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import os
import pandas as pd
import numpy as np
import joblib
from mastml.feature_generators import ElementalFeatureGenerator, OneHotGroupGenerator
from pymatgen.analysis.cost import CostAnalyzer, CostDBElements
def get_cost(comp_list):
ca = CostAnalyzer(costdb=CostDBElements())
costs = list()
for comp in comp_list:
cost = ca.get_cost_per_kg(comp=comp)
costs.append(cost)
return costs
def get_stability(df_test):
d = 'ASR_model/Stability_model'
scaler = joblib.load(os.path.join(d, 'StandardScaler.pkl'))
model = joblib.load(os.path.join(d, 'RandomForestRegressor.pkl'))
df_features = pd.read_csv(os.path.join(d, 'X_train.csv'))
features = df_features.columns.tolist()
df_test2 = df_test[features]
X_stab = scaler.transform(df_test2)
stabilities = model.predict(X_stab)
return stabilities
def get_barrier(df_test):
d = 'ASR_model/Barrier_model'
scaler = joblib.load(os.path.join(d, 'StandardScaler.pkl'))
model = joblib.load(os.path.join(d, 'RandomForestRegressor.pkl'))
df_features = pd.read_csv(os.path.join(d, 'X_train.csv'))
features = df_features.columns.tolist()
X_barrier = df_test[features]
X_barrier = scaler.transform(X_barrier)
barriers = model.predict(X_barrier)
return barriers
def get_asr_rf(df_test):
d = 'ASR_model/ASR_RF_model'
scaler = joblib.load(os.path.join(d, 'StandardScaler.pkl'))
model = joblib.load(os.path.join(d, 'RandomForestRegressor.pkl'))
df_features = pd.read_csv(os.path.join(d, 'X_train.csv'))
features = df_features.columns.tolist()
df_test = df_test[features]
X_ASR = scaler.transform(df_test)
asrs = model.predict(X_ASR)
# Get ebars and recalibrate them
a = 0.42824232546669644
b = 0.36341790743237223
errs_list = list()
for i, x in X_ASR.iterrows():
preds_list = list()
for pred in model.model.estimators_:
preds_list.append(pred.predict(np.array(x).reshape(1, -1))[0])
errs_list.append(np.std(preds_list))
asr_ebars = a * np.array(errs_list) + b
return asrs, asr_ebars
def get_asr_gpr(df_test):
d = 'ASR_model/ASR_GPR_model'
scaler = joblib.load(os.path.join(d, 'StandardScaler.pkl'))
model = joblib.load(os.path.join(d, 'GaussianProcessRegressor.pkl'))
df_features = pd.read_csv(os.path.join(d, 'X_train.csv'))
features = df_features.columns.tolist()
df_test = df_test[features]
X_ASR = scaler.transform(df_test)
asrs, errs_list = model.model.predict(X_ASR, return_std=True)
# Get ebars and recalibrate them
a = 1.18033360971506
b = -0.0660773887574826
asr_ebars = a * np.array(errs_list) + b
return asrs, asr_ebars
def process_data(comp_list, elec_list):
X = pd.DataFrame(np.empty((len(comp_list),)))
y = pd.DataFrame(np.empty((len(comp_list),)))
df_test = pd.DataFrame({'Material composition': comp_list})
# Try this both ways depending on mastml version used.
try:
X, y = ElementalFeatureGenerator(composition_df=df_test['Material composition'],
feature_types=['composition_avg', 'arithmetic_avg', 'max', 'min','difference'],
remove_constant_columns=False).evaluate(X=X, y=y, savepath=os.getcwd(), make_new_dir=False)
except:
X, y = ElementalFeatureGenerator(featurize_df=df_test['Material composition'],
feature_types=['composition_avg', 'arithmetic_avg', 'max', 'min',
'difference'], remove_constant_columns=False).evaluate(X=X, y=y, savepath=os.getcwd(), make_new_dir=False)
df_test = pd.concat([df_test, X], axis=1)
elec_cls_0 = list()
elec_cls_1 = list()
elec_cls_2 = list()
elec_cls_3 = list()
for elec in elec_list:
if elec == 'ceria':
elec_cls_0.append(1)
elec_cls_1.append(0)
elec_cls_2.append(0)
elec_cls_3.append(0)
elif elec == 'mixed':
elec_cls_0.append(0)
elec_cls_1.append(1)
elec_cls_2.append(0)
elec_cls_3.append(0)
elif elec == 'perovskite':
elec_cls_0.append(0)
elec_cls_1.append(0)
elec_cls_2.append(1)
elec_cls_3.append(0)
elif elec == 'zirconia':
elec_cls_0.append(0)
elec_cls_1.append(0)
elec_cls_2.append(0)
elec_cls_3.append(1)
else:
raise ValueError('Invalid electrolyte choice detected. Valid choices are "ceria", "mixed", "perovskite", "zirconia"')
df_test['Electrolyte class_0'] = elec_cls_0 # ceria
df_test['Electrolyte class_1'] = elec_cls_1 # mixed
df_test['Electrolyte class_2'] = elec_cls_2 # perovskite
df_test['Electrolyte class_3'] = elec_cls_3 # zirconia
return df_test
def make_predictions(comp_list, elec_list):
# Check comp and elec list lengths match
assert len(comp_list) == len(elec_list)
# Process data
df_test = process_data(comp_list, elec_list)
# Calculate the cost of the materials
costs = get_cost(comp_list)
# Get the ML-predicted stability of the materials
stabilities = get_stability(df_test)
# Get the ML-predicted ASR barrier of the materials
barriers = get_barrier(df_test)
df_test['ML pred ASR barrier (eV)'] = barriers
# Get the ML (RF) predicted ASRs
asrs, asr_ebars = get_asr_rf(df_test)
# Get the ML (GPR) predicted ASRs
asrs_gpr, asr_ebars_gpr = get_asr_gpr(df_test)
pred_dict = {'Compositions': comp_list,
'Electrolytes': elec_list,
'Cost ($/kg)': costs,
'Stability @ 500C (meV/atom)': stabilities,
'ASR barrier (eV)': barriers,
'log ASR at 500C (Ohm-cm2) (RF)': asrs,
'log ASR error (Ohm-cm2) (RF)': asr_ebars,
'log ASR at 500C (Ohm-cm2) (GPR)': asrs_gpr,
'log ASR error (Ohm-cm2) (GPR)': asr_ebars_gpr}
return pd.DataFrame(pred_dict)
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