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Added unittest for MAP RL
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@hiyoneda
hiyoneda committed Oct 29, 2024
1 parent 79540f9 commit c3f4283
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Showing 2 changed files with 150 additions and 10 deletions.
2 changes: 1 addition & 1 deletion tests/image_deconvolution/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
def dataset():

event_binned_data = Histogram.open(test_data.path / "test_event_histogram_galacticCDS.hdf5").project(["Em", "Phi", "PsiChi"])
dict_bkg_binned_data = {"bkg": Histogram.open(test_data.path / "test_event_histogram_galacticCDS.hdf5").project(["Em", "Phi", "PsiChi"])}
dict_bkg_binned_data = {"bkg": Histogram.open(test_data.path / "test_event_histogram_galacticCDS.hdf5").project(["Em", "Phi", "PsiChi"]) * 0.1}
precomputed_response = Histogram.open(test_data.path / "test_precomputed_response.h5")

data = DataIF_COSI_DC2.load(name = "testdata_galacticCDS",
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158 changes: 149 additions & 9 deletions tests/image_deconvolution/test_algorithm.py
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@@ -1,11 +1,15 @@
import pytest
import numpy as np
from yayc import Configurator

from cosipy.image_deconvolution import RichardsonLucySimple, RichardsonLucy
from cosipy.image_deconvolution import RichardsonLucySimple, RichardsonLucy, MAP_RichardsonLucy

def test_RicharsonLucySimple(dataset, model, mask, tmp_path):

parameter = Configurator({"iteration_max": 2,
num_iteration = 3

parameter = Configurator({"iteration_max": num_iteration,
"response_weighting": {"activate": True, "index": 0.5},
"minimum_flux": {"value": 0.0, "unit": "cm-2 s-1 sr-1"},
"background_normalization_optimization": {"activate": True,
"range": {"bkg": [0.9, 1.1]}},
Expand All @@ -19,14 +23,18 @@ def test_RicharsonLucySimple(dataset, model, mask, tmp_path):

algorithm.initialization()

algorithm.iteration()
algorithm.iteration()
for i in range(num_iteration):
stop = algorithm.iteration()
if stop:
break

algorithm.finalization()

def test_RicharsonLucy(dataset, model, mask, tmp_path):

parameter = Configurator({"iteration_max": 2,
num_iteration = 3

parameter = Configurator({"iteration_max": num_iteration,
"minimum_flux": {"value": 0.0, "unit": "cm-2 s-1 sr-1"},
"acceleration": {"activate": True, "alpha_max": 10.0},
"response_weighting": {"activate": True, "index": 0.5},
Expand All @@ -44,10 +52,14 @@ def test_RicharsonLucy(dataset, model, mask, tmp_path):

algorithm.initialization()

algorithm.iteration()
algorithm.iteration()
for i in range(num_iteration):
stop = algorithm.iteration()
if stop:
break

algorithm.finalization()

assert np.isclose(algorithm.results[-1]['log-likelihood'][0], 5259.242894137228)

# wo/ acceleration and overwrite the directory
parameter["acceleration:activate"] = False
Expand All @@ -59,7 +71,135 @@ def test_RicharsonLucy(dataset, model, mask, tmp_path):

algorithm.initialization()

algorithm.iteration()
algorithm.iteration()
for i in range(num_iteration):
stop = algorithm.iteration()
if stop:
break

algorithm.finalization()

assert np.isclose(algorithm.results[-1]['log-likelihood'][0], 5226.135408675148)

def test_MAP_RichardsonLucy(dataset, model, mask, tmp_path):

num_iteration = 10

parameter = Configurator({"iteration_max": num_iteration,
"minimum_flux": {"value": 0.0, "unit": "cm-2 s-1 sr-1"},
"response_weighting": {"activate": True, "index": 0.5},
"background_normalization_optimization": {"activate": True,
"range": {"bkg": [0.9, 1.1]}},
"stopping_criteria": {"statistics": "log-posterior",
"threshold": 1e-2},
"prior": {"TSV" :{"coefficient": 1.e-10},
"gamma":{"model":{"theta": {"value": np.inf, "unit": "cm-2 s-1 sr-1"},
"k": {"value": 0.999}},
"background": {"theta": {"value": np.inf}, "k": {"value": 1.0}}
}
},
"save_results": {"activate": True, "directory": f"{str(tmp_path)}", "only_final_result": True}
})

# first run
algorithm = MAP_RichardsonLucy(initial_model = model,
dataset = dataset,
mask = mask,
parameter = parameter)

algorithm.initialization()

for i in range(num_iteration):
stop = algorithm.iteration()
if stop:
break

algorithm.finalization()

assert np.isclose(algorithm.results[-1]['log-posterior'], 5298.642098311226)

# background fixed
parameter["background_normalization_optimization:activate"] = False

algorithm = MAP_RichardsonLucy(initial_model = model,
dataset = dataset,
mask = mask,
parameter = parameter)

algorithm.initialization()

for i in range(num_iteration):
stop = algorithm.iteration()
if stop:
break

algorithm.finalization()

assert np.isclose(algorithm.results[-1]['log-posterior'], 4951.657349512739)

# with large threshold
parameter["stopping_criteria:threshold"] = 1e10

algorithm = MAP_RichardsonLucy(initial_model = model,
dataset = dataset,
mask = mask,
parameter = parameter)

algorithm.initialization()

for i in range(num_iteration):
stop = algorithm.iteration()
if stop:
break

algorithm.finalization()

assert len(algorithm.results) == 2

# with log-likelihood
parameter["stopping_criteria:statistics"] = "log-likelihood"

algorithm = MAP_RichardsonLucy(initial_model = model,
dataset = dataset,
mask = mask,
parameter = parameter)

algorithm.initialization()

for i in range(num_iteration):
stop = algorithm.iteration()
if stop:
break

algorithm.finalization()

assert np.isclose(algorithm.results[-1]['log-likelihood'][0], 4850.641606406938)

# without gamma prior
parameter["stopping_criteria:statistics"] = 'log-posterior'
parameter["prior"] = {"TSV":{"coefficient": 1.e-10}}

algorithm = MAP_RichardsonLucy(initial_model = model,
dataset = dataset,
mask = mask,
parameter = parameter)

algorithm.initialization()

for i in range(num_iteration):
stop = algorithm.iteration()
if stop:
break

algorithm.finalization()

assert np.isclose(algorithm.results[-1]['log-posterior'], 4850.604374561122)

# wrong statistics
parameter["stopping_criteria:statistics"] = "likelihooooooooood!!!"

with pytest.raises(ValueError) as e_info:
algorithm = MAP_RichardsonLucy(initial_model = model,
dataset = dataset,
mask = mask,
parameter = parameter)

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