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predict_test_loans.py
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predict_test_loans.py
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import pandas as pd
import numpy as np
import glob, sys
from datetime import date
import pickle
from random import randint
import time
import json
import os
from datetime import datetime
from pandas.io import sql
import pymysql as mdb
import mysql_connector
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.tree import export_graphviz
from sklearn.decomposition import PCA
from sklearn.metrics import recall_score
from scipy.stats import randint as sp_randint
import matplotlib.pyplot as plt
import data_manipulation as dm
class Predictor():
def __init__(self):
self.load_data()
self.load_classifiers()
self.clean_data()
self.add_unemp_rates()
self.add_swap_rates()
self.rearrange_loans()
self.remove_60mths()
self.define_x_test()
self.predict()
self.calculate_roi()
self.upload_to_db()
self.close_db_connection()
def load_data(self):
with open('credentials.json') as credentials_file:
credentials = json.load(credentials_file)
passwd = credentials['mysql']['password']
self.con = mdb.connect(host='127.0.0.1', port=3307, user='root', passwd=passwd, db='insight', autocommit=True)
sql_query = "SELECT * FROM test_loans;"
self.loanData = sql.read_sql(sql_query, self.con)
def load_classifiers(self):
self.regrs = []
self.clfs = []
for i in range(0, 2):
rfr_file = './pickles/rfr_%s.pickle' %i
rfc_file = './pickles/rfc_%s.pickle' %i
print "loading rf classifiers %s" %i
f = open(rfr_file, 'rb')
self.regrs.append(pickle.load(f))
f.close()
f = open(rfc_file, 'rb')
self.clfs.append(pickle.load(f))
f.close()
def clean_data(self):
self.loanData = self.loanData.drop(['asOfDate',
'memberId',
'expDefaultRate',
'serviceFeeRate',
'acceptD',
'expD',
'listD',
'creditPullD',
'reviewStatusD',
'reviewStatus',
'investorCount',
'ilsExpD',
'initialListStatus',
'bcOpenToBuy',
'percentBcGt75',
'bcUtil',
'ficoRangeLow',
'ficoRangeHigh',
'mthsSinceRecentRevolDelinq',
'mthsSinceRecentBc',
'mortAcc',
'totalBalExMort',
'totalBcLimit',
'totalIlHighCreditLimit',
'mthsSinceRecentBcDlq',
'pubRecBankruptcies',
'numAcctsEver120Ppd',
'chargeoffWithin12Mths',
'taxLiens',
'numSats',
'numTlOpPast12m',
'avgCurBal',
'numBcTl',
'numActvBcTl',
'numBcSats',
'pctTlNvrDlq',
'numTl90gDpd24m',
'numTl30Dpd',
'numTl120dpd2m',
'numIlTl',
'moSinOldIlAcct',
'numActvRevTl',
'moSinOldRevTlOp',
'moSinRcntRevTlOp',
'totalRevHiLim',
'numRevTlBalGt0',
'numOpRevTl',
'totCollAmt',
'totHiCredLim',
'moSinRcntTl',
'accNowDelinq',
'delinqAmnt',
'mthsSinceRecentInq',
'numRevAccts',
'totCurBal',
'fundedAmount',
'accOpenPast24Mths'], 1)
##addr_state
statesBinarized = pd.get_dummies(self.loanData['addrState'])
self.loanData = pd.concat([self.loanData, statesBinarized], axis=1)
##Fill in the missing states:
cols = self.loanData.columns
states = ['AK',
'AL',
'AR',
'AZ',
'CA',
'CO',
'CT',
'DC',
'DE',
'FL',
'GA',
'HI',
'IA',
'IL',
'IDAHO',
'INDIANA',
'KS',
'KY',
'LA',
'MA',
'MD',
'ME',
'MI',
'MN',
'MO',
'MS',
'MT',
'NC',
'NE',
'NH',
'NJ',
'NM',
'NV',
'NY',
'OH',
'OK',
'OREGON',
'PA',
'RI',
'SC',
