update samples from Release-6 as a part of 1.1.5rc0 SDK stable release

Co-authored-by: vizhur <vizhur@live.com>

README.md

@@ -2,7 +2,7 @@
 
 This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
 
-![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/concept-azure-machine-learning-architecture/workflow.png)
+![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/media/concept-azure-machine-learning-architecture/workflow.png)
 
 
 ## Quick installation
@@ -13,15 +13,15 @@ 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...
 
- * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/img-classification-part2-deploy.ipynb).
+ * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb).
  * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
  * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
- * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
- * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
+ * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
+ * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
  * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb).
 
 ## Tutorials

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.83 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.1.5rc0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

how-to-use-azureml/README.md

@@ -9,7 +9,6 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
 * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
 * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
 * [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
-* [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
 * [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
 * [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
  

how-to-use-azureml/automated-machine-learning/README.md

@@ -144,7 +144,7 @@ jupyter notebook
     - Dataset: forecasting for a bike-sharing
     - Example of training an automated ML forecasting model on multiple time-series
 
-- [automl-forecasting-function.ipynb](forecasting-high-frequency/automl-forecasting-function.ipynb)
+- [auto-ml-forecasting-function.ipynb](forecasting-high-frequency/auto-ml-forecasting-function.ipynb)
     - Example of training an automated ML forecasting model on multiple time-series
 
 - [auto-ml-forecasting-beer-remote.ipynb](forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)
@@ -197,6 +197,17 @@ If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execut
 4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
 5) Check that you have access to the region using the Azure Portal.
 
+## import AutoMLConfig fails after upgrade from before 1.0.76 to 1.0.76 or later
+There were package changes in automated machine learning version 1.0.76, which require the previous version to be uninstalled before upgrading to the new version.
+If you have manually upgraded from a version of automated machine learning before 1.0.76 to 1.0.76 or later, you may get the error:
+`ImportError: cannot import name 'AutoMLConfig'`
+
+This can be resolved by running:
+`pip uninstall azureml-train-automl` and then 
+`pip install azureml-train-automl`
+
+The automl_setup.cmd script does this automatically.
+
 ## workspace.from_config fails
 If the call `ws = Workspace.from_config()` fails:
 1) Make sure that you have run the `configuration.ipynb` notebook successfully.

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -2,8 +2,9 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip
+- pip<=19.3.1
 - python>=3.5.2,<3.6.8
+- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2
@@ -20,18 +21,17 @@ dependencies:
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
+  - azureml-dataprep[pandas]
   - azureml-train-automl
   - azureml-train
   - azureml-widgets
-  - azureml-explain-model
   - azureml-pipeline
   - 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
 
 channels:
 - conda-forge
-- pytorch
+- pytorch

how-to-use-azureml/automated-machine-learning/automl_env_mac.yml

@@ -2,9 +2,10 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip
+- pip<=19.3.1
 - nomkl
 - python>=3.5.2,<3.6.8
+- wheel==0.30.0
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2
@@ -21,18 +22,17 @@ dependencies:
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
   - azureml-defaults
+  - azureml-dataprep[pandas]
   - azureml-train-automl
   - azureml-train
   - azureml-widgets
-  - azureml-explain-model
   - azureml-pipeline
   - 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  
 
 channels:
 - conda-forge
-- pytorch
+- pytorch

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

@@ -92,6 +92,32 @@
         "from azureml.explain.model._internal.explanation_client import ExplanationClient"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Accessing the Azure ML workspace requires authentication with Azure.\n",
+        "\n",
+        "The default authentication is interactive authentication using the default tenant.  Executing the `ws = Workspace.from_config()` line in the cell below will prompt for authentication the first time that it is run.\n",
+        "\n",
+        "If you have multiple Azure tenants, you can specify the tenant by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
+        "\n",
+        "```\n",
+        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
+        "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "```\n",
+        "\n",
+        "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
+        "\n",
+        "```\n",
+        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
+        "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
+        "ws = Workspace.from_config(auth = auth)\n",
+        "```\n",
+        "For more details, see [aka.ms/aml-notebook-auth](http://aka.ms/aml-notebook-auth)"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -294,7 +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)"
       ]
@@ -326,7 +351,6 @@
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
         "                             validation_data = validation_dataset,\n",
-        "                             model_explainability=True,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -474,11 +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",

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

@@ -2,3 +2,10 @@ name: auto-ml-classification-bank-marketing-all-features
 dependencies:
 - pip:
   - azureml-sdk
+  - 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

@@ -210,10 +210,9 @@
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'average_precision_score_weighted',\n",
-        "    \"preprocess\": True,\n",
         "    \"enable_early_stopping\": True,\n",
         "    \"max_concurrent_iterations\": 2, # This is a limit for testing purpose, please increase it as per cluster size\n",
-        "    \"experiment_timeout_hours\": 0.2, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
+        "    \"experiment_timeout_hours\": 0.25, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ablity to find the best model possible\n",
         "    \"verbosity\": logging.INFO,\n",
         "}\n",
         "\n",
@@ -283,7 +282,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "widget-rundetails-sample"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.widgets import RunDetails\n",

