Add heusler

.gitattributes

@@ -61,3 +61,4 @@ model_perovskite_conductivity/model.dill filter=lfs diff=lfs merge=lfs -text
 model_metallicglass_Rc_LLM/model.dill filter=lfs diff=lfs merge=lfs -text
 model_metallicglass_Rc/model.dill filter=lfs diff=lfs merge=lfs -text
 model_metallicglass_Dmax/model.dill filter=lfs diff=lfs merge=lfs -text
+model_heusler/model.dill filter=lfs diff=lfs merge=lfs -text

model_heusler/.gitattributes

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

model_heusler/README.md

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+# model_heusler
+Random forest model to predict the magnetization of Heusler alloys

model_heusler/RandomForestRegressor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e27c34ee725460bf7f312646ee7da4fb8d98b689007647d0059b8a95e5b0b551
+size 38383015

model_heusler/StandardScaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5da6984fcb813e4006ca05afdedf98efb16e95d4684b84fd614d45bf15f2f8b
+size 1364

model_heusler/model.dill

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6b05d10d6e3480523e605593a0046b62ce07948862c4479c067255b130457364
+size 45232823

model_heusler/predict_heusler.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_heusler'
+    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, numelec_list, heuslertype_list):
+
+    # Process data
+    df_test = process_data(comp_list)
+
+    # Process data
+    X_train = pd.read_csv('model_heusler/X_train.csv')
+    feature_names = X_train.columns.tolist()
+
+    # Convert heusler type encoding to numbers
+    pf_0 = list()
+    pf_1 = list()
+    pf_2 = list()
+    for i in heuslertype_list:
+        if i == 'Full Heusler':
+            pf_0.append(1)
+            pf_1.append(0)
+            pf_2.append(0)
+        elif i == 'Inverse Heusler':
+            pf_0.append(0)
+            pf_1.append(1)
+            pf_2.append(0)
+        elif i == 'Half Heusler':
+            pf_0.append(0)
+            pf_1.append(0)
+            pf_2.append(1)
+        else:
+            raise ValueError('Heusler type must be one of Full Heusler, Half Heusler, or Inverse Heusler')
+    
+    df_test['heusler type_0'] = pf_0
+    df_test['heusler type_1'] = pf_1
+    df_test['heusler type_2'] = pf_2
+    df_test['num_electron'] = numelec_list
+
+    # Get the ML predicted values
+    preds, ebars, domains = get_preds_ebars_domains(df_test)
+
+    pred_dict = {'Predicted magnetization (emu/cm3)': preds,
+                 'Ebar magnetization (emu/cm3)': 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_heusler/recal_dict.csv

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+a,b
+0.877099152681331,-3.798959368401773e-11

model_heusler/requirements.txt

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