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!pip install imblearn==0.0
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!pip install xgboost==1.4.1
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!pip install lightgbm==3.2.1
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import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split, cross_validate, GridSearchCV, cross_val_score
from imblearn.under_sampling import RandomUnderSampler
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, classification_report
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import StratifiedKFold
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier
sns.set_style()
import warnings
warnings.filterwarnings('ignore')
COLOR = '#ababab'
mpl.rcParams['figure.titlesize'] = 16
mpl.rcParams['text.color'] = 'black'
mpl.rcParams['axes.labelcolor'] = COLOR
mpl.rcParams['xtick.color'] = COLOR
mpl.rcParams['ytick.color'] = COLOR
mpl.rcParams['grid.color'] = COLOR
mpl.rcParams['grid.alpha'] = 0.1
df_credit = pd.read_csv('http://dl.dropboxusercontent.com/s/xn2a4kzf0zer0xu/acquisition_train.csv?dl=0')
df_credit.head()
print('Number of rows: ', df_credit.shape[0])
print('Number of columns: ', df_credit.shape[1])
df_credit.info()
Number of rows: 45000
Number of columns: 43
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 45000 entries, 0 to 44999
Data columns (total 43 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ids 45000 non-null object
1 target_default 41741 non-null object
2 score_1 44438 non-null object
3 score_2 44438 non-null object
4 score_3 44438 non-null float64
5 score_4 45000 non-null float64
6 score_5 45000 non-null float64
7 score_6 45000 non-null float64
8 risk_rate 44438 non-null float64
9 last_amount_borrowed 15044 non-null float64
10 last_borrowed_in_months 15044 non-null float64
11 credit_limit 31200 non-null float64
12 reason 44434 non-null object
13 income 44438 non-null float64
14 facebook_profile 40542 non-null object
15 state 44438 non-null object
16 zip 44438 non-null object
17 channel 44438 non-null object
18 job_name 41664 non-null object
19 real_state 44438 non-null object
20 ok_since 18455 non-null float64
21 n_bankruptcies 44303 non-null float64
22 n_defaulted_loans 44426 non-null float64
23 n_accounts 44438 non-null float64
24 n_issues 33456 non-null float64
25 application_time_applied 45000 non-null object
26 application_time_in_funnel 45000 non-null int64
27 email 45000 non-null object
28 external_data_provider_credit_checks_last_2_year 22372 non-null float64
29 external_data_provider_credit_checks_last_month 45000 non-null int64
30 external_data_provider_credit_checks_last_year 29876 non-null float64
31 external_data_provider_email_seen_before 42767 non-null float64
32 external_data_provider_first_name 45000 non-null object
33 external_data_provider_fraud_score 45000 non-null int64
34 lat_lon 43637 non-null object
35 marketing_channel 41422 non-null object
36 profile_phone_number 45000 non-null object
37 reported_income 45000 non-null float64
38 shipping_state 45000 non-null object
39 shipping_zip_code 45000 non-null int64
40 profile_tags 45000 non-null object
41 user_agent 44278 non-null object
42 target_fraud 1522 non-null object
dtypes: float64(18), int64(4), object(21)
memory usage: 14.8+ MB
df_credit.dropna(subset=['target_default'], inplace=True)
print('Number of rows: ', df_credit.shape[0])
print('Number of columns: ', df_credit.shape[1])
df_credit.info()
Number of rows: 41741
Number of columns: 42
<class 'pandas.core.frame.DataFrame'>
Int64Index: 41741 entries, 0 to 44999
Data columns (total 42 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ids 41741 non-null object
1 target_default 41741 non-null object
2 score_1 41741 non-null object
3 score_2 41741 non-null object
4 score_3 41741 non-null float64
5 score_4 41741 non-null float64
6 score_5 41741 non-null float64
7 score_6 41741 non-null float64
8 risk_rate 41741 non-null float64
9 last_amount_borrowed 14133 non-null float64
10 last_borrowed_in_months 14133 non-null float64
11 credit_limit 28632 non-null float64
12 reason 41737 non-null object
13 income 41741 non-null float64
14 facebook_profile 37588 non-null object
15 state 41741 non-null object
16 zip 41741 non-null object
17 channel 41741 non-null object
18 job_name 39124 non-null object
19 real_state 41741 non-null object
20 ok_since 17276 non-null float64
21 n_bankruptcies 41606 non-null float64
22 n_defaulted_loans 41729 non-null float64
