Notes

main_flare.py

# author : makcin

# Importing Libraries
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
import copy
from sklearn.metrics import mean_absolute_error
import openpyxl
from datetime import date, timedelta
from sklearn.linear_model import HuberRegressor, LinearRegression, TheilSenRegressor, RANSACRegressor
from sklearn.linear_model import Ridge, Lasso
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import ElasticNetCV
from sklearn.linear_model import ElasticNet
from sklearn.utils.validation import check_consistent_length, check_array
pd.set_option('display.max_rows', 6000)
from scripts import _check_reg_targets,mean_absolute_percentage_error,best_model_pipeline,taking_average
import warnings
warnings.filterwarnings("ignore")
import re
from openpyxl import load_workbook
from datetime import datetime

# ------- WASTE Models --------------------
# 1- WASTE FG Prediction Model (Average)

df_org = pd.read_excel("FLARE_Tags_17082022.xlsx",index_col = 0,engine='openpyxl')

df_org.rename(columns = {'0101.FQR2002':'ETHYLENE_Fuelgas_Export',
'7501.FQI9100':'WASTE_Fuelgas_Import',
'0401.FI449.PV':'VCM_Fuelgas_Import',
'1701.FI226':'PP_Fuelgas_Import',
'2101.FQT029.DACA.PV':'ACN_Fuelgas_Import',
'4001.FT9000.DACA.PV':'PA_Fuelgas_Import'}, inplace = True)

df_flare = copy.deepcopy(df_org)

# Confidence 0 olan dataları veri setinden düşürmek.
df_flare_manipulated = copy.deepcopy(df_flare)

confidence_cols = list([col for col in df_flare_manipulated.columns if 'Confidence' in col])

for i in confidence_cols:
df_flare_manipulated.drop(
df_flare_manipulated.loc[(df_flare_manipulated[i] == 0)].index,
inplace=True)

df_flare_manipulated.drop(confidence_cols,axis=1,inplace=True)


# Sıfırdan küçük değerlerin sıfıra eşitlenmesi

main_features = list(df_flare_manipulated.columns)

for i in main_features:
df_flare_manipulated.loc[df_flare_manipulated[i] < 0, i] = 0

# Average alma ve excel'e yazma

# VCM Fuelgas Import (Average)

wb = load_workbook('Draft_Final_Output.xlsx')

fg_import_sheet = wb['FG_Import_Other_Units']

vcm_fuelgas = taking_average(df_flare_manipulated,'VCM_Fuelgas_Import')

fg_import_sheet['K3'] = round(vcm_fuelgas,2)

# PP Fuelgas Import (Average)

pp_fuelgas = taking_average(df_flare_manipulated,'PP_Fuelgas_Import')

fg_import_sheet['K4'] = round(pp_fuelgas,2)

# PA Fuelgas Import (Average)

pa_fuelgas = taking_average(df_flare_manipulated,'PA_Fuelgas_Import')

fg_import_sheet['K5'] = round(pa_fuelgas,2)


# Flare Fuelgas Import (Average)

# Ethylene FG Export ve ACN Fuelgas Import tahminleri yapıldıktan sonra, Flare Fuelgas Import değeri de hesaplanabilecektir.


fg_import_sheet['R1'] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

wb.save(r'C:Usersmert.akcinDesktopDaily NG DemandNG_Demand_NewNg_Demand_Prod_CodeDraft_Final_Output.xlsx')
wb.close()