def test_miedema_all(self):
miedema_df = pd.DataFrame({
"composition": [
Composition("TiZr"),
Composition("Mg10Cu50Ca40"),
Composition("Fe2O3")
]
})
df_miedema = Miedema(struct_types='all').featurize_dataframe(
miedema_df, col_id="composition")
self.assertAlmostEqual(df_miedema['formation_enthalpy_inter'][0],
-0.0034450221522328503)
self.assertAlmostEqual(df_miedema['formation_enthalpy_amor'][0],
0.070765883630040161)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_min'][0],
0.036635997549833224)
self.assertAlmostEqual(df_miedema['formation_enthalpy_inter'][1],
-0.23512597842733007)
self.assertAlmostEqual(df_miedema['formation_enthalpy_amor'][1],
-0.16454184827089643)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_min'][1],
-0.052808433113994087)
self.assertAlmostEqual(
math.isnan(df_miedema['formation_enthalpy_inter'][2]), True)
self.assertAlmostEqual(
math.isnan(df_miedema['formation_enthalpy_amor'][2]), True)
self.assertAlmostEqual(
math.isnan(df_miedema['formation_enthalpy_ss_min'][2]), True)
def test_miedema_all(self):
df = pd.DataFrame({
"composition": [
Composition("TiZr"),
Composition("Mg10Cu50Ca40"),
Composition("Fe2O3")
]
})
miedema = Miedema(struct_types='all')
self.assertFalse(miedema.precheck(df["composition"].iloc[-1]))
self.assertAlmostEqual(miedema.precheck_dataframe(df, "composition"),
2 / 3)
mfps = miedema.featurize_dataframe(df, col_id="composition")
self.assertAlmostEqual(mfps['Miedema_deltaH_inter'][0],
-0.003445022152)
self.assertAlmostEqual(mfps['Miedema_deltaH_amor'][0], 0.0707658836300)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_min'][0], 0.03663599755)
self.assertAlmostEqual(mfps['Miedema_deltaH_inter'][1],
-0.235125978427)
self.assertAlmostEqual(mfps['Miedema_deltaH_amor'][1], -0.164541848271)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_min'][1],
-0.05280843311)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_inter'][2]),
True)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_amor'][2]),
True)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_ss_min'][2]),
True)
def featurize_composition(df: pd.DataFrame) -> pd.DataFrame:
""" Decorate input `pandas.DataFrame` of structures with composition
features from matminer.
Currently applies the set of all matminer composition features.
Args:
df (pandas.DataFrame): the input dataframe with `"structure"`
column containing `pymatgen.Structure` objects.
Returns:
pandas.DataFrame: the decorated DataFrame.
"""
logging.info("Applying composition featurizers...")
df = df.copy()
df['composition'] = df['structure'].apply(lambda s: s.composition)
featurizer = MultipleFeaturizer([ElementProperty.from_preset("magpie"),
AtomicOrbitals(),
BandCenter(),
# ElectronAffinity(), - This descriptor was not used in the paper preset
Stoichiometry(),
ValenceOrbital(),
IonProperty(),
ElementFraction(),
TMetalFraction(),
# CohesiveEnergy(), - This descriptor was not used in the paper preset
Miedema(),
YangSolidSolution(),
AtomicPackingEfficiency(),
])
df = featurizer.featurize_dataframe(df, "composition", multiindex=True, ignore_errors=True)
df.columns = df.columns.map('|'.join).str.strip('|')
ox_featurizer = MultipleFeaturizer([OxidationStates(),
ElectronegativityDiff()
])
df = CompositionToOxidComposition().featurize_dataframe(df, "Input Data|composition")
df = ox_featurizer.featurize_dataframe(df, "composition_oxid", multiindex=True, ignore_errors=True)
df = df.rename(columns={'Input Data': ''})
df.columns = df.columns.map('|'.join).str.strip('|')
_orbitals = {"s": 1, "p": 2, "d": 3, "f": 4}
df['AtomicOrbitals|HOMO_character'] = df['AtomicOrbitals|HOMO_character'].map(_orbitals)
df['AtomicOrbitals|LUMO_character'] = df['AtomicOrbitals|LUMO_character'].map(_orbitals)
df['AtomicOrbitals|HOMO_element'] = df['AtomicOrbitals|HOMO_element'].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z
)
df['AtomicOrbitals|LUMO_element'] = df['AtomicOrbitals|LUMO_element'].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z
)
df = df.replace([np.inf, -np.inf, np.nan], 0)
return clean_df(df)
def test_miedema_all(self):
miedema_df = pd.DataFrame({"composition": [Composition("TiZr")]})
df_miedema_all = Miedema(struct='all').featurize_dataframe(
miedema_df, col_id="composition")
self.assertAlmostEqual(df_miedema_all['formation_enthalpy_inter'][0],
-0.0034450221522328503)
self.assertAlmostEqual(df_miedema_all['formation_enthalpy_amor'][0],
