In [1]:

    

import pandas as pd
import numpy as np
from numpy import sort
from sklearn.metrics import matthews_corrcoef, accuracy_score,confusion_matrix
from sklearn.feature_selection import SelectFromModel
from matplotlib import pyplot
import pylab as pl
from sklearn import svm

%matplotlib inline


    

In [2]:

    

SEED = 1234
## Selected set of most important features

featureSet=['L3_S31_F3846','L1_S24_F1578','L3_S33_F3857','L1_S24_F1406','L3_S29_F3348','L3_S33_F3863',
            'L3_S29_F3427','L3_S37_F3950','L0_S9_F170', 'L3_S29_F3321','L1_S24_F1346','L3_S32_F3850',
            'L3_S30_F3514','L1_S24_F1366','L2_S26_F3036']

train_x = pd.read_csv("../data/train_numeric.csv", usecols=featureSet)
train_y = pd.read_csv("../data/train_numeric.csv", usecols=['Response'])


    

In [3]:

    

test_x = pd.read_csv("../data/test_numeric.csv", usecols=featureSet)


    

In [4]:

    

train_x = train_x.fillna(9999999)
msk = np.random.rand(len(train_x)) < 0.7  # creating Training and validation set 


X_train = train_x[msk]

Y_train = train_y.Response.ravel()[msk]

X_valid = train_x[~msk]
Y_valid = train_y.Response.ravel()[~msk]


    

In [5]:

    

def showconfusionmatrix(cm, typeModel):
    pl.matshow(cm)
    pl.title('Confusion matrix for '+typeModel)
    pl.colorbar()
    pl.show()


    

In [7]:

    

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier

C=4
lin_svc = svm.LinearSVC(C=C).fit(X_train, Y_train)
print "LibSVM fitted"


    

    




LibSVM fitted

In [10]:

    

title = 'LinearSVC (linear kernel)'

predicted = lin_svc.predict(X_valid)
mcc= matthews_corrcoef(Y_valid, predicted)
print "MCC Score \t +"+title+str(mcc)

cm = confusion_matrix(predicted, Y_valid)
showconfusionmatrix(cm, title)
print "Confusion Matrix"
print (cm)


    

    




MCC Score 	 +LinearSVC (linear kernel)0.0






    













    




Confusion Matrix
[[352988   2074]
 [     0      0]]

In [22]:

    

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier

C=4
rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X_train, Y_train)
print "RBF fitted"


title = 'SVC with RBF kernel'

predicted = rbf_svc.predict(X_valid)
mcc= matthews_corrcoef(Y_valid, predicted)
print "MCC Score \t +"+title+str(mcc)

cm = confusion_matrix(predicted, Y_valid)
showconfusionmatrix(cm, title)
print "Confusion Matrix"
print (cm)


    

    




MCC Score 	 +SVC with RBF kernel0.0322577344939






    













    




Confusion Matrix
[[811252   4470]
 [ 11860    314]]

In [10]:

    

from sklearn.naive_bayes import GaussianNB

gnb = GaussianNB()

clf = gnb.fit(X_train,Y_train)
print "Naive Bayes Fitted"


title = 'Naive Bayes'

predicted = clf.predict(X_valid)


mcc= matthews_corrcoef(Y_valid, predicted)
print "MCC Score \t +"+title+str(mcc)

cm = confusion_matrix(predicted, Y_valid)
showconfusionmatrix(cm, title)
print "Confusion Matrix"
print (cm)


    

    




Naive Bayes Fitted
MCC Score 	 +Naive Bayes0.0337046126992






    













    




Confusion Matrix
[[749710   3757]
 [ 72970   1032]]

In [21]:

    

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.cross_validation import cross_val_score
from sklearn.model_selection import GridSearchCV


    

In [23]:

    

rf = RandomForestClassifier(n_estimators=20, n_jobs=2)
param_grid = {
                 'n_estimators': [5, 10, 15, 20],
                 'max_depth': [2, 5, 7, 9]
             }


    

In [24]:

    

grid_rf = GridSearchCV(rf, param_grid, cv=10)
rf_model=grid_rf.fit(X_train, Y_train)


    

In [30]:

    

print "RF fitted"

titles = 'Random Forest'

predicted = rf_model.predict(X_valid)
mcc= matthews_corrcoef(Y_valid, predicted)
print "MCC Score \t +"+titles[0]+str(mcc)

cm = confusion_matrix(predicted, Y_valid)
showconfusionmatrix(cm, titles[0])


    

    




RF fitted
MCC Score 	 +Random Forest0.397684988803






    

In [31]:

    

gb = GradientBoostingClassifier(learning_rate=0.5)
param_grid = {
                 'n_estimators': [5, 10, 15, 20],
                 'max_depth': [2, 5, 7, 9]
             }


    

In [32]:

    

grid_gb = GridSearchCV(gb, param_grid, cv=10)
gb_model=grid_gb.fit(X_train, Y_train)


    

In [36]:

    

print "GB fitted"

title = 'Gradient Boosting'

predicted = gb_model.predict(X_valid)
mcc= matthews_corrcoef(Y_valid, predicted)
print "MCC Score \t +"+title+str(mcc)

cm = confusion_matrix(predicted, Y_valid)
showconfusionmatrix(cm, title)


    

    




GB fitted
MCC Score 	 +Gradient Boosting0.41425519667