

In [1]:

    
from preamble import *
%matplotlib notebook

German Credit Card Fraud



In [2]:

    
data = pd.read_csv("data/german_cc_fraud.csv")



In [3]:

    
data['class'].value_counts()









    Out[3]:





good    700
bad     300
Name: class, dtype: int64



In [4]:

    
data.head()









    Out[4]:






  
    
      
      over_draft
      credit_usage
      credit_history
      purpose
      current_balance
      Average_Credit_Balance
      employment
      location
      personal_status
      other_parties
      ...
      property_magnitude
      cc_age
      other_payment_plans
      housing
      existing_credits
      job
      num_dependents
      own_telephone
      foreign_worker
      class
    
  
  
    
      0
      <0
      6.0
      critical/other existing credit
      radio/tv
      1169.0
      no known savings
      >=7
      4.0
      male single
      none
      ...
      real estate
      67.0
      none
      own
      2.0
      skilled
      1.0
      False
      False
      good
    
    
      1
      0<=X<200
      48.0
      existing paid
      radio/tv
      5951.0
      <100
      1<=X<4
      2.0
      female div/dep/mar
      none
      ...
      real estate
      22.0
      none
      own
      1.0
      skilled
      1.0
      False
      False
      bad
    
    
      2
      no checking
      12.0
      critical/other existing credit
      education
      2096.0
      <100
      4<=X<7
      2.0
      male single
      none
      ...
      real estate
      49.0
      none
      own
      1.0
      unskilled resident
      2.0
      False
      False
      good
    
    
      3
      <0
      42.0
      existing paid
      furniture/equipment
      7882.0
      <100
      4<=X<7
      2.0
      male single
      guarantor
      ...
      life insurance
      45.0
      none
      for free
      1.0
      skilled
      2.0
      False
      False
      good
    
    
      4
      <0
      24.0
      delayed previously
      new car
      4870.0
      <100
      1<=X<4
      3.0
      male single
      none
      ...
      no known property
      53.0
      none
      for free
      2.0
      skilled
      2.0
      False
      False
      bad
    
  

5 rows × 21 columns



In [5]:

    
data_dummies = pd.get_dummies(data.drop("class", axis=1))



In [6]:

    
data_dummies.columns









    Out[6]:





Index(['credit_usage', 'current_balance', 'location', 'residence_since',
       'cc_age', 'existing_credits', 'num_dependents', 'own_telephone',
       'foreign_worker', 'over_draft_0<=X<200', 'over_draft_<0',
       'over_draft_>=200', 'over_draft_no checking', 'credit_history_all paid',
       'credit_history_critical/other existing credit',
       'credit_history_delayed previously', 'credit_history_existing paid',
       'credit_history_no credits/all paid', 'purpose_domestic appliance',
       'purpose_education', 'purpose_furniture/equipment', 'purpose_new car',
       'purpose_other', 'purpose_radio/tv', 'purpose_repairs',
       'purpose_retraining', 'purpose_used car', 'purpose_usiness',
       'Average_Credit_Balance_100<=X<500',
       'Average_Credit_Balance_500<=X<1000', 'Average_Credit_Balance_<100',
       'Average_Credit_Balance_>=1000',
       'Average_Credit_Balance_no known savings', 'employment_1<=X<4',
       'employment_4<=X<7', 'employment_<1', 'employment_>=7',
       'employment_unemployed', 'personal_status_female div/dep/mar',
       'personal_status_male div/sep', 'personal_status_male mar/wid',
       'personal_status_male single', 'other_parties_co applicant',
       'other_parties_guarantor', 'other_parties_none',
       'property_magnitude_car', 'property_magnitude_life insurance',
       'property_magnitude_no known property',
       'property_magnitude_real estate', 'other_payment_plans_ank',
       'other_payment_plans_none', 'other_payment_plans_stores',
       'housing_for free', 'housing_own', 'housing_rent',
       'job_high qualif/self emp/mgmt', 'job_skilled',
       'job_unemp/unskilled non res', 'job_unskilled resident'],
      dtype='object')



In [7]:

