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(KS2022RS) added a gentle exit on KeyboardInterrupt that still retu…
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…rns the result (after warning the user)
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@amkrajewski
amkrajewski committed Mar 27, 2024
1 parent 96c3292 commit ff18418
Showing 1 changed file with 124 additions and 108 deletions.
232 changes: 124 additions & 108 deletions pysipfenn/descriptorDefinitions/KS2022_randomSolutions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -291,115 +291,131 @@ def generate_descriptor(struct: Structure,
raise AssertionError('Invalid convergence criteria (compositionDistance > compositionConvergenceCriterion).')
if minOccupationCount > minimumElementOccurrences:
raise AssertionError('Invalid convergence criteria (minOccupationCount > minimumElementOccurrences).')

while maxDiff > featureConvergenceCriterion \
or compositionDistance > compositionConvergenceCriterion \
or minOccupationCount < minimumElementOccurrences:
# Choose random structure occupation
randomOccupation = random.choices(list(elementalFrequencies.keys()),
weights=elementalFrequencies.values(),
k=adjustedStruct.num_sites)
allOccupations += randomOccupation
occupationCount = dict(Counter(allOccupations))
minOccupationCount = min(occupationCount.values())
currentComposition = Composition.from_dict(occupationCount)
# Adjust current elemental frequencies to push the current composition towards the target composition

compositionDistance = 0
for element in elementalFrequencies.keys():
difference = currentComposition.fractional_composition.get_atomic_fraction(element) \
- comp.fractional_composition.get_atomic_fraction(element)
compositionDistance += abs(difference)
elementalFrequencies[element] -= difference * 0.1
compositionDistance /= len(elementalFrequencies.keys())

for site, occupation in zip(adjustedStruct.sites, randomOccupation):
site.species = occupation

diff_properties_instance, attribute_properties_instance = generate_voronoi_attributes(struct=adjustedStruct)

diff_properties = np.concatenate((diff_properties, diff_properties_instance), axis=0)
attribute_properties = np.concatenate((attribute_properties, attribute_properties_instance), axis=0)

properties = np.concatenate(
(np.stack(
(np.mean(diff_properties, axis=0),
np.mean(np.abs(diff_properties - np.mean(diff_properties, axis=0)), axis=0),
np.min(diff_properties, axis=0),
np.max(diff_properties, axis=0),
np.max(diff_properties, axis=0) - np.min(diff_properties, axis=0)), axis=-1).reshape((-1)),
np.stack(
(np.mean(attribute_properties, axis=0),
np.max(attribute_properties, axis=0) - np.min(attribute_properties, axis=0),
np.mean(np.abs(attribute_properties - np.mean(attribute_properties, axis=0)), axis=0),
np.max(attribute_properties, axis=0),
np.min(attribute_properties, axis=0),
most_common(attribute_properties)), axis=-1).reshape((-1))))
# Normalize Bond Length properties.
properties[6] /= properties[5]
properties[7] /= properties[5]
properties[8] /= properties[5]
# Normalize the Cell Volume Deviation.
properties[16] /= properties[15]
# Remove properties not in magpie.
properties = np.delete(properties, [4, 5, 9, 14, 15, 17, 18, 19, 21, 22, 23, 24])
# Renormalize the packing efficiency.
properties[12] *= adjustedStruct.num_sites / adjustedStruct.volume
# Calculate and insert stoichiometry attributes.
element_dict = currentComposition.fractional_composition.as_dict()
position = 118
for p in [10, 7, 5, 3, 2]:
properties = np.insert(properties, position,
math.pow(sum(math.pow(value, p) for value in element_dict.values()), 1.0 / p))
properties = np.insert(properties, position, len(element_dict))
# Calculate Valence Electron Statistics
electron_occupation_dict = {'s': 0, 'p': 0, 'd': 0, 'f': 0}
for key, value in element_dict.items():
electron_occupation_dict['s'] += value * attribute_matrix[Element(key).Z - 1][8]
electron_occupation_dict['p'] += value * attribute_matrix[Element(key).Z - 1][9]
electron_occupation_dict['d'] += value * attribute_matrix[Element(key).Z - 1][10]
electron_occupation_dict['f'] += value * attribute_matrix[Element(key).Z - 1][11]
total_valence_factor = sum([val for (key, val) in electron_occupation_dict.items()])
for orb in ['s', 'p', 'd', 'f']:
properties = np.append(properties, electron_occupation_dict[orb] / total_valence_factor)
# Calculate ionic compound attributes.
max_ionic_char = 0
av_ionic_char = 0
for key1, value1 in element_dict.items():
for key2, value2 in element_dict.items():
ionic_char = 1.0 - math.exp(-0.25 * (Element(key1).X - Element(key2).X) ** 2)
if ionic_char > max_ionic_char:
max_ionic_char = ionic_char
av_ionic_char += ionic_char * value1 * value2
properties = np.append(properties, max_ionic_char)
properties = np.append(properties, av_ionic_char)
properties = properties.astype(np.float32)