23 n_accounts 41741 non-null float64
24 n_issues 30818 non-null float64
25 application_time_applied 41741 non-null object
26 application_time_in_funnel 41741 non-null int64
27 email 41741 non-null object
28 external_data_provider_credit_checks_last_2_year 20711 non-null float64
29 external_data_provider_credit_checks_last_month 41741 non-null int64
30 external_data_provider_credit_checks_last_year 27720 non-null float64
31 external_data_provider_email_seen_before 39656 non-null float64
32 external_data_provider_first_name 41741 non-null object
33 external_data_provider_fraud_score 41741 non-null int64
34 lat_lon 40479 non-null object
35 marketing_channel 38433 non-null object
36 profile_phone_number 41741 non-null object
37 reported_income 41741 non-null float64
38 shipping_state 41741 non-null object
39 shipping_zip_code 41741 non-null int64
40 profile_tags 41741 non-null object
41 user_agent 41085 non-null object
dtypes: float64(18), int64(4), object(20)
memory usage: 13.7+ MB
df_credit.nunique().sort_values()
df_credit.drop(labels=['channel', 'external_data_provider_credit_checks_last_2_year'], axis=1, inplace=True)
df_credit.drop(labels=['email', 'reason', 'zip', 'job_name', 'external_data_provider_first_name', 'lat_lon',
'shipping_zip_code', 'user_agent', 'profile_tags', 'marketing_channel',
'profile_phone_number', 'application_time_applied', 'ids'], axis=1, inplace=True)
df_credit.describe()
np.isinf(df_credit['reported_income']).sum()
df_credit['reported_income'] = df_credit['reported_income'].replace(np.inf, np.nan)
df_credit.loc[df_credit['external_data_provider_email_seen_before'] == -999, 'external_data_provider_email_seen_before'] = np.nan
df_credit_numerical = df_credit[['score_3', 'risk_rate', 'last_amount_borrowed',
'last_borrowed_in_months', 'credit_limit', 'income', 'ok_since',
'n_bankruptcies', 'n_defaulted_loans', 'n_accounts', 'n_issues',
'external_data_provider_email_seen_before']]
nrows = 3
ncols = 4
fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(25, 16))
r = 0
c = 0
for i in df_credit_numerical:
sns.distplot(df_credit_numerical[i], bins=15,kde=False, ax=ax[r][c])
if c == ncols - 1:
r += 1
c = 0
else:
c += 1
plt.show()
df_credit_num = df_credit.select_dtypes(exclude='object').columns
df_credit_cat = df_credit.select_dtypes(include='object').columns
# fill missing values for "last_amount_borrowed", "last_borrowed_in_months" and "n_issues"
df_credit['last_amount_borrowed'].fillna(value=0, inplace=True)
df_credit['last_borrowed_in_months'].fillna(value=0, inplace=True)
df_credit['n_issues'].fillna(value=0, inplace=True)
# fill missing values for numerical variables
imputer = SimpleImputer(missing_values=np.nan, strategy='median')
imputer = imputer.fit(df_credit.loc[:, df_credit_num])
df_credit.loc[:, df_credit_num] = imputer.transform(df_credit.loc[:, df_credit_num])
# fill missing values for categorical variables
imputer = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
imputer = imputer.fit(df_credit.loc[:, df_credit_cat])
df_credit.loc[:, df_credit_cat] = imputer.transform(df_credit.loc[:, df_credit_cat])
nrows = 3
ncols = 4
fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(25, 16))
r = 0
c = 0
for i in df_credit_numerical:
sns.distplot(df_credit_numerical[i], bins=15,kde=False, ax=ax[r][c])
if c == ncols - 1:
r += 1
c = 0
else:
c += 1
plt.show()
bin_var = df_credit.nunique()[df_credit.nunique() == 2].keys().tolist()
num_var = [col for col in df_credit.select_dtypes(['int', 'float']).columns.tolist() if col not in bin_var]
cat_var = [col for col in df_credit.select_dtypes(['object']).columns.tolist() if col not in bin_var]
df_credit_encoded = df_credit.copy()
# label encoding for the binary variables
le = LabelEncoder()
for col in bin_var:
df_credit_encoded[col] = le.fit_transform(df_credit_encoded[col])
# encoding with get_dummies for the categorical variables
df_credit_encoded = pd.get_dummies(df_credit_encoded, columns=cat_var)
df_credit_encoded.head()
# feature matrix
X = df_credit_encoded.drop('target_default', axis=1)
# target vector
y = df_credit_encoded['target_default']
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True, stratify=y)
# standardize numerical variables
scaler = StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
# resample
rus = RandomUnderSampler()
X_train_rus, y_train_rus = rus.fit_resample(X_train, y_train)
def val_model(X, y, clf, show=True):
"""
Apply cross-validation on the training set.