'SD',
'TN',
'TX',
'UT',
'VA',
'VT',
'WA',
'WI',
'WV',
'WY']
for state in states:
if state not in cols:
self.loanData[state] = 0
##term
print "term"
self.loanData['term'] = [0 if x == 36 else 1 for x in self.loanData['term']]
##grade
print "grade"
#self.loanData = self.loanData[pd.isnull(self.loanData['grade']) == 0]
#self.loanData.index = range(len(self.loanData))
#Binarize the grade
gradesBinarized = pd.get_dummies(self.loanData['grade'])
self.loanData = pd.concat([self.loanData, gradesBinarized], axis=1)
self.loanData = self.loanData.drop(['grade'], 1)
grades = ['A', 'B', 'C', 'D', 'E', 'F', 'G']
cols = self.loanData.columns
for grade in grades:
if grade not in cols:
self.loanData[grade] = 0
#Subgrade - numeric part of LendingClub's subgrade (ie: b3 -> grade=b, subgrade=3)
self.loanData['subGrade'] = [x[1:] for x in self.loanData['subGrade']]
##emp_length
emp_years = dict(zip(self.loanData['empLength'].unique(), np.arange(self.loanData['empLength'].nunique())))
self.loanData['empLength'] = self.loanData['empLength'].map(lambda x: emp_years[x])
homeOwnershipBinarized = pd.get_dummies(self.loanData['homeOwnership'])
self.loanData = pd.concat([self.loanData, homeOwnershipBinarized], axis=1)
ownership = ['ANY', 'MORTGAGE', 'NONE', 'OWN', 'RENT', 'OTHER']
cols = self.loanData.columns
for val in ownership:
if val not in cols:
self.loanData[val] = 0
self.loanData = self.loanData.drop(['homeOwnership'], 1)
self.loanData['isIncV'] = [1 if x == "SOURCE_VERIFIED" else x for x in self.loanData['isIncV']]
self.loanData['isIncV'] = [2 if x == "VERIFIED" else x for x in self.loanData['isIncV']]
self.loanData['isIncV'] = [0 if x == "NOT_VERIFIED" else x for x in self.loanData['isIncV']]
purposeBinarized = pd.get_dummies(self.loanData['purpose'])
self.loanData = pd.concat([self.loanData, purposeBinarized], axis=1)
purposes = ['car',
'credit_card',
'debt_consolidation',
'home_improvement',
'house',
'major_purchase',
'medical',
'moving',
'renewable_energy',
'small_business',
'vacation',
'wedding',
'other']
cols = self.loanData.columns
for purpose in purposes:
if purpose not in cols:
self.loanData[purpose] = 0
self.loanData['addrZip'] = [x[:3] for x in self.loanData['addrZip']]
##yrs_since_first_cr_line
yrs_since_first_cr_line = []
self.loanData['yrs_since_first_cr_line'] = 0
for i in range(len(self.loanData['earliestCrLine'])):
earliest_year = pd.to_datetime(self.loanData['earliestCrLine'][i]).year
yrs_since_first_cr_line.append(date.today().year - earliest_year )
self.loanData['yrs_since_first_cr_line'] = yrs_since_first_cr_line
self.loanData['issue_month'] = date.today().month
self.loanData['issue_year'] = date.today().year
self.loanData['desc_length'] = [len(str(x)) for x in self.loanData['description']]
self.loanData['install_frac_of_monthly_inc'] = self.loanData['installment']/self.loanData['annualInc']*12.0
def add_unemp_rates(self):
self.cur = self.con.cursor()
self.cur.execute("SELECT * FROM unemployment_rates")
unemp_rate_tuple = self.cur.fetchall()
print unemp_rate_tuple[0]
unemp_rate_dict = dict()
for entry in unemp_rate_tuple:
unemp_state = entry[1]
unemp_year = entry[2]
unemp_month = entry[3]
unemp_rate = entry[4]