0.070765883630040161)
self.assertAlmostEqual(df_miedema_all['formation_enthalpy_ss_min'][0],
0.036635997549833224)
def test_miedema_ss(self):
miedema_df = pd.DataFrame({"composition": [Composition("TiZr")]})
df_miedema_all = Miedema(
struct='ss|ss,none,bcc,fcc,hcp').featurize_dataframe(
miedema_df, col_id="composition")
self.assertAlmostEqual(df_miedema_all['formation_enthalpy_ss_min'][0],
0.036635997549833224)
self.assertAlmostEqual(df_miedema_all['formation_enthalpy_ss_none'][0],
0.036635997549833224)
self.assertAlmostEqual(df_miedema_all['formation_enthalpy_ss_bcc'][0],
0.083275226530828264)
self.assertAlmostEqual(df_miedema_all['formation_enthalpy_ss_fcc'][0],
0.047000270656721008)
self.assertAlmostEqual(df_miedema_all['formation_enthalpy_ss_hcp'][0],
0.036635997549833224)
def test_miedema_all(self):
df = pd.DataFrame({
"composition": [
Composition("TiZr"),
Composition("Mg10Cu50Ca40"),
Composition("Fe2O3")
]
})
miedema = Miedema(struct_types='all')
self.assertTrue(miedema.precheck(df["composition"].iloc[0]))
self.assertFalse(miedema.precheck(df["composition"].iloc[-1]))
self.assertAlmostEqual(miedema.precheck_dataframe(df, "composition"),
2 / 3)
# test precheck for oxidation-state decorated compositions
df = CompositionToOxidComposition(return_original_on_error=True).\
featurize_dataframe(df, 'composition')
self.assertTrue(miedema.precheck(df["composition_oxid"].iloc[0]))
self.assertFalse(miedema.precheck(df["composition_oxid"].iloc[-1]))
self.assertAlmostEqual(
miedema.precheck_dataframe(df, "composition_oxid"), 2 / 3)
mfps = miedema.featurize_dataframe(df, col_id="composition")
self.assertAlmostEqual(mfps['Miedema_deltaH_inter'][0],
-0.003445022152)
self.assertAlmostEqual(mfps['Miedema_deltaH_amor'][0], 0.0707658836300)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_min'][0], 0.03663599755)
self.assertAlmostEqual(mfps['Miedema_deltaH_inter'][1],
-0.235125978427)
self.assertAlmostEqual(mfps['Miedema_deltaH_amor'][1], -0.164541848271)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_min'][1],
-0.05280843311)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_inter'][2]),
True)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_amor'][2]),
True)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_ss_min'][2]),
True)
# make sure featurization works equally for compositions with or without
# oxidation states
mfps = miedema.featurize_dataframe(df, col_id="composition_oxid")
self.assertAlmostEqual(mfps['Miedema_deltaH_inter'][0],
-0.003445022152)
self.assertAlmostEqual(mfps['Miedema_deltaH_amor'][0], 0.0707658836300)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_min'][0], 0.03663599755)
def test_miedema_ss(self):
miedema_df = pd.DataFrame({
"composition": [
Composition("TiZr"),
Composition("Mg10Cu50Ca40"),
Composition("Fe2O3")
]
})
df_miedema = Miedema(struct_types='ss',
ss_types=['min', 'fcc', 'bcc', 'hcp',
'no_latt']).featurize_dataframe(
miedema_df, col_id="composition")
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_min'][0],
0.036635997549833224)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_fcc'][0],
0.047000270656721008)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_bcc'][0],
0.083275226530828264)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_hcp'][0],
0.036635997549833224)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_no_latt'][0],
0.036635997549833224)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_min'][1],
-0.052808433113994087)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_fcc'][1],
0.030105751741108196)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_bcc'][1],
-0.052808433113994087)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_hcp'][1],
0.030105751741108196)
self.assertAlmostEqual(df_miedema['formation_enthalpy_ss_no_latt'][1],
-0.0035781358562771083)
self.assertAlmostEqual(
math.isnan(df_miedema['formation_enthalpy_ss_min'][2]), True)
self.assertAlmostEqual(
math.isnan(df_miedema['formation_enthalpy_ss_fcc'][2]), True)
self.assertAlmostEqual(
math.isnan(df_miedema['formation_enthalpy_ss_bcc'][2]), True)
self.assertAlmostEqual(
math.isnan(df_miedema['formation_enthalpy_ss_hcp'][2]), True)
self.assertAlmostEqual(
math.isnan(df_miedema['formation_enthalpy_ss_no_latt'][2]), True)
def _generate_pairwise_features(self):
"""Generates features for each bonding pair.
