Update README.md

README.md

@@ -98,32 +98,30 @@ The trained model is valid for 14:2:1 phases only, which are stoichiometric comp
 
 **Model pipeline**
 
-The voting regressor to predict the Tc combines four base models.
+The voting regressor to predict the Tc combines the following four base models and equal weight is assigned to each base models.
+
+1. Extra tree regressor (ET)
+2. Extreme gradient boosting (XGB)
+3. Random forest regressor (RF)
+4. Adaptive boosted RF regressor (AB)
 
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-                            ('XGB',
-                             XGBRegressor(alpha=0.5, base_score=0.5,
-                                          booster='gbtree', colsample_bylevel=1,
-                                          colsample_bynode=1,
-                                          colsample_bytree=0.4,
-                                          enable_categorical=False, gamma=0,
-                                          gpu_id=-1, importance_type=None,
-                                          interaction_constraints='',
-                                          learning_rate=0.2, max_delta_step=0,
-                                          max_depth=2, min_child_weight=1,
-                                          missing=nan,
-                                          mo...
-                                          n_estimators=1000, n_jobs=8,
-                                          num_parallel_tree=1, predictor='auto',
-                                          random_state=0, reg_alpha=0.5,
-                                          reg_lambda=1, scale_pos_weight=1,
-                                          subsample=1, tree_method='exact',
-                                          validate_parameters=1,
-                                          verbosity=None)),
-                            ('RF', RandomForestRegressor(max_depth=100)),
-                            ('AB',
-                             AdaBoostRegressor(base_estimator=RandomForestRegressor(max_depth=50,
-                                                                                    n_estimators=50),
-                                               learning_rate=0.001))])
                                                
+# How to use the trained model for inference
+
+```python
+import json
+from joblib
+import pandas as pd
+
+Tc_predictor = load('Magnet_Tc_predictor.joblib') # trained model
+config = json.load(open('config.json')) # config file
+features = config['features'] # feature extraction
+
+#data = pd.read_excel("data.xlsx") # read test file with new compositions 
+data = data[features]
+#data.columns = ["feat_" + str(col) for col in data.columns]
+
+Predicted_value = Tc_predictor.predict(data) # predict Tc values
+print("Predicted Tc value is: {0:.2f}'.format(predictions)")
 
+```