Upload thermal_conductivity

.gitattributes

@@ -41,3 +41,4 @@ model_thermalcond_aflow/model.dill filter=lfs diff=lfs merge=lfs -text
 model_thermalexp_aflow/model.dill filter=lfs diff=lfs merge=lfs -text
 model_RPV_TTS/model.dill filter=lfs diff=lfs merge=lfs -text
 model_phonon_freq/model.dill filter=lfs diff=lfs merge=lfs -text
+model_thermal_conductivity/model.dill filter=lfs diff=lfs merge=lfs -text

model_thermal_conductivity/.gitattributes

@@ -0,0 +1,2 @@
+# Auto detect text files and perform LF normalization
+* text=auto

model_thermal_conductivity/README.md

@@ -0,0 +1,2 @@
+# model_thermal_conductivity
+Random forest model to predict the thermal conductivity of materials

model_thermal_conductivity/RandomForestRegressor.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:764b2577944e9770dd88b4be2ed9dcbc789558898609c04eb8a0e739e14d8a61
+size 9464148

model_thermal_conductivity/StandardScaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0af2333aed167ba9d3c74e14226476f8cff50c2d9d579a90f96cffd68e58c5dc
+size 1364

model_thermal_conductivity/model.dill

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:731567814a657ddd70ee7027077086f8c621e53f11203d01c772bb3adfda94b6
+size 15277719

model_thermal_conductivity/predict_thermal_conductivity.py

@@ -0,0 +1,83 @@
+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_thermal_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]
+    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, temp_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, 'k_condition': temp_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):
+
+    # Process data
+    df_test = process_data(comp_list, temp_list)
+
+    # Get the ML predicted values
+    preds, ebars, domains = get_preds_ebars_domains(df_test)
+
+    pred_dict = {'Compositions': comp_list,
+                 'Predicted log thermal conductivity (W/m-K)': preds,
+                 'Ebar log 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_thermal_conductivity/recal_dict.csv

@@ -0,0 +1,2 @@
+a,b,c
+0.963780687843677,0.28978088616088316,0.08759892406567099

model_thermal_conductivity/requirements.txt

@@ -0,0 +1,5 @@
+scikit-learn
+numpy 
+pandas
+mastml
+pymatgen