Column labels contain a prefix "pairwise_feature {}", where the
string in parethesis is either "bcc_tet1", "bcc_tet2", or "tet1_tet2".
"""
# initializes requested matminer featurizers
feat_element_property = PairwiseElementProperty(
data_source='deml',
features=[
'atom_radius', 'electronegativity', 'first_ioniz', 'col_num',
'row_num', 'molar_vol', 'heat_fusion', 'melting_point',
'GGAU_Etot', 'mus_fere', 'FERE correction'
],
stats=['difference', 'mean'])
feat_miedema = Miedema(struct_types=['inter', 'amor'])
# generates freatures for each bonding pair
for bonding_pair in ['bcc_tet1', 'bcc_tet2', 'tet1_tet2']:
# gets the string composition of the bonding pairs
composition_index = 'pairwise_composition {}'.format(bonding_pair)
composition = self.memory[[composition_index]]
# adds ElementProperty features
features = feat_element_property.featurize_dataframe(
df=composition, col_id=composition_index, inplace=False).drop(
labels=composition_index, axis=1).add_prefix(
prefix='pairwise_feature {} '.format(bonding_pair))
self.memory = concat([self.memory, features], axis=1)
# gets the pymatgen.Composition of the bonding pairs
composition[composition_index] = [
Composition(i) for i in composition[composition_index]
]
# adds Miedema features
features = feat_miedema.featurize_dataframe(
df=composition, col_id=composition_index, inplace=False).drop(
labels=composition_index, axis=1).add_prefix(
prefix='pairwise_feature {} '.format(bonding_pair))
self.memory = concat([self.memory, features], axis=1)
def test_miedema_ss(self):
df = pd.DataFrame({
"composition": [
Composition("TiZr"),
Composition("Mg10Cu50Ca40"),
Composition("Fe2O3")
]
})
miedema = Miedema(struct_types='ss',
ss_types=['min', 'fcc', 'bcc', 'hcp', 'no_latt'])
mfps = miedema.featurize_dataframe(df, col_id="composition")
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_min'][0], 0.03663599755)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_fcc'][0], 0.04700027066)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_bcc'][0], 0.08327522653)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_hcp'][0], 0.03663599755)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_no_latt'][0],
0.036635998)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_min'][1],
-0.05280843311)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_fcc'][1], 0.03010575174)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_bcc'][1],
-0.05280843311)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_hcp'][1], 0.03010575174)
self.assertAlmostEqual(mfps['Miedema_deltaH_ss_no_latt'][1],
-0.0035781359)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_ss_min'][2]),
True)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_ss_fcc'][2]),
True)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_ss_bcc'][2]),
True)
self.assertAlmostEqual(math.isnan(mfps['Miedema_deltaH_ss_hcp'][2]),
True)
self.assertAlmostEqual(
math.isnan(mfps['Miedema_deltaH_ss_no_latt'][2]), True)
class DeBreuck2020Featurizer(modnet.featurizers.MODFeaturizer):
""" Featurizer presets used for the paper 'Machine learning
materials properties for small datasets' by Pierre-Paul De Breuck,
Geoffroy Hautier & Gian-Marco Rignanese, arXiv:2004.14766 (2020).
