Dont run samples for any checkin (Azure#1799)

* Dont run samples for any checkin

* Reformat with black

Author: balapv

.github/workflows/sdk-endpoints-online-kubernetes-kubernetes-online-endpoints-safe-rollout.yml

@@ -12,7 +12,6 @@ on:
     branches:
       - main
     paths:
-      - sdk/python/**
       - .github/workflows/sdk-endpoints-online-kubernetes-kubernetes-online-endpoints-safe-rollout.yml
       - sdk/python/dev-requirements.txt
       - .github/kubernetes-compute/tool.sh

.github/workflows/sdk-endpoints-online-kubernetes-kubernetes-online-endpoints-simple-deployment.yml

@@ -12,7 +12,6 @@ on:
     branches:
       - main
     paths:
-      - sdk/python/**
       - .github/workflows/sdk-endpoints-online-kubernetes-kubernetes-online-endpoints-simple-deployment.yml
       - sdk/python/dev-requirements.txt
       - .github/kubernetes-compute/tool.sh

sdk/python/resources/connections/connections.ipynb

@@ -111,15 +111,14 @@
     "acr_password = os.environ[\"ACR_PASSWORD\"]\n",
     "\n",
     "credentials = UsernamePasswordConfiguration(\n",
-    "    username=acr_username,\n",
-    "    password=acr_password\n",
+    "    username=acr_username, password=acr_password\n",
     ")\n",
     "\n",
     "ws_connection = WorkspaceConnection(\n",
     "    name=\"<connection_name>\",\n",
-    "    target=\"<acr_url>\", # testacr.azurecr.io,\n",
+    "    target=\"<acr_url>\",  # testacr.azurecr.io,\n",
     "    type=\"container_registry\",\n",
-    "    credentials=credentials\n",
+    "    credentials=credentials,\n",
     ")\n",
     "\n",
     "ml_client.connections.create_or_update(ws_connection)"
@@ -147,15 +146,10 @@
     "# fetching secrets from env var to secure access, these secrets can be set outside or source code\n",
     "git_pat = os.environ[\"GIT_PAT\"]\n",
     "\n",
-    "credentials = PatTokenConfiguration(\n",
-    "    pat=git_pat\n",
-    ")\n",
+    "credentials = PatTokenConfiguration(pat=git_pat)\n",
     "\n",
     "ws_connection = WorkspaceConnection(\n",
-    "    name=\"<connection_name>\",\n",
-    "    target=\"<git_url>\",\n",
-    "    type=\"git\",\n",
-    "    credentials=credentials\n",
+    "    name=\"<connection_name>\", target=\"<git_url>\", type=\"git\", credentials=credentials\n",
     ")\n",
     "\n",
     "ml_client.connections.create_or_update(ws_connection)"
@@ -183,15 +177,13 @@
     "# fetching secrets from env var to secure access, these secrets can be set outside or source code\n",
     "python_feed_sas = os.environ[\"PYTHON_FEED_SAS\"]\n",
     "\n",
-    "credentials = SasTokenConfiguration(\n",
-    "    pat=python_feed_sas\n",
-    ")\n",
+    "credentials = SasTokenConfiguration(pat=python_feed_sas)\n",
     "\n",
     "ws_connection = WorkspaceConnection(\n",
     "    name=\"<connection_name>\",\n",
     "    target=\"<python_feed_url>\",\n",
     "    type=\"python_feed\",\n",
-    "    credentials=credentials\n",
+    "    credentials=credentials,\n",
     ")\n",
     "\n",
     "ml_client.connections.create_or_update(ws_connection)"

v1/python-sdk/tutorials/automl-with-azureml/automl-nlp-ner/automl-nlp-ner.ipynb

@@ -448,9 +448,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\n",
-    "    named_entity_recognition_report(output_prediction_file, data_dir + \"/test.txt\")\n",
-    ")"
+    "print(named_entity_recognition_report(output_prediction_file, data_dir + \"/test.txt\"))"
    ]
   }
  ],

v1/python-sdk/tutorials/automl-with-azureml/automl-nlp-ner/utils.py

@@ -2,11 +2,12 @@
 from seqeval.metrics.sequence_labeling import precision_recall_fscore_support
 from azureml.automl.core.shared.constants import Metric
 
