diff options
Diffstat (limited to 'test')
| -rwxr-xr-x | test/test_parameters.py | 34 |
1 files changed, 18 insertions, 16 deletions
diff --git a/test/test_parameters.py b/test/test_parameters.py index 6cd9c71..63562a4 100755 --- a/test/test_parameters.py +++ b/test/test_parameters.py @@ -100,12 +100,14 @@ class TestModels(unittest.TestCase): lambda x: 42 + 7 * x[0] + 10 * np.log(x[1]) - 0.5 * x[2] * x[2], signature="(n)->()", ) - f_ll = np.vectorize(lambda x: 23 + 5 * x[0] - 3 * x[1], signature="(n)->()") + f_ll = np.vectorize( + lambda x: 23 + 5 * x[0] - 3 * x[0] / x[1], signature="(n)->()" + ) Y_lls = f_lls(X) + rng.normal(size=X.shape[0]) Y_ll = f_ll(X) + rng.normal(size=X.shape[0]) - parameter_names = ["lin_lin", "log_lin", "square_none"] + parameter_names = ["lin_lin", "log_inv", "square_none"] by_name = { "someKey": { @@ -119,24 +121,24 @@ class TestModels(unittest.TestCase): stats = parameters.ParamStats(by_name, by_param, parameter_names, dict()) self.assertEqual(stats.depends_on_param("someKey", "lls", "lin_lin"), True) - self.assertEqual(stats.depends_on_param("someKey", "lls", "log_lin"), True) + self.assertEqual(stats.depends_on_param("someKey", "lls", "log_inv"), True) self.assertEqual(stats.depends_on_param("someKey", "lls", "square_none"), True) self.assertEqual(stats.depends_on_param("someKey", "ll", "lin_lin"), True) - self.assertEqual(stats.depends_on_param("someKey", "ll", "log_lin"), True) + self.assertEqual(stats.depends_on_param("someKey", "ll", "log_inv"), True) self.assertEqual(stats.depends_on_param("someKey", "ll", "square_none"), False) paramfit = dt.ParallelParamFit(by_param) paramfit.enqueue("someKey", "lls", 0, "lin_lin") - paramfit.enqueue("someKey", "lls", 1, "log_lin") + paramfit.enqueue("someKey", "lls", 1, "log_inv") paramfit.enqueue("someKey", "lls", 2, "square_none") paramfit.enqueue("someKey", "ll", 0, "lin_lin") - paramfit.enqueue("someKey", "ll", 1, "log_lin") + paramfit.enqueue("someKey", "ll", 1, "log_inv") paramfit.fit() fit_lls = paramfit.get_result("someKey", "lls") self.assertEqual(fit_lls["lin_lin"]["best"], "linear") - self.assertEqual(fit_lls["log_lin"]["best"], "logarithmic") + self.assertEqual(fit_lls["log_inv"]["best"], "logarithmic") self.assertEqual(fit_lls["square_none"]["best"], "square") combined_fit_lls = analytic.function_powerset(fit_lls, parameter_names, 0) @@ -144,12 +146,12 @@ class TestModels(unittest.TestCase): self.assertEqual( combined_fit_lls.model_function, "0 + regression_arg(0) + regression_arg(1) * parameter(lin_lin)" - " + regression_arg(2) * np.log(parameter(log_lin))" + " + regression_arg(2) * np.log(parameter(log_inv))" " + regression_arg(3) * (parameter(square_none))**2" - " + regression_arg(4) * parameter(lin_lin) * np.log(parameter(log_lin))" + " + regression_arg(4) * parameter(lin_lin) * np.log(parameter(log_inv))" " + regression_arg(5) * parameter(lin_lin) * (parameter(square_none))**2" - " + regression_arg(6) * np.log(parameter(log_lin)) * (parameter(square_none))**2" - " + regression_arg(7) * parameter(lin_lin) * np.log(parameter(log_lin)) * (parameter(square_none))**2", + " + regression_arg(6) * np.log(parameter(log_inv)) * (parameter(square_none))**2" + " + regression_arg(7) * parameter(lin_lin) * np.log(parameter(log_inv)) * (parameter(square_none))**2", ) combined_fit_lls.fit(by_param, "someKey", "lls") @@ -181,7 +183,7 @@ class TestModels(unittest.TestCase): fit_ll = paramfit.get_result("someKey", "ll") self.assertEqual(fit_ll["lin_lin"]["best"], "linear") - self.assertEqual(fit_ll["log_lin"]["best"], "linear") + self.assertEqual(fit_ll["log_inv"]["best"], "inverse") self.assertEqual("quare_none" not in fit_ll, True) combined_fit_ll = analytic.function_powerset(fit_ll, parameter_names, 0) @@ -189,8 +191,8 @@ class TestModels(unittest.TestCase): self.assertEqual( combined_fit_ll.model_function, "0 + regression_arg(0) + regression_arg(1) * parameter(lin_lin)" - " + regression_arg(2) * parameter(log_lin)" - " + regression_arg(3) * parameter(lin_lin) * parameter(log_lin)", + " + regression_arg(2) * 1/(parameter(log_inv))" + " + regression_arg(3) * parameter(lin_lin) * 1/(parameter(log_inv))", ) combined_fit_ll.fit(by_param, "someKey", "ll") @@ -200,8 +202,8 @@ class TestModels(unittest.TestCase): # Verify that f_ll parameters have been found self.assertAlmostEqual(combined_fit_ll.model_args[0], 23, places=0) self.assertAlmostEqual(combined_fit_ll.model_args[1], 5, places=0) - self.assertAlmostEqual(combined_fit_ll.model_args[2], -3, places=0) - self.assertAlmostEqual(combined_fit_ll.model_args[3], 0, places=2) + self.assertAlmostEqual(combined_fit_ll.model_args[2], 0, places=1) + self.assertAlmostEqual(combined_fit_ll.model_args[3], -3, places=0) self.assertEqual(combined_fit_ll.is_predictable([None, None, None]), False) self.assertEqual(combined_fit_ll.is_predictable([None, None, 11]), False) |