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forecast_helper.py
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import numpy as np
import pandas as pd
import seaborn as sns
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler
from sklearn.tree import DecisionTreeRegressor
import random
from commodity import Commodity
#commodity_list = []
annual_rainfall = [29, 21, 37.5, 30.7, 52.6, 150, 299, 251.7, 179.2, 70.5, 39.8, 10.9]
base = {
"Paddy": 1245.5,
"Arhar": 3200,
"Bajra": 1175,
"Barley": 980,
"Copra": 5100,
"Cotton": 3600,
"Sesamum": 4200,
"Gram": 2800,
"Groundnut": 3700,
"Jowar": 1520,
"Maize": 1175,
"Masoor": 2800,
"Moong": 3500,
"Niger": 3500,
"Ragi": 1500,
"Rape": 2500,
"Jute": 1675,
"Safflower": 2500,
"Soyabean": 2200,
"Sugarcane": 2250,
"Sunflower": 3700,
"Urad": 4300,
"Wheat": 1350
}
commodity_dict = {
"arhar": "crop_data/Arhar.csv",
"bajra": "crop_data/Bajra.csv",
"barley": "crop_data/Barley.csv",
"copra": "crop_data/Copra.csv",
"cotton": "crop_data/Cotton.csv",
"sesamum": "crop_data/Sesamum.csv",
"gram": "crop_data/Gram.csv",
"groundnut": "crop_data/Groundnut.csv",
"jowar": "crop_data/Jowar.csv",
"maize": "crop_data/Maize.csv",
"masoor": "crop_data/Masoor.csv",
"moong": "crop_data/Moong.csv",
"niger": "crop_data/Niger.csv",
"paddy": "crop_data/Paddy.csv",
"ragi": "crop_data/Ragi.csv",
"rape": "crop_data/Rape.csv",
"jute": "crop_data/Jute.csv",
"safflower": "crop_data/Safflower.csv",
"soyabean": "crop_data/Soyabean.csv",
"sugarcane": "crop_data/Sugarcane.csv",
"sunflower": "crop_data/Sunflower.csv",
"urad": "crop_data/Urad.csv",
"wheat": "crop_data/Wheat.csv"
}
from update_commodities import update_list
commodity_list=update_list()
def SixMonthsForecast():
month1=[]
month2=[]
month3=[]
month4=[]
month5=[]
month6=[]
for i in commodity_list:
crop=SixMonthsForecastHelper(i.getCropName())
k=0
for j in crop:
time = j[0]
price = j[1]
change = j[2]
if k==0:
month1.append((price,change,i.getCropName().split("/")[1],time))
elif k==1:
month2.append((price,change,i.getCropName().split("/")[1],time))
elif k==2:
month3.append((price,change,i.getCropName().split("/")[1],time))
elif k==3:
month4.append((price,change,i.getCropName().split("/")[1],time))
elif k==4:
month5.append((price,change,i.getCropName().split("/")[1],time))
elif k==5:
month6.append((price,change,i.getCropName().split("/")[1],time))
k+=1
month1.sort()
month2.sort()
month3.sort()
month4.sort()
month5.sort()
month6.sort()
crop_month_wise=[]
crop_month_wise.append([month1[0][3],month1[len(month1)-1][2],month1[len(month1)-1][0],month1[len(month1)-1][1],month1[0][2],month1[0][0],month1[0][1]])
crop_month_wise.append([month2[0][3],month2[len(month2)-1][2],month2[len(month2)-1][0],month2[len(month2)-1][1],month2[0][2],month2[0][0],month2[0][1]])
crop_month_wise.append([month3[0][3],month3[len(month3)-1][2],month3[len(month3)-1][0],month3[len(month3)-1][1],month3[0][2],month3[0][0],month3[0][1]])
crop_month_wise.append([month4[0][3],month4[len(month4)-1][2],month4[len(month4)-1][0],month4[len(month4)-1][1],month4[0][2],month4[0][0],month4[0][1]])
crop_month_wise.append([month5[0][3],month5[len(month5)-1][2],month5[len(month5)-1][0],month5[len(month5)-1][1],month5[0][2],month5[0][0],month5[0][1]])
crop_month_wise.append([month6[0][3],month6[len(month6)-1][2],month6[len(month6)-1][0],month6[len(month6)-1][1],month6[0][2],month6[0][0],month6[0][1]])
return crop_month_wise
#supporting function for six months forecast
def SixMonthsForecastHelper(name):
current_month = datetime.now().month
current_year = datetime.now().year
current_rainfall = annual_rainfall[current_month - 1]
name = name.split("/")[1]
name = name.lower()
commodity = commodity_list[0]
for i in commodity_list:
if name == str(i):
commodity = i
break
month_with_year = []
for i in range(1, 7):
if current_month + i <= 12:
month_with_year.append((current_month + i, current_year, annual_rainfall[current_month + i - 1]))
else:
month_with_year.append((current_month + i - 12, current_year + 1, annual_rainfall[current_month + i - 13]))
