def setUpClass():
TestClassificationMetrics.test_knn_classifier = knn.KNN(
"classifier", 4)
TestClassificationMetrics.test_knn_classifier.load_csv(
'datasets/Iris.csv', 'Species')
TestClassificationMetrics.test_knn_classifier.train_test_split(0.2)
print('from setUpClass')
def setUpClass(self):
# Create example observations for auto_mpg dataset
self.reg_obs1 = [4, 97, 46, 1835, 20.5]
self.reg_obs2 = [8, 318, 210, 4382, 13.5]
self.reg_obs3 = [-4, -97, -46, -1835, -20.5]
# Create example observations for iris dataset
self.clf_obs1 = [5.2, 3.4, 1.4, 0.2]
self.clf_obs2 = [7.2, 3, 5.8, 1.6]
self.clf_obs3 = [-5.2, -3.4, -1.4, -0.2]
# Create KNN regressor
self.knn_regressor = knn.KNN('regressor')
# Create KNN classifier
self.knn_classifier = knn.KNN('classifier', 4)
# Compare our model's predictions with sklearn's KNN implementation
# Create sklearn regressor
self.sklearn_regressor = KNeighborsRegressor(n_neighbors=3)
# Create sklearn classifier
self.sklearn_classifier = KNeighborsClassifier(n_neighbors=4)
def setUpClass():
TestRegressionMetrics.test_knn_regressor = knn.KNN("regressor",7)
TestRegressionMetrics.test_knn_regressor.load_csv('datasets/auto_mpg.csv','mpg')
TestRegressionMetrics.test_knn_regressor.train_test_split(0.25)
print('from setUpClass')
def setUpClass():
TestVariables.test_knn_classifier = knn.KNN("classifier", 4)
def setUpClass():
Testload_csv.test_knn_regressor = knn.KNN("regressor", 2)
def setUpClass():
TestDataCollection_classifier.test_knn_classifier = knn.KNN(
"classifier", 3)
def setUpClass():
TestDataCollection_regressor.test_knn_regressor = knn.KNN(
"regressor", 2)