-def named_entity_recognition_report(predictions_file, test_data_file, use_str = True):
-    
+
+def named_entity_recognition_report(predictions_file, test_data_file, use_str=True):
+
     # getting the predicted and true labels
     def get_labels_from_file(file):
-        
+
         labels = []
         current_labels = []
         with open(file) as opened:
@@ -22,39 +23,65 @@ def get_labels_from_file(file):
 
             labels.append(current_labels)
         return labels
-    
+
     prediction_labels = get_labels_from_file(predictions_file)
     true_labels = get_labels_from_file(test_data_file)
-    
+
     # accuracy
     accuracy = accuracy_score(y_true=true_labels, y_pred=prediction_labels)
     # micro averages
     precision_micro, recall_micro, f1_micro, _ = precision_recall_fscore_support(
-        y_true=true_labels, y_pred=prediction_labels, average='micro'
+        y_true=true_labels, y_pred=prediction_labels, average="micro"
     )
     # macro averages
     precision_macro, recall_macro, f1_macro, _ = precision_recall_fscore_support(
-        y_true=true_labels, y_pred=prediction_labels, average='macro'
+        y_true=true_labels, y_pred=prediction_labels, average="macro"
     )
     # weighted averages
-    precision_weighted, recall_weighted, f1_weighted, _ = precision_recall_fscore_support(
-        y_true=true_labels, y_pred=prediction_labels, average='weighted'
+    (
+        precision_weighted,
+        recall_weighted,
+        f1_weighted,
+        _,
+    ) = precision_recall_fscore_support(
+        y_true=true_labels, y_pred=prediction_labels, average="weighted"
     )
-    
+
     if use_str:
         results = Metric.Accuracy + "\t" + "{:.3f}".format(accuracy) + "\n"
         results += "\t\t" + "F1-score" + "\t" + "Precision" + "\t" + "Recall" + "\n"
-        results += "micro" + "\t\t" + "{:.3f}".format(f1_micro) + "\t\t" \
-                    + "{:.3f}".format(precision_micro) + "\t\t" \
-                    + "{:.3f}".format(recall_micro) + "\n"
-        results += "macro" + "\t\t" + "{:.3f}".format(f1_macro) + "\t\t" \
-                    + "{:.3f}".format(precision_macro) + "\t\t" \
-                    + "{:.3f}".format(recall_macro) + "\n"
-        results += "weighted" + "\t" + "{:.3f}".format(f1_weighted) + "\t\t" \
-                    + "{:.3f}".format(precision_weighted) + "\t\t" \
-                    + "{:.3f}".format(recall_weighted) + "\n"
-        
-    else: 
+        results += (
+            "micro"
+            + "\t\t"
+            + "{:.3f}".format(f1_micro)
+            + "\t\t"
+            + "{:.3f}".format(precision_micro)
+            + "\t\t"
+            + "{:.3f}".format(recall_micro)
+            + "\n"
+        )
+        results += (
+            "macro"
+            + "\t\t"
+            + "{:.3f}".format(f1_macro)
+            + "\t\t"
+            + "{:.3f}".format(precision_macro)
+            + "\t\t"
+            + "{:.3f}".format(recall_macro)
+            + "\n"
+        )
+        results += (
+            "weighted"
+            + "\t"
+            + "{:.3f}".format(f1_weighted)
+            + "\t\t"
+            + "{:.3f}".format(precision_weighted)
+            + "\t\t"
+            + "{:.3f}".format(recall_weighted)
+            + "\n"
+        )
+
+    else:
         results = dict()
         results[Metric.Accuracy] = accuracy
         results[Metric.F1Micro] = f1_micro
@@ -66,5 +93,5 @@ def get_labels_from_file(file):
         results[Metric.RecallMicro] = recall_micro
         results[Metric.RecallMacro] = recall_macro
         results[Metric.RecallWeighted] = recall_weighted
-        
-    return results
+
+    return results