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In [1]:



    


import h2o
h2o.init(max_mem_size = 2)



    


















    






Checking whether there is an H2O instance running at http://localhost:54321. connected.
Warning: Your H2O cluster version is too old (8 months)! Please download and install the latest version from http://h2o.ai/download/










    







H2O cluster uptime:
4 hours 34 mins


H2O cluster version:
3.10.3.4


H2O cluster version age:
8 months !!!


H2O cluster name:
H2O_from_python_unknownUser_ljpv4e


H2O cluster total nodes:
1


H2O cluster free memory:
1.404 Gb


H2O cluster total cores:
8


H2O cluster allowed cores:
8


H2O cluster status:
locked, healthy


H2O connection url:
http://localhost:54321


H2O connection proxy:
None


Python version:
2.7.6 final

















In [2]:



    


h2o.remove_all()



    











In [3]:



    


get_ipython().magic(u'matplotlib inline')



    











In [4]:



    


import numpy as np
import pandas as pd
import os

from h2o.estimators.deeplearning import H2OAutoEncoderEstimator, H2ODeepLearningEstimator



    











In [5]:



    


credit_card_data = h2o.import_file(path = os.path.realpath("../data/fraud/creditcard.csv"))



    


















    






Parse progress: |█████████████████████████████████████████████████████████| 100%

















In [6]:



    


credit_card_data.head()



    


















    










  Time       V1        V2        V3       V4        V5        V6       V7        V8       V9       V10      V11       V12      V13       V14       V15      V16        V17       V18       V19       V20       V21       V22       V23       V24      V25       V26       V27        V28  Amount  Class




     0-1.35981 -0.0727812 2.53635   1.37816 -0.338321  0.462388  0.239599 0.0986979 0.363787 0.0907942-0.5516  -0.617801 -0.99139 -0.311169  1.46818  -0.470401 0.207971   0.0257906 0.403993  0.251412 -0.0183068 0.277838 -0.110474  0.0669281 0.128539-0.189115  0.133558 -0.0210531   149.62      0


     0 1.19186  0.266151  0.16648   0.448154 0.0600176-0.0823608-0.078803 0.0851017-0.255425-0.166974  1.61273  1.06524   0.489095-0.143772  0.635558  0.463917-0.114805  -0.183361 -0.145783 -0.0690831-0.225775 -0.638672  0.101288 -0.339846  0.16717  0.125895 -0.0089831 0.0147242     2.69      0


     1-1.35835 -1.34016   1.77321   0.37978 -0.503198  1.8005    0.791461 0.247676 -1.51465  0.207643  0.624501 0.0660837 0.717293-0.165946  2.34586  -2.89008  1.10997   -0.121359 -2.26186   0.52498   0.247998  0.771679  0.909412 -0.689281 -0.327642-0.139097 -0.0553528-0.0597518   378.66      0


     1-0.966272-0.185226  1.79299  -0.863291-0.0103089 1.2472    0.237609 0.377436 -1.38702 -0.0549519-0.226487 0.178228  0.507757-0.287924 -0.631418 -1.05965 -0.684093   1.96578  -1.23262  -0.208038 -0.1083    0.0052736-0.190321 -1.17558   0.647376-0.221929  0.0627228 0.0614576   123.5       0


     2-1.15823  0.877737  1.54872   0.403034-0.407193  0.0959215 0.592941-0.270533  0.817739 0.753074 -0.822843 0.538196  1.34585 -1.11967   0.175121 -0.451449-0.237033  -0.0381948 0.803487  0.408542 -0.0094307 0.798278 -0.137458  0.141267 -0.20601  0.502292  0.219422  0.215153     69.99      0


     2-0.425966 0.960523  1.14111  -0.168252 0.420987 -0.0297276 0.476201 0.260314 -0.568671-0.371407  1.34126  0.359894 -0.358091-0.137134  0.517617  0.401726-0.0581328  0.0686531-0.0331938 0.0849677-0.208254 -0.559825 -0.0263977-0.371427 -0.232794 0.105915  0.253844  0.0810803     3.67      0


     4 1.22966  0.141004  0.0453708 1.20261  0.191881  0.272708 -0.005159 0.0812129 0.46496 -0.0992543-1.41691 -0.153826 -0.751063 0.167372  0.0501436-0.443587 0.00282051-0.611987 -0.045575 -0.219633 -0.167716 -0.27071  -0.154104 -0.780055  0.750137-0.257237  0.0345074 0.00516777    4.99      0


     7-0.644269 1.41796   1.07438  -0.492199 0.948934  0.428118  1.12063 -3.80786   0.615375 1.24938  -0.619468 0.291474  1.75796 -1.32387   0.686133 -0.076127-1.22213   -0.358222  0.324505 -0.156742  1.94347  -1.01545   0.0575035-0.649709 -0.415267-0.0516343-1.20692  -1.08534      40.8       0


     7-0.894286 0.286157 -0.113192 -0.271526 2.6696    3.72182   0.370145 0.851084 -0.392048-0.41043  -0.705117-0.110452 -0.286254 0.0743554-0.328783 -0.210077-0.499768   0.118765  0.570328  0.0527357-0.0734251-0.268092 -0.204233  1.01159   0.373205-0.384157  0.0117474 0.142404     93.2       0


     9-0.338262 1.11959   1.04437  -0.222187 0.499361 -0.246761  0.651583 0.0695386-0.736727-0.366846  1.01761  0.83639   1.00684 -0.443523  0.150219  0.739453-0.54098    0.476677  0.451773  0.203711 -0.246914 -0.633753 -0.120794 -0.38505  -0.069733 0.0941988 0.246219  0.0830756     3.68      0













    
Out[6]:

































In [7]:



    


credit_card_data_df = credit_card_data.as_data_frame(use_pandas = True)



    



















In [8]:



    


credit_card_data_df['Class'].describe()



    


















    
Out[8]:








count    284807.000000
mean          0.001727
std           0.041527
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max           1.000000
Name: Class, dtype: float64

























Data Exploration ...
















