Add concrete

.gitattributes

@@ -65,3 +65,4 @@ model_heusler/model.dill filter=lfs diff=lfs merge=lfs -text
 model_hea_hardness/model.dill filter=lfs diff=lfs merge=lfs -text
 model_elastic_tensor/model.dill filter=lfs diff=lfs merge=lfs -text
 model_double_perovskite_gap/model.dill filter=lfs diff=lfs merge=lfs -text
+model_concrete/model.dill filter=lfs diff=lfs merge=lfs -text

model_concrete/.gitattributes

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

model_concrete/README.md

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+# model_concrete
+Random forest model to predict the compressive strength of concrete mixtures

model_concrete/RandomForestRegressor.pkl

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+oid sha256:0ba2a0b9f483e9f3c90954c8d6c528af240d05e9d6fb4012b4b1f8ec5769bfce
+size 7889907

model_concrete/StandardScaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ac53cb7d1fb0708da56ba9fefc881a66a8a40ff0ff694ebd071f2cf2e9dd7680
+size 1573

model_concrete/X_train.csv

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+Cement (component 1)(kg in a m^3 mixture),Age (day),Water  (component 4)(kg in a m^3 mixture),Blast Furnace Slag (component 2)(kg in a m^3 mixture),Superplasticizer (component 5)(kg in a m^3 mixture),Fine Aggregate (component 7)(kg in a m^3 mixture),Coarse Aggregate  (component 6)(kg in a m^3 mixture),Fly Ash (component 3)(kg in a m^3 mixture)
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model_concrete/model.dill

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c935becba3226203b02ffbe675e736e7fa39a97ca7394cd5dae923491968cd1c
+size 17608749

model_concrete/predict_concrete.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_concrete'
+    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_concrete/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 concrete compressive strength (MPa)': preds,
+                 'Ebar concrete compressive 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_concrete/recal_dict.csv

@@ -0,0 +1,2 @@
+a,b
+0.638067716635479,1.2482931234381782

model_concrete/requirements.txt

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