wpis = []
current_wpi = commodity.getPredictedValue([float(current_month), current_year, current_rainfall])
change = []
for m, y, r in month_with_year:
current_predict = commodity.getPredictedValue([float(m), y, r])
wpis.append(current_predict)
change.append(((current_predict - current_wpi) * 100) / current_wpi)
crop_price = []
for i in range(0, len(wpis)):
m, y, r = month_with_year[i]
x = datetime(y, m, 1)
x = x.strftime("%b %y")
crop_price.append([x, round((wpis[i]* base[name.capitalize()]) / 100, 2) , round(change[i], 2)])
#
return crop_price
#get current month forecast
def CurrentMonth(name):
current_month = datetime.now().month
current_year = datetime.now().year
current_rainfall = annual_rainfall[current_month - 1]
name = name.lower()
commodity = commodity_list[0]
for i in commodity_list:
if name == str(i):
commodity = i
break
current_wpi = commodity.getPredictedValue([float(current_month), current_year, current_rainfall])
current_price = (base[name.capitalize()]*current_wpi)/100
return current_price
#twelve months forecast
def TwelveMonthsForecast(name):
current_month = datetime.now().month
current_year = datetime.now().year
current_rainfall = annual_rainfall[current_month - 1]
name = name.lower()
commodity = commodity_list[0]
for i in commodity_list:
if name == str(i):
commodity = i
break
month_with_year = []
for i in range(1, 13):
if current_month + i <= 12:
month_with_year.append((current_month + i, current_year, annual_rainfall[current_month + i - 1]))
else:
month_with_year.append((current_month + i - 12, current_year + 1, annual_rainfall[current_month + i - 13]))
max_index = 0
min_index = 0
max_value = 0
min_value = 9999
wpis = []
current_wpi = commodity.getPredictedValue([float(current_month), current_year, current_rainfall])
change = []
for m, y, r in month_with_year:
current_predict = commodity.getPredictedValue([float(m), y, r])
if current_predict > max_value:
max_value = current_predict
max_index = month_with_year.index((m, y, r))
if current_predict < min_value:
min_value = current_predict
min_index = month_with_year.index((m, y, r))
wpis.append(current_predict)
change.append(((current_predict - current_wpi) * 100) / current_wpi)
max_month, max_year, r1 = month_with_year[max_index]
min_month, min_year, r2 = month_with_year[min_index]
min_value = min_value * base[name.capitalize()] / 100
max_value = max_value * base[name.capitalize()] / 100
crop_price = []
for i in range(0, len(wpis)):
m, y, r = month_with_year[i]
x = datetime(y, m, 1)
x = x.strftime("%b %y")
crop_price.append([x, round((wpis[i]* base[name.capitalize()]) / 100, 2) , round(change[i], 2)])
x = datetime(max_year,max_month,1)
x = x.strftime("%b %y")
max_crop = [x, round(max_value,2)]
x = datetime(min_year, min_month, 1)
x = x.strftime("%b %y")
min_crop = [x, round(min_value,2)]
return max_crop, min_crop, crop_price
#twelve months previous for creating windows
def TwelveMonthPrevious(name):
name = name.lower()
current_month = datetime.now().month
current_year = datetime.now().year
current_rainfall = annual_rainfall[current_month - 1]
commodity = commodity_list[0]
wpis = []
crop_price = []
#Define Forecast via commondity
for i in commodity_list:
if name == str(i):
commodity = i
break
month_with_year = []
#for 12 months range
for i in range(1, 13):
if current_month - i >= 1:
month_with_year.append((current_month - i, current_year, annual_rainfall[current_month - i - 1]))
else:
month_with_year.append((current_month - i + 12, current_year - 1, annual_rainfall[current_month - i + 11]))
for m, y, r in month_with_year:
current_predict = commodity.getPredictedValue([float(m), 2013, r])
wpis.append(current_predict)
for i in range(0, len(wpis)):
m, y, r = month_with_year[i]
x = datetime(y,m,1)
x = x.strftime("%b %y")
crop_price.append([x, round((wpis[i]* base[name.capitalize()]) / 100, 2)])
new_crop_price =[]
for i in range(len(crop_price)-1,-1,-1):
new_crop_price.append(crop_price[i])
return new_crop_price