx], inpDataL1[idx:len(inpDataL1)]
# 전체 데이터
# trainData = inpDataL1
# 2021년 기준으로 데이터 분할
trainData, testData = inpDataL1[0:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_7D'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
timedelta(days=7)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_15D'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
timedelta(days=15)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_1M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=1)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_3M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=3)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_6M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=6)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
# 2021년 기준으로 변경
trainDataL1 = trainData[[
'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR',
'pv', 'sza', 'aza', 'et'
]]
# trainDataL1.describe()
# **********************************************************************************************************
# 머신러닝
# **********************************************************************************************************
# 시게열
# https://towardsdatascience.com/time-series-forecasting-with-pycaret-regression-module-237b703a0c63
#
# saveCsvFile = '{}/{}_{}.csv'.format(globalVar['outPath'], serviceName, "trainDataL4")
# # trainDataL4.to_csv(saveCsvFile, index=False)
# log.info('[CHECK] saveCsvFile : {}'.format(saveCsvFile))
#
# trainDataL4 = pd.read_csv(saveCsvFile)
# trainDataL4.describe()
saveMlModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model.pkl'.format(
globalVar['outPath'], modelDirKey, serviceName, posId,
'final', 'pycaret', 'act', '*')
saveMlModelList = sorted(glob.glob(saveMlModel),
reverse=True)
# 학습 모델이 없을 경우
if (sysOpt['isOverWrite']) or (len(saveMlModelList) < 1):
pyModel = setup(data=trainDataL1,
session_id=123,
silent=True,
target='pv')
# 각 모형에 따른 자동 머신러닝
modelList = compare_models(sort='RMSE', n_select=10)
# 앙상블 모형
blendModel = blend_models(estimator_list=modelList,
fold=10)
# 앙상블 튜닝
tuneModel = tune_model(blendModel,
fold=2,
choose_better=True)
log.info("[CHECK] tuneModel : {}".format(tuneModel))
# 학습 모델
fnlModel = finalize_model(tuneModel)
log.info("[CHECK] fnlModel : {}".format(fnlModel))
# 학습 모델 저장
saveModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName,
posId, 'final', 'pycaret', 'act',
datetime.now().strftime("%Y%m%d"))
log.info("[CHECK] saveModel : {}".format(saveModel))
os.makedirs(os.path.dirname(saveModel), exist_ok=True)
save_model(fnlModel, saveModel)
# **********************************************************************************************************
# 딥러닝
# **********************************************************************************************************
saveDlModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName, posId,
'final', 'h2o', 'act', '*')
saveDlModelList = sorted(glob.glob(saveDlModel),
reverse=True)
# 학습 모델이 없을 경우
if (sysOpt['isOverWrite']) or (len(saveDlModelList) < 1):
# 개수 제한
# 10초 제한
# dnModel = H2OAutoML(max_models=20, max_runtime_secs=10000, balance_classes=True, seed=123)
dnModel = H2OAutoML(max_models=40,
max_runtime_secs=20000,
balance_classes=True,
seed=123)
# trainSet, validSet = np.split(trainDataL1, [int(0.70 * len(trainDataL1))])
dnModel.train(x=[
'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS',
'SWR', 'sza', 'aza', 'et'
],
y='pv',
training_frame=h2o.H2OFrame(trainDataL1))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainSetL1), validation_frame=h2o.H2OFrame(validSetL1))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL2), validation_frame=h2o.H2OFrame(testData))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainSet), validation_frame=h2o.H2OFrame(validSet))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL2))
# 학습 모델 저장
saveModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName,
posId, 'final', 'h2o', 'act',
datetime.now().strftime("%Y%m%d"))
log.info("[CHECK] saveModel : {}".format(saveModel))
os.makedirs(os.path.dirname(saveModel), exist_ok=True)
h2o.save_model(model=dnModel.get_best_model(),
path=os.path.dirname(saveModel),
filename=os.path.basename(saveModel),
force=True)
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
