def get_acc_auc_kfold(X,Y,k=5):
#TODO:First get the train indices and test indices for each iteration
#Then train the classifier accordingly
#Report the mean accuracy and mean auc of all the folds
a=KFold(len(Y),k)
acc=[]
auc=[]
for train_index, test_index in a:
X_train, X_test = X[train_index], X[test_index]
Y_train, Y_test = Y[train_index], Y[test_index]
Y_pred=models.logistic_regression_pred(X_train, Y_train, X_test)
'''
false_positive_rate, true_positive_rate, thresholds = roc_curve(Y_test, Y_pred)
roc_auc = sklearn.metrics.roc_auc_score(Y_test, Y_pred)
plt.title('Receiver Operating Characteristic')
plt.plot(false_positive_rate, true_positive_rate, 'b',label='AUC = %0.2f'% roc_auc)
plt.legend(loc='lower right')
plt.plot([0,1],[0,1],'r--')
plt.xlim([-0.1,1.2])
plt.ylim([-0.1,1.2])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
'''
acc_1=sklearn.metrics.accuracy_score(Y_test, Y_pred)
auc_1=sklearn.metrics.roc_auc_score(Y_test, Y_pred)
acc.append(acc_1)
auc.append(auc_1)
acc_mean=mean(acc)
auc_mean=mean(auc)
return acc_mean,auc_mean
def get_acc_auc_kfold(X, Y, k=5):
#TODO:First get the train indices and test indices for each iteration
#Then train the classifier accordingly
#Report the mean accuracy and mean auc of all the folds
kf = KFold(len(Y), n_folds=k, random_state=545510477)
acc = []
auc = []
for train, test in kf:
Y_pred = models.logistic_regression_pred(X[train], Y[train], X[test])
acc.append(accuracy_score(Y[test], Y_pred))
auc.append(roc_auc_score(Y[test], Y_pred))
return mean(acc), mean(auc)
def get_acc_auc_randomisedCV(X, Y, iterNo=5, test_percent=0.2):
#TODO: First get the train indices and test indices for each iteration
#Then train the classifier accordingly
#Report the mean accuracy and mean auc of all the iterations
kf = ShuffleSplit(len(Y),
n_iter=iterNo,
test_size=test_percent,
random_state=545510477)
acc = []
auc = []
for train, test in kf:
Y_pred = models.logistic_regression_pred(X[train], Y[train], X[test])
acc.append(accuracy_score(Y[test], Y_pred))
auc.append(roc_auc_score(Y[test], Y_pred))
return mean(acc), mean(auc)
def get_acc_auc_kfold(X, Y, k=5):
# TODO:First get the train indices and test indices for each iteration
# Then train the classifier accordingly
# Report the mean accuracy and mean auc of all the folds
accuracy_arr = []
auc_arr = []
kfold = KFold(n=X.get_shape()[0], n_folds=k, random_state=545510477)
for train_i, test_i in kfold:
X_train, X_test = X[train_i], X[test_i]
Y_train, Y_test = Y[train_i], Y[test_i]
Y_pred = models.logistic_regression_pred(X_train, Y_train, X_test)
acc, auc_, precision, recall, f1score = models.classification_metrics(Y_pred, Y_test)
accuracy_arr.append(acc)
auc_arr.append(auc_)
return sum(accuracy_arr) / len(accuracy_arr), sum(auc_arr) / len(auc_arr)
def get_acc_auc_kfold(X, Y, k=5):
# TODO:First get the train indices and test indices for each iteration
# Then train the classifier accordingly
# Report the mean accuracy and mean auc of all the folds
accuracy_arr = []
auc_arr = []
kfold = KFold(n=X.get_shape()[0], n_folds=k, random_state=545510477)
for train_i, test_i in kfold:
X_train, X_test = X[train_i], X[test_i]
Y_train, Y_test = Y[train_i], Y[test_i]
Y_pred = models.logistic_regression_pred(X_train, Y_train, X_test)
acc, auc_, precision, recall, f1score = models.classification_metrics(
Y_pred, Y_test)
accuracy_arr.append(acc)
auc_arr.append(auc_)
return sum(accuracy_arr) / len(accuracy_arr), sum(auc_arr) / len(auc_arr)
def get_acc_auc_randomisedCV(X, Y, iterNo=5, test_percent=0.2):
# TODO: First get the train indices and test indices for each iteration
# Then train the classifier accordingly
# Report the mean accuracy and mean auc of all the iterations
accuracy_arr = []
