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deliverable4_deploymodel.py
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# -*- coding: utf-8 -*-
"""Deliverable4_DeployModel.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/14piiWBHlDwenlq21jTVXUs7aOwG7w55i
"""
import numpy as np
import pandas as pd
import pickle
#READING THE PREVIOUSLY SAVED MINI COMBINED_DF
mini_df = pd.read_csv('df.csv')
#LOAD THE MODEL FROM DISK
filename = 'finalized_model.sav'
model = pickle.load(open(filename, 'rb'))
def getListOfFinancialRatios(mini_df):
listOfFinancialRatios = mini_df.columns.values.tolist()
listOfFinancialRatios.remove('Next Year Stock Return')
return listOfFinancialRatios
#FUNCTION THAT VECTORIZES INPUT DATA
def vectorizeInput(listOfFinancialRatios, listOfVars):
dictOfRatios = dict.fromkeys(listOfFinancialRatios , 0)
if listOfVars[0] == 0:
dictOfRatios['Revenue Growth'] = 0
else:
dictOfRatios['Revenue Growth'] = (listOfVars[1] - listOfVars[0]) / listOfVars[0]
if listOfVars[5] == 0:
dictOfRatios['interestCoverage'] = 0
else:
dictOfRatios['interestCoverage'] = listOfVars[4] / listOfVars[5]
if listOfVars[8] == 0:
dictOfRatios['Receivables Turnover'] = 0
else:
dictOfRatios['Receivables Turnover'] = listOfVars[1] / listOfVars[8]
if listOfVars[9] == 0:
dictOfRatios['Inventory Turnover'] = 0
else:
dictOfRatios['Inventory Turnover'] = listOfVars[2] / listOfVars[9]
if listOfVars[12] == 0:
dictOfRatios['Payables Turnover'] = 0
else:
dictOfRatios['Payables Turnover'] = listOfVars[2] / listOfVars[12]
if listOfVars[13] == 0:
dictOfRatios['currentRatio'] = 0
dictOfRatios['quickRatio'] = 0
dictOfRatios['cashRatio'] = 0
else:
dictOfRatios['currentRatio'] = listOfVars[10] / listOfVars[13]
dictOfRatios['quickRatio'] = (listOfVars[10] - listOfVars[9]) / listOfVars[13]
dictOfRatios['cashRatio'] = listOfVars[7] / listOfVars[13]
if listOfVars[16] == 0:
dictOfRatios['returnOnEquity'] = 0
dictOfRatios['debtEquityRatio'] = 0
else:
dictOfRatios['returnOnEquity'] = listOfVars[6] / listOfVars[16]
dictOfRatios['debtEquityRatio'] = listOfVars[14] / listOfVars[16]
if listOfVars[11] == 0:
dictOfRatios['debtRatio'] = 0
else:
dictOfRatios['debtRatio'] = listOfVars[14] / listOfVars[11]
if listOfVars[20] == 0:
dictOfRatios['dividendYield'] = 0
else:
dictOfRatios['dividendYield'] = listOfVars[19] / listOfVars[20]
if listOfVars[18] == 0:
dictOfRatios['EPS'] = 0
else:
dictOfRatios['EPS'] = (listOfVars[6] - listOfVars[15]) / listOfVars[18]
if (dictOfRatios['EPS'] == 0):
dictOfRatios['PE ratio'] = 0
else:
dictOfRatios['PE ratio'] = listOfVars[20] / dictOfRatios['EPS']
if listOfVars[6] == listOfVars[17]:
dictOfRatios['priceToFreeCashFlowsRatio'] = 0
else:
dictOfRatios['priceToFreeCashFlowsRatio'] = (listOfVars[20] * listOfVars[18]) / (listOfVars[6] - listOfVars[17])
if listOfVars[1] == 0:
dictOfRatios['EBITDA Margin'] = 0
dictOfRatios['Net Profit Margin'] = 0
dictOfRatios['priceToSalesRatio'] = 0
dictOfRatios['grossProfitMargin'] = 0
else:
dictOfRatios['EBITDA Margin'] = listOfVars[3] / listOfVars[1]
dictOfRatios['Net Profit Margin'] = listOfVars[6] / listOfVars[1]
dictOfRatios['priceToSalesRatio'] = (listOfVars[20] * listOfVars[18]) / listOfVars[1]
dictOfRatios['grossProfitMargin'] = (listOfVars[1] - listOfVars[2]) / listOfVars[1]
dictOfRatios['Dividend per Share'] = listOfVars[19]
if (listOfVars[21] == "basicMaterials"):
dictOfRatios['Sector_Basic Materials'] = 1
elif (listOfVars[21] == "communicationServices"):
dictOfRatios['Sector_Communication Services'] = 1
elif (listOfVars[21] == "consumerCyclical"):
dictOfRatios['Sector_Consumer Cyclical'] = 1
elif (listOfVars[21] == "consumerDefensive"):
dictOfRatios['Sector_Consumer Defensive'] = 1
elif (listOfVars[21] == "energy"):
dictOfRatios['Sector_Energy'] = 1
elif (listOfVars[21] == "financialServices"):
dictOfRatios['Sector_Financial Services'] = 1
elif (listOfVars[21] == "healthcare"):
dictOfRatios['Sector_Healthcare'] = 1
elif (listOfVars[21] == "industrials"):
dictOfRatios['Sector_Industrials'] = 1
elif (listOfVars[21] == "realEstate"):
dictOfRatios['Sector_Real Estate'] = 1
elif (listOfVars[21] == "technology"):
dictOfRatios['Sector_Technology'] = 1
elif (listOfVars[21] == "utilities"):
dictOfRatios['Sector_Utilities'] = 1
listOfCalculatedRatios = list(dictOfRatios.values())
vector = np.asarray(listOfCalculatedRatios)
return vector
def preprocess_sample_point(vector):
vector = vector.reshape(1, -1)
result = model.predict(vector)
return str(round(result[0], 2)) + "%"
def run_model(listOfVars):
vector = vectorizeInput(getListOfFinancialRatios(mini_df), listOfVars)
return (preprocess_sample_point(vector))