Upload perovskite_conductivity

.gitattributes

@@ -57,3 +57,4 @@ model_perovskite_workfunction/model.dill filter=lfs diff=lfs merge=lfs -text
 model_perovskite_tec/model.dill filter=lfs diff=lfs merge=lfs -text
 model_perovskite_stability/model.dill filter=lfs diff=lfs merge=lfs -text
 model_perovskite_formationE/model.dill filter=lfs diff=lfs merge=lfs -text
+model_perovskite_conductivity/model.dill filter=lfs diff=lfs merge=lfs -text

model_perovskite_conductivity/.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.dill filter=lfs diff=lfs merge=lfs -text

model_perovskite_conductivity/README.md

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+# model_perovskite_conductivity
+Random forest model to predict the conductivity of perovskite oxides

model_perovskite_conductivity/RandomForestRegressor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5011de1281ab77ca5d9b41d8f8f055fe71de6d586b14636a08871af59b1abb3b
+size 129170291

model_perovskite_conductivity/StandardScaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:199f3182f0b2bf65840f08cc3c63a9579e03ca89fc8e27d4fa0a54cb2be7011d
+size 2461

model_perovskite_conductivity/model.dill

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+version https://git-lfs.github.com/spec/v1
+oid sha256:39fe363b85e9bfed7a986447919d192054bb491d94487ccb0b54e571374d7b28
+size 152598144

model_perovskite_conductivity/predict_perovskite_conductivity.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_conductivity'
+    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, temp_list, ph2o_list, po2_list):
+
+    # Process data
+    df_test = process_data(comp_list)
+
+    # Add other features to df_test
+    df_test['Temperature (degC)'] = temp_list
+    df_test['PH2O (%)'] = ph2o_list
+    df_test['PO2 (%)'] = po2_list
+
+    # Get the ML predicted values
+    preds, ebars, domains = get_preds_ebars_domains(df_test)
+
+    pred_dict = {'Compositions': comp_list,
+                 'Predicted log perovskite conductivity (S/cm)': preds,
+                 'Ebar log perovskite conductivity (S/cm)': 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_conductivity/recal_dict.csv

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+a,b
+0.6074274320025865,0.0684379339445645

model_perovskite_conductivity/requirements.txt

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