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

@@ -2,3 +2,8 @@ name: auto-ml-classification-credit-card-fraud
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - interpret
+  - azureml-explain-model

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb

@@ -121,9 +121,9 @@
       "metadata": {},
       "source": [
         "## Set up a compute cluster\n",
-        "This section uses a user-provided compute cluster (named \"cpu-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n",
+        "This section uses a user-provided compute cluster (named \"dnntext-cluster\" in this example). If a cluster with this name does not exist in the user's workspace, the below code will create a new cluster. You can choose the parameters of the cluster as mentioned in the comments.\n",
         "\n",
-        "Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively."
+        "Whether you provide/select a CPU or GPU cluster, AutoML will choose the appropriate DNN for that setup - BiLSTM or BERT text featurizer will be included in the candidate featurizers on CPU and GPU respectively.  If your goal is to obtain the most accurate model, we recommend you use GPU clusters since BERT featurizers usually outperform BiLSTM featurizers."
       ]
     },
     {
@@ -133,7 +133,7 @@
       "outputs": [],
       "source": [
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-dnntext\"\n",
+        "amlcompute_cluster_name = \"dnntext-cluster\"\n",
         "\n",
         "found = False\n",
         "# Check if this compute target already exists in the workspace.\n",
@@ -145,11 +145,11 @@
         "\n",
         "if not found:\n",
         "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # CPU for BiLSTM\n",
-        "                                                                # To use BERT, select a GPU such as \"STANDARD_NC6\" \n",
+        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_NC6\", # CPU for BiLSTM, such as \"STANDARD_D2_V2\" \n",
+        "                                                                # To use BERT (this is recommended for best performance), select a GPU such as \"STANDARD_NC6\" \n",
         "                                                                # or similar GPU option\n",
         "                                                                # available in your workspace\n",
-        "                                                                max_nodes = 6)\n",
+        "                                                                max_nodes = 1)\n",
         "\n",
         "    # Create the cluster\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
@@ -218,7 +218,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Featch data and upload to datastore for use in training"
+        "#### Fetch data and upload to datastore for use in training"
       ]
     },
     {
@@ -275,7 +275,6 @@
         "automl_settings = {\n",
         "    \"experiment_timeout_minutes\": 20,\n",
         "    \"primary_metric\": 'accuracy',\n",
-        "    \"preprocess\": True,\n",
         "    \"max_concurrent_iterations\": 4, \n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"enable_dnn\": True,\n",
@@ -348,7 +347,26 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#best_run, fitted_model = automl_run.get_output()"
+        "best_run, fitted_model = automl_run.get_output()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can now see what text transformations are used to convert text data to features for this dataset, including deep learning transformations based on BiLSTM or Transformer (BERT is one implementation of a Transformer) models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "text_transformations_used = []\n",
+        "for column_group in fitted_model.named_steps['datatransformer'].get_featurization_summary():\n",
+        "    text_transformations_used.extend(column_group['Transformations'])\n",
+        "text_transformations_used"
       ]
     },
     {

how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.yml

@@ -2,3 +2,12 @@ name: auto-ml-classification-text-dnn
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - azurmel-train
+  - https://download.pytorch.org/whl/cpu/torch-1.1.0-cp35-cp35m-win_amd64.whl
+  - sentencepiece==0.1.82
+  - pytorch-transformers==1.0
+  - spacy==2.1.8
+  - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb

@@ -197,7 +197,7 @@
         "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
         "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', 'applicationinsights', 'azureml-opendatasets'], \n",
-        "                              conda_packages=['numpy', 'py-xgboost'], \n",
+        "                              conda_packages=['numpy==1.16.2'], \n",
         "                              pin_sdk_version=False)\n",
         "#cd.add_pip_package('azureml-explain-model')\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
@@ -277,7 +277,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "data_pipeline_run.wait_for_completion()"
+        "data_pipeline_run.wait_for_completion(show_output=False)"
       ]
     },
     {
@@ -343,14 +343,13 @@
       "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",
-        "    \"experiment_timeout_hours\": 0.2,\n",
+        "    \"experiment_timeout_hours\": 0.25,\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'r2_score',\n",
-        "    \"preprocess\": True,\n",
         "    \"max_concurrent_iterations\": 3,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"verbosity\": logging.INFO,\n",

how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.yml

@@ -2,3 +2,7 @@ name: auto-ml-continuous-retraining
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - azureml-pipeline

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

@@ -1,4 +1,10 @@
 name: auto-ml-forecasting-beer-remote
 dependencies:
+- fbprophet==0.5
+- py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - azureml-train

