def test_fscore_dt():
expected = 0.591715976331
Y_pred = decisionTree_pred(X_train, Y_train, X_test)
actual = classification_metrics(Y_pred, Y_test)[4]
assert_almost_equals(expected, actual, places=2, msg="UNEQUAL Expected:%s, Actual:%s" % (expected, actual))
def test_auc_svm():
expected = 0.738888888889
Y_pred = svm_pred(X_train, Y_train, X_test)
actual = classification_metrics(Y_pred, Y_test)[1]
assert_almost_equals(expected, actual, places=1, msg="UNEQUAL Expected:%s, Actual:%s" % (expected, actual))
def test_accuracy_lr():
expected = 0.738095238095
Y_pred = logistic_regression_pred(X_train, Y_train, X_test)
actual = classification_metrics(Y_pred, Y_test)[0]
assert_almost_equals(expected, actual, places=2, msg="UNEQUAL Expected:%s, Actual:%s" % (expected, actual))
def test_fscore_dt(): expected = 0.591715976331 Y_pred = decisionTree_pred(X_train,Y_train,X_test) actual = classification_metrics(Y_pred,Y_test)[4] assert_almost_equals(expected, actual,places=2, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual))
def test_auc_svm(): expected = 0.738888888889 Y_pred = svm_pred(X_train,Y_train,X_test) actual = classification_metrics(Y_pred,Y_test)[1] assert_almost_equals(expected, actual,places=1, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual))
def test_accuracy_lr(): expected = 0.738095238095 Y_pred = logistic_regression_pred(X_train,Y_train,X_test) actual = classification_metrics(Y_pred,Y_test)[0] assert_almost_equals(expected, actual,places=2, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual))