key = "%s%s%s" %(unemp_state, unemp_year, unemp_month)
unemp_rate_dict[key] = unemp_rate
self.loanData['unemp_rate_12mths'] = 0
self.loanData['unemp_rate_6mths'] = 0
self.loanData['unemp_rate_3mths'] = 0
for i, loan in enumerate(self.loanData['id']):
key_12mths = "%s%s%s" %(self.loanData['addrState'][i],
(self.loanData['issue_year'][i]-1),
self.loanData['issue_month'][i])
if self.loanData['issue_month'][i] <= 6:
key_6mths = "%s%s%s" %(self.loanData['addrState'][i],
(self.loanData['issue_year'][i]-1),
self.loanData['issue_month'][i]+6)
else:
key_6mths = "%s%s%s" %(self.loanData['addr_state'][i],
(self.loanData['issue_year'][i]),
self.loanData['issue_month'][i]-6)
if self.loanData['issue_month'][i] <= 3:
key_3mths = "%s%s%s" %(self.loanData['addrState'][i],
(self.loanData['issue_year'][i]-1),
self.loanData['issue_month'][i]+3)
else:
key_3mths = "%s%s%s" %(self.loanData['addrState'][i],
(self.loanData['issue_year'][i]),
self.loanData['issue_month'][i]-3)
try:
self.loanData['unemp_rate_12mths'].iloc[i] = unemp_rate_dict[key_12mths]
self.loanData['unemp_rate_6mths'].iloc[i] = unemp_rate_dict[key_6mths]
self.loanData['unemp_rate_3mths'].iloc[i] = unemp_rate_dict[key_3mths]
except KeyError:
print KeyError, "loan ", i
#self.loanData = self.loanData.drop(self.loanData.index[i])
#self.loanData.index = range(len(self.loanData))
def add_swap_rates(self):
self.cur.execute("SELECT * FROM interest_rate_swaps")
int_rate_swap_tuple = self.cur.fetchall()
int_rate_swap_dict = dict()
for entry in int_rate_swap_tuple:
int_rate_swap_year = entry[1]
int_rate_swap_rate = entry[3]
int_rate_swap_dict[int_rate_swap_year] = int_rate_swap_rate
self.loanData['implied_risk'] = 0
self.loanData.index = range(len(self.loanData))
for i in range(len(self.loanData)):
year_i = self.loanData['issue_year'][i]
swap_rate_i = int_rate_swap_dict[year_i - 1]
int_rate_i = self.loanData['intRate'][i]
impl_risk = int_rate_i - swap_rate_i
try:
self.loanData['implied_risk'].iloc[i] = impl_risk
except:
print "Error ", "loan ", i
#self.loanData = self.loanData[self.loanData['implied_risk'] != 0]
#self.loanData.index = range(len(self.loanData))
def rearrange_loans(self):
self.listedLoanData = self.loanData[[
'id',
'loanAmount',
'term',
'intRate',
'installment',
'empLength',
'annualInc',
'isIncV',
'addrZip',
'dti',
'delinq2Yrs',
'inqLast6Mths',
'mthsSinceLastDelinq',
'mthsSinceLastRecord',
'openAcc',
'pubRec',
'revolBal',
'revolUtil',
'totalAcc',
'collections12MthsExMed',
'mthsSinceLastMajorDerog',
'A',
'B',
'C',
'D',
'E',
'F',
'G',
'MORTGAGE',
'OWN',
'RENT',
'issue_month',
#'issue_year',
'car',
'credit_card',
'debt_consolidation',
'home_improvement',
'house',
'major_purchase',
'medical',
'moving',
'renewable_energy',
'small_business',
'vacation',
'wedding',
'AK',
'AL',
'AR',
'AZ',
'CA',
'CO',
'CT',
'DC',
'DE',
'FL',
'GA',
'HI',
'IL',
'INDIANA',
'KS',
'KY',
'LA',
'MA',
'MD',
'MI',
'MN',
'MO',
'MS',
'MT',
'NC',
'NH',
'NJ',
'NM',
'NV',
'NY',
'OH',
'OK',
'OREGON',
'PA',
'RI',
'SC',
'SD',
'TN',
'TX',
'UT',
'VA',
'VT',
'WA',
'WI',
'WV',
'WY',
'yrs_since_first_cr_line',
'desc_length',
'unemp_rate_12mths',
'unemp_rate_6mths',
'unemp_rate_3mths',
'subGrade',
'other',