def exec(self):
log.info('[START] {}'.format("exec"))
# h2o.init()
import pandas as pd
try:
if (platform.system() == 'Windows'):
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': '2019-01-01',
'endDate': '2021-12-31',
'isOverWrite': True
# , 'isOverWrite': False
}
globalVar['inpPath'] = 'E:/DATA'
globalVar['outPath'] = 'E:/DATA'
else:
# 옵션 설정
sysOpt = {
# 시작/종료 시간
'srtDate': globalVar['srtDate'],
'endDate': globalVar['endDate']
# , 'isOverWrite': True
,
'isOverWrite': False
}
isDlModelInit = False
inpPosFile = '{}/{}'.format(globalVar['cfgPath'],
'stnInfo/GA_STN_INFO.xlsx')
posData = pd.read_excel(inpPosFile, engine='openpyxl')
posDataL1 = posData[['id', 'lat', 'lon']]
modelDirKeyList = ['AI_2Y']
# modelDirKeyList = ['AI_1Y6M']
# modelDirKeyList = ['AI_2Y', 'AI_7D', 'AI_15D', 'AI_1M', 'AI_3M', 'AI_6M']
for k, modelDirKey in enumerate(modelDirKeyList):
log.info("[CHECK] modelDirKey : {}".format(modelDirKey))
for i, posInfo in posDataL1.iterrows():
posId = int(posInfo['id'])
posLat = posInfo['lat']
posLon = posInfo['lon']
# if (not re.search('51', str(posId))): continue
# if (not re.search('17', str(posId))): continue
# if (re.search('17|50|51|58|60|67|72|81|85|87', str(posId))): continue
log.info('[CHECK] posId : {}'.format(posId))
# break
inpFile = '{}/{}/{}-SRV{:05d}-{}-{}-{}.xlsx'.format(
globalVar['outPath'], 'FOR', serviceName, posId,
'final', 'proc', 'for')
fileList = sorted(glob.glob(inpFile))
# 파일 없을 경우 예외 처리
if fileList is None or len(fileList) < 1:
log.error('[ERROR] inpFile : {} / {}'.format(
inpFile, '입력 자료를 확인해주세요.'))
continue
fileInfo = fileList[0]
inpData = pd.read_excel(fileInfo, engine='openpyxl')
inpData['CA_TOT'][inpData['CA_TOT'] < 0] = np.nan
inpData['WS'][inpData['WS'] < 0] = np.nan
inpData['WD'][inpData['WD'] < 0] = np.nan
inpData['SWR'][inpData['SWR'] < 0] = np.nan
inpData['pv'][inpData['pv'] < 0] = np.nan
inpDataL1 = inpData.dropna().reset_index(drop=True)
inpDataL1 = inpDataL1.sort_values(by=['dtDateKst'], axis=0)
# idxInfo = inpDataL1.loc[inpDataL1['dtDateKst'] >= pd.to_datetime('2021-01-01', format='%Y-%m-%d')].index.to_numpy()[0]
# idxInfo = inpDataL1.loc[inpDataL1['dtDateKst'] >= pd.to_datetime('2021-11-30', format='%Y-%m-%d')].index.to_numpy()
idxInfo = inpDataL1.loc[
inpDataL1['dtDateKst'] >= pd.to_datetime(
'2021-06-01', format='%Y-%m-%d')].index.to_numpy()
# idxInfo = inpDataL1.loc[inpDataL1['dtDateKst'] >= pd.to_datetime('2022-01-01', format='%Y-%m-%d')].index.to_numpy()
if (len(idxInfo) < 1): continue
idx = idxInfo[0]
# 7일, 15일, 1달, 3달, 6달, 2년
if (modelDirKey == 'AI_2Y'):
# 전체 데이터
# trainData = inpDataL1
# 2021년 기준으로 데이터 분할
trainData, testData = inpDataL1[0:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_7D'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
timedelta(days=7)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_15D'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
timedelta(days=15)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_1M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=1)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_3M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=3)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
elif (modelDirKey == 'AI_6M'):
srtIdx = inpDataL1.loc[
inpDataL1['dtDateKst'] >=
pd.to_datetime('2021-01-01', format='%Y-%m-%d') -
relativedelta(months=6)].index.to_numpy()[0]
trainData, testData = inpDataL1[srtIdx:idx], inpDataL1[
idx:len(inpDataL1)]
log.info('[CHECK] len(trainData) : {}'.format(
len(trainData)))
log.info('[CHECK] len(testData) : {}'.format(
len(testData)))
log.info('[CHECK] trainData : {} - {}'.format(
trainData['dtDateKst'].min(),
trainData['dtDateKst'].max()))
