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#!/usr/bin/env python3
from dfatool import dfatool as dt
from dfatool import parameters
from dfatool.utils import by_name_to_by_param
from dfatool.functions import analytic
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"),
[list(range(5)), list(range(7))],
)
def test_parameter_detection_linear(self):
rng = np.random.default_rng(seed=1312)
X = np.arange(200) % 50
Y = X + rng.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"],
}
}
by_param = by_name_to_by_param(by_name)
stats = parameters.ParamStats(by_name, by_param, parameter_names, dict())
self.assertEqual(stats.depends_on_param("TX", "power", "p_mod5"), False)
self.assertEqual(stats.depends_on_param("TX", "power", "p_linear"), True)
# Fit individual functions for each parameter (only "p_linear" in this case)
paramfit = dt.ParallelParamFit(by_param)
paramfit.enqueue("TX", "power", 1, "p_linear")
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(by_param, "TX", "power")
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)
if __name__ == "__main__":
unittest.main()
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