In [9]:



    


import matplotlib.pyplot as plt

credit_card_data_df['Counter_on_Class'] =1
print(credit_card_data_df.groupby(['Class'])['Counter_on_Class'].sum())

plt.style.use('ggplot')
credit_card_data_df['Class'].hist()



    


















    






Class
0    284315
1       492
Name: Counter_on_Class, dtype: int64










    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0x7fd913e3ca10>










    
































In [12]:



    


# Feature: Time is in seconds 1hr = 60*60 = 3600sec
# 1day = 24*3600 =  86,400          
print(credit_card_data_df['Time'].describe())
credit_card_data_df['Time'].hist(bins=20)
credit_card_data_df['Time'].hist(bins=100, by=credit_card_data_df['Class'])
#or credit_card_data_df['Time'].plot(kind='hist', alpha=0.5)



    


















    






count    284807.000000
mean      94813.859575
std       47488.145955
min           0.000000
25%       54201.500000
50%       84692.000000
75%      139320.500000
max      172792.000000
Name: Time, dtype: float64










    
Out[12]:








array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fd913697c50>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7fd9136145d0>], dtype=object)










    

















    
































In [39]:



    


credit_card_data_df.groupby(['Time', 'Class']).size().reset_index(name='Counter').head()



    


















    
Out[39]:











  
    

      
      Time
      Class
      Counter
    

  
  
    

      0
      0
      0
      2
    

    

      1
      1
      0
      2
    

    

      2
      2
      0
      2
    

    

      3
      4
      0
      1
    

    

      4
      7
      0
      2
    

  




























In [ ]:



    


# import matplotlib.cm as cm
# gg = credit_card_data_df.groupby(['Time', 'Class']).sum().unstack()



    



















In [ ]:



    


# gg.columns = gg.columns.droplevel()
# gg.plot(kind = 'bar', colormap = cm.Accent, width = 1)



    



















In [14]:



    


# Visualize the class distribution ...
from ggplot import *
ggplot(aes(x='Time', y='Class', color='Class'), data=credit_card_data_df) + geom_point()



    


















    

















    
Out[14]:








<ggplot: (8785624934465)>

























In [20]:



    


print("Class 0")
print(credit_card_data_df[credit_card_data_df['Class'] == 0]['Amount'].describe())
print("Class 1")
print(credit_card_data_df[credit_card_data_df['Class'] == 1]['Amount'].describe())



    


















    






Class 0
count    284315.000000
mean         88.291022
std         250.105092
min           0.000000
25%           5.650000
50%          22.000000
75%          77.050000
max       25691.160000
Name: Amount, dtype: float64
Class 1
count     492.000000
mean      122.211321
std       256.683288
min         0.000000
25%         1.000000
50%         9.250000
75%       105.890000
max      2125.870000
Name: Amount, dtype: float64

























In [32]:



    


credit_card_data_df[credit_card_data_df.columns.difference(['Class'])].head(2)



    


















    
Out[32]:











  
    

      
      Amount
      Counter_on_Class
      Time
      V1
      V10
      V11
      V12
      V13
      V14
      V15
      ...
      V26
      V27
      V28
      V3
      V4
      V5
      V6
      V7
      V8
      V9
    

  
  
    

      0
      149.62
      1
      0
      -1.359807
      0.090794
      -0.551600
      -0.617801
      -0.991390
      -0.311169
      1.468177
      ...
      -0.189115
      0.133558
      -0.021053
      2.536347
      1.378155
      -0.338321
      0.462388
      0.239599
      0.098698
      0.363787
    

    

      1
      2.69
      1
      0
      1.191857
      -0.166974
      1.612727
      1.065235
      0.489095
      -0.143772
      0.635558
      ...
      0.125895
      -0.008983
      0.014724
      0.166480
      0.448154
      0.060018
      -0.082361
      -0.078803
      0.085102
      -0.255425
    

  



2 rows × 31 columns



























In [35]:



    


credit_card_h2o_frame = h2o.H2OFrame(credit_card_data_df)
train, valid, test = credit_card_h2o_frame.split_frame(ratios=[0.6, 0.2], seed=0)



    


















    






Parse progress: |█████████████████████████████████████████████████████████| 100%

























In [34]:



    


model = H2OAutoEncoderEstimator(activation="Tanh", hidden=[50, 50, 50],
                                ignore_const_cols=False, epochs=100)



    



















In [ ]:



    


model.train(x=credit_card_h2o_frame.col_names, training_frame=credit_card_h2o_frame)



    
















    


    

        

            

            

                Content source: pramitchoudhary/Experiments
            

            
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