In [47]:
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.svm import LinearSVC
from sklearn.calibration import CalibratedClassifierCV
from sklearn.feature_selection import RFE
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
from sklearn.model_selection import cross_val_score
import matplotlib.pylab as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = 10, 8
In [2]:
## Load cleaned approved loan data
df = pd.read_csv('approved_loan_2015_clean.csv',low_memory=False)
In [22]:
p_optimal='l1'
In [4]:
df.head()
Out[4]:
Unnamed: 0
loan_amnt
annual_inc
fico_range_low
fico_range_high
num_actv_bc_tl
tot_cur_bal
mort_acc
num_actv_rev_tl
pub_rec_bankruptcies
...
sub_grade_F2
sub_grade_F3
sub_grade_F4
sub_grade_F5
sub_grade_G1
sub_grade_G2
sub_grade_G3
sub_grade_G4
sub_grade_G5
int_rate
0
0
16000.0
62000.0
720.0
724.0
4.0
227708.0
3.0
5.0
0.0
...
0
0
0
0
0
0
0
0
0
8.49
1
1
8000.0
45000.0
670.0
674.0
3.0
148154.0
2.0
11.0
0.0
...
0
0
0
0
0
0
0
0
0
10.78
2
2
10000.0
41600.0
695.0
699.0
2.0
168304.0
2.0
5.0
0.0
...
0
0
0
0
0
0
0
0
0
10.78
3
3
24700.0
65000.0
715.0
719.0
5.0
204396.0
4.0
5.0
0.0
...
0
0
0
0
0
0
0
0
0
11.99
4
4
10000.0
42500.0
705.0
709.0
4.0
41166.0
0.0
4.0
0.0
...
0
0
0
0
0
0
0
0
0
11.99
5 rows × 78 columns
In [10]:
c=0.0820849986238988
In [13]:
df2 = df.copy()
df2 = df2.ix[:,1:]
In [15]:
df2.head()
Out[15]:
loan_amnt
annual_inc
fico_range_low
fico_range_high
num_actv_bc_tl
tot_cur_bal
mort_acc
num_actv_rev_tl
pub_rec_bankruptcies
dti
...
sub_grade_F2
sub_grade_F3
sub_grade_F4
sub_grade_F5
sub_grade_G1
sub_grade_G2
sub_grade_G3
sub_grade_G4
sub_grade_G5
int_rate
0
16000.0
62000.0
720.0
724.0
4.0
227708.0
3.0
5.0
0.0
28.92
...
0
0
0
0
0
0
0
0
0
8.49
1
8000.0
45000.0
670.0
674.0
3.0
148154.0
2.0
11.0
0.0
21.23
...
0
0
0
0
0
0
0
0
0
10.78
2
10000.0
41600.0
695.0
699.0
2.0
168304.0
2.0
5.0
0.0
15.78
...
0
0
0
0
0
0
0
0
0
10.78
3
24700.0
65000.0
715.0
719.0
5.0
204396.0
4.0
5.0
0.0
16.06
...
0
0
0
0
0
0
0
0
0
11.99
4
10000.0
42500.0
705.0
709.0
4.0
41166.0
0.0
4.0
0.0
31.04
...
0
0
0
0
0
0
0
0
0
11.99
5 rows × 77 columns
In [16]:
from sklearn.model_selection import train_test_split
# Split training(labeled) and test(unlabled)
#df_train = df_bi[df_chin_bi['cuisine_Chinese'] != 2]
#df_test = df_chin_bi[df_chin_bi['Loan_status'] == 2]
# reduce data volumn by randomly selecting instances
np.random.seed(99)
ind = np.random.randint(0, len(df2), 100000)
df_reduced = df2.ix[ind, :]
df_labeled = df_reduced[df_reduced['Target']!=2]
df_unlabeled = df_reduced[df_reduced['Target']==2]
X = df_labeled.drop('Target',axis=1)
Y = df_labeled['Target']
X_train_val, X_test, Y_train_val, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
X_train, X_vali , Y_train , Y_vali = train_test_split(X_train_val, Y_train_val, test_size=0.25, random_state=42)
In [23]:
lr = LogisticRegression(C=c, penalty=p_optimal, random_state=99)
lr.fit(X_train, Y_train)
Out[23]:
LogisticRegression(C=0.0820849986239, class_weight=None, dual=False,
fit_intercept=True, intercept_scaling=1, max_iter=100,
multi_class='ovr', n_jobs=1, penalty='l1', random_state=99,
solver='liblinear', tol=0.0001, verbose=0, warm_start=False)
In [24]:
cost_matrix = pd.DataFrame([[0.101, -0.647], [0, 0]], columns=['p', 'n'], index=['Y', 'N'])
print ("Cost matrix")
print (cost_matrix)
Cost matrix
p n
Y 0.101 -0.647
N 0.000 0.000
In [25]:
probabilities = lr.predict_proba(X_test)[:, 1]
In [26]:
prediction = probabilities > 0.5
# Build and print a confusion matrix
confusion_matrix_large = pd.DataFrame(metrics.confusion_matrix(Y_test, prediction, labels=[1, 0]).T,
columns=['p', 'n'], index=['Y', 'N'])
print (confusion_matrix_large)
p n
Y 4209 1522
N 329 475
In [42]:
#MANUAL ROC CALCULATION
n=[1,2,3,4,5,6,7,8,9]
TPR=[]
FPR=[]
for i in n:
prediction = probabilities > ((i*1.0)/10)
confusion_matrix_large = pd.DataFrame(metrics.confusion_matrix(Y_test, prediction, labels=[1, 0]).T,columns=['p', 'n'], index=['Y', 'N'])
TPR.append(1.0*confusion_matrix_large['p']['Y']/(1.0*(confusion_matrix_large['p']['Y']+confusion_matrix_large['p']['N'])))
FPR.append(1.0*confusion_matrix_large['n']['Y']/(1.0*(confusion_matrix_large['n']['Y']+confusion_matrix_large['n']['N'])))
In [51]:
fpr, tpr, thresholds = metrics.roc_curve(Y_test, lr.predict_proba(X_test)[:,1])
In [56]:
thresholds
Out[56]:
array([ 0.9834676 , 0.95883673, 0.95877152, ..., 0.20111337,
0.20065485, 0.15679253])
In [57]:
profits=[]
for i in thresholds:
prediction = probabilities > i
confusion_matrix_large = pd.DataFrame(metrics.confusion_matrix(Y_test, prediction, labels=[1, 0]).T,columns=['p', 'n'], index=['Y', 'N'])
profits.append((1.0*confusion_matrix_large['p']['Y'])*cost_matrix['p']['Y']+(1.0*confusion_matrix_large['n']['Y'])*cost_matrix['n']['Y'])
In [58]:
profits
Out[58]:
[0.0,
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3.3929999999999998,
3.4939999999999998,
4.5640000000000001,
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10.826000000000001,
10.927,
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13.370000000000001,
12.824000000000002,
13.288000000000002,
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16.277000000000001,
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In [64]:
plt.plot(thresholds,profits)
plt.xlabel("Threshold")
plt.ylabel("Profit")
plt.title("Profits")
Out[64]:
<matplotlib.text.Text at 0x124c08cd0>
In [ ]:
Content source: kayzhou22/DSBiz_Project_LendingClub
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