Uses most of the featurizers implemented by matminer at the time of
writing with their default hyperparameters and presets.
"""
from matminer.featurizers.composition import (
AtomicOrbitals,
AtomicPackingEfficiency,
BandCenter,
# CohesiveEnergy, - This descriptor was not used in the paper preset
# ElectronAffinity, - This descriptor was not used in the paper preset
ElectronegativityDiff,
ElementFraction,
ElementProperty,
IonProperty,
Miedema,
OxidationStates,
Stoichiometry,
TMetalFraction,
ValenceOrbital,
YangSolidSolution,
)
from matminer.featurizers.structure import (
# BagofBonds, - This descriptor was not used in the paper preset
BondFractions,
ChemicalOrdering,
CoulombMatrix,
DensityFeatures,
EwaldEnergy,
GlobalSymmetryFeatures,
MaximumPackingEfficiency,
# PartialRadialDistributionFunction,
RadialDistributionFunction,
SineCoulombMatrix,
StructuralHeterogeneity,
XRDPowderPattern,
)
from matminer.featurizers.site import (
AGNIFingerprints,
AverageBondAngle,
AverageBondLength,
BondOrientationalParameter,
ChemEnvSiteFingerprint,
CoordinationNumber,
CrystalNNFingerprint,
GaussianSymmFunc,
GeneralizedRadialDistributionFunction,
LocalPropertyDifference,
OPSiteFingerprint,
VoronoiFingerprint,
)
composition_featurizers = (
AtomicOrbitals(),
AtomicPackingEfficiency(),
BandCenter(),
ElementFraction(),
ElementProperty.from_preset("magpie"),
IonProperty(),
Miedema(),
Stoichiometry(),
TMetalFraction(),
ValenceOrbital(),
YangSolidSolution(),
)
oxide_composition_featurizers = (
ElectronegativityDiff(),
OxidationStates(),
)
structure_featurizers = (
DensityFeatures(),
GlobalSymmetryFeatures(),
RadialDistributionFunction(),
CoulombMatrix(),
# PartialRadialDistributionFunction(),
SineCoulombMatrix(),
EwaldEnergy(),
BondFractions(),
StructuralHeterogeneity(),
MaximumPackingEfficiency(),
ChemicalOrdering(),
XRDPowderPattern(),
# BagofBonds(),
)
site_featurizers = (
AGNIFingerprints(),
AverageBondAngle(VoronoiNN()),
AverageBondLength(VoronoiNN()),
BondOrientationalParameter(),
ChemEnvSiteFingerprint.from_preset("simple"),
CoordinationNumber(),
CrystalNNFingerprint.from_preset("ops"),
GaussianSymmFunc(),
GeneralizedRadialDistributionFunction.from_preset("gaussian"),
LocalPropertyDifference(),
OPSiteFingerprint(),
VoronoiFingerprint(),
)
def featurize_composition(self, df):
""" Applies the preset composition featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_composition(df)
_orbitals = {"s": 1, "p": 2, "d": 3, "f": 4}
df['AtomicOrbitals|HOMO_character'] = df[
'AtomicOrbitals|HOMO_character'].map(_orbitals)
df['AtomicOrbitals|LUMO_character'] = df[
'AtomicOrbitals|LUMO_character'].map(_orbitals)
df['AtomicOrbitals|HOMO_element'] = df[
'AtomicOrbitals|HOMO_element'].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z)
df['AtomicOrbitals|LUMO_element'] = df[
'AtomicOrbitals|LUMO_element'].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z)
df = df.replace([np.inf, -np.inf, np.nan], 0)
return modnet.featurizers.clean_df(df)
def featurize_structure(self, df):
""" Applies the preset structural featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_structure(df)
dist = df[
"RadialDistributionFunction|radial distribution function"].iloc[0][
'distances'][:50]
for i, d in enumerate(dist):
_rdf_key = "RadialDistributionFunction|radial distribution function|d_{:.2f}".format(
d)
df[_rdf_key] = df[
"RadialDistributionFunction|radial distribution function"].apply(