'install_frac_of_monthly_inc',
'implied_risk']]
def remove_60mths(self):
#Only keep 36 month terms:
self.listedLoanData = self.listedLoanData[self.listedLoanData['term']==0]
self.listedLoanData.index = range(len(self.listedLoanData))
def define_x_test(self):
#self.X_test = self.listedLoanData.drop(['id'], 1).values
self.X_test = self.listedLoanData.drop(['id'], 1).astype(float).values
(self.X_test, _) = dm.standardize_samples(self.X_test, self.X_test)
(self.X_test, _) = dm.scale_samples_to_range(self.X_test, self.X_test)
def predict(self):
#arrays to hold final prediction values
self.prediction_clf_0 = []
self.prediction_clf_1 = []
self.prediction_regr = []
self.error_clf = []
self.error_regr = []
print "predicting loans"
for loan in self.X_test:
#temporary variables to hold predictions for each new loan
preds_clf = []
preds_regr = []
for i in range(0, 2):
preds_clf.append(self.clfs[i].predict_proba(loan))
preds_regr.append(self.regrs[i].predict(loan))
#break up positive and negative probabilities for clf:
preds_clf_0 = [x[0][0] for x in preds_clf]
preds_clf_1 = [x[0][1] for x in preds_clf]
mean_clf_0 = np.mean(preds_clf_0)
mean_clf_1 = np.mean(preds_clf_1)
std_clf = np.std(preds_clf_0)
self.prediction_clf_0.append(mean_clf_0)
self.prediction_clf_1.append(mean_clf_1)
self.error_clf.append(std_clf)
mean_regr = np.mean(preds_regr)
std_regr = np.std(preds_regr)
self.prediction_regr.append(mean_regr)
self.error_regr.append(std_regr)
print self.error_clf
print len(self.error_clf)
def calculate_roi(self):
#Calculate expected ROI
self.roi = []
for index, pred in enumerate(self.prediction_regr):
income = 0
installment = self.listedLoanData['installment'][index]
loanAmount = self.listedLoanData['loanAmount'][index]
mths_till_default = 36
if self.prediction_clf_1[index] < 0.7:
mths_till_default = int(pred/30)
for i in range(0, mths_till_default):
income += installment
self.roi.append(income/loanAmount)
def upload_to_db(self):
##Upload predicted default times to database
for i, val in enumerate(self.prediction_regr):
#default time
sql_query = "UPDATE listed_loans SET pred_default_time='%s' WHERE id='%s';" %(int(val), self.listedLoanData['id'][i])
self.cur.execute(sql_query)
#default time error
sql_query = "UPDATE listed_loans SET pred_default_time_error='%s' WHERE id='%s';" %(self.error_regr[i], self.listedLoanData['id'][i])
self.cur.execute(sql_query)
for i, val in enumerate(self.prediction_clf_0):
#default prediction
sql_query = "UPDATE listed_loans SET pred_default='%s' WHERE id='%s';" %(val, self.listedLoanData['id'][i])
self.cur.execute(sql_query)
#default prediction error
sql_query = "UPDATE listed_loans SET pred_default_error='%s' WHERE id='%s';" %(self.error_clf[i], self.listedLoanData['id'][i])
self.cur.execute(sql_query)
for i, val in enumerate(self.prediction_clf_1):
#paid prediction
sql_query = "UPDATE listed_loans SET pred_paid='%s' WHERE id='%s';" %(val, self.listedLoanData['id'][i])
self.cur.execute(sql_query)
#roi
sql_query = "UPDATE listed_loans SET pred_roi='%s' WHERE id='%s';" %(self.roi[i], self.listedLoanData['id'][i])
self.cur.execute(sql_query)
def close_db_connection(self):
self.cur.close()
p = Predictor()