Upload Habs model

.gitattributes

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 *tfevents* filter=lfs diff=lfs merge=lfs -text
 model_bandgap-main/model.dill filter=lfs diff=lfs merge=lfs -text
 model_piezoelectric-main/model.dill filter=lfs diff=lfs merge=lfs -text
+model_perovskite_Habs-main/model.dill filter=lfs diff=lfs merge=lfs -text

model_perovskite_Habs-main/.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

model_perovskite_Habs-main/README.md

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+# model_perovskite_Habs
+Random forest model to predict the H absorption uptake of perovskites

model_perovskite_Habs-main/RandomForestRegressor.pkl

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+oid sha256:cf8c73212018afc54a27beff63147e3a1dfbe320a37be23142f63515bae14d8c
+size 1579913

model_perovskite_Habs-main/StandardScaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3adc564442ba56f999bec43a159e442700f81dc5481d81295e0dd98ee5861299
+size 1364

model_perovskite_Habs-main/model.dill

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f859f5cf2a2c8fe68df13bcd63f7c099b63e1653f99078e691abbe2394aaf76d
+size 11627572

model_perovskite_Habs-main/predict_perovskite_Habs.py

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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_Habs'
+    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, temp_list, ph2o_list):
+
+    # Process data
+    df_test = process_data(comp_list)
+
+    # Add other features to df_test
+    df_test['temperature_C'] = temp_list
+    df_test['pH2O'] = ph2o_list
+
+    # Get the ML predicted values
+    preds, ebars, domains = get_preds_ebars_domains(df_test)
+
+    pred_dict = {'Compositions': comp_list,
+                 'Predicted H absorption (mol/formula unit)': preds,
+                 'Ebar H absorption (mol/formula unit)': 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)

model_perovskite_Habs-main/recal_dict.csv

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+a,b,c
+-1.1747331419299594,0.8780103485479195,1.9018284230405332e-05

model_perovskite_Habs-main/requirements.txt

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+scikit-learn
+numpy 
+pandas
+mastml
+pymatgen