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/helper.py

@@ -76,9 +76,12 @@ def get_result_df(remote_run):
 def run_inference(test_experiment, compute_target, script_folder, train_run,
                   test_dataset, lookback_dataset, max_horizon,
                   target_column_name, time_column_name, freq):
-    train_run.download_file('outputs/model.pkl', 'inference/model.pkl')
-    train_run.download_file('outputs/conda_env_v_1_0_0.yml',
-                            'inference/condafile.yml')
+    model_base_name = 'model.pkl'
+    if 'model_data_location' in train_run.properties:
+        model_location = train_run.properties['model_data_location']
+        _, model_base_name = model_location.rsplit('/', 1)
+    train_run.download_file('outputs/{}'.format(model_base_name), 'inference/{}'.format(model_base_name))
+    train_run.download_file('outputs/conda_env_v_1_0_0.yml', 'inference/condafile.yml')
 
     inference_env = Environment("myenv")
     inference_env.docker.enabled = True
@@ -91,7 +94,8 @@ def run_inference(test_experiment, compute_target, script_folder, train_run,
                         '--max_horizon': max_horizon,
                         '--target_column_name': target_column_name,
                         '--time_column_name': time_column_name,
-                        '--frequency': freq
+                        '--frequency': freq,
+                        '--model_path': model_base_name
                     },
                     inputs=[test_dataset.as_named_input('test_data'),
                             lookback_dataset.as_named_input('lookback_data')],

how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/infer.py

@@ -232,6 +232,9 @@ parser.add_argument(
 parser.add_argument(
     '--frequency', type=str, dest='freq',
     help='Frequency of prediction')
+parser.add_argument(
+    '--model_path', type=str, dest='model_path',
+    default='model.pkl', help='Filename of model to be loaded')
 
 
 args = parser.parse_args()
@@ -239,6 +242,7 @@ max_horizon = args.max_horizon
 target_column_name = args.target_column_name
 time_column_name = args.time_column_name
 freq = args.freq
+model_path = args.model_path
 
 
 print('args passed are: ')
@@ -246,6 +250,7 @@ print(max_horizon)
 print(target_column_name)
 print(time_column_name)
 print(freq)
+print(model_path)
 
 run = Run.get_context()
 # get input dataset by name
@@ -267,7 +272,8 @@ X_lookback_df = lookback_dataset.drop_columns(columns=[target_column_name])
 y_lookback_df = lookback_dataset.with_timestamp_columns(
     None).keep_columns(columns=[target_column_name])
 
-fitted_model = joblib.load('model.pkl')
+fitted_model = joblib.load(model_path)
+
 
 if hasattr(fitted_model, 'get_lookback'):
     lookback = fitted_model.get_lookback()

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

@@ -1,4 +1,9 @@
 name: auto-ml-forecasting-bike-share
 dependencies:
+- fbprophet==0.5
+- py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib

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

@@ -253,7 +253,7 @@
       "source": [
         "# split into train based on time\n",
         "train = dataset.time_before(datetime(2017, 8, 8, 5), include_boundary=True)\n",
-        "train.to_pandas_dataframe().sort_values(time_column_name).tail(5).reset_index(drop=True)"
+        "train.to_pandas_dataframe().reset_index(drop=True).sort_values(time_column_name).tail(5)"
       ]
     },
     {
@@ -264,7 +264,7 @@
       "source": [
         "# split into test based on time\n",
         "test = dataset.time_between(datetime(2017, 8, 8, 6), datetime(2017, 8, 10, 5))\n",
-        "test.to_pandas_dataframe().head(5).reset_index(drop=True)"
+        "test.to_pandas_dataframe().reset_index(drop=True).head(5)"
       ]
     },
     {

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

@@ -2,3 +2,9 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - interpret
+  - azureml-explain-model
+  - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb

@@ -1,551 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "\n",
-        "_**Forecasting with grouping using Pipelines**_\n",
-        "\n",
-        "## Contents\n",
-        "\n",
-        "1. [Introduction](#Introduction)\n",
-        "2. [Setup](#Setup)\n",
-        "3. [Data](#Data)\n",
-        "4. [Compute](#Compute)\n",
-        "4. [AutoMLConfig](#AutoMLConfig)\n",
-        "5. [Pipeline](#Pipeline)\n",
-        "5. [Train](#Train)\n",
-        "6. [Test](#Test)\n",
-        "\n",
-        "\n",
-        "## Introduction\n",
-        "In this example we use Automated ML and Pipelines to train, select, and operationalize forecasting models for multiple time-series.\n",
-        "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace.\n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "\n",
-        "* Create an Experiment in an existing Workspace.\n",
-        "* Configure AutoML using AutoMLConfig.\n",
-        "* Use our helper script to generate pipeline steps to split, train, and deploy the models.\n",
-        "* Explore the results.\n",
-        "* Test the models.\n",
-        "\n",
-        "It is advised you ensure your cluster has at least one node per group.\n",
-        "\n",
-        "An Enterprise workspace is required for this notebook. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade)\n",
-        "\n",
-        "## Setup\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "import logging\n",
-        "import warnings\n",
-        "\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "\n",
-        "import azureml.core\n",
-        "\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.train.automl import AutoMLConfig"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Accessing the Azure ML workspace requires authentication with Azure.\n",
-        "\n",
-        "The default authentication is interactive authentication using the default tenant. Executing the ws = Workspace.from_config() line in the cell below will prompt for authentication the first time that it is run.\n",
-        "\n",
-        "If you have multiple Azure tenants, you can specify the tenant by replacing the ws = Workspace.from_config() line in the cell below with the following:\n",
-        "```\n",
-        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
-        "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "```\n",
-        "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the ws = Workspace.from_config() line in the cell below with the following:\n",
-        "```\n",
-        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
-        "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "```\n",
-        "For more details, see aka.ms/aml-notebook-auth"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "ds = ws.get_default_datastore()\n",
-        "\n",
-        "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automl-grouping-oj'\n",
-        "# project folder\n",
-        "project_folder = './sample_projects/{}'.format(experiment_name)\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Project Directory'] = project_folder\n",
-        "output['Run History Name'] = experiment_name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Data\n",
-        "Upload data to your default datastore and then load it as a `TabularDataset`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.dataset import Dataset"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# upload training and test data to your default datastore\n",
-        "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path='groupdata', overwrite=True, show_progress=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# load data from your datastore\n",
-        "data = Dataset.Tabular.from_delimited_files(path=ds.path('groupdata/dominicks_OJ_2_5_8_train.csv'))\n",
-        "data_test = Dataset.Tabular.from_delimited_files(path=ds.path('groupdata/dominicks_OJ_2_5_8_test.csv'))\n",
-        "\n",
-        "data.take(5).to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Compute \n",
-        "\n",
-        "#### Create or Attach existing AmlCompute\n",
-        "\n",
-        "You will need to create a compute target for your automated ML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
-        "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
-        "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-11\"\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 == 'AmlCompute':\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_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\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()."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## AutoMLConfig\n",
-        "#### Create a base AutoMLConfig\n",
-        "This configuration will be used for all the groups in the pipeline."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "target_column = 'Quantity'\n",
-        "time_column_name = 'WeekStarting'\n",
-        "grain_column_names = ['Brand']\n",
-        "group_column_names = ['Store']\n",
-        "max_horizon = 20"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\" : 5,\n",
-        "    \"experiment_timeout_hours\" : 0.25,\n",
-        "    \"primary_metric\" : 'normalized_mean_absolute_error',\n",
-        "    \"time_column_name\": time_column_name,\n",
-        "    \"grain_column_names\": grain_column_names,\n",
-        "    \"max_horizon\": max_horizon,\n",
-        "    \"drop_column_names\": ['logQuantity'],\n",
-        "    \"max_concurrent_iterations\": 2,\n",
-        "    \"max_cores_per_iteration\": -1\n",
-        "}\n",
-        "base_configuration = AutoMLConfig(task = 'forecasting',\n",
-        "                             path = project_folder,\n",
-        "                             n_cross_validations=3,\n",
-        "                             **automl_settings\n",
-        "                            )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Pipeline\n",
-        "We've written a script to generate the individual pipeline steps used to create each automl step. Calling this script will return a list of PipelineSteps that will train multiple groups concurrently and then deploy these models.\n",
-        "\n",
-        "This step requires an Enterprise workspace to gain access to this feature. To learn more about creating an Enterprise workspace or upgrading to an Enterprise workspace from the Azure portal, please visit our [Workspace page.](https://docs.microsoft.com/azure/machine-learning/service/concept-workspace#upgrade).\n",
-        "\n",
-        "### Call the method to build pipeline steps\n",
-        "\n",
-        "`build_pipeline_steps()` takes as input:\n",
-        "* **automlconfig**: This is the configuration used for every automl step\n",
-        "* **df**: This is the dataset to be used for training\n",
-        "* **target_column**: This is the target column of the dataset\n",
-        "* **compute_target**: The compute to be used for training\n",
-        "* **deploy**: The option on to deploy the models after training, if set to true an extra step will be added to deploy a webservice with all the models (default is `True`)\n",
-        "* **service_name**: The service name for the model query endpoint\n",
-        "* **time_column_name**: The time column of the data"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "\n",
-        "service_name = 'grouped-model'\n",
-        "try:\n",
-        "    # if you want to get existing service below is the command\n",
-        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
-        "    # we use aci_service_name to create azure aci\n",
-        "    service = Webservice(ws, name=service_name)\n",
-        "    if service:\n",
-        "        service.delete()\n",
-        "except WebserviceException as e:\n",
-        "    pass"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from build import build_pipeline_steps\n",
-        "\n",
-        "steps = build_pipeline_steps(\n",
-        "    base_configuration, \n",
-        "    data, \n",
-        "    target_column,\n",
-        "    compute_target, \n",
-        "    group_column_names=group_column_names, \n",
-        "    deploy=True, \n",
-        "    service_name=service_name, \n",
-        "    time_column_name=time_column_name\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "Use the list of steps generated from above to build the pipeline and submit it to your compute for remote training."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import Pipeline\n",
-        "pipeline = Pipeline(\n",
-        "    description=\"A pipeline with one model per data group using Automated ML.\",\n",
-        "    workspace=ws,    \n",
-        "    steps=steps)\n",
-        "\n",
-        "pipeline_run = experiment.submit(pipeline)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pipeline_run.wait_for_completion(show_output=False)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Test\n",
-        "\n",
-        "Now we can use the holdout set to test our models and ensure our web-service is running as expected."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "service = AciWebservice(ws, service_name)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_test = data_test.to_pandas_dataframe()\n",
-        "# Drop the column we are trying to predict (target column)\n",
-        "x_pred = X_test.drop(target_column, inplace=False, axis=1)\n",
-        "x_pred.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Get Predictions\n",
-        "test_sample = X_test.drop(target_column, inplace=False, axis=1).to_json()\n",
-        "predictions = service.run(input_data=test_sample)\n",
-        "print(predictions)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Convert predictions from JSON to DataFrame\n",
-        "pred_dict =json.loads(predictions)\n",
-        "X_pred = pd.read_json(pred_dict['predictions'])\n",
-        "X_pred.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Fix the index\n",
-        "PRED = 'pred_target'\n",
-        "X_pred[time_column_name] = pd.to_datetime(X_pred[time_column_name], unit='ms')\n",
-        "\n",
-        "X_pred.set_index([time_column_name] + grain_column_names, inplace=True, drop=True)\n",
-        "X_pred.rename({'_automl_target_col': PRED}, inplace=True, axis=1)\n",
-        "# Drop all but the target column and index\n",
-        "X_pred.drop(list(set(X_pred.columns.values).difference({PRED})), axis=1, inplace=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_test[time_column_name] = pd.to_datetime(X_test[time_column_name])\n",
-        "X_test.set_index([time_column_name] + grain_column_names, inplace=True, drop=True)\n",
-        "# Merge predictions with raw features\n",
-        "pred_test = X_test.merge(X_pred, left_index=True, right_index=True)\n",
-        "pred_test.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
-        "def MAPE(actual, pred):\n",
-        "    \"\"\"\n",
-        "    Calculate mean absolute percentage error.\n",
-        "    Remove NA and values where actual is close to zero\n",
-        "    \"\"\"\n",
-        "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
-        "    not_zero = ~np.isclose(actual, 0.0)\n",
-        "    actual_safe = actual[not_na & not_zero]\n",
-        "    pred_safe = pred[not_na & not_zero]\n",
-        "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
-        "    return np.mean(APE)\n",
-        "\n",
-        "def get_metrics(actuals, preds):\n",
-        "    return pd.Series(\n",
-        "    {\n",
-        "        \"RMSE\": np.sqrt(mean_squared_error(actuals, preds)),\n",
-        "        \"NormRMSE\": np.sqrt(mean_squared_error(actuals, preds))/np.abs(actuals.max()-actuals.min()),\n",
-        "        \"MAE\": mean_absolute_error(actuals, preds),\n",
-        "        \"MAPE\": MAPE(actuals, preds)},\n",
-        "    )"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "get_metrics(pred_test[PRED].values, pred_test[target_column].values)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "alyerman"
-      }
-    ],
-    "category": "other",
-    "compute": [
-      "AML Compute"
-    ],
-    "datasets": [
-      "Orange Juice Sales"
-    ],
-    "deployment": [
-      "Azure Container Instance"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "Scikit-learn",
-      "Pytorch"
-    ],
-    "friendly_name": "Automated ML Grouping with Pipeline.",
-    "index_order": 10,
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    },
-    "tags": [
-      "AutomatedML"
-    ],
-    "task": "Use AzureML Pipeline to trigger multiple Automated ML runs."
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -1,4 +0,0 @@
-name: auto-ml-forecasting-grouping
-dependencies:
-- pip:
-  - azureml-sdk

how-to-use-azureml/automated-machine-learning/forecasting-grouping/build.py

@@ -1,144 +0,0 @@
-from typing import List, Dict
-import copy
-import json
-import pandas as pd
-import re
-
-from azureml.core import RunConfiguration
-from azureml.core.compute import ComputeTarget
-from azureml.core.conda_dependencies import CondaDependencies
-from azureml.core.dataset import Dataset
-from azureml.data import TabularDataset
-from azureml.pipeline.core import PipelineData, PipelineParameter, TrainingOutput, StepSequence
-from azureml.pipeline.steps import PythonScriptStep
-from azureml.train.automl import AutoMLConfig
-from azureml.train.automl.runtime import AutoMLStep
-
-
-def _get_groups(data: Dataset, group_column_names: List[str]) -> pd.DataFrame:
-    return data._dataflow.distinct(columns=group_column_names)\
-        .keep_columns(columns=group_column_names).to_pandas_dataframe()
-
-
-def _get_configs(automlconfig: AutoMLConfig,
-                 data: Dataset,
-                 target_column: str,
-                 compute_target: ComputeTarget,
-                 group_column_names: List[str]) -> Dict[str, AutoMLConfig]:
-    # remove invalid characters regex
-    valid_chars = re.compile('[^a-zA-Z0-9-]')
-    groups = _get_groups(data, group_column_names)
-    configs = {}
-    for i, group in groups.iterrows():
-        single = data._dataflow
-        group_name = "#####".join(str(x) for x in group.values)
-        group_name = valid_chars.sub('', group_name)
-        for key in group.index:
-            single = single.filter(data._dataflow[key] == group[key])
-        t_dataset = TabularDataset._create(single)
-        group_conf = copy.deepcopy(automlconfig)
-        group_conf.user_settings['training_data'] = t_dataset
-        group_conf.user_settings['label_column_name'] = target_column
-        group_conf.user_settings['compute_target'] = compute_target
-        configs[group_name] = group_conf
-    return configs
-
-
-def build_pipeline_steps(automlconfig: AutoMLConfig,
-                         data: Dataset,
-                         target_column: str,
-                         compute_target: ComputeTarget,
-                         group_column_names: list,
-                         time_column_name: str,
-                         deploy: bool,
-                         service_name: str = 'grouping-demo') -> StepSequence:
-    steps = []
-
-    metrics_output_name = 'metrics_{}'
-    best_model_output_name = 'best_model_{}'
-    count = 0
-    model_names = []
-
-    # get all automl configs by group
-    configs = _get_configs(automlconfig, data, target_column, compute_target, group_column_names)
-
-    # build a runconfig for register model
-    register_config = RunConfiguration()
-    cd = CondaDependencies()
-    cd.add_pip_package('azureml-pipeline')
-    register_config.environment.python.conda_dependencies = cd
-
-    # create each automl step end-to-end (train, register)
-    for group_name, conf in configs.items():
-        # create automl metrics output
-        metrics_data = PipelineData(
-            name='metrics_data_{}'.format(group_name),
-            pipeline_output_name=metrics_output_name.format(group_name),
-            training_output=TrainingOutput(type='Metrics'))
-        # create automl model output
-        model_data = PipelineData(
-            name='model_data_{}'.format(group_name),
-            pipeline_output_name=best_model_output_name.format(group_name),
-            training_output=TrainingOutput(type='Model', metric=conf.user_settings['primary_metric']))
-
-        automl_step = AutoMLStep(
-            name='automl_{}'.format(group_name),
-            automl_config=conf,
-            outputs=[metrics_data, model_data],
-            allow_reuse=True)
-        steps.append(automl_step)
-
-        # pass the group name as a parameter to the register step ->
-        # this will become the name of the model for this group.
-        group_name_param = PipelineParameter("group_name_{}".format(count), default_value=group_name)
-        count += 1
-
-        reg_model_step = PythonScriptStep(
-            'register.py',
-            name='register_{}'.format(group_name),
-            arguments=["--model_name", group_name_param, "--model_path", model_data],
-            inputs=[model_data],
-            compute_target=compute_target,
-            runconfig=register_config,
-            source_directory="register",
-            allow_reuse=True
-        )
-        steps.append(reg_model_step)
-        model_names.append(group_name)
-
-    final_steps = steps
-    if deploy:
-        # modify the conda dependencies to ensure we pick up correct
-        # versions of azureml-defaults and azureml-train-automl
-        cd = CondaDependencies.create(pip_packages=['azureml-defaults', 'azureml-train-automl'])
-        automl_deps = CondaDependencies(conda_dependencies_file_path='deploy/myenv.yml')
-        cd._merge_dependencies(automl_deps)
-        cd.save('deploy/myenv.yml')
-
-        # add deployment step
-        pp_group_column_names = PipelineParameter(
-            "group_column_names",
-            default_value="#####".join(list(reversed(group_column_names))))
-
-        pp_model_names = PipelineParameter(
-            "model_names",
-            default_value=json.dumps(model_names))
-
-        pp_service_name = PipelineParameter(
-            "service_name",
-            default_value=service_name)
-
-        deployment_step = PythonScriptStep(
-            'deploy.py',
-            name='service_deploy',
-            arguments=["--group_column_names", pp_group_column_names,
-                       "--model_names", pp_model_names,
-                       "--service_name", pp_service_name,
-                       "--time_column_name", time_column_name],
-            compute_target=compute_target,
-            runconfig=RunConfiguration(),
-            source_directory="deploy"
-        )
-        final_steps = StepSequence(steps=[steps, deployment_step])
-
-    return final_steps

how-to-use-azureml/automated-machine-learning/forecasting-grouping/data/dominicks_OJ_2_5_8_test.csv

