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import os
import pandas as pd
import numpy as np
import joblib
import dill
from mastml.feature_generators import ElementalFeatureGenerator, OneHotGroupGenerator
def get_preds_ebars_domains(df_test):
d = 'model_perovskite_tec'
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'))
recal_params = pd.read_csv(os.path.join(d, 'recal_dict.csv'))
features = df_features.columns.tolist()
df_test = df_test[features]
X = scaler.transform(df_test)
# Make predictions
preds = model.predict(X)
# Get ebars and recalibrate them
errs_list = list()
a = recal_params['a'][0]
b = recal_params['b'][0]
c = recal_params['c'][0]
for i, x in X.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))
ebars = a * np.array(errs_list)**2 + b * np.array(errs_list) + c
# Get domains
with open(os.path.join(d, 'model.dill'), 'rb') as f:
model_domain = dill.load(f)
domains = model_domain.predict(X)
return preds, ebars, domains
def process_data(comp_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)
return df_test
def make_predictions(comp_list):
# Process data
df_test = process_data(comp_list)
# Get the ML predicted values
preds, ebars, domains = get_preds_ebars_domains(df_test)
pred_dict = {'Compositions': comp_list,
'Predicted perovskite TEC (x10^-6 1/K)': preds,
'Ebar perovskite TEC (x10^-6 1/K)': ebars}
for d in domains.columns.tolist():
pred_dict[d] = domains[d]
del pred_dict['y_pred']
#del pred_dict['d_pred']
del pred_dict['y_stdu_pred']
del pred_dict['y_stdc_pred']
return pd.DataFrame(pred_dict) |