[Sample] Add back visualization in XGBoost sample

samples/core/xgboost_training_cm/README.md

@@ -3,7 +3,10 @@
 The `xgboost_training_cm.py` pipeline creates XGBoost models on structured data in CSV format. Both classification and regression are supported.
 
 The pipeline starts by creating an Google DataProc cluster, and then running analysis, transformation, distributed training and 
-prediction in the created cluster. Finally, a delete cluster operation runs to destroy the cluster it creates
+prediction in the created cluster. 
+Then a single node confusion-matrix and ROC aggregator is used (for classification case) to	
+provide the confusion matrix data, and ROC data to the front end, respectively.
+Finally, a delete cluster operation runs to destroy the cluster it creates
 in the beginning. The delete cluster operation is used as an exit handler, meaning it will run regardless of whether the pipeline fails
 or not.
 
@@ -45,3 +48,11 @@ Delete Cluster:
 
 The container file is located [here](https://github.com/kubeflow/pipelines/tree/master/components/gcp/container) 
 
+For visualization, we use confusion matrix and ROC.
+Confusion Matrix:	
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/local/confusion_matrix/src),
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/local/confusion_matrix)
+and ROC: 
+  [source code](https://github.com/kubeflow/pipelines/tree/master/components/local/roc/src), 
+  [container](https://github.com/kubeflow/pipelines/tree/master/components/local/roc)
+

samples/core/xgboost_training_cm/xgboost_training_cm.py

@@ -22,7 +22,9 @@
 import os
 import subprocess
 
-# TODO(numerology): add ROC and CM back once UI metadata is enabled in this sample.
+confusion_matrix_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e598176c02f45371336ccaa819409e8ec83743df/components/local/confusion_matrix/component.yaml')
+
+roc_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/e598176c02f45371336ccaa819409e8ec83743df/components/local/roc/component.yaml')
 
 dataproc_create_cluster_op = components.load_component_from_url(
     'https://raw.githubusercontent.com/kubeflow/pipelines/677fbaa281125fd604b81eab2488513efee7b600/components/gcp/dataproc/create_cluster/component.yaml')
@@ -211,6 +213,7 @@ def xgb_train_pipeline(
     target='resolution',
     rounds=200,
     workers=2,
+    true_label='ACTION',
 ):
     output_template = str(output) + '/' + dsl.RUN_ID_PLACEHOLDER + '/data'
 
@@ -227,7 +230,7 @@ def xgb_train_pipeline(
         region=region,
         name=cluster_name
     )):
-        create_cluster_op = dataproc_create_cluster_op(
+        _create_cluster_op = dataproc_create_cluster_op(
             project_id=project,
             region=region,
             name=cluster_name,
@@ -238,16 +241,16 @@ def xgb_train_pipeline(
             image_version='1.2'
         )
 
-        analyze_op = dataproc_analyze_op(
+        _analyze_op = dataproc_analyze_op(
             project=project,
             region=region,
             cluster_name=cluster_name,
             schema=schema,
             train_data=train_data,
             output=output_template
-        ).after(create_cluster_op).set_display_name('Analyzer')
+        ).after(_create_cluster_op).set_display_name('Analyzer')
 
-        transform_op = dataproc_transform_op(
+        _transform_op = dataproc_transform_op(
             project=project,
             region=region,
             cluster_name=cluster_name,
@@ -256,9 +259,9 @@ def xgb_train_pipeline(
             target=target,
             analysis=analyze_output,
             output=output_template
-        ).after(analyze_op).set_display_name('Transformer')
+        ).after(_analyze_op).set_display_name('Transformer')
 
-        train_op = dataproc_train_op(
+        _train_op = dataproc_train_op(
             project=project,
             region=region,
             cluster_name=cluster_name,
@@ -269,9 +272,9 @@ def xgb_train_pipeline(
             workers=workers,
             rounds=rounds,
             output=train_output
-        ).after(transform_op).set_display_name('Trainer')
+        ).after(_transform_op).set_display_name('Trainer')
 
-        predict_op = dataproc_predict_op(
+        _predict_op = dataproc_predict_op(
             project=project,
             region=region,
             cluster_name=cluster_name,
@@ -280,7 +283,19 @@ def xgb_train_pipeline(
             target=target,
             analysis=analyze_output,
             output=predict_output
-        ).after(train_op).set_display_name('Predictor')
+        ).after(_train_op).set_display_name('Predictor')
+
+        _cm_op = confusion_matrix_op(
+            predictions=os.path.join(predict_output, 'part-*.csv'),
+            output_dir=output_template
+        ).after(_predict_op)
+
+        _roc_op = roc_op(
+            predictions_dir=os.path.join(predict_output, 'part-*.csv'),
+            true_class=true_label,
+            true_score_column=true_label,
+            output_dir=output_template
+        ).after(_predict_op)
 
     dsl.get_pipeline_conf().add_op_transformer(
         gcp.use_gcp_secret('user-gcp-sa'))

test/sample-test/run_sample_test.py

@@ -152,21 +152,20 @@ def check(self):
         exit(1)
 
       ###### Validate the results for specific test cases ######
-      #TODO: Add result check for tfx-cab-classification after launch.
-      # if self._testname == 'xgboost_training_cm':
-      #   # For xgboost sample, check its confusion matrix.
-      #   cm_tar_path = './confusion_matrix.tar.gz'
-      #   utils.get_artifact_in_minio(workflow_json, 'confusion-matrix', cm_tar_path,
-      #                               'mlpipeline-ui-metadata')
-      #   with tarfile.open(cm_tar_path) as tar_handle:
-      #     file_handles = tar_handle.getmembers()
-      #     assert len(file_handles) == 1
-      #
-      #     with tar_handle.extractfile(file_handles[0]) as f:
-      #       cm_data = f.read()
-      #       utils.add_junit_test(self._test_cases, 'confusion matrix format',
-      #                            (len(cm_data) > 0),
-      #                            'the confusion matrix file is empty')
+      if self._testname == 'xgboost_training_cm':
+        # For xgboost sample, check its confusion matrix.
+        cm_tar_path = './confusion_matrix.tar.gz'
+        utils.get_artifact_in_minio(workflow_json, 'confusion-matrix', cm_tar_path,
+                                    'mlpipeline-ui-metadata')
+        with tarfile.open(cm_tar_path) as tar_handle:
+          file_handles = tar_handle.getmembers()
+          assert len(file_handles) == 1
+
+          with tar_handle.extractfile(file_handles[0]) as f:
+            cm_data = f.read()
+            utils.add_junit_test(self._test_cases, 'confusion matrix format',
+                                 (len(cm_data) > 0),
+                                 'the confusion matrix file is empty')
 
     ###### Delete Job ######
     #TODO: add deletion when the backend API offers the interface.