# log.info('[CHECK] testData : {} - {}'.format(trainData['testData'].min(), trainData['testData'].max()))
# trainData['year'] = trainData['dtDateKst'].dt.strftime('%Y').astype('int64')
# trainData['month'] = trainData['dtDateKst'].dt.strftime('%m').astype('int64')
# trainData['day'] = trainData['dtDateKst'].dt.strftime('%d').astype('int64')
# trainDataL1 = trainData[['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'pv', 'sza', 'aza', 'et']]
# trainDataL1 = trainData[['year', 'month', 'day', 'hour', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'pv', 'sza', 'aza', 'et']]
# trainDataL1.describe()
# trainDataL1 = trainDataL1.loc[(trainDataL1['CA_TOT'] == 0)]
# CA_TOT = 0 (전운량)
# trainData.info()
# trainData['dtDateKst'] = pd.to_datetime(trainData['dtDateKst'])
# plt.scatter(trainData['dtDateKst'][0], trainData['CA_TOT'][0])
# plt.scatter(trainData['dtDate'], trainData['CA_TOT'])
# plt.scatter(trainData['dtDateKst'], trainData['SWR'])
# plt.scatter(trainData['pv'], trainData['SWR'])
# plt.scatter(trainDataL1['CA_TOT'], trainDataL1['SWR'])
# plt.scatter(trainData['dtDateKst'], trainData['SWR'])
log.info('[CHECK] min-max : {} - {}'.format(
int(trainData['pv'].min()),
int(trainData['pv'].max())))
mainTitle = '[{:05d}] {}'.format(
posId, '기상 예보 정보 (수치모델)를 활용한 입력데이터 (발전량) 시계열')
saveImg = '{}/{}/{}/{}.png'.format(globalVar['figPath'],
serviceName,
modelDirKey, mainTitle)
os.makedirs(os.path.dirname(saveImg), exist_ok=True)
plt.scatter(trainData['dtDateKst'], trainData['pv'])
plt.title('{:05d}'.format(posId))
plt.savefig(saveImg, dpi=600, bbox_inches='tight')
# plt.scatter(trainData['dtDateKst'], trainData['SWR'])
# plt.scatter(trainData['dtDateKst'], trainData['sza'])
# plt.scatter(trainData['dtDateKst'], trainData['aza'])
plt.show()
plt.close()
continue
# trainData.plot()
# plt.show()
# plt.close()
# trainDataL1 = trainData[['dtDateKst', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'pv']]
# # )[['dtDate', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'PM10', 'DSR', 'CLD', 'CF', 'SWR', 'pv']]
# # )[['dtDate', 'dtDateKst', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'PM10', 'DSR', 'CF', 'CLD', 'SWR', 'pv']].dropna()
# # )[['dtDate', 'dtDateKst', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'PM10', 'CF', 'CLD', 'SWR', 'pv']]
# import pandas as pd
# from autofeat import AutoFeatClassifier
# from sklearn.model_selection import train_test_split
# from sklearn.datasets import load_breast_cancer
# from sklearn.linear_model import LogisticRegression
# from sklearn.metrics import accuracy_score, confusion_matrix
# #
# # load_breast_cancer = load_breast_cancer(as_frame=True)
# # X = load_breast_cancer.data
# # y = load_breast_cancer.target
# # trainData, testData
# model = AutoFeatClassifier(verbose=1)
# X_train_feature_creation = model.fit_transform(trainData, testData)
#
# import pandas as pd # 기본라이브러리
# # from prophet import Prophet # Prophet
# from neuralprophet import NeuralProphet # NeuralProphet
# from sklearn.metrics import mean_absolute_error # 평가 지표 MAE
# from statistics import mean # 평균값 계산
# import matplotlib.pyplot as plt # 그래프묘사
#
# df1_nprophet_model = NeuralProphet(seasonality_mode='multiplicative')
# df1_nprophet_model_result = df1_nprophet_model.fit(trainData, freq="H")
# trainData['ds'] = trainData['dtDateKst']
#
# import pandas as pd
# from pycaret.datasets import get_data
# data = get_data('pycaret_downloads')
# data['Date'] = pd.to_datetime(data['Date'])
# data = data.groupby('Date').sum()
# data = data.asfreq('D')
# data.head()
#
# # plot the data
# data.plot()
# plt.show()
#
# trainData.drop_duplicates(subset=['dtDateKst'], inplace=True)
# trainDataL2 = trainData[['pv']]
# trainDataL2.index = trainData['dtDateKst']
# import pycaret.classification
# from pycaret.time_series import *
# from pycaret.internal.pycaret_experiment import TimeSeriesExperiment
# pyModel = setup(trainDataL2, fh=7, fold=3, session_id=123)
# pyModel = setup(trainData, target = 'Price', fh=7, fold=3, session_id=123)
# 각 모형에 따른 자동 머신러닝
# modelList = compare_models(sort='RMSE', n_select=3)
# modelList = compare_models(sort='RMSE')
# tuneModel = stack_models(modelList)
# 앙상블 모형
# blendModel = blend_models(estimator_list=modelList, fold=5)
# 앙상블 튜닝
# tuneModel = tune_model(modelList, fold=5, choose_better=True)
# # 학습 모델
# fnlModel = finalize_model(tuneModel)
#
# predict_model(fnlModel, fh=90)
#
# plot_model(fnlModel, plot='forecast', data_kwargs = { 'fh' : 30 })
# # plot_model(modelList[0], plot='forecast', data_kwargs = { 'fh' : 30 })
# # plot_model(modelList[0], plot='forecast', data_kwargs = { 'fh' : 30 })
# plot_model(fnlModel, plot='insample')
# **********************************************************************************************************
# 머신러닝
# **********************************************************************************************************
# 시게열
# https://towardsdatascience.com/time-series-forecasting-with-pycaret-regression-module-237b703a0c63
#
# saveCsvFile = '{}/{}_{}.csv'.format(globalVar['outPath'], serviceName, "trainDataL4")
# # trainDataL4.to_csv(saveCsvFile, index=False)
# log.info('[CHECK] saveCsvFile : {}'.format(saveCsvFile))
#
# trainDataL4 = pd.read_csv(saveCsvFile)
# trainDataL4.describe()
saveMlModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model.pkl'.format(
