fixed imports

template/steps/model_evaluator.py

@@ -4,20 +4,22 @@
 
 import pandas as pd
 from sklearn.base import ClassifierMixin
-from zenml import log_metadata, step, Client
+
+from zenml import log_metadata, step
+from zenml.client import Client
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
 
 
 @step
 def model_evaluator(
-    model: ClassifierMixin,
-    dataset_trn: pd.DataFrame,
-    dataset_tst: pd.DataFrame,
-    min_train_accuracy: float = 0.0,
-    min_test_accuracy: float = 0.0,
-    target: Optional[str] = "target",
+        model: ClassifierMixin,
+        dataset_trn: pd.DataFrame,
+        dataset_tst: pd.DataFrame,
+        min_train_accuracy: float = 0.0,
+        min_test_accuracy: float = 0.0,
+        target: Optional[str] = "target",
 ) -> float:
     """Evaluate a trained model.
 
@@ -63,17 +65,17 @@ def model_evaluator(
         dataset_tst.drop(columns=[target]),
         dataset_tst[target],
     )
-    logger.info(f"Train accuracy={trn_acc*100:.2f}%")
-    logger.info(f"Test accuracy={tst_acc*100:.2f}%")
+    logger.info(f"Train accuracy={trn_acc * 100:.2f}%")
+    logger.info(f"Test accuracy={tst_acc * 100:.2f}%")
 
     messages = []
     if trn_acc < min_train_accuracy:
         messages.append(
-            f"Train accuracy {trn_acc*100:.2f}% is below {min_train_accuracy*100:.2f}% !"
+            f"Train accuracy {trn_acc * 100:.2f}% is below {min_train_accuracy * 100:.2f}% !"
         )
     if tst_acc < min_test_accuracy:
         messages.append(
-            f"Test accuracy {tst_acc*100:.2f}% is below {min_test_accuracy*100:.2f}% !"
+            f"Test accuracy {tst_acc * 100:.2f}% is below {min_test_accuracy * 100:.2f}% !"
         )
     else:
         for message in messages: