Upload Mg alloy models

.gitattributes

@@ -36,3 +36,4 @@ saved_model/**/* 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_Mg_alloy-main/model.dill filter=lfs diff=lfs merge=lfs -text

model_Mg_alloy-main/.gitattributes

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

model_Mg_alloy-main/README.md

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+# model_Mg_alloy
+Random forest model to predict the yield strength of Mg alloys

model_Mg_alloy-main/RandomForestRegressor.pkl

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model_Mg_alloy-main/StandardScaler.pkl

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model_Mg_alloy-main/X_train.csv

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model_Mg_alloy-main/model.dill

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a074c0a0f6993bd3367b8e75ec158cebd9932c10d66268d23a0bc5c21f4eb0c
+size 5005081

model_Mg_alloy-main/predict_Mg_alloy.py

@@ -0,0 +1,74 @@
+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_Mg_alloy'
+    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 make_predictions(df_test):
+
+    # Process data
+    X_train = pd.read_csv('model_Mg_alloy/X_train.csv')
+    feature_names = X_train.columns.tolist()
+    
+    # Check the data
+    cols_in = df_test.columns.tolist()
+    for c_in in cols_in:
+        if c_in not in feature_names:
+            print('Error with input feature', c_in)
+            print('Input features should be', feature_names)
+            break
+
+    # Get the ML predicted values
+    preds, ebars, domains = get_preds_ebars_domains(df_test)
+
+    pred_dict = {'Predicted Mg alloy yield strength (MPa)': preds,
+                 'Ebar Mg alloy yield strength (MPa)': 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']
+
+    for f in feature_names:
+        pred_dict[f] = np.array(df_test[f]).ravel()
+
+    return pd.DataFrame(pred_dict)

model_Mg_alloy-main/recal_dict.csv

@@ -0,0 +1,2 @@
+a,b
+0.742278188398772,17.279200210523392

model_Mg_alloy-main/requirements.txt

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