def test_experiment_not_transformed_test(self):
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
loaded_data = FileLoader.load_file(file_path)
data_manager = DataManager()
data_manager.set_data(loaded_data)
data_manager.split_data(test_split=0.19, train_split=0.62)
learning_model = FakePredictionModel()
exp = Experiment(data_manager, learning_model)
exp.run_experiment()
self.assertEquals(0, exp.get_r2(SplitTypes.Test))
def test_experiment(self):
output_filename_header = FileLoader.create_output_file()
time.sleep(1)
loaded_algorithm_combinations = FileLoader.read_csv_file("../Datasets/test.csv")
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
loaded_data = FileLoader.load_file(file_path)
# feature_eliminator = SelectKBest(f_regression,k=k_value)
print (loaded_algorithm_combinations[0])
output_filename = FileLoader.create_output_file()
for i in range(0, 80):
normalizer = self.getnormalizer(loaded_algorithm_combinations[i][0])
feature_eliminator = self.getfeature_eliminator(loaded_algorithm_combinations[i][1])
the_model = self.get_model(loaded_algorithm_combinations[i][2])
print "taking ", type(normalizer).__name__, "and feature selector ", type(
feature_eliminator
).__name__, "model", type(the_model).__name__
FileLoader.write_model_in_file(
output_filename_header,
type(normalizer).__name__,
type(feature_eliminator).__name__,
type(the_model).__name__,
"",
"",
"",
"",
"",
)
the_data_manager = DataManager(feature_eliminator, normalizer=normalizer)
the_data_manager.set_data(loaded_data)
the_data_manager.split_data(test_split=0.15, train_split=0.70)
exp = Experiment(the_data_manager, the_model)
exp.run_experiment()
# arr_selected = feature_eliminator.get_support(indices=True)
# if(exp.get_r2(SplitTypes.Train) > 0 and exp.get_r2(SplitTypes.Valid) > 0 and exp.get_r2(SplitTypes.Test) > 0):
FileLoader.write_model_in_file(
output_filename,
type(normalizer).__name__,
type(feature_eliminator).__name__,
type(the_model).__name__,
"",
exp.fitness_matrix[0],
exp.get_r2(SplitTypes.Train),
exp.get_r2(SplitTypes.Valid),
exp.get_r2(SplitTypes.Test),
)
def test_experiment_sum_of_squares_zeros_test(self):
the_data_manager = DataManager()
an_array_of_all_ones = np.ones((37, 397))
the_model = svm.SVR()
the_data_manager.set_data(an_array_of_all_ones)
the_data_manager.split_data(test_split=0.19, train_split=0.62)
exp = Experiment(the_data_manager, the_model)
exp.run_experiment()
sum_of_squares_test = exp.get_sum_of_squares(SplitTypes.Test)
expected = 0
self.assertEquals(expected, sum_of_squares_test)
def test_experiment_svm_svr_37dataset_r2_train(self):
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
loaded_data = FileLoader.load_file(file_path)
the_data_manager = DataManager()
the_data_manager.set_data(loaded_data)
the_data_manager.split_data(test_split=0.19, train_split=0.62)
the_model = svm.SVR()
exp = Experiment(the_data_manager, the_model)
exp.run_experiment()
r2_train = exp.get_r2(SplitTypes.Train)
expected_svm_r2_value = 0.93994377385638073
self.assertEqual(r2_train, expected_svm_r2_value)
def test_experiment_all_zeros_r2_1(self):
the_data_manager = DataManager()
array_all_zeroes = np.zeros((37, 397))
the_data_manager.set_data(array_all_zeroes)
the_data_manager.split_data(test_split=0.19, train_split=0.62)
the_model = svm.SVR()
exp = Experiment(the_data_manager, the_model)
exp.run_experiment()
r2_train = exp.get_r2(SplitTypes.Train)
expected = 1.0
self.assertEqual(r2_train, expected)
def test_experiment_svr_37dataset_r2_test(self):
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
loaded_data = FileLoader.load_file(file_path)
the_data_manager = DataManager()
the_data_manager.set_data(loaded_data)
the_data_manager.split_data(test_split=0.19, train_split=0.62)
the_model = svm.SVR()
exp = Experiment(the_data_manager, the_model)
exp.run_experiment()
r2_test = exp.get_r2(SplitTypes.Test)
expected_svm_r2_value = -0.33005242525900247
self.assertEqual(r2_test, expected_svm_r2_value)
def test_split_merge_csv_4_25_8(self):
file_loader = FileLoader()
data_manager = DataManager()
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
result = file_loader.load_file(file_path)
data_manager.set_data(result)
data_manager.split_data(test_split=0.11,train_split=0.22)
test_shapes = np.zeros((4, 397)).shape
valid_shapes = np.zeros((25,397)).shape
train_shapes = np.zeros((8, 397)).shape
expected = np.array([test_shapes, valid_shapes, train_shapes])
result = np.array([data_manager.datum[SplitTypes.Test].shape, data_manager.datum[SplitTypes.Valid].shape, data_manager.datum[SplitTypes.Train].shape])
self.assertTrue(np.array_equal(result, expected))
def test_experiment_sum_of_squares_real37_test(self):
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
loaded_data = FileLoader.load_file(file_path)
the_data_manager = DataManager()
the_data_manager.set_data(loaded_data)
the_model = svm.SVR()
the_data_manager.split_data(test_split=0.19, train_split=0.62)
exp = Experiment(the_data_manager, the_model)
exp.run_experiment()
sum_of_squares_test = exp.get_sum_of_squares(SplitTypes.Test)
expected = 6.708898437500002
self.assertAlmostEqual(expected, sum_of_squares_test)
def test_split_merge_csv_7_7_23(self):
file_loader = FileLoader()
data_manager = DataManager()
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
result = file_loader.load_file(file_path)
data_manager.set_data(result)
data_manager.split_data(test_split=0.19,train_split=0.62)
valid_and_test_shapes = (7, 397)
train_shapes = (23, 397)
expected = np.array([valid_and_test_shapes, valid_and_test_shapes, train_shapes])
result = np.array([data_manager.datum[SplitTypes.Test].shape, data_manager.datum[SplitTypes.Valid].shape, data_manager.datum[SplitTypes.Train].shape])
self.assertTrue(np.array_equal(result, expected))
def test_split_into_target_and_input(self):
file_loader = FileLoader()
data_manager = DataManager()
file_path = "../Datasets/HIV_37_Samples/MergedDataset.csv"
result = file_loader.load_file(file_path)
data_manager.set_data(result)
data_manager.split_data(test_split=0.11,train_split=0.22)
test_shapes_input = np.zeros((4, 396)).shape
valid_shapes_input = np.zeros((25,396)).shape
train_shapes_input = np.zeros((8, 396)).shape
test_shapes_target = np.zeros((4, )).shape
valid_shapes_target = np.zeros((25,)).shape
train_shapes_target = np.zeros((8, )).shape
expected = np.array([test_shapes_input, valid_shapes_input, train_shapes_input, test_shapes_target, valid_shapes_target, train_shapes_target])
result = np.array([data_manager.inputs[SplitTypes.Test].shape, data_manager.inputs[SplitTypes.Valid].shape, data_manager.inputs[SplitTypes.Train].shape, data_manager.targets[SplitTypes.Test].shape, data_manager.targets[SplitTypes.Valid].shape, data_manager.targets[SplitTypes.Train].shape])
self.assertTrue(np.array_equal(result, expected))
from FileLoader import FileLoader
from DataManager import DataManager
from src.Population import Population
file_path = "../Dataset/00-91-Drugs-All-In-One-File.csv"
loaded_data = FileLoader.load_file(file_path)
data_manager = DataManager(normalizer=None)
data_manager.set_data(loaded_data)
data_manager.split_data(test_split=0.15, train_split=0.70)
population = Population()
population.load_data()
for i in range (1,50):
print("row", i, population.data[i].sum())