propHistory.append(properties)
# Calculate the difference between the current step and the previous step and divide it by maximum value of
# each feature found in OQMD to normalize the difference.
if len(propHistory) > 2:
# Current iteration diff
diff = np.subtract(properties, propHistory[-2])
diff /= maxFeaturesInOQMD
diffHistory.append(diff)
# Calculate the additional diff to one level older iteration
diff2 = np.subtract(properties, propHistory[-3])
diff2 /= maxFeaturesInOQMD
# Calculate the maximum difference across both differences
maxDiff = max(np.concatenate((diff, diff2), axis=0))
if printProgress:
print(f'{attribute_properties.shape[0]:^6} | '
f'{compositionDistance: 18.6f} | '
f'{maxDiff: 21.6f} | '
f'{minOccupationCount:^4}')
try:
while maxDiff > featureConvergenceCriterion \
or compositionDistance > compositionConvergenceCriterion \
or minOccupationCount < minimumElementOccurrences:
# Choose random structure occupation
randomOccupation = random.choices(list(elementalFrequencies.keys()),
weights=elementalFrequencies.values(),
k=adjustedStruct.num_sites)
allOccupations += randomOccupation
occupationCount = dict(Counter(allOccupations))
minOccupationCount = min(occupationCount.values())
currentComposition = Composition.from_dict(occupationCount)
# Adjust current elemental frequencies to push the current composition towards the target composition

compositionDistance = 0
for element in elementalFrequencies.keys():
difference = currentComposition.fractional_composition.get_atomic_fraction(element) \
- comp.fractional_composition.get_atomic_fraction(element)
compositionDistance += abs(difference)
elementalFrequencies[element] -= difference * 0.1
compositionDistance /= len(elementalFrequencies.keys())

for site, occupation in zip(adjustedStruct.sites, randomOccupation):
site.species = occupation

diff_properties_instance, attribute_properties_instance = generate_voronoi_attributes(struct=adjustedStruct)

diff_properties = np.concatenate((diff_properties, diff_properties_instance), axis=0)
attribute_properties = np.concatenate((attribute_properties, attribute_properties_instance), axis=0)

