retrained alarm model

ml/src/model/train_survival_model.py

@@ -7,7 +7,6 @@ from utility.settings import load_settings
 from sksurv.ensemble import RandomSurvivalForest
 from io import BytesIO
 import shutil
-from sklearn.model_selection import train_test_split
 
 def main():
     data_settings = load_settings(pt.CONFIGS_DIR, "data.yaml")
@@ -27,13 +26,9 @@ def main():
         labels_enc[col_name] = le
         X.loc[:, col_name] = le.transform(X.loc[:, col_name].astype(str))
         
-    X_train, X_test, y_train, y_test = train_test_split(
-        X, y, stratify=y['Status'], test_size=0.25, random_state=0)
-        
     model = RandomSurvivalForest(n_estimators=200, max_depth=6,
                                  n_jobs=-1, random_state=0)
-    model.fit(X_train, y_train)
-    print(model.score(X_test, y_test))
+    model.fit(X, y)
     
     with open(Path.joinpath(pt.MODELS_DIR, "alarm_labels.pkl"), 'wb') as fd:
         outfile = BytesIO()