update samples from Release-43 as a part of SDK release

how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.yml

@@ -5,7 +5,6 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - interpret
   - onnxruntime==1.0.0
   - azureml-explain-model
   - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb

@@ -122,35 +122,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your AmlCompute cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-1\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpu-cluster-1\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'cpu-cluster-1':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_DS12_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml

@@ -5,5 +5,4 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - interpret
   - azureml-explain-model

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.yml

@@ -1,10 +1,9 @@
 name: auto-ml-forecasting-beer-remote
 dependencies:
-- fbprophet==0.5
-- numpy==1.16.2
 - py-xgboost<=0.90
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml

@@ -1,10 +1,9 @@
 name: auto-ml-forecasting-bike-share
 dependencies:
-- fbprophet==0.5
-- numpy==1.16.2
 - py-xgboost<=0.90
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml

@@ -2,9 +2,9 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - interpret
   - azureml-explain-model
   - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.yml

@@ -1,10 +1,9 @@
 name: auto-ml-forecasting-function
 dependencies:
-- fbprophet==0.5
-- numpy==1.16.2
 - py-xgboost<=0.90
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml

@@ -1,10 +1,9 @@
 name: auto-ml-forecasting-orange-juice-sales
 dependencies:
-- fbprophet==0.5
-- numpy==1.16.2
 - py-xgboost<=0.90
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
   - pandas==0.23.4
   - azureml-train-automl
   - azureml-widgets

how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.ipynb

@@ -49,8 +49,8 @@
         "2. Configure AutoML using `AutoMLConfig`.\n",
         "3. Train the model.\n",
         "4. Explore the results.\n",
-        "5. Visualization model's feature importance in widget\n",
-        "6. Explore any model's explanation\n",
+        "5. Visualization model's feature importance in azure portal\n",
+        "6. Explore any model's explanation and explore feature importance in azure portal\n",
         "7. Test the fitted model."
       ]
     },
@@ -272,7 +272,7 @@
         "Retrieve the explanation from the best_run which includes explanations for engineered features and raw features.\n",
         "\n",
         "#### Download engineered feature importance from artifact store\n",
-        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run."
+        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
       ]
     },
     {
@@ -283,7 +283,8 @@
       "source": [
         "client = ExplanationClient.from_run(best_run)\n",
         "engineered_explanations = client.download_model_explanation(raw=False)\n",
-        "print(engineered_explanations.get_feature_importance_dict())"
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
       ]
     },
     {
@@ -376,7 +377,7 @@
       "metadata": {},
       "source": [
         "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
-        "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the generated engineered features."
+        "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
       ]
     },
     {
@@ -386,7 +387,8 @@
       "outputs": [],
       "source": [
         "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
-        "print(engineered_explanations.get_feature_importance_dict())\n"
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/local-run-classification-credit-card-fraud/auto-ml-classification-credit-card-fraud-local.yml

@@ -5,5 +5,4 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - interpret
   - azureml-explain-model

how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb

@@ -51,8 +51,8 @@
         "4. Explore the results and featurization transparency options\n",
         "5. Setup remote compute for computing the model explanations for a given AutoML model.\n",
         "6. Start an AzureML experiment on your remote compute to compute explanations for an AutoML model.\n",
-        "7. Download the feature importance for engineered features and visualize the explanations for engineered features. \n",
-        "8. Download the feature importance for raw features and visualize the explanations for raw features. \n"
+        "7. Download the feature importance for engineered features and visualize the explanations for engineered features on azure portal. \n",
+        "8. Download the feature importance for raw features and visualize the explanations for raw features on azure portal. \n"
       ]
     },
     {
@@ -598,46 +598,16 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Feature importance  and  explanation dashboard\n",
-        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Setup for visualizing the model explanation results\n",
-        "For visualizing the explanation results for the *fitted_model* we need to perform the following steps:-\n",
-        "1. Featurize test data samples.\n",
-        "\n",
-        "The *automl_explainer_setup_obj* contains all the structures from above list. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_test = test_data.drop_columns([label]).to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
-        "explainer_setup_class = automl_setup_model_explanations(fitted_model, 'regression', X_test=X_test)"
+        "### Feature importance  and  visualizing explanation dashboard\n",
+        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model on the azure portal."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "#### Download engineered feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the engineered features."
+        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
       ]
     },
     {
@@ -647,19 +617,18 @@
       "outputs": [],
       "source": [
         "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
-        "from interpret_community.widget import ExplanationDashboard\n",
         "client = ExplanationClient.from_run(automl_run)\n",
         "engineered_explanations = client.download_model_explanation(raw=False)\n",
         "print(engineered_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, datasetX=explainer_setup_class.X_test_transform)"
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "#### Download raw feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the raw features."
+        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use azure portal url to view the dash board visualization of the feature importance values of the raw features."
       ]
     },
     {
@@ -670,7 +639,7 @@
       "source": [
         "raw_explanations = client.download_model_explanation(raw=True)\n",
         "print(raw_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, datasetX=explainer_setup_class.X_test_raw)"
+        "print(\"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
       ]
     },
     {
@@ -803,6 +772,7 @@
       "outputs": [],
       "source": [
         "if service.state == 'Healthy':\n",
+        "    X_test = test_data.drop_columns([label]).to_pandas_dataframe()\n",
         "    # Serialize the first row of the test data into json\n",
         "    X_test_json = X_test[:1].to_json(orient='records')\n",
         "    print(X_test_json)\n",

how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.yml

@@ -5,7 +5,6 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - interpret
   - azureml-explain-model
   - azureml-explain-model
   - azureml-contrib-interpret

how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml

@@ -2,7 +2,6 @@ name: explain-model-on-amlcompute
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-interpret
   - azureml-contrib-interpret
   - sklearn-pandas

how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml

@@ -2,7 +2,6 @@ name: save-retrieve-explanations-run-history
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-interpret
   - azureml-contrib-interpret
   - ipywidgets

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml

@@ -2,7 +2,6 @@ name: train-explain-model-locally-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-interpret
   - azureml-contrib-interpret
   - sklearn-pandas

how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml

@@ -2,7 +2,6 @@ name: train-explain-model-on-amlcompute-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-interpret
   - azureml-contrib-interpret
   - sklearn-pandas