    
X = data_dummies.values.astype(np.float)



In [11]:

    
X.shape









    Out[11]:





(1000, 59)

Elliptic Envelope



In [8]:

    
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.covariance import EllipticEnvelope



In [9]:

    
X_scaled = StandardScaler().fit_transform(X)
pca = PCA(n_components=.8)
X_preprocessed = pca.fit_transform(X_scaled)



In [10]:

    
pca.n_components_









    Out[10]:





30



In [12]:

    
ee = EllipticEnvelope(contamination=.3).fit(X_preprocessed)



In [15]:

    
np.bincount(ee.predict(X_preprocessed) + 1)









    Out[15]:





array([300,   0, 700])



In [16]:

    
from sklearn.metrics import confusion_matrix
confusion_matrix(data['class'] == "good", ee.predict(X_preprocessed) == 1)









    Out[16]:





array([[100, 200],
       [200, 500]])



In [17]:

    
from sklearn.metrics import roc_auc_score
roc_auc_score(data['class'] == "good", ee.decision_function(X_preprocessed))









    Out[17]:





0.55953809523809528

PCA



In [19]:

    
pca_full = PCA().fit(X_scaled)
plt.figure()
plt.plot(pca_full.explained_variance_ratio_)









    














    











    Out[19]:





[<matplotlib.lines.Line2D at 0x7f2911139e10>]



In [16]:

    
roc_auc_score(data['class'] == "good", pca.score_samples(X_scaled))









    Out[16]:





0.59285714285714286

Robust PCA



In [20]:

    
from robust_pca import RobustPCA
rpca = RobustPCA().fit(X_scaled)



In [21]:

    
roc_auc_score(data['class'] == "good", rpca.score_samples(X_scaled))









    Out[21]:





0.56573809523809526

KDE



In [22]:

    
from sklearn.neighbors import KernelDensity



In [23]:

    
kde = KernelDensity(bandwidth=5).fit(X_scaled)



In [24]:

    
plt.figure()
plt.hist(kde.score_samples(X_scaled), bins=100);



In [25]:

    
roc_auc_score(data['class'] == "good", kde.score_samples(X_scaled))









    Out[25]:





0.58804761904761904

Isolation Forest



In [26]:

    
from sklearn.ensemble import IsolationForest
iso = IsolationForest(contamination=.3).fit(data_dummies.values)



In [27]:

    
from sklearn.metrics import confusion_matrix, roc_auc_score
confusion_matrix(data['class'] == "good", iso.predict(data_dummies.values) == 1)









    Out[27]:





array([[122, 178],
       [178, 522]])



In [28]:

    
roc_auc_score(data['class'] == "good", iso.decision_function(data_dummies.values))









    Out[28]:





0.59531428571428568

Exercise

Apply the above techniques to the Adult dataset (the rich people are the outliers).

	over_draft	credit_usage	credit_history	purpose	current_balance	Average_Credit_Balance	employment	location	personal_status	other_parties	...	property_magnitude	cc_age	other_payment_plans	housing	existing_credits	job	num_dependents	own_telephone	foreign_worker	class
0	<0	6.0	critical/other existing credit	radio/tv	1169.0	no known savings	>=7	4.0	male single	none	...	real estate	67.0	none	own	2.0	skilled	1.0	False	False	good
1	0<=X<200	48.0	existing paid	radio/tv	5951.0	<100	1<=X<4	2.0	female div/dep/mar	none	...	real estate	22.0	none	own	1.0	skilled	1.0	False	False	bad
2	no checking	12.0	critical/other existing credit	education	2096.0	<100	4<=X<7	2.0	male single	none	...	real estate	49.0	none	own	1.0	unskilled resident	2.0	False	False	good
3	<0	42.0	existing paid	furniture/equipment	7882.0	<100	4<=X<7	2.0	male single	guarantor	...	life insurance	45.0	none	for free	1.0	skilled	2.0	False	False	good
4	<0	24.0	delayed previously	new car	4870.0	<100	1<=X<4	3.0	male single	none	...	no known property	53.0	none	for free	2.0	skilled	2.0	False	False	bad