@@ -1,61 +0,0 @@
-WeekStarting,Store,Brand,Quantity,logQuantity,Advert,Price,Age60,COLLEGE,INCOME,Hincome150,Large HH,Minorities,WorkingWoman,SSTRDIST,SSTRVOL,CPDIST5,CPWVOL5
-1992-08-20,2,minute.maid,23488,10.06424493,1,1.94,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-20,2,tropicana,13376,9.501217335,1,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-27,2,tropicana,8128,9.00307017,0,2.75,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-27,2,minute.maid,19008,9.852615222,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-27,2,dominicks,9024,9.107642974,0,1.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-03,2,tropicana,19456,9.875910785,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-03,2,minute.maid,11584,9.357380115,0,1.81,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-03,2,dominicks,2048,7.624618986000001,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-10,2,tropicana,10048,9.215128888999999,0,2.64,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-10,2,minute.maid,26752,10.19436452,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-10,2,dominicks,1984,7.592870287999999,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-17,2,tropicana,6336,8.754002933999999,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-17,2,minute.maid,3904,8.269756948,0,2.83,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-17,2,dominicks,4160,8.333270353,0,1.77,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-24,2,tropicana,16192,9.692272572,1,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-24,2,minute.maid,3712,8.219326094,0,2.67,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-09-24,2,dominicks,35264,10.47061789,0,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-10-01,2,dominicks,8640,9.064157862,0,1.82,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-10-01,2,minute.maid,41216,10.62658181,1,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-10-01,2,tropicana,5824,8.66974259,0,2.97,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-20,5,tropicana,17728,9.78290059,1,2.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-20,5,minute.maid,27072,10.20625526,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-27,5,tropicana,9600,9.169518378,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-27,5,minute.maid,3840,8.253227646000001,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-27,5,dominicks,1856,7.526178913,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-03,5,tropicana,25664,10.15284451,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-03,5,minute.maid,6144,8.723231275,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-03,5,dominicks,3712,8.219326094,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-10,5,tropicana,9984,9.208739091,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-10,5,dominicks,2688,7.896552702,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-10,5,minute.maid,36416,10.50276352,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-17,5,tropicana,8576,9.056722882999999,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-17,5,minute.maid,5440,8.60153434,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-17,5,dominicks,6464,8.774003599999999,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-24,5,tropicana,13184,9.486759252,1,2.78,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-24,5,dominicks,40896,10.61878754,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-09-24,5,minute.maid,7680,8.946374826,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-10-01,5,dominicks,6144,8.723231275,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-10-01,5,minute.maid,50304,10.82583988,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-10-01,5,tropicana,7488,8.921057017999999,0,2.78,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-20,8,minute.maid,55552,10.9250748,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-20,8,tropicana,8576,9.056722882999999,1,2.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-27,8,tropicana,8000,8.987196821,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-27,8,minute.maid,18688,9.835636886,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-27,8,dominicks,19200,9.862665558,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-03,8,tropicana,21760,9.987828701,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-03,8,minute.maid,14656,9.592605087,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-03,8,dominicks,12800,9.45720045,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-10,8,tropicana,12800,9.45720045,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-10,8,minute.maid,30144,10.31374118,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-10,8,dominicks,15296,9.635346635,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-17,8,tropicana,10112,9.221478116,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-17,8,minute.maid,6208,8.733594062,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-17,8,dominicks,20992,9.951896692,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-24,8,tropicana,10304,9.240287448,1,2.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-24,8,minute.maid,7104,8.868413285,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-09-24,8,dominicks,73856,11.20987253,0,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-10-01,8,minute.maid,65856,11.09522582,1,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-10-01,8,dominicks,16192,9.692272572,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-10-01,8,tropicana,6400,8.764053269,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947

how-to-use-azureml/automated-machine-learning/forecasting-grouping/data/dominicks_OJ_2_5_8_train.csv

