Upload RPV TTS models

.gitattributes

@@ -39,3 +39,4 @@ 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_thermalexp_aflow/model.dill filter=lfs diff=lfs merge=lfs -text
+model_RPV_TTS/model.dill filter=lfs diff=lfs merge=lfs -text

model_RPV_TTS/.gitattributes

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

model_RPV_TTS/README.md

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+# model_RPV_TTS
+Random forest model to predict the transition temperature shift (TTS) of reactor pressure vessel (RPV) steels

model_RPV_TTS/RandomForestRegressor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86f924788562e4e81c05df3a557d26673f2238f54fff06666090643f4b0a541c
+size 166993571

model_RPV_TTS/StandardScaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd721d5060f293a045bff734f7cf489b44544091830112bb5909835f03a3f4db
+size 1645

model_RPV_TTS/model.dill

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eeb1a8465c13f16a5b21afb1ba4d59d696c57bd71c3348c40fe442a95e2badab
+size 343058384

model_RPV_TTS/predict_RPV_TTS.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_RPV_TTS'
+    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 make_predictions(df_test):
+
+    # Process data
+    X_train = pd.read_csv('model_RPV_TTS/X_train.csv')
+    feature_names = X_train.columns.tolist()
+
+    # Convert Product form encoding to numbers
+    pf = df_test['Product Form']
+    pf_0 = list()
+    pf_1 = list()
+    pf_2 = list()
+    pf_3 = list()
+    pf_4 = list()
+    pf_5 = list()
+    for i in pf:
+        if i == 'F':
+            pf_0.append(1)
+            pf_1.append(0)
+            pf_2.append(0)
+            pf_3.append(0)
+            pf_4.append(0)
+            pf_5.append(0)
+        elif i == 'HAZ':
+            pf_0.append(0)
+            pf_1.append(1)
+            pf_2.append(0)
+            pf_3.append(0)
+            pf_4.append(0)
+            pf_5.append(0)
+        elif i == 'P':
+            pf_0.append(0)
+            pf_1.append(0)
+            pf_2.append(1)
+            pf_3.append(0)
+            pf_4.append(0)
+            pf_5.append(0)
+        elif i == 'PCE':
+            pf_0.append(0)
+            pf_1.append(0)
+            pf_2.append(0)
+            pf_3.append(1)
+            pf_4.append(0)
+            pf_5.append(0)
+        elif i == 'SRM':
+            pf_0.append(0)
+            pf_1.append(0)
+            pf_2.append(0)
+            pf_3.append(0)
+            pf_4.append(1)
+            pf_5.append(0)
+        elif i == 'W':
+            pf_0.append(0)
+            pf_1.append(0)
+            pf_2.append(0)
+            pf_3.append(0)
+            pf_4.append(0)
+            pf_5.append(1)
+        else:
+            raise ValueError('Product form must be one of F, HAZ, P, PCE, SRM, W')
+    
+    df_test['Product Form_0'] = pf_0
+    df_test['Product Form_1'] = pf_1
+    df_test['Product Form_2'] = pf_2
+    df_test['Product Form_3'] = pf_3
+    df_test['Product Form_4'] = pf_4
+    df_test['Product Form_5'] = pf_5
+
+    del df_test['Product Form']
+
+    # 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 TTS (degC)': preds,
+                 'Ebar TTS (degC)': 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_RPV_TTS/recal_dict.csv

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+a,b,c
+-0.0014836483656973376,0.7730364503167175,2.4083842823104162

model_RPV_TTS/requirements.txt

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