globalVar['outPath'], modelDirKey, serviceName, posId,
'final', 'pycaret', 'for', '*')
saveMlModelList = sorted(glob.glob(saveMlModel),
reverse=True)
# 학습 모델이 없을 경우
# if (sysOpt['isOverWrite']) or (len(saveMlModelList) < 1):
if (len(saveMlModelList) < 1):
pyModel = setup(data=trainDataL1,
session_id=123,
silent=True,
target='pv')
# 각 모형에 따른 자동 머신러닝
modelList = compare_models(sort='RMSE', n_select=3)
# 앙상블 모형
blendModel = blend_models(estimator_list=modelList,
fold=10)
# 앙상블 튜닝
tuneModel = tune_model(blendModel,
fold=10,
choose_better=True)
# 학습 모델
fnlModel = finalize_model(tuneModel)
# 학습 모델 저장
saveModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName,
posId, 'final', 'pycaret', 'for',
datetime.now().strftime("%Y%m%d"))
os.makedirs(os.path.dirname(saveModel), exist_ok=True)
save_model(fnlModel, saveModel)
# **********************************************************************************************************
# 딥러닝
# **********************************************************************************************************
saveDlModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName, posId,
'final', 'h2o', 'for', '*')
saveDlModelList = sorted(glob.glob(saveDlModel),
reverse=True)
# 학습 모델이 없을 경우
if (sysOpt['isOverWrite']) or (len(saveDlModelList) < 1):
if (isDlModelInit == False):
h2o.init()
isDlModelInit = True
# dnModel = H2OAutoML(max_models=20, max_runtime_secs=10000, balance_classes=True, seed=123)
# 2022-03-29
# dnModel = H2OAutoML(max_models=2, max_runtime_secs=20000, balance_classes=True, seed=123)
dnModel = H2OAutoML(max_models=20,
max_runtime_secs=99999,
balance_classes=True,
seed=123)
# java.lang.OutOfMemoryError: Java heap space
# dnModel = H2OAutoML(max_models=None, max_runtime_secs=99999, balance_classes=True, seed=123)
# dnModel = H2OAutoML(max_models=40, max_runtime_secs=99999, balance_classes=True, seed=123)
# dnModel = H2OAutoML(max_models=30, max_runtime_secs=99999, balance_classes=True, seed=123)
# dnModel = H2OAutoML(max_models=40, max_runtime_secs=20000, balance_classes=True, seed=123)
dnModel.train(x=[
'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS',
'SWR', 'sza', 'aza', 'et'
],
y='pv',
training_frame=h2o.H2OFrame(trainDataL1))
# dnModel.train(x=['year', 'month', 'day', 'hour', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL1))
# dnModel.train(x=['hour', 'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL1))
# trainSet, validSet = np.split(trainDataL1, [int(0.70 * len(trainDataL1))])
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainSetL1), validation_frame=h2o.H2OFrame(validSetL1))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL2), validation_frame=h2o.H2OFrame(testData))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainSet), validation_frame=h2o.H2OFrame(validSet))
# aml.train(x=['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza', 'aza', 'et'], y='pv', training_frame=h2o.H2OFrame(trainDataL2))
# 학습 모델 저장
saveModel = '{}/{}/{}-SRV{:05d}-{}-{}-{}-{}.model'.format(
globalVar['outPath'], modelDirKey, serviceName,
posId, 'final', 'h2o', 'for',
datetime.now().strftime("%Y%m%d"))
os.makedirs(os.path.dirname(saveModel), exist_ok=True)
# h2o.save_model(model=dnModel.get_best_model(), path=os.path.dirname(saveModel), filename=os.path.basename(saveModel), force=True)
dnModel.get_best_model().save_mojo(
path=os.path.dirname(saveModel),
filename=os.path.basename(saveModel),
force=True)
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
'Default Task'].tolist()[0]
# Describe data
st.write(
f'This dataset has {df.shape[0]} samples and {df.shape[1]} features. Target variable is {df_target}.'
