feat: allows for deepdog bayesrun with ss to not print csv to make snapshot testing possible
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@ -71,6 +71,7 @@ class BayesRunWithSubspaceSimulation:
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ss_default_w_log_step=0.01,
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ss_default_upper_w_log_step=4,
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ss_dump_last_generation=False,
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write_output_to_bayesruncsv=True,
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) -> None:
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self.dot_inputs = pdme.inputs.inputs_with_frequency_range(
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dot_positions, frequency_range
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@ -138,11 +139,19 @@ class BayesRunWithSubspaceSimulation:
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self.run_count = run_count
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def go(self) -> None:
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self.write_output_to_csv = write_output_to_bayesruncsv
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def go(self) -> Sequence:
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if self.write_output_to_csv:
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(outfile, fieldnames=self.csv_fields, dialect="unix")
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writer = csv.DictWriter(
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outfile, fieldnames=self.csv_fields, dialect="unix"
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)
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writer.writeheader()
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return_result = []
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for run in range(1, self.run_count + 1):
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# Generate the actual dipoles
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@ -222,7 +231,9 @@ class BayesRunWithSubspaceSimulation:
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for name, probability in zip(self.model_names, self.probabilities):
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row[f"{name}_prob"] = probability
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_logger.info(row)
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return_result.append(row)
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if self.write_output_to_csv:
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with open(self.filename, "a", newline="") as outfile:
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writer = csv.DictWriter(
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outfile, fieldnames=self.csv_fields, dialect="unix"
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@ -236,3 +247,5 @@ class BayesRunWithSubspaceSimulation:
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f"Aborting early, because {max_prob} is greater than {self.end_threshold}"
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)
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break
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return return_result
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24
poetry.lock
generated
24
poetry.lock
generated
@ -92,6 +92,14 @@ category = "dev"
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optional = false
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python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
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[[package]]
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name = "colored"
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version = "1.4.4"
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description = "Simple library for color and formatting to terminal"
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category = "dev"
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optional = false
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python-versions = "*"
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[[package]]
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name = "coverage"
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version = "7.2.7"
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@ -633,6 +641,18 @@ category = "dev"
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optional = false
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python-versions = ">=3.6"
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[[package]]
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name = "syrupy"
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version = "4.0.8"
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description = "Pytest Snapshot Test Utility"
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category = "dev"
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optional = false
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python-versions = ">=3.8.1,<4"
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[package.dependencies]
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colored = ">=1.3.92,<2.0.0"
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pytest = ">=7.0.0,<8.0.0"
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[[package]]
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name = "tomli"
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version = "2.0.1"
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@ -730,7 +750,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-co
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[metadata]
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lock-version = "1.1"
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python-versions = ">=3.8.1,<3.10"
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content-hash = "111972d04616ce3ddfc9039a0b38c7eb7c4a41f10390139b27e958aedac7e979"
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content-hash = "e1531b1493bac50ffe5e8f9a46a64d9b66198f7021f6d643c72f21cb53dc77ec"