lambda x: x['distribution'][i])
df = df.drop("RadialDistributionFunction|radial distribution function",
axis=1)
_crystal_system = {
"cubic": 1,
"tetragonal": 2,
"orthorombic": 3,
"hexagonal": 4,
"trigonal": 5,
"monoclinic": 6,
"triclinic": 7
}
def _int_map(x):
if x == np.nan:
return 0
elif x:
return 1
else:
return 0
df["GlobalSymmetryFeatures|crystal_system"] = df[
"GlobalSymmetryFeatures|crystal_system"].map(_crystal_system)
df["GlobalSymmetryFeatures|is_centrosymmetric"] = df[
"GlobalSymmetryFeatures|is_centrosymmetric"].map(_int_map)
return modnet.featurizers.clean_df(df)
def featurize_site(self, df):
""" Applies the preset site featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
# rename some features for backwards compatibility with pretrained models
aliases = {
"GeneralizedRadialDistributionFunction": "GeneralizedRDF",
"AGNIFingerprints": "AGNIFingerPrint",
"BondOrientationalParameter": "BondOrientationParameter",
"GaussianSymmFunc": "ChemEnvSiteFingerprint|GaussianSymmFunc",
}
df = super().featurize_site(df, aliases=aliases)
df = df.loc[:, (df != 0).any(axis=0)]
return modnet.featurizers.clean_df(df)
class FUTURE_PROSPECTS_2021(featurizer.extendedMODFeaturizer):
from matminer.featurizers.composition import (
AtomicOrbitals,
AtomicPackingEfficiency,
BandCenter,
CohesiveEnergy,
ElectronAffinity,
ElectronegativityDiff,
ElementFraction,
ElementProperty,
IonProperty,
Miedema,
OxidationStates,
Stoichiometry,
TMetalFraction,
ValenceOrbital,
YangSolidSolution,
)
from matminer.featurizers.structure import (
BagofBonds,
BondFractions,
ChemicalOrdering,
CoulombMatrix,
DensityFeatures,
EwaldEnergy,
GlobalSymmetryFeatures,
MaximumPackingEfficiency,
PartialRadialDistributionFunction,
RadialDistributionFunction,
SineCoulombMatrix,
StructuralHeterogeneity,
XRDPowderPattern,
)
from matminer.featurizers.site import (
AGNIFingerprints,
AverageBondAngle,
AverageBondLength,
BondOrientationalParameter,
ChemEnvSiteFingerprint,
CoordinationNumber,
CrystalNNFingerprint,
GaussianSymmFunc,
GeneralizedRadialDistributionFunction,
LocalPropertyDifference,
OPSiteFingerprint,
VoronoiFingerprint,
)
from matminer.featurizers.dos import (
DOSFeaturizer,
SiteDOS,
Hybridization,
DosAsymmetry,
)
from matminer.featurizers.bandstructure import (
BandFeaturizer,
BranchPointEnergy
)
composition_featurizers = (
AtomicOrbitals(),
AtomicPackingEfficiency(),
BandCenter(),
ElementFraction(),
ElementProperty.from_preset("magpie"),
IonProperty(),
Miedema(),
Stoichiometry(),
TMetalFraction(),
ValenceOrbital(),
YangSolidSolution(),
)
oxid_composition_featurizers = (
ElectronegativityDiff(),
OxidationStates(),
)
structure_featurizers = (
DensityFeatures(),
GlobalSymmetryFeatures(),
RadialDistributionFunction(),
CoulombMatrix(),
#PartialRadialDistributionFunction(), #Introduces a large amount of features
SineCoulombMatrix(),
EwaldEnergy(),
BondFractions(),
StructuralHeterogeneity(),
MaximumPackingEfficiency(),
ChemicalOrdering(),
XRDPowderPattern(),
)
site_featurizers = (
AGNIFingerprints(),
AverageBondAngle(VoronoiNN()),
AverageBondLength(VoronoiNN()),
BondOrientationalParameter(),
ChemEnvSiteFingerprint.from_preset("simple"),
CoordinationNumber(),
CrystalNNFingerprint.from_preset("ops"),
GaussianSymmFunc(),
GeneralizedRadialDistributionFunction.from_preset("gaussian"),
LocalPropertyDifference(),
OPSiteFingerprint(),
VoronoiFingerprint(),
)
