#!/usr/bin/env python3 from dfatool import parameters from dfatool.utils import by_name_to_by_param from dfatool.functions import analytic from dfatool.model import ParallelParamFit import unittest import numpy as np class TestModels(unittest.TestCase): def test_distinct_param_values(self): X = np.arange(35) by_name = { "TX": { "param": [(x % 5, x % 7) for x in X], "power": X, "attributes": ["power"], } } self.assertEqual( parameters.distinct_param_values(by_name["TX"]["param"]), [list(range(5)), list(range(7))], ) def test_parameter_detection_linear(self): # rng = np.random.default_rng(seed=1312) # requiresy NumPy >= 1.17 np.random.seed(1312) X = np.arange(200) % 50 # Y = X + rng.normal(size=X.size) # requiry NumPy >= 1.17 Y = X + np.random.normal(size=X.size) parameter_names = ["p_mod5", "p_linear"] # Test input data: # * param[0] ("p_mod5") == X % 5 (bogus data to test detection of non-influence) # * param[1] ("p_linear") == X # * TX power == X ± gaussian noise # -> TX power depends linearly on "p_linear" by_name = { "TX": { "param": [(x % 5, x) for x in X], "power": Y, "attributes": ["power"], } } stats = parameters.ParamStats( parameters._compute_param_statistics( by_name["TX"]["power"], parameter_names, by_name["TX"]["param"] ) ) self.assertEqual(stats.depends_on_param("p_mod5"), False) self.assertEqual(stats.depends_on_param("p_linear"), True) # Fit individual functions for each parameter (only "p_linear" in this case) paramfit = ParallelParamFit() paramfit.enqueue(("TX", "power", "p_linear"), (stats.by_param, 1, False)) paramfit.fit() fit_result = paramfit.get_result("TX", "power") self.assertEqual(fit_result["p_linear"]["best"], "linear") self.assertEqual("p_mod5" not in fit_result, True) # Fit a single function for all parameters (still only "p_linear" in this case) combined_fit = analytic.function_powerset(fit_result, parameter_names, 0) self.assertEqual( combined_fit.model_function, "0 + regression_arg(0) + regression_arg(1) * parameter(p_linear)", ) self.assertEqual( combined_fit._function_str, "0 + reg_param[0] + reg_param[1] * model_param[1]", ) combined_fit.fit(stats.by_param) self.assertEqual(combined_fit.fit_success, True) self.assertEqual(combined_fit.is_predictable([None, None]), False) self.assertEqual(combined_fit.is_predictable([None, 0]), True) self.assertEqual(combined_fit.is_predictable([None, 50]), True) self.assertEqual(combined_fit.is_predictable([0, None]), False) self.assertEqual(combined_fit.is_predictable([50, None]), False) self.assertEqual(combined_fit.is_predictable([0, 0]), True) self.assertEqual(combined_fit.is_predictable([0, 50]), True) self.assertEqual(combined_fit.is_predictable([50, 0]), True) self.assertEqual(combined_fit.is_predictable([50, 50]), True) # The function should be linear without offset or skew for i in range(100): self.assertAlmostEqual(combined_fit.eval([None, i]), i, places=0) def test_parameter_detection_multi_dimensional(self): # rng = np.random.default_rng(seed=1312) # requires NumPy >= 1.17 np.random.seed(1312) # vary each parameter from 1 to 10 Xi = (np.arange(50) % 10) + 1 # Three parameters -> Build input array [[1, 1, 1], [1, 1, 2], ..., [10, 10, 10]] X = np.array(np.meshgrid(Xi, Xi, Xi)).T.reshape(-1, 3) f_lls = np.vectorize( 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[0] / x[1], signature="(n)->()" ) # Y_lls = f_lls(X) + rng.normal(size=X.shape[0]) # requires NumPy >= 1.17 # Y_ll = f_ll(X) + rng.normal(size=X.shape[0]) # requires NumPy >= 1.17 Y_lls = f_lls(X) + np.random.normal(size=X.shape[0]) Y_ll = f_ll(X) + np.random.normal(size=X.shape[0]) parameter_names = ["lin_lin", "log_inv", "square_none"] by_name = { "someKey": { "param": X, "lls": Y_lls, "ll": Y_ll, "attributes": ["lls", "ll"], } } by_param = by_name_to_by_param(by_name) lls_stats = parameters.ParamStats( parameters._compute_param_statistics( by_name["someKey"]["lls"], parameter_names, by_name["someKey"]["param"] ) ) ll_stats = parameters.ParamStats( parameters._compute_param_statistics( by_name["someKey"]["ll"], parameter_names, by_name["someKey"]["param"] ) ) self.assertEqual(lls_stats.depends_on_param("lin_lin"), True) self.assertEqual(lls_stats.depends_on_param("log_inv"), True) self.assertEqual(lls_stats.depends_on_param("square_none"), True) self.assertEqual(ll_stats.depends_on_param("lin_lin"), True) self.assertEqual(ll_stats.depends_on_param("log_inv"), True) self.assertEqual(ll_stats.depends_on_param("square_none"), False) paramfit = ParallelParamFit() paramfit.enqueue(("someKey", "lls", "lin_lin"), (lls_stats.by_param, 0, False)) paramfit.enqueue(("someKey", "lls", "log_inv"), (lls_stats.by_param, 1, False)) paramfit.enqueue( ("someKey", "lls", "square_none"), (lls_stats.by_param, 2, False) ) paramfit.enqueue(("someKey", "ll", "lin_lin"), (ll_stats.by_param, 0, False)) paramfit.enqueue(("someKey", "ll", "log_inv"), (ll_stats.by_param, 1, False)) paramfit.fit() fit_lls = paramfit.get_result("someKey", "lls") self.assertEqual(fit_lls["lin_lin"]["best"], "linear") 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) self.assertEqual( combined_fit_lls.model_function, "0 + regression_arg(0) + regression_arg(1) * parameter(lin_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_inv))" " + regression_arg(5) * parameter(lin_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(lls_stats.by_param) self.assertEqual(combined_fit_lls.fit_success, True) # Verify that f_lls parameters have been found self.assertAlmostEqual(combined_fit_lls.model_args[0], 42, places=0) self.assertAlmostEqual(combined_fit_lls.model_args[1], 7, places=0) self.assertAlmostEqual(combined_fit_lls.model_args[2], 10, places=0) self.assertAlmostEqual(combined_fit_lls.model_args[3], -0.5, places=1) self.assertAlmostEqual(combined_fit_lls.model_args[4], 0, places=2) self.assertAlmostEqual(combined_fit_lls.model_args[5], 0, places=2) self.assertAlmostEqual(combined_fit_lls.model_args[6], 0, places=2) self.assertAlmostEqual(combined_fit_lls.model_args[7], 0, places=2) self.assertEqual(combined_fit_lls.is_predictable([None, None, None]), False) self.assertEqual(combined_fit_lls.is_predictable([None, None, 11]), False) self.assertEqual(combined_fit_lls.is_predictable([None, 11, None]), False) self.assertEqual(combined_fit_lls.is_predictable([None, 11, 11]), False) self.assertEqual(combined_fit_lls.is_predictable([11, None, None]), False) self.assertEqual(combined_fit_lls.is_predictable([11, None, 11]), False) self.assertEqual(combined_fit_lls.is_predictable([11, 11, None]), False) self.assertEqual(combined_fit_lls.is_predictable([11, 11, 11]), True) # Verify that fitted function behaves like input function for i, x in enumerate(X): self.assertAlmostEqual(combined_fit_lls.eval(x), f_lls(x), places=0) fit_ll = paramfit.get_result("someKey", "ll") self.assertEqual(fit_ll["lin_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) self.assertEqual( combined_fit_ll.model_function, "0 + regression_arg(0) + regression_arg(1) * parameter(lin_lin)" " + regression_arg(2) * 1/(parameter(log_inv))" " + regression_arg(3) * parameter(lin_lin) * 1/(parameter(log_inv))", ) combined_fit_ll.fit(ll_stats.by_param) self.assertEqual(combined_fit_ll.fit_success, True) # 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], 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) self.assertEqual(combined_fit_ll.is_predictable([None, 11, None]), False) self.assertEqual(combined_fit_ll.is_predictable([None, 11, 11]), False) self.assertEqual(combined_fit_ll.is_predictable([11, None, None]), False) self.assertEqual(combined_fit_ll.is_predictable([11, None, 11]), False) self.assertEqual(combined_fit_ll.is_predictable([11, 11, None]), True) self.assertEqual(combined_fit_ll.is_predictable([11, 11, 11]), True) # Verify that fitted function behaves like input function for i, x in enumerate(X): self.assertAlmostEqual(combined_fit_ll.eval(x), f_ll(x), places=0) if __name__ == "__main__": unittest.main()