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[metadata.files]
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black = []
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@ -741,6 +761,7 @@ charset-normalizer = []
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click = []
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click-log = []
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colorama = []
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colored = []
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coverage = []
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cryptography = []
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docutils = []
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@ -786,6 +807,7 @@ secretstorage = []
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semver = []
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six = []
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smmap = []
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syrupy = []
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tomli = []
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tomlkit = []
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tqdm = []
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@ -17,6 +17,7 @@ pytest-cov = "^4.1.0"
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mypy = "^0.971"
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python-semantic-release = "^7.24.0"
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black = "^22.3.0"
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syrupy = "^4.0.8"
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[build-system]
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requires = ["poetry-core>=1.0.0"]
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177
tests/__snapshots__/test_bayes_run_with_ss.ambr
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177
tests/__snapshots__/test_bayes_run_with_ss.ambr
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@ -0,0 +1,177 @@
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# serializer version: 1
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# name: test_basic_analysis
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list([
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.3333333333333333,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.3333333333333333,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.3333333333333333,
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'dipole_frequency_1': 0.006029931414230269,
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'dipole_frequency_2': 85436.78758379082,
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'dipole_location_1': array([-4.76615152, -6.33160296, 5.29522808]),
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'dipole_location_2': array([-4.72700391, -2.06478573, 6.52467702]),
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'dipole_moment_1': array([ 860.14181416, -450.27082062, -239.60852996]),
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'dipole_moment_2': array([ 908.18325588, -208.52681777, -362.93214244]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.45,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.3103448275862069,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.9,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.6206896551724138,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.06896551724137932,
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'dipole_frequency_1': 102275.63477261562,
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'dipole_frequency_2': 1755280.9783485082,
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'dipole_location_1': array([ 4.71515397, -9.70362197, 5.43016546]),
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'dipole_location_2': array([3.42476038, 3.88562934, 5.15034328]),
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'dipole_moment_1': array([-502.60742674, -790.60222587, 349.7626267 ]),
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'dipole_moment_2': array([-192.42708465, -434.81009148, -879.7226844 ]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.7,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.6631578947368421,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.18947368421052635,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.7,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.1473684210526316,
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'dipole_frequency_1': 2896.799464036654,
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'dipole_frequency_2': 9.980565189326681e-05,
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'dipole_location_1': array([-4.97465789, 12.54716531, 6.06324588]),
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'dipole_location_2': array([ 9.84518459, -11.1183876 , 7.35028226]),
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'dipole_moment_1': array([997.67961917, 19.6376112 , 65.19004305]),
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'dipole_moment_2': array([305.63093655, 440.57669389, 844.08643362]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.663157894736842,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.18947368421052635,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.1473684210526316,
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'dipole_frequency_1': 1.4522667818288244,
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'dipole_frequency_2': 2704.9795645301197,
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'dipole_location_1': array([ 7.38183022, 16.6745801 , 7.10428414]),
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'dipole_location_2': array([-8.15636906, -9.56609132, 6.34141559]),
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'dipole_moment_1': array([-145.9924693 , 738.74936496, 657.97839986]),
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'dipole_moment_2': array([-960.16113239, 104.96824669, -258.98314046]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.9,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.9465776293823038,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.030050083472454105,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.1,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.02337228714524208,
