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_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)
and exp.get_r2(SplitTypes.Test) > 0
):
print(
feature_eliminator.get_support(indices=True),
type(normalizer).__name__,
type(feature_eliminator).__name__,
type(the_model).__name__,
"Fitness",
exp.fitness_matrix[0],
"Train",
exp.get_r2(SplitTypes.Train),
"Valid",
exp.get_r2(SplitTypes.Valid),
"Test",
exp.get_r2(SplitTypes.Test),
exp.get_sum_of_squares(SplitTypes.Test),
)
FileLoader.write_model_in_file(
output_filename,
type(normalizer).__name__,
type(feature_eliminator).__name__,
type(the_model).__name__,
feature_eliminator.get_support(indices=True),
exp.fitness_matrix[0],
exp.get_r2(SplitTypes.Train),
exp.get_r2(SplitTypes.Valid),
exp.get_r2(SplitTypes.Test),
)
# exp.plot_true_vs_predicted(SplitTypes.Train)
