classifier_code_unbalanced

import pandas  as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

#----------loading the data
data = pd.read_csv('../input/creditcard.csv')
data.head(3)
#-----------------------------

#--------------------ploting ther data labels-------------------------------------------
pd.value_counts(data['Class']).plot.bar()
plt.title('Fraud class histogram')
plt.xlabel('Class')
plt.ylabel('Frequency')
data['Class'].value_counts()

#--------applying standard scaler  on th e column which are not in good shape----

from sklearn.preprocessing import StandardScaler

data['normAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Time', 'Amount'], axis=1)
data.head()



#-----------------------converting the X and Y for train test split
X = np.array(data.ix[:, data.columns != 'Class'])
y = np.array(data.ix[:, data.columns == 'Class'])
print('Shape of X: {}'.format(X.shape))
print('Shape of y: {}'.format(y.shape))


#------------------handling the class imbalace 
from imblearn.over_sampling import SMOTE

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

print("Number transactions X_train dataset: ", X_train.shape)
print("Number transactions y_train dataset: ", y_train.shape)
print("Number transactions X_test dataset: ", X_test.shape)
print("Number transactions y_test dataset: ", y_test.shape)


print("Before OverSampling, counts of label '1': {}".format(sum(y_train==1)))
print("Before OverSampling, counts of label '0': {} \n".format(sum(y_train==0)))


#----------after smote aplying
sm = SMOTE(random_state=2)
X_train_res, y_train_res = sm.fit_sample(X_train, y_train.ravel())

print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))
print('After OverSampling, the shape of train_y: {} \n'.format(y_train_res.shape))

print("After OverSampling, counts of label '1': {}".format(sum(y_train_res==1)))
print("After OverSampling, counts of label '0': {}".format(sum(y_train_res==0)))



#-----------------model 
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, precision_recall_curve, auc, roc_auc_score, roc_curve, recall_score, classification_report ,f1_score

parameters = {
    'C': np.linspace(1, 10, 10)
             }
lr = LogisticRegression()
clf = GridSearchCV(lr, parameters, cv=5, verbose=5, n_jobs=3)
clf.fit(X_train_res, y_train_res.ravel())
clf.best_params_

#---final fit

lr1 = LogisticRegression(C=4,penalty='l1', verbose=5)
lr1.fit(X_train_res, y_train_res.ravel())


y_pre = lr1.predict(X_test)
cnf_matrix = confusion_matrix(y_test, y_pre)


target_names = ['STANDING', 'SITTING', 'LAYING', 'WALKING', 'WALKING_DOWNSTAIRS',
       'WALKING_UPSTAIRS']

print(classification_report(y_test,y_pred,target_names=target_names))

F1score = f1_score(y_test, pred, average="macro")

#-----------------ROC CURVE
y_pred_sample_score = tmp.decision_function(X_test)


fpr, tpr, thresholds = roc_curve(y_test, y_pred_sample_score)
roc_auc = auc(fpr,tpr)