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'dipole_frequency_1': 3827.2315421318913,
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'dipole_frequency_2': 1.9301094166184413e-05,
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'dipole_location_1': array([ 5.02067673, -0.9783039 , 6.1431897 ]),
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'dipole_location_2': array([ 4.66628999, 10.80907459, 7.21771744]),
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'dipole_moment_1': array([ 871.30659253, -299.17389491, -388.99846068]),
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'dipole_moment_2': array([-189.87268624, 677.28285845, 710.79975568]),
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}),
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])
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# ---
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# name: test_bayesss_with_tighter_cost
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list([
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.33333333333333337,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.33333333333333337,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.33333333333333337,
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'dipole_frequency_1': 0.006029931414230269,
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'dipole_frequency_2': 85436.78758379082,
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'dipole_location_1': array([-4.76615152, -6.33160296, 5.29522808]),
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'dipole_location_2': array([-4.72700391, -2.06478573, 6.52467702]),
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'dipole_moment_1': array([ 860.14181416, -450.27082062, -239.60852996]),
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'dipole_moment_2': array([ 908.18325588, -208.52681777, -362.93214244]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.0109375,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.1044776119402985,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.03125,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.2985074626865672,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.0625,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.5970149253731344,
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'dipole_frequency_1': 102275.63477261562,
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'dipole_frequency_2': 1755280.9783485082,
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'dipole_location_1': array([ 4.71515397, -9.70362197, 5.43016546]),
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'dipole_location_2': array([3.42476038, 3.88562934, 5.15034328]),
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'dipole_moment_1': array([-502.60742674, -790.60222587, 349.7626267 ]),
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'dipole_moment_2': array([-192.42708465, -434.81009148, -879.7226844 ]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 7.291135021404688e-05,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.021875,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.4666326413699001,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.0125,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.5332944472798858,
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'dipole_frequency_1': 2896.799464036654,
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'dipole_frequency_2': 9.980565189326681e-05,
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'dipole_location_1': array([-4.97465789, 12.54716531, 6.06324588]),
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'dipole_location_2': array([ 9.84518459, -11.1183876 , 7.35028226]),
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'dipole_moment_1': array([997.67961917, 19.6376112 , 65.19004305]),
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'dipole_moment_2': array([305.63093655, 440.57669389, 844.08643362]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 7.291135021404688e-05,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.4666326413699001,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.5332944472798858,
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'dipole_frequency_1': 1.4522667818288244,
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'dipole_frequency_2': 2704.9795645301197,
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'dipole_location_1': array([ 7.38183022, 16.6745801 , 7.10428414]),
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'dipole_location_2': array([-8.15636906, -9.56609132, 6.34141559]),
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'dipole_moment_1': array([-145.9924693 , 738.74936496, 657.97839986]),
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'dipole_moment_2': array([-960.16113239, 104.96824669, -258.98314046]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 0.175,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 0.00012008361740869356,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.05625,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.24702915581216964,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.15,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.7528507605704217,
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'dipole_frequency_1': 3827.2315421318913,
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'dipole_frequency_2': 1.9301094166184413e-05,
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'dipole_location_1': array([ 5.02067673, -0.9783039 , 6.1431897 ]),
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'dipole_location_2': array([ 4.66628999, 10.80907459, 7.21771744]),
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'dipole_moment_1': array([ 871.30659253, -299.17389491, -388.99846068]),
