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chore: increasing search cutoff
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Kevin M. Jablonka committed Dec 17, 2020
1 parent f9b3c1b commit bf2ab57
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Showing 3 changed files with 83 additions and 68 deletions.
2 changes: 1 addition & 1 deletion oximachine_featurizer/crystalnn.py
View file
Expand Up @@ -32,7 +32,7 @@ def __init__(
distance_cutoffs=(0.5, 1),
x_diff_weight=3.0,
porous_adjustment=True,
search_cutoff=8,
search_cutoff=10,
fingerprint_length=None,
):
"""
Expand Down
2 changes: 1 addition & 1 deletion oximachine_featurizer/featurize.py
View file
Expand Up @@ -537,7 +537,7 @@ def _run_featurization(self):
)
self._get_metal_sites()
try:
self.logger.debug(
self.logger.info(
"iterating over {} metal sites".format(len(self.metal_sites))
)
for idx, metal_site in enumerate(self.metal_sites):
Expand Down
147 changes: 81 additions & 66 deletions oximachine_featurizer/featurizer_local_property.py
View file
Expand Up @@ -3,7 +3,11 @@

import numpy as np
from matminer.featurizers.base import BaseFeaturizer
from matminer.featurizers.site import (LocalStructOrderParams, cn_motif_op_params, cn_target_motif_op)
from matminer.featurizers.site import (
LocalStructOrderParams,
cn_motif_op_params,
cn_target_motif_op,
)
from matminer.utils.caching import get_nearest_neighbors
from matminer.utils.data import MagpieData
from pymatgen.analysis.local_env import VoronoiNN
Expand All @@ -30,8 +34,10 @@ class LocalPropertyStatsNew(BaseFeaturizer):
`Ward et al. _PRB_ 2017 <http://link.aps.org/doi/10.1103/PhysRevB.96.024104>`_
"""

def __init__(self, data_source=MagpieData(), weight='area', properties=('Electronegativity',)):
""" Initialize the featurizer
def __init__(
self, data_source=MagpieData(), weight="area", properties=("Electronegativity",)
):
"""Initialize the featurizer
Args:
data_source (AbstractData) - Class from which to retrieve
elemental properties
Expand All @@ -53,44 +59,44 @@ def from_preset(preset):
preset (str) - Name of preset
"""

if preset == 'interpretable':
if preset == "interpretable":
return LocalPropertyStatsNew(
data_source=MagpieData(),
properties=[
'MendeleevNumber',
'Column',
'Row',
'Electronegativity',
'NsValence',
'NpValence',
'NdValence',
'NfValence',
'NValence',
'NsUnfilled',
'NpUnfilled',
'NdUnfilled',
'NfUnfilled',
'NUnfilled',
'GSbandgap',
"MendeleevNumber",
"Column",
"Row",
"Electronegativity",
"NsValence",
"NpValence",
"NdValence",
"NfValence",
"NValence",
"NsUnfilled",
"NpUnfilled",
"NdUnfilled",
"NfUnfilled",
"NUnfilled",
"GSbandgap",
],
)
else:
raise ValueError('Unrecognized preset: ' + preset)
raise ValueError("Unrecognized preset: " + preset)

def featurize(self, strc, idx):
# Get the targeted site
my_site = strc[idx]

# Get the tessellation of a site
nn = get_nearest_neighbors(
VoronoiNN(weight=self.weight, cutoff=8, compute_adj_neighbors=False),
VoronoiNN(weight=self.weight, cutoff=10, compute_adj_neighbors=False),
strc,
idx,
)

# Get the element and weight of each site
elems = [n['site'].specie for n in nn]
weights = [n['weight'] for n in nn]
elems = [n["site"].specie for n in nn]
weights = [n["weight"] for n in nn]

# Compute the difference for each property
output = np.zeros((len(self.properties),))
Expand All @@ -102,45 +108,51 @@ def featurize(self, strc, idx):
for i, p in enumerate(self.properties):
my_prop = self.data_source.get_elemental_property(my_site.specie, p)
n_props = self.data_source.get_elemental_properties(elems, p)
output[i] = (np.dot(weights, np.abs(np.subtract(n_props, my_prop))) / total_weight)
output_signed[i] = (np.dot(weights, np.subtract(n_props, my_prop)) / total_weight)
output[i] = (
np.dot(weights, np.abs(np.subtract(n_props, my_prop))) / total_weight
)
output_signed[i] = (
np.dot(weights, np.subtract(n_props, my_prop)) / total_weight
)
output_max[i] = np.max(np.subtract(n_props, my_prop))
output_min[i] = np.min(np.subtract(n_props, my_prop))
return np.hstack([output, output_signed, output_max, output_min])

def feature_labels(self):

return (['local difference in ' + p for p in self.properties] +
['local signed difference in ' + p for p in self.properties] +
['maximum local difference in ' + p for p in self.properties] +
['minimum local difference in ' + p for p in self.properties])
return (
["local difference in " + p for p in self.properties]
+ ["local signed difference in " + p for p in self.properties]
+ ["maximum local difference in " + p for p in self.properties]
+ ["minimum local difference in " + p for p in self.properties]
)