)
st.dataframe(df.head())
if df_task in ['NLP / Regression', 'Regression']:
# Setup PyCaret
with st.spinner('PyCaret setup is running...'):
pycset = regression.setup(data=df, target=df_target)
# Compare models
st.dataframe(regression.compare_models())
# End
st.success('End of execution!')
if df_task in ['Classification (Binary)', 'Classification (Multiclass)']:
# Setup PyCaret
with st.spinner('PyCaret setup is running...'):
pycset = classification.setup(data=df, target=df_target)
# Compare models
st.dataframe(classification.compare_models())
# End
st.success('End of execution!')
def exec(self):
log.info('[START] {}'.format("exec"))
try:
import pandas as pd
globalVar['inpPath'] = 'E:/DATA/OUTPUT'
globalVar['outPath'] = 'E:/DATA/OUTPUT'
inpCsvFile = '{}/{}_{}.csv'.format(globalVar['outPath'],
serviceName, 'TrainData')
# CSV 파일 저장
# umDataL10.to_csv(saveCsvFile, index=False)
data = pd.read_csv(inpCsvFile)
# test = trainDataL7.dropna().reset_index(drop=True)
data = data.drop(['ML', 'DL'], axis=1)
data['dtDateKst'] = pd.to_datetime(data['dtDateKst'])
#
# testL1 = test[['CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'PM10', 'SWR', 'sza', 'aza', 'et', 'pv']]
dataL1 = data[[
'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza',
'aza', 'et', 'pv'
]]
pyModel = setup(data=dataL1, target='pv', session_id=123)
try:
# 각 모형에 따른 자동 머신러닝
modelList = compare_models(sort='RMSE', n_select=3)
# 앙상블 모형
blendModel = blend_models(estimator_list=modelList, fold=2)
# 앙상블 튜닝
tuneModel = tune_model(blendModel, fold=2, choose_better=True)
# 학습 모델
fnlModel = finalize_model(tuneModel)
except Exception as e:
log.error("Exception : {}".format(e))
# evaluate_model(tuneModel)
# pred_holdout = predict_model(fnlModel)
# print(fnlModel)
# 회귀 시각화
# plot_model(fnlModel, plot='error')
# plt.show()
mlModel = fnlModel
# predData = predict_model(fnlModel, data=dataL1)
# 24.4427
# check_metric(dataL1['pv'], dataL1['Label'], metric='RMSE')
# h2o
h2o.init()
aml = H2OAutoML(max_models=20,
max_runtime_secs=10000,
balance_classes=True,
seed=1)
aml.train(x=[
'CA_TOT', 'HM', 'PA', 'TA', 'TD', 'WD', 'WS', 'SWR', 'sza',
'aza', 'et'
],
y='pv',
training_frame=h2o.H2OFrame(dataL1),
validation_frame=h2o.H2OFrame(dataL1))
dlModel = aml.get_best_model()
dataL2 = data
dataL3 = predict_model(mlModel,
data=dataL2).rename({'Label': 'ML'},
axis='columns')
dataL3['DL'] = dlModel.predict(
h2o.H2OFrame(dataL2)).as_data_frame()
anaTimeList = dataL3['anaTime'].unique()
for j, anaTimeInfo in enumerate(anaTimeList):
dataL4 = dataL3.loc[dataL3['anaTime'] == anaTimeInfo].dropna(
).reset_index(drop=True)
mainTitle = '[{}] {}'.format(
anaTimeInfo, '기상 예보 정보 (수치모델)를 활용한 48시간 예측 시계열')
saveImg = '{}/{}/{}.png'.format(globalVar['figPath'],
serviceName, mainTitle)
makeUserTimeSeriesPlot(pd.to_datetime(dataL4['dtDateKst']),
dataL4['ML'], dataL4['DL'],
dataL4['pv'], '예측 (머신러닝)', '예측 (딥러닝)',
'실측 (발전량)', '시간 (시)', '발전량', mainTitle,
saveImg, True)
mainTitle = '[{}-{}] {}'.format(
min(anaTimeList), max(anaTimeList),
'기상 예보 정보 (수치모델)를 활용한 머신러닝 (48시간 예측) 산점도')
saveImg = '{}/{}/{}.png'.format(globalVar['figPath'], serviceName,
mainTitle)
makeUserScatterPlot(dataL3['ML'], dataL3['pv'], '머신러닝', '실측',
mainTitle, saveImg, 0, 1000, 20, 60, True)
