Upload thermalcond_aflow models

.gitattributes

@@ -37,3 +37,4 @@ 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_Mg_alloy-main/model.dill filter=lfs diff=lfs merge=lfs -text
+model_thermalcond_aflow/model.dill filter=lfs diff=lfs merge=lfs -text

model_thermalcond_aflow/.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_thermalcond_aflow/README.md

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+# model_thermalcond_aflow
+Random forest model to predict the thermal conductivity of materials, trained from AFLOW database

model_thermalcond_aflow/RandomForestRegressor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d4aaf522d442f9ec47485f3a8bbf756e7482c1b78ac1085a9ab8f525f821859c
+size 99013264

model_thermalcond_aflow/StandardScaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fbf7d44096a2605e57fd064ca80a9099a1e0a9111f7ce231dcf6825335631ad8
+size 1364

model_thermalcond_aflow/model.dill

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f25862e70cb2f88322d84c555660fe2bfbcb846e01bbc537bf2c96e16c62836f
+size 132167605

model_thermalcond_aflow/predict_thermalcond_aflow.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_thermalcond_aflow'
+    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):
+
+    # 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 thermal conductivity (W/m-K)': preds,
+                 'Ebar thermal conductivity (W/m-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)

model_thermalcond_aflow/recal_dict.csv

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+a,b
+0.9548670317937368,-0.038849973163035206

model_thermalcond_aflow/requirements.txt

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