# Arguments
X: DataFrame containing the independent variables.
y: Series containing the target vector.
clf: Scikit-learn estimator instance.
# Returns
float, mean value of the cross-validation scores.
"""
X = np.array(X)
y = np.array(y)
pipeline = make_pipeline(StandardScaler(), clf)
scores = cross_val_score(pipeline, X, y, scoring='recall')
if show == True:
print(f'Recall: {scores.mean()}, {scores.std()}')
return scores.mean()
#evaluate the models
xgb = XGBClassifier()
lgb = LGBMClassifier()
cb = CatBoostClassifier()
model = []
recall = []
for clf in (xgb, lgb, cb):
model.append(clf.__class__.__name__)
recall.append(val_model(X_train_rus, y_train_rus, clf, show=False))
pd.DataFrame(data=recall, index=model, columns=['Recall'])
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# final XGBoost model
xgb = XGBClassifier(max_depth=3, learning_rate=0.0001, n_estimators=50, gamma=1, min_child_weight=6)
xgb.fit(X_train_rus, y_train_rus)
# prediction
X_test_xgb = scaler.transform(X_test)
y_pred_xgb = xgb.predict(X_test_xgb)
# classification report
print(classification_report(y_test, y_pred_xgb))
# confusion matrix
fig, ax = plt.subplots()
sns.heatmap(confusion_matrix(y_test, y_pred_xgb, normalize='true'), annot=True, ax=ax)
ax.set_title('Confusion Matrix - XGBoost')
ax.set_xlabel('Predicted Value')
ax.set_ylabel('Real Value')
plt.show()
[10:22:29] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
precision recall f1-score support
0 0.92 0.44 0.60 8771
1 0.22 0.81 0.34 1665
accuracy 0.50 10436
macro avg 0.57 0.63 0.47 10436
weighted avg 0.81 0.50 0.56 10436
# final LightGBM model
lgb = LGBMClassifier(num_leaves=70, max_depth=5, learning_rate=0.01, min_data_in_leaf=400)
lgb.fit(X_train_rus, y_train_rus)
# prediction
X_test_lgb = scaler.transform(X_test)
y_pred_lgb = lgb.predict(X_test_lgb)
# classification report
print(classification_report(y_test, y_pred_lgb))
# confusion matrix
fig, ax = plt.subplots()
sns.heatmap(confusion_matrix(y_test, y_pred_lgb, normalize='true'), annot=True, ax=ax)
ax.set_title('Confusion Matrix - LightGBM')
ax.set_xlabel('Predicted Value')
ax.set_ylabel('Real Value')
plt.show()
[LightGBM] [Warning] min_data_in_leaf is set=400, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=400
precision recall f1-score support
0 0.84 1.00 0.91 8771
1 0.18 0.01 0.01 1665
accuracy 0.84 10436
macro avg 0.51 0.50 0.46 10436
weighted avg 0.73 0.84 0.77 10436
# final CatBoost model
cb = CatBoostClassifier(learning_rate=0.03, depth=6, l2_leaf_reg=5, logging_level='Silent')
cb.fit(X_train_rus, y_train_rus)
# prediction
X_test_cb = scaler.transform(X_test)
y_pred_cb = cb.predict(X_test_cb)
# classification report
print(classification_report(y_test, y_pred_cb))
# confusion matrix
fig, ax = plt.subplots()
sns.heatmap(confusion_matrix(y_test, y_pred_cb, normalize='true'), annot=True, ax=ax)
ax.set_title('Confusion Matrix - CatBoost')
ax.set_xlabel('Predicted Value')
ax.set_ylabel('Real Value')
plt.show()
precision recall f1-score support
0 0.84 0.99 0.91 8771
1 0.21 0.01 0.03 1665
accuracy 0.83 10436
macro avg 0.53 0.50 0.47 10436
weighted avg 0.74 0.83 0.77 10436