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#!/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()
|