properties = np.concatenate(
(np.stack(
(np.mean(diff_properties, axis=0),
np.mean(np.abs(diff_properties - np.mean(diff_properties, axis=0)), axis=0),
np.min(diff_properties, axis=0),
np.max(diff_properties, axis=0),
np.max(diff_properties, axis=0) - np.min(diff_properties, axis=0)), axis=-1).reshape((-1)),
np.stack(
(np.mean(attribute_properties, axis=0),
np.max(attribute_properties, axis=0) - np.min(attribute_properties, axis=0),
np.mean(np.abs(attribute_properties - np.mean(attribute_properties, axis=0)), axis=0),
np.max(attribute_properties, axis=0),
np.min(attribute_properties, axis=0),
most_common(attribute_properties)), axis=-1).reshape((-1))))
# Normalize Bond Length properties.
properties[6] /= properties[5]
properties[7] /= properties[5]
properties[8] /= properties[5]
# Normalize the Cell Volume Deviation.
properties[16] /= properties[15]
# Remove properties not in magpie.
properties = np.delete(properties, [4, 5, 9, 14, 15, 17, 18, 19, 21, 22, 23, 24])
# Renormalize the packing efficiency.
properties[12] *= adjustedStruct.num_sites / adjustedStruct.volume
# Calculate and insert stoichiometry attributes.
element_dict = currentComposition.fractional_composition.as_dict()
position = 118
for p in [10, 7, 5, 3, 2]:
properties = np.insert(properties, position,
math.pow(sum(math.pow(value, p) for value in element_dict.values()), 1.0 / p))
properties = np.insert(properties, position, len(element_dict))
# Calculate Valence Electron Statistics
electron_occupation_dict = {'s': 0, 'p': 0, 'd': 0, 'f': 0}
for key, value in element_dict.items():
electron_occupation_dict['s'] += value * attribute_matrix[Element(key).Z - 1][8]
electron_occupation_dict['p'] += value * attribute_matrix[Element(key).Z - 1][9]
electron_occupation_dict['d'] += value * attribute_matrix[Element(key).Z - 1][10]
electron_occupation_dict['f'] += value * attribute_matrix[Element(key).Z - 1][11]
total_valence_factor = sum([val for (key, val) in electron_occupation_dict.items()])
for orb in ['s', 'p', 'd', 'f']:
properties = np.append(properties, electron_occupation_dict[orb] / total_valence_factor)
# Calculate ionic compound attributes.
max_ionic_char = 0
av_ionic_char = 0
for key1, value1 in element_dict.items():
for key2, value2 in element_dict.items():
ionic_char = 1.0 - math.exp(-0.25 * (Element(key1).X - Element(key2).X) ** 2)
if ionic_char > max_ionic_char:
max_ionic_char = ionic_char
av_ionic_char += ionic_char * value1 * value2
properties = np.append(properties, max_ionic_char)
properties = np.append(properties, av_ionic_char)
properties = properties.astype(np.float32)

propHistory.append(properties)
# Calculate the difference between the current step and the previous step and divide it by maximum value of
# each feature found in OQMD to normalize the difference.
if len(propHistory) > 2:
# Current iteration diff
diff = np.subtract(properties, propHistory[-2])
diff /= maxFeaturesInOQMD
diffHistory.append(diff)
# Calculate the additional diff to one level older iteration
diff2 = np.subtract(properties, propHistory[-3])
diff2 /= maxFeaturesInOQMD
# Calculate the maximum difference across both differences
maxDiff = max(np.concatenate((diff, diff2), axis=0))
if printProgress:
print(f'{attribute_properties.shape[0]:^6} | '
f'{compositionDistance: 18.6f} | '
f'{maxDiff: 21.6f} | '
f'{minOccupationCount:^4}')
else:
if printProgress:
print(f'{attribute_properties.shape[0]:^6} | '
f'{compositionDistance: 18.6f} | '
f'{"(init)":^21} | '
f'{minOccupationCount:^4}')
# ^^^ End of the long while-loop above
except KeyboardInterrupt:
if len(propHistory) == 0:
raise KeyboardInterrupt(
'KS2022_randomSolution descriptor calculation was interrupted before it managed to complete the '
'first iteration.')
else:
if printProgress:
print(f'{attribute_properties.shape[0]:^6} | '
f'{compositionDistance: 18.6f} | '
f'{"(init)":^21} | '
f'{minOccupationCount:^4}')
# ^^^ End of the long while-loop above
if len(diffHistory) == 0:
print(
'Warning: The feature calculation was interrupted before it managed to begin convergence process. '
'The first one or two iterations was completed and partial results are available but there was not '
'enough data to calculate the convergence level.')
else:
print(
'The feature calculation was interrupted before it managed to converge. Partial results are '
'available, including the level of convergence achieved before the interruption.')
pass

if plotParameters:
import plotly.express as px
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