auc_arr = []
shuffle_split = ShuffleSplit(n=X.get_shape()[0], n_iter=5, test_size=.2, random_state=545510477)
for train_i, test_i in shuffle_split:
X_train, X_test = X[train_i], X[test_i]
Y_train, Y_test = Y[train_i], Y[test_i]
Y_pred = models.logistic_regression_pred(X_train, Y_train, X_test)
acc, auc_, precision, recall, f1score = models.classification_metrics(Y_pred, Y_test)
accuracy_arr.append(acc)
auc_arr.append(auc_)
return sum(accuracy_arr) / len(accuracy_arr), sum(auc_arr) / len(auc_arr)
def get_acc_auc_randomisedCV(X,Y,iterNo=5,test_percent=0.2): #TODO: First get the train indices and test indices for each iteration #Then train the classifier accordingly #Report the mean accuracy and mean auc of all the iterations a=ShuffleSplit(len(Y),iterNo,test_percent) acc=[] auc=[] for train_index, test_index in a: X_train, X_test = X[train_index], X[test_index] Y_train, Y_test = Y[train_index], Y[test_index] Y_pred=models.logistic_regression_pred(X_train, Y_train, X_test) acc_1=sklearn.metrics.accuracy_score(Y_test, Y_pred) auc_1=sklearn.metrics.roc_auc_score(Y_test, Y_pred) acc.append(acc_1) auc.append(auc_1) acc_mean=mean(acc) auc_mean=mean(auc) return acc_mean,auc_mean
def get_acc_auc_randomisedCV(X,Y,iterNo=5,test_percent=0.2):
randomizedCV = ShuffleSplit(n = len(Y) , n_iter = iterNo, test_size = test_percent)
# Train logistic regression classifier on each fold train set
lr_pred = [models.logistic_regression_pred(X[train_index] , Y[train_index], X[test_index]) for train_index,test_index in randomizedCV]
# Use the kfold object to get the true Y values for each fold test set
Y_true_values = [Y[test_index] for train_index , test_index in randomizedCV]
# Get the various classications metrics for the logistic regression models implemented on each fold
metrics = [ models.classification_metrics(Y_pred,Y_true) for Y_pred,Y_true in zip(lr_pred , Y_true_values) ]
# Calculate mean accuracy across all iterations
mean_accuracy = mean([elt[0] for elt in metrics])
# Calculate mean AUC across all iterations
mean_auc = mean([elt[1] for elt in metrics])
return mean_accuracy,mean_auc
def get_acc_auc_randomisedCV(X, Y, iterNo=5, test_percent=0.2):
# TODO: First get the train indices and test indices for each iteration
# Then train the classifier accordingly
# Report the mean accuracy and mean auc of all the iterations
accuracy_arr = []
auc_arr = []
shuffle_split = ShuffleSplit(n=X.get_shape()[0],
n_iter=5,
test_size=.2,
random_state=545510477)
for train_i, test_i in shuffle_split:
X_train, X_test = X[train_i], X[test_i]
Y_train, Y_test = Y[train_i], Y[test_i]
Y_pred = models.logistic_regression_pred(X_train, Y_train, X_test)
acc, auc_, precision, recall, f1score = models.classification_metrics(
Y_pred, Y_test)
accuracy_arr.append(acc)
auc_arr.append(auc_)
return sum(accuracy_arr) / len(accuracy_arr), sum(auc_arr) / len(auc_arr)
def get_acc_auc_kfold(X,Y,k=5):
# Get the train indices and test indices for each k-fold iteration
kfold = KFold(n = len(Y) , n_folds = k)
# Train logistic regression classifier on each fold train set
lr_pred = [models.logistic_regression_pred(X[train_index] , Y[train_index], X[test_index]) for train_index,test_index in kfold]
# Use the kfold object to get the true Y values for each fold test set
Y_true_values = [Y[test_index] for train_index , test_index in kfold]
# Get the various classications metrics for the logistic regression models implemented on each fold
metrics = [ models.classification_metrics(Y_pred,Y_true) for Y_pred,Y_true in zip(lr_pred , Y_true_values) ]
mean_accuracy = mean([elt[0] for elt in metrics])
mean_auc = mean([elt[1] for elt in metrics])
return mean_accuracy,mean_auc