def test_objective05_knn_regressor(self):
automator = MLAutomator(
self.x,
self.y,
iterations=self.iters,
algo_type='regressor',
specific_algos=['05'],
num_cv_folds=self.folds,
score_metric='neg_mean_squared_error',
)
automator.find_best_algorithm()
self.assertEqual(automator.best_algo, 'KNeighborRegressor')
def test_objective07_knn(self):
automator=MLAutomator(self.x, self.y, iterations=self.iters, specific_algos=['07'], num_cv_folds=self.folds)
automator.find_best_algorithm()
self.assertEqual(automator.best_algo, 'KNeighborClassifier')
def test_objective06_logistic_regression(self):
automator=MLAutomator(self.x, self.y, iterations=self.iters, specific_algos=['06'], num_cv_folds=self.folds)
automator.find_best_algorithm()
self.assertEqual(automator.best_algo, 'LogisticRegression')
def test_objective05_naive_bayes(self):
automator=MLAutomator(self.x, self.y, iterations=self.iters, specific_algos=['05'], num_cv_folds=self.folds)
automator.find_best_algorithm()
self.assertEqual(automator.best_algo, 'GaussianNB')
def test_objective04_bag_of_svc(self):
automator=MLAutomator(self.x, self.y, iterations=self.iters, specific_algos=['04'], num_cv_folds=self.folds)
automator.find_best_algorithm()
self.assertEqual(automator.best_algo, 'SVC')
def test_objective03_sgd_classifier(self):
automator=MLAutomator(self.x, self.y, iterations=self.iters, specific_algos=['03'], num_cv_folds=self.folds)
automator.find_best_algorithm()
self.assertEqual(automator.best_algo, 'RandomForestClassifier')
def test_objective01_xgboost(self):
automator=MLAutomator(self.x, self.y, iterations=self.iters, specific_algos=['01'], num_cv_folds=self.folds)
automator.find_best_algorithm()
self.assertEqual(automator.best_algo, 'xgboost_classifier')
from data.utilities import clf_prep
from mlautomator.mlautomator import MLAutomator
if __name__ == '__main__':
x, y = clf_prep('boston_housing.csv')
automator = MLAutomator(x, y, iterations=20)
automator.find_best_algorithm()
automator.print_best_space()