Do not handle test data when upload_only is True.

When the upload_only flag is set, the test data generation is not required. This
commit adds logic to check for this and create the test data accordingly.

PiperOrigin-RevId: 677013478

spade_anomaly_detection/runner.py

@@ -98,7 +98,8 @@ def __init__(self, runner_parameters: parameters.RunnerParameters):
       self.input_data_loader = cast(
           data_loader.DataLoader, self.input_data_loader
       )
-      self.test_data_loader = self.input_data_loader
+      if not self.runner_parameters.upload_only:
+        self.test_data_loader = self.input_data_loader
     else:
       self.input_data_loader = csv_data_loader.CsvDataLoader(
           self.runner_parameters
@@ -107,13 +108,14 @@ def __init__(self, runner_parameters: parameters.RunnerParameters):
       self.input_data_loader = cast(
           csv_data_loader.CsvDataLoader, self.input_data_loader
       )
-      self.test_data_loader = csv_data_loader.CsvDataLoader(
-          self.runner_parameters
-      )
-      # Type hint to prevent linter errors.
-      self.test_data_loader = cast(
-          csv_data_loader.CsvDataLoader, self.test_data_loader
-      )
+      if not self.runner_parameters.upload_only:
+        self.test_data_loader = csv_data_loader.CsvDataLoader(
+            self.runner_parameters
+        )
+        # Type hint to prevent linter errors.
+        self.test_data_loader = cast(
+            csv_data_loader.CsvDataLoader, self.test_data_loader
+        )
 
     # TODO(b/247116870): Evaluate performance implications of using a global
     # testing array - the machine may not have enough memory to store the test
@@ -555,6 +557,8 @@ def preprocess_train_test_split(
     features = features[random_indices]
     labels = labels[random_indices]
 
+    test_x, test_y = None, None
+
     if self.runner_parameters.train_setting == parameters.TrainSetting.PNU:
       ground_truth_label_indices = np.where(
           labels != self.runner_parameters.unlabeled_data_value
@@ -570,8 +574,9 @@ def preprocess_train_test_split(
       train_x = np.delete(features, test_index_subset, axis=0)
       train_y = np.delete(labels, test_index_subset, axis=0)
 
-      test_x = features[test_index_subset]
-      test_y = labels[test_index_subset]
+      if not self.runner_parameters.upload_only:
+        test_x = features[test_index_subset]
+        test_y = labels[test_index_subset]
 
     # TODO(b/247116870): Investigate the performance implications and user
     # interest for including some of the negative data in the training set
@@ -606,51 +611,57 @@ def preprocess_train_test_split(
           axis=0,
       )
 
-      test_x = np.concatenate(
-          [features[negative_indices], features[test_positive_indices]], axis=0
-      )
-      test_y = np.concatenate(
-          [labels[negative_indices], labels[test_positive_indices]], axis=0
-      )
+      if not self.runner_parameters.upload_only:
+        test_x = np.concatenate(
+            [features[negative_indices], features[test_positive_indices]],
+            axis=0,
+        )
+        test_y = np.concatenate(
+            [labels[negative_indices], labels[test_positive_indices]], axis=0
+        )
 
     else:
       raise ValueError(
           'Unknown train setting for preparing train/test '
           f'datasets: {self.runner_parameters.train_setting}'
       )
 
-    # TODO(b/247116870): Implement a dedicated function in the runner class
-    # to load BQ test sets before evaluating the supervised model.
-    if (
-        self.runner_parameters.test_bigquery_table_path
-        or self.runner_parameters.data_test_gcs_uri
-    ):
-      test_tf_dataset = self._get_test_data()
-      test_x, test_y = test_tf_dataset.as_numpy_iterator().next()
-      self.test_x = np.array(test_x)
-      self.test_y = np.array(test_y)
-
-      if not (
-          np.any(test_y == self.runner_parameters.positive_data_value)
-          and np.any(test_y == self.runner_parameters.negative_data_value)
+    if not self.runner_parameters.upload_only:
+      # TODO(b/247116870): Implement a dedicated function in the runner class
+      # to load BQ test sets before evaluating the supervised model.
+      if (
+          self.runner_parameters.test_bigquery_table_path
+          or self.runner_parameters.data_test_gcs_uri
       ):
-        raise ValueError(
-            'Positive and negative labels must be in the testing set. Please '
-            'check the test table provided in the test_bigquery_table_path '
-            'parameter.'
-        )
-    else:
-      if self.test_x is not None:
-        self.test_x = np.concatenate([self.test_x, test_x], axis=0)
-        self.test_y = np.concatenate([self.test_y, test_y], axis=0)
+        test_tf_dataset = self._get_test_data()
+        test_x, test_y = test_tf_dataset.as_numpy_iterator().next()
+        self.test_x = np.array(test_x)
+        self.test_y = np.array(test_y)
+
+        if not (
+            np.any(test_y == self.runner_parameters.positive_data_value)
+            and np.any(test_y == self.runner_parameters.negative_data_value)
+        ):
+          raise ValueError(
+              'Positive and negative labels must be in the testing set. Please '
+              'check the test table provided in the test_bigquery_table_path '
+              'parameter.'
+          )
       else:
-        self.test_x = test_x
-        self.test_y = test_y
-
-    # Adjust the testing labels to values of 1 and 0 to align with the class
-    # the supervised model is trained on.
-    self.test_y[self.test_y == self.runner_parameters.positive_data_value] = 1
-    self.test_y[self.test_y == self.runner_parameters.negative_data_value] = 0
+        if self.test_x is not None:
+          self.test_x = np.concatenate([self.test_x, test_x], axis=0)
+          self.test_y = np.concatenate([self.test_y, test_y], axis=0)
+        else:
+          self.test_x = test_x
+          self.test_y = test_y
+
+      # Adjust the testing labels to values of 1 and 0 to align with the class
+      # the supervised model is trained on.
+      if self.test_y is not None:
+        self.test_y[self.test_y ==
+                    self.runner_parameters.positive_data_value] = 1
+        self.test_y[self.test_y ==
+                    self.runner_parameters.negative_data_value] = 0
 
     return (train_x, train_y)
 

spade_anomaly_detection/runner_test.py

@@ -776,7 +776,7 @@ def test_evaluate_set_throw_error_not_initialized(self):
     ):
       runner_object.evaluate_model()
 
-  def test_supervised_model_not_instantiated_throw_error(self):
+  def test_upload_only_true_throw_error(self):
     self.runner_parameters.upload_only = True
     self.runner_parameters.output_bigquery_table_path = (
         'project.dataset.output_table'
@@ -785,7 +785,7 @@ def test_supervised_model_not_instantiated_throw_error(self):
     runner_object.run()
 
     with self.assertRaisesRegex(
-        ValueError, r'Evaluate called without a trained supervised model'
+        ValueError, r'There is no test set to evaluate on'
     ):
       runner_object.evaluate_model()