dos_featurizers = (
DOSFeaturizer(),
SiteDOS(),
Hybridization()
)
band_featurizers = (
BandFeaturizer(),
BranchPointEnergy()
)
def __init__(self, n_jobs=None):
self._n_jobs = n_jobs
def featurize_composition(self, df):
"""Applies the preset composition featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_composition(df)
_orbitals = {"s": 1, "p": 2, "d": 3, "f": 4}
df["AtomicOrbitals|HOMO_character"] = df["AtomicOrbitals|HOMO_character"].map(
_orbitals
)
df["AtomicOrbitals|LUMO_character"] = df["AtomicOrbitals|LUMO_character"].map(
_orbitals
)
df["AtomicOrbitals|HOMO_element"] = df["AtomicOrbitals|HOMO_element"].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z
)
df["AtomicOrbitals|LUMO_element"] = df["AtomicOrbitals|LUMO_element"].apply(
lambda x: -1 if not isinstance(x, str) else Element(x).Z
)
return clean_df(df)
def featurize_structure(self, df):
"""Applies the preset structural featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_structure(df)
dist = df["RadialDistributionFunction|radial distribution function"].iloc[0][
"distances"
][:50]
for i, d in enumerate(dist):
_rdf_key = "RadialDistributionFunction|radial distribution function|d_{:.2f}".format(
d
)
df[_rdf_key] = df[
"RadialDistributionFunction|radial distribution function"
].apply(lambda x: x["distribution"][i])
df = df.drop("RadialDistributionFunction|radial distribution function", axis=1)
_crystal_system = {
"cubic": 1,
"tetragonal": 2,
"orthorombic": 3,
"hexagonal": 4,
"trigonal": 5,
"monoclinic": 6,
"triclinic": 7,
}
def _int_map(x):
if x == np.nan:
return 0
elif x:
return 1
else:
return 0
df["GlobalSymmetryFeatures|crystal_system"] = df[
"GlobalSymmetryFeatures|crystal_system"
].map(_crystal_system)
df["GlobalSymmetryFeatures|is_centrosymmetric"] = df[
"GlobalSymmetryFeatures|is_centrosymmetric"
].map(_int_map)
return clean_df(df)
def featurize_dos(self, df):
"""Applies the presetdos featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_dos(df)
hotencodeColumns = ["DOSFeaturizer|vbm_specie_1","DOSFeaturizer|cbm_specie_1"]
one_hot = pd.get_dummies(df[hotencodeColumns])
df = df.drop(hotencodeColumns, axis = 1).join(one_hot)
_orbitals = {"s": 1, "p": 2, "d": 3, "f": 4}
df["DOSFeaturizer|vbm_character_1"] = df[
"DOSFeaturizer|vbm_character_1"
].map(_orbitals)
df["DOSFeaturizer|cbm_character_1"] = df[
"DOSFeaturizer|cbm_character_1"
].map(_orbitals)
# Splitting one feature into several floating features
# e.g. number;number;number into three columns
splitColumns = ["DOSFeaturizer|cbm_location_1", "DOSFeaturizer|vbm_location_1"]
for column in splitColumns:
try:
newColumns = df[column].str.split(";", n = 2, expand = True)
for i in range(0,3):
df[column + "_" + str(i)] = np.array(newColumns[i]).astype(np.float)
except:
continue
df = df.drop(splitColumns, axis=1)
df = df.drop(["dos"], axis=1)
return clean_df(df)
def featurize_bandstructure(self, df):
"""Applies the preset band structure featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
df = super().featurize_bandstructure(df)
def _int_map(x):
if str(x) == "False":
return 0
elif str(x) == "True":
return 1
df["BandFeaturizer|is_gap_direct"] = df[
"BandFeaturizer|is_gap_direct"
].map(_int_map)
df = df.drop(["bandstructure"], axis=1)
return clean_df(df)
def featurize_site(self, df):
"""Applies the preset site featurizers to the input dataframe,
renames some fields and cleans the output dataframe.
"""
aliases = {
"GeneralizedRadialDistributionFunction": "GeneralizedRDF",
"AGNIFingerprints": "AGNIFingerPrint",
"BondOrientationalParameter": "BondOrientationParameter",
"GaussianSymmFunc": "ChemEnvSiteFingerprint|GaussianSymmFunc",
}
df = super().featurize_site(df, aliases=aliases)
df = df.loc[:, (df != 0).any(axis=0)]
return clean_df(df)
def AddFeatures(df): # Add features by Matminer
from matminer.featurizers.conversions import StrToComposition
df = StrToComposition().featurize_dataframe(df, "formula")
from matminer.featurizers.composition import ElementProperty
ep_feat = ElementProperty.from_preset(preset_name="magpie")
df = ep_feat.featurize_dataframe(
df, col_id="composition"
) # input the "composition" column to the featurizer
from matminer.featurizers.conversions import CompositionToOxidComposition
from matminer.featurizers.composition import OxidationStates
df = CompositionToOxidComposition().featurize_dataframe(df, "composition")
os_feat = OxidationStates()
df = os_feat.featurize_dataframe(df, "composition_oxid")
from matminer.featurizers.composition import ElectronAffinity
ea_feat = ElectronAffinity()
df = ea_feat.featurize_dataframe(df,
"composition_oxid",
ignore_errors=True)
from matminer.featurizers.composition import BandCenter
bc_feat = BandCenter()
df = bc_feat.featurize_dataframe(df,
"composition_oxid",
ignore_errors=True)
from matminer.featurizers.composition import CohesiveEnergy
ce_feat = CohesiveEnergy()
df = ce_feat.featurize_dataframe(df,
"composition_oxid",
ignore_errors=True)
from matminer.featurizers.composition import Miedema
m_feat = Miedema()
df = m_feat.featurize_dataframe(df, "composition_oxid", ignore_errors=True)
from matminer.featurizers.composition import TMetalFraction
tmf_feat = TMetalFraction()
df = tmf_feat.featurize_dataframe(df,
"composition_oxid",
ignore_errors=True)
from matminer.featurizers.composition import ValenceOrbital
vo_feat = ValenceOrbital()
df = vo_feat.featurize_dataframe(df,
"composition_oxid",
ignore_errors=True)
from matminer.featurizers.composition import YangSolidSolution
yss_feat = YangSolidSolution()
df = yss_feat.featurize_dataframe(df,
"composition_oxid",
ignore_errors=True)
from matminer.featurizers.structure import GlobalSymmetryFeatures
# This is the border between compositional features and structural features. Comment out the following featurizers to use only compostional features.
gsf_feat = GlobalSymmetryFeatures()
df = gsf_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.structure import StructuralComplexity
sc_feat = StructuralComplexity()
df = sc_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.structure import ChemicalOrdering
co_feat = ChemicalOrdering()
df = co_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.structure import MaximumPackingEfficiency
mpe_feat = MaximumPackingEfficiency()
df = mpe_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.structure import MinimumRelativeDistances
mrd_feat = MinimumRelativeDistances()
df = mrd_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.structure import StructuralHeterogeneity
sh_feat = StructuralHeterogeneity()
df = sh_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.structure import SiteStatsFingerprint
from matminer.featurizers.site import AverageBondLength
from pymatgen.analysis.local_env import CrystalNN
bl_feat = SiteStatsFingerprint(
AverageBondLength(CrystalNN(search_cutoff=20)))
df = bl_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.site import AverageBondAngle
ba_feat = SiteStatsFingerprint(
AverageBondAngle(CrystalNN(search_cutoff=20)))
df = ba_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.site import BondOrientationalParameter
bop_feat = SiteStatsFingerprint(BondOrientationalParameter())
df = bop_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.site import CoordinationNumber
cn_feat = SiteStatsFingerprint(CoordinationNumber())
df = cn_feat.featurize_dataframe(df, "structure", ignore_errors=True)
from matminer.featurizers.structure import DensityFeatures
df_feat = DensityFeatures()
df = df_feat.featurize_dataframe(df, "structure", ignore_errors=True)
return (df)