update samples from Release-25 as a part of 1.1.2rc0 SDK experimental…

… release
fomensah · Mar 2, 2020 · 6948535 · 6948535
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diff --git a/README.md b/README.md
@@ -13,7 +13,7 @@ Read more detailed instructions on [how to set up your environment](./NBSETUP.md
 
 ## How to navigate and use the example notebooks?
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
-This [index](.index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+This [index](./index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
 
 If you want to...
 

diff --git a/configuration.ipynb b/configuration.ipynb
@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.1.1rc0 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.1.2rc0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

diff --git a/how-to-use-azureml/automated-machine-learning/automl_env.yml b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -28,7 +28,6 @@ dependencies:
   - azureml-contrib-interpret
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - joblib
   - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
 

diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -29,7 +29,6 @@ dependencies:
   - azureml-contrib-interpret
   - pytorch-transformers==1.0.0
   - spacy==2.1.8
-  - joblib
   - onnxruntime==1.0.0
   - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz  
 

diff --git a/...tion-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb b/...tion-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb
@@ -320,7 +320,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
-        "|**model_explainability**|Indicate to explain each trained pipeline or not.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -352,7 +351,6 @@
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
         "                             validation_data = validation_dataset,\n",
-        "                             model_explainability=True,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -500,11 +498,11 @@
       "outputs": [],
       "source": [
         "# Wait for the best model explanation run to complete\n",
-        "from azureml.train.automl.run import AutoMLRun\n",
+        "from azureml.core.run import Run\n",
         "model_explainability_run_id = remote_run.get_properties().get('ModelExplainRunId')\n",
         "print(model_explainability_run_id)\n",
         "if model_explainability_run_id is not None:\n",
-        "    model_explainability_run = AutoMLRun(experiment=experiment, run_id=model_explainability_run_id)\n",
+        "    model_explainability_run = Run(experiment=experiment, run_id=model_explainability_run_id)\n",
         "    model_explainability_run.wait_for_completion()\n",
         "\n",
         "# Get the best run object\n",

diff --git a/...reml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb b/...reml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb
@@ -343,7 +343,7 @@
       "outputs": [],
       "source": [
         "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl import AutoMLStep\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\": 10,\n",

diff --git a/...l/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb b/...l/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb
@@ -459,8 +459,8 @@
         "# use forecast_quantiles function, not the forecast() one\n",
         "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test)\n",
         "\n",
-        "# it all nicely aligns column-wise\n",
-        "pd.concat([X_test.reset_index(), y_pred_quantiles], axis=1)"
+        "# quantile forecasts returned in a Dataframe along with the time and grain columns \n",
+        "y_pred_quantiles"
       ]
     },
     {

diff --git a/...featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb b/...featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb
@@ -514,7 +514,7 @@
         "    content = cefr.read()\n",
         "\n",
         "# Replace the values in train_explainer.py file with the appropriate values\n",
-        "content = content.replace('<<experimnet_name>>', automl_run.experiment.name) # your experiment name.\n",
+        "content = content.replace('<<experiment_name>>', automl_run.experiment.name) # your experiment name.\n",
         "content = content.replace('<<run_id>>', automl_run.id) # Run-id of the AutoML run for which you want to explain the model.\n",
         "content = content.replace('<<target_column_name>>', 'ERP') # Your target column name\n",
         "content = content.replace('<<task>>', 'regression') # Training task type\n",

diff --git a/...learning/regression-hardware-performance-explanation-and-featurization/train_explainer.py b/...learning/regression-hardware-performance-explanation-and-featurization/train_explainer.py
@@ -22,7 +22,7 @@
 ws = run.experiment.workspace
 
 # Get the AutoML run object from the experiment name and the workspace
-experiment = Experiment(ws, '<<experimnet_name>>')
+experiment = Experiment(ws, '<<experiment_name>>')
 automl_run = Run(experiment=experiment, run_id='<<run_id>>')
 
 # Check if this AutoML model is explainable