Hugging Face's logo

willengler-uc
/
perovskite-asr

Model card Files Community
perovskite-asr
File size: 4,513 Bytes 
f935bff
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
 
import os
import pandas as pd
import numpy as np
import joblib
import dill
from mastml.feature_generators import ElementalFeatureGenerator, OneHotGroupGenerator

def get_barrier(df_test):
    d = 'model_perovskite_ASR/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_preds_ebars_domains(df_test):
    d = 'model_perovskite_ASR'
    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]
    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) + b 

    # 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, elec_list):

    # Process data
    df_test = process_data(comp_list)

    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

    barriers = get_barrier(df_test)
    df_test['ML pred ASR barrier (eV)'] = barriers

    # Get the ML predicted values
    preds, ebars, domains = get_preds_ebars_domains(df_test)

    pred_dict = {'Predicted log ASR at 500C (Ohm-cm2)': preds,
                 'Ebar log ASR at 500C (Ohm-cm2)': 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)