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'dipole_moment_2': array([-189.87268624, 677.28285845, 710.79975568]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 4.9116305003549454e-08,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 0.0109375,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.11316396672817797,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.028125,
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'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.886835984155517,
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'dipole_frequency_1': 1.1715179359592061e-05,
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'dipole_frequency_2': 0.0019103783276337497,
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'dipole_location_1': array([-0.95736547, 1.09273812, 7.47158641]),
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'dipole_location_2': array([ -3.18510322, -15.64493131, 5.81623624]),
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'dipole_moment_1': array([-184.64961369, 956.56786553, 225.57136075]),
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'dipole_moment_2': array([ -34.63395137, 801.17771816, -597.42342885]),
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}),
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dict({
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_fixedxy-pfixexp_3-dipole_count_2_prob': 1.977090156727901e-10,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_likelihood': 9.765625e-06,
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'connors_geom-5height-orientation_fixedz-pfixexp_3-dipole_count_2_prob': 0.00045552157211010855,
|
||||
'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_likelihood': 0.002734375,
|
||||
'connors_geom-5height-orientation_free-pfixexp_3-dipole_count_2_prob': 0.9995444782301809,
|
||||
'dipole_frequency_1': 999786.9069039805,
|
||||
'dipole_frequency_2': 186034.67996840767,
|
||||
'dipole_location_1': array([-5.59679125, 6.3411602 , 5.33602522]),
|
||||
'dipole_location_2': array([-0.03412955, -6.83522954, 5.58551513]),
|
||||
'dipole_moment_1': array([826.38270589, 491.81526944, 274.24325726]),
|
||||
'dipole_moment_2': array([ 202.74745884, -656.07483714, -726.95204519]),
|
||||
}),
|
||||
])
|
||||
# ---
|
||||
156
tests/test_bayes_run_with_ss.py
Normal file
156
tests/test_bayes_run_with_ss.py
Normal file
@ -0,0 +1,156 @@
|
||||
import deepdog
|
||||
import logging
|
||||
import logging.config
|
||||
|
||||
import numpy.random
|
||||
|
||||
from pdme.model import (
|
||||
LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
|
||||
LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
|
||||
LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel,
|
||||
)
|
||||
|
||||
|
||||
_logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def fixed_z_model_func(
|
||||
xmin,
|
||||
xmax,
|
||||
ymin,
|
||||
ymax,
|
||||
zmin,
|
||||
zmax,
|
||||
wexp_min,
|
||||
wexp_max,
|
||||
pfixed,
|
||||
n_max,
|
||||
prob_occupancy,
|
||||
):
|
||||
return LogSpacedRandomCountMultipleDipoleFixedMagnitudeFixedOrientationModel(
|
||||
xmin,
|
||||
xmax,
|
||||
ymin,
|
||||
ymax,
|
||||
zmin,
|
||||
zmax,
|
||||
wexp_min,
|
||||
wexp_max,
|
||||
pfixed,
|
||||
0,
|
||||
0,
|
||||
n_max,
|
||||
prob_occupancy,
|
||||
)
|
||||
|
||||
|
||||
def get_model(orientation):
|
||||
model_funcs = {
|
||||
"fixedz": fixed_z_model_func,
|
||||
"free": LogSpacedRandomCountMultipleDipoleFixedMagnitudeModel,
|
||||
"fixedxy": LogSpacedRandomCountMultipleDipoleFixedMagnitudeXYModel,
|
||||
}
|
||||
model = model_funcs[orientation](
|
||||
-10,
|
||||
10,
|
||||
-17.5,
|
||||
17.5,
|
||||
5,
|
||||
7.5,
|
||||
-5,
|
||||
6.5,
|
||||
10**3,
|
||||
2,
|
||||
0.99999999,
|
||||
)
|
||||
model.n = 2
|
||||
model.rng = numpy.random.default_rng(1234)
|
||||
|
||||
return (
|
||||
f"connors_geom-5height-orientation_{orientation}-pfixexp_{3}-dipole_count_{2}",
|
||||
model,
|
||||
)
|
||||
|
||||
|
||||
def test_basic_analysis(snapshot):
|
||||
|
||||
dot_positions = [[0, 0, 0], [0, 1, 0]]
|
||||
|
||||
freqs = [1, 10, 100]
|
||||
models = []
|
||||
|
||||
orientations = ["free", "fixedxy", "fixedz"]
|
||||
for orientation in orientations:
|
||||
models.append(get_model(orientation))
|
||||
|
||||
_logger.info(f"have {len(models)} models to look at")
|
||||
if len(models) == 1:
|
||||
_logger.info(f"only one model, name: {models[0][0]}")
|
||||
|
||||
square_run = deepdog.BayesRunWithSubspaceSimulation(
|
||||
dot_positions,
|
||||
freqs,
|
||||
models,
|
||||
models[0][1],
|
||||
filename_slug="test",
|
||||
end_threshold=0.9,
|
||||
ss_n_c=5,
|
||||
ss_n_s=2,
|
||||
ss_m_max=10,
|
||||
ss_target_cost=150,
|
||||
ss_level_0_seed=200,
|
||||
ss_mcmc_seed=20,
|
||||
ss_use_adaptive_steps=True,
|
||||
ss_default_phi_step=0.01,
|
||||
ss_default_theta_step=0.01,
|
||||
ss_default_r_step=0.01,
|
||||
ss_default_w_log_step=0.01,
|
||||
ss_default_upper_w_log_step=4,
|
||||
ss_dump_last_generation=False,
|
||||
write_output_to_bayesruncsv=False,
|
||||
)
|
||||
result = square_run.go()
|
||||
|
||||
assert result == snapshot
|
||||
|
||||
|
||||
def test_bayesss_with_tighter_cost(snapshot):
|
||||
|
||||
dot_positions = [[0, 0, 0], [0, 1, 0]]
|
||||
|
||||
freqs = [1, 10, 100]
|
||||
models = []
|
||||
|
||||
orientations = ["free", "fixedxy", "fixedz"]
|
||||
for orientation in orientations:
|
||||
models.append(get_model(orientation))
|
||||
|
||||
_logger.info(f"have {len(models)} models to look at")
|
||||
if len(models) == 1:
|
||||
_logger.info(f"only one model, name: {models[0][0]}")
|
||||
|
||||
square_run = deepdog.BayesRunWithSubspaceSimulation(
|
||||
dot_positions,
|
||||
freqs,
|
||||
models,
|
||||
models[0][1],
|
||||
filename_slug="test",
|
||||
end_threshold=0.9,
|
||||
ss_n_c=5,
|
||||
ss_n_s=2,
|
||||
ss_m_max=10,
|
||||
ss_target_cost=1.5,
|
||||
ss_level_0_seed=200,
|
||||
ss_mcmc_seed=20,
|
||||
ss_use_adaptive_steps=True,
|
||||
ss_default_phi_step=0.01,
|
||||
ss_default_theta_step=0.01,
|
||||
ss_default_r_step=0.01,
|
||||
ss_default_w_log_step=0.01,
|
||||
ss_default_upper_w_log_step=4,
|
||||
ss_dump_last_generation=False,
|
||||
write_output_to_bayesruncsv=False,
|
||||
)
|
||||
result = square_run.go()
|
||||
|
||||
assert result == snapshot
|
||||
Loading…
x
Reference in New Issue
Block a user