def citations(self):
return [
'@article{Ward2017,'
'author = {Ward, Logan and Liu, Ruoqian '
'and Krishna, Amar and Hegde, Vinay I. '
'and Agrawal, Ankit and Choudhary, Alok '
'and Wolverton, Chris},'
'doi = {10.1103/PhysRevB.96.024104},'
'journal = {Physical Review B},'
'pages = {024104},'
'title = {{Including crystal structure attributes '
'in machine learning models of formation energies '
'via Voronoi tessellations}},'
'url = {http://link.aps.org/doi/10.1103/PhysRevB.96.014107},'
'volume = {96},year = {2017}}',
'@article{jong_chen_notestine_persson_ceder_jain_asta_gamst_2016,'
'title={A Statistical Learning Framework for Materials Science: '
'Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds}, '
'volume={6}, DOI={10.1038/srep34256}, number={1}, journal={Scientific Reports}, '
'author={Jong, Maarten De and Chen, Wei and Notestine, Randy and Persson, '
'Kristin and Ceder, Gerbrand and Jain, Anubhav and Asta, Mark and Gamst, Anthony}, '
'year={2016}, month={Mar}}',
"@article{Ward2017,"
"author = {Ward, Logan and Liu, Ruoqian "
"and Krishna, Amar and Hegde, Vinay I. "
"and Agrawal, Ankit and Choudhary, Alok "
"and Wolverton, Chris},"
"doi = {10.1103/PhysRevB.96.024104},"
"journal = {Physical Review B},"
"pages = {024104},"
"title = {{Including crystal structure attributes "
"in machine learning models of formation energies "
"via Voronoi tessellations}},"
"url = {http://link.aps.org/doi/10.1103/PhysRevB.96.014107},"
"volume = {96},year = {2017}}",
"@article{jong_chen_notestine_persson_ceder_jain_asta_gamst_2016,"
"title={A Statistical Learning Framework for Materials Science: "
"Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds}, "
"volume={6}, DOI={10.1038/srep34256}, number={1}, journal={Scientific Reports}, "
"author={Jong, Maarten De and Chen, Wei and Notestine, Randy and Persson, "
"Kristin and Ceder, Gerbrand and Jain, Anubhav and Asta, Mark and Gamst, Anthony}, "
"year={2016}, month={Mar}}",
]

def implementors(self):
return ['Logan Ward', 'Aik Rui Tan']
return ["Logan Ward", "Aik Rui Tan"]


class CrystalNNFingerprint(BaseFeaturizer):
Expand All @@ -163,22 +175,24 @@ def from_preset(preset, **kwargs):
preset (str): name of preset ("cn" or "ops")
**kwargs: other settings to be passed into CrystalNN class
"""
if preset == 'cn':
op_types = {k + 1: ['wt'] for k in range(24)}
if preset == "cn":
op_types = {k + 1: ["wt"] for k in range(24)}
return CrystalNNFingerprint(op_types, **kwargs)

elif preset == 'ops':
elif preset == "ops":
op_types = copy.deepcopy(cn_target_motif_op)
for k in range(24):
if k + 1 in op_types:
op_types[k + 1].insert(0, 'wt')
op_types[k + 1].insert(0, "wt")
else:
op_types[k + 1] = ['wt']
op_types[k + 1] = ["wt"]

return CrystalNNFingerprint(op_types, chem_info=None, **kwargs)

else:
raise RuntimeError('preset "{}" is not supported in ' 'CrystalNNFingerprint'.format(preset))
raise RuntimeError(
'preset "{}" is not supported in ' "CrystalNNFingerprint".format(preset)
)

def __init__(self, op_types, chem_info=None, **kwargs):
"""
Expand All @@ -205,7 +219,7 @@ def __init__(self, op_types, chem_info=None, **kwargs):
for cn, t_list in self.op_types.items():
self.ops[cn] = []
for t in t_list:
if t == 'wt':
if t == "wt":
self.ops[cn].append(t)
else:
ot = t
Expand Down Expand Up @@ -246,13 +260,13 @@ def featurize(self, struct, idx):
wt = nndata.cn_weights.get(cn, 0)
if cn in self.ops:
for op in self.ops[cn]:
if op == 'wt':
if op == "wt":
cn_fingerprint.append(wt)

if self.chem_info is not None and wt != 0:
# Compute additional chemistry-related features
sum_wt += wt
neigh_sites = [d['site'] for d in nndata.cn_nninfo[cn]]
neigh_sites = [d["site"] for d in nndata.cn_nninfo[cn]]

for prop in self.chem_props:
# get the value for specie, if not fall back to
Expand All @@ -270,17 +284,18 @@ def featurize(self, struct, idx):
self.chem_info[prop].get(elem_neigh),
)

prop_delta[prop] += (wt * (prop_neigh - prop_central) / cn)
prop_delta[prop] += (
wt * (prop_neigh - prop_central) / cn
)

elif wt == 0:
cn_fingerprint.append(wt)
else:
neigh_sites = [d['site'] for d in nndata.cn_nninfo[cn]]
neigh_sites = [d["site"] for d in nndata.cn_nninfo[cn]]
opval = op.get_order_parameters(
[struct[idx]] + neigh_sites,
0,
indices_neighs=list(range(1,
len(neigh_sites) + 1)),
indices_neighs=list(range(1, len(neigh_sites) + 1)),
)[0]
opval = opval or 0 # handles None
cn_fingerprint.append(wt * opval)
Expand All @@ -299,14 +314,14 @@ def feature_labels(self):
cn = k + 1
if cn in list(self.ops.keys()):
for op in self.op_types[cn]:
labels.append('{} CN_{}'.format(op, cn))
labels.append("{} CN_{}".format(op, cn))
if self.chem_info is not None:
for prop in self.chem_props:
labels.append('{} local diff'.format(prop))
labels.append("{} local diff".format(prop))
return labels

def citations(self):
return []

def implementors(self):
return ['Anubhav Jain', 'Nils E.R. Zimmermann']
return ["Anubhav Jain", "Nils E.R. Zimmermann"]

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