mainTitle = '[{}-{}] {}'.format(
min(anaTimeList), max(anaTimeList),
'기상 예보 정보 (수치모델)를 활용한 딥러닝 (48시간 예측) 산점도')
saveImg = '{}/{}/{}.png'.format(globalVar['figPath'], serviceName,
mainTitle)
makeUserScatterPlot(dataL3['DL'], dataL3['pv'], '딥러닝', '실측',
mainTitle, saveImg, 0, 1000, 20, 60, True)
except Exception as e:
log.error("Exception : {}".format(e))
raise e
finally:
log.info('[END] {}'.format("exec"))
def regression_model(*, y_col, training_set, normalize, test_size, folds,
metric, model_name, testing_set, imbalanced, seed,
include_models, normalize_method):
"""
Build a regression model for prediction.
Parameters
----------
y_col : str
the name of the target column.
training_set : pd.DataFrame
DataFrame containing the training data.
normalize : bool
if True the dataset will be normalized before training.
test_size : float
Between [0.0-1.0]. The size of the split for test within the training set.
folds : int
number of folds for cross validation.
metric : str
the metric used for evaluating the best model.
model_name : str
the name to save the model.
testing_set : pd.DataFrame
the external dataset for evaluating the best model.
imbalanced
seed : int
random number to initilize the process.
include_models : List
a list of models to be included in the process.
normalize_method : str
The method used for normalizing the data.
Returns
-------
Final regression model
"""
if not metric:
metric = 'RMSE'
setup = pyreg.setup(target=y_col,
data=training_set,
normalize=normalize,
normalize_method=normalize_method,
train_size=1 - test_size,
fold=folds,
silent=True,
session_id=seed)
best_model = pyreg.compare_models(sort=metric, include=include_models)
pyreg.pull().to_csv(model_name + '_compare_models.tsv',
sep='\t',
index=False)
reg_model = pyreg.create_model(best_model)
reg_tuned_model = pyreg.tune_model(reg_model, optimize=metric)
pyreg.pull().to_csv(model_name + '_tuned_model.tsv', sep='\t', index=False)
final_model = pyreg.finalize_model(reg_tuned_model)
pyreg.plot_model(final_model, save=True)
pyreg.plot_model(final_model, plot='feature', save=True)
pyreg.plot_model(final_model, plot='error', save=True)
pyreg.save_model(final_model, model_name)
if len(testing_set.index) != 0:
unseen_predictions = test_regressor(
model_path=model_name + '.pkl',
x_set=testing_set.drop(columns=[y_col]),
y_col=testing_set[y_col],
output=model_name)
unseen_predictions.to_csv(model_name + '_external_testing_results.tsv',
sep='\t',
index=True)
return final_model
target=y_col,
numeric_features=x_cols,
verbose=False,
remove_multicollinearity=False,
# multicollinearity_threshold = 0.6,
ignore_low_variance=False,
silent=True,
n_jobs=2)
# ---- 模型选择 ---------------------------------------------------------------------------------
best_model = compare_models(
include=[
'rf', 'lightgbm', 'lasso', 'ridge', 'xgboost', 'en', 'knn', 'mlp',
'lr', 'dt'
],
sort='R2',
verbose=True,
fold=3,
round=5,
)
# ---- 模型调参 ---------------------------------------------------------------------------------
# 初始化模型, 固定参数.
params = {'max_features': 'auto'}
rgsr = create_model('rf', verbose=False, **params)
# 模型调参.
params4tuning = {
"n_estimators": np.arange(30, 250, 30),
"min_samples_leaf": [10, 15, 20, 30, 40, 50],