@@ -1,973 +0,0 @@
-WeekStarting,Store,Brand,Quantity,logQuantity,Advert,Price,Age60,COLLEGE,INCOME,Hincome150,Large HH,Minorities,WorkingWoman,SSTRDIST,SSTRVOL,CPDIST5,CPWVOL5
-1990-06-14,2,dominicks,10560,9.264828557000001,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-06-14,2,minute.maid,4480,8.407378325,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-06-14,2,tropicana,8256,9.018695487999999,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-07-26,2,dominicks,8000,8.987196821,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-07-26,2,minute.maid,4672,8.449342525,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-07-26,2,tropicana,6144,8.723231275,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-02,2,tropicana,3840,8.253227646000001,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-02,2,minute.maid,20160,9.911455722000001,1,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-02,2,dominicks,6848,8.831711918,1,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-09,2,dominicks,2880,7.965545572999999,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-09,2,minute.maid,2688,7.896552702,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-09,2,tropicana,8000,8.987196821,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-23,2,dominicks,1600,7.377758908,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-23,2,minute.maid,3008,8.009030685,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-23,2,tropicana,8896,9.093357017,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-30,2,tropicana,7168,8.877381955,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-30,2,minute.maid,4672,8.449342525,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-08-30,2,dominicks,25344,10.140297300000002,1,1.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-06,2,dominicks,10752,9.282847063,0,1.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-06,2,minute.maid,2752,7.920083199,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-06,2,tropicana,10880,9.29468152,0,3.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-13,2,minute.maid,26176,10.17259824,1,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-13,2,dominicks,6656,8.803273982999999,0,1.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-13,2,tropicana,7744,8.954673629,0,3.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-20,2,dominicks,6592,8.793612072,0,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-20,2,minute.maid,3712,8.219326094,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-09-20,2,tropicana,8512,9.049232212,0,3.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-11,2,tropicana,5504,8.61323038,0,3.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-11,2,minute.maid,30656,10.33058368,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-11,2,dominicks,1728,7.454719948999999,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-18,2,tropicana,5888,8.68067166,0,3.56,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-18,2,minute.maid,3840,8.253227646000001,0,2.98,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-18,2,dominicks,33792,10.42797937,1,1.24,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-25,2,tropicana,8384,9.034080407000001,0,3.56,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-25,2,minute.maid,2816,7.943072717000001,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-10-25,2,dominicks,1920,7.560080465,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-01,2,tropicana,5952,8.691482577,0,3.56,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-01,2,minute.maid,23104,10.04776104,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-01,2,dominicks,8960,9.100525506,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-08,2,dominicks,11392,9.340666634,0,1.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-08,2,tropicana,6848,8.831711918,0,3.56,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-08,2,minute.maid,3392,8.129174997,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-15,2,tropicana,9216,9.128696383,0,3.87,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-15,2,minute.maid,26304,10.1774763,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-15,2,dominicks,28416,10.25470765,0,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-22,2,dominicks,17152,9.749870064,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-22,2,tropicana,12160,9.405907156,0,2.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-22,2,minute.maid,6336,8.754002933999999,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-29,2,tropicana,12672,9.447150114,0,2.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-29,2,minute.maid,9920,9.2023082,0,3.17,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-11-29,2,dominicks,26560,10.1871616,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-06,2,dominicks,6336,8.754002933999999,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-06,2,minute.maid,25280,10.13776885,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-06,2,tropicana,6528,8.783855897,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-13,2,dominicks,26368,10.17990643,1,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-13,2,tropicana,6144,8.723231275,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-13,2,minute.maid,14848,9.605620455,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-20,2,tropicana,21120,9.957975738,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-20,2,minute.maid,12288,9.416378455,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-20,2,dominicks,896,6.797940412999999,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-27,2,tropicana,12416,9.426741242,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-27,2,minute.maid,6272,8.743850562,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-12-27,2,dominicks,1472,7.294377299,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-03,2,tropicana,9472,9.156095357,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-03,2,minute.maid,9152,9.121727714,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-03,2,dominicks,1344,7.2034055210000005,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-10,2,tropicana,17920,9.793672686,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-10,2,minute.maid,4160,8.333270353,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-10,2,dominicks,111680,11.62339292,1,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-17,2,tropicana,9408,9.14931567,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-17,2,minute.maid,10176,9.227787286,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-17,2,dominicks,1856,7.526178913,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-24,2,tropicana,6272,8.743850562,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-24,2,minute.maid,29056,10.27698028,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-24,2,dominicks,5568,8.624791202,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-31,2,tropicana,6912,8.841014311,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-31,2,minute.maid,7104,8.868413285,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-01-31,2,dominicks,32064,10.37548918,1,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-07,2,tropicana,16768,9.727227587,0,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-07,2,dominicks,4352,8.378390789,0,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-07,2,minute.maid,7488,8.921057017999999,0,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-14,2,dominicks,704,6.556778356000001,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-14,2,minute.maid,4224,8.348537825,0,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-14,2,tropicana,6272,8.743850562,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-21,2,tropicana,7936,8.979164649,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-21,2,minute.maid,8960,9.100525506,0,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-21,2,dominicks,13760,9.529521112000001,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-28,2,tropicana,6144,8.723231275,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-28,2,minute.maid,22464,10.01966931,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-02-28,2,dominicks,43328,10.67655436,1,1.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-07,2,tropicana,7936,8.979164649,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-07,2,minute.maid,3840,8.253227646000001,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-07,2,dominicks,57600,10.96127785,1,1.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-14,2,tropicana,7808,8.962904128,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-14,2,minute.maid,12992,9.472089062,0,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-14,2,dominicks,704,6.556778356000001,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-21,2,tropicana,6080,8.712759975,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-21,2,minute.maid,70144,11.15830555,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-21,2,dominicks,6016,8.702177866,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-28,2,tropicana,42176,10.64960662,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-28,2,dominicks,10368,9.246479419,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-03-28,2,minute.maid,21248,9.964018052,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-04,2,dominicks,12608,9.442086812000001,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-04,2,minute.maid,5696,8.647519453,1,2.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-04,2,tropicana,4928,8.502688505,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-11,2,tropicana,29504,10.29228113,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-11,2,minute.maid,7680,8.946374826,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-11,2,dominicks,6336,8.754002933999999,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-18,2,tropicana,9984,9.208739091,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-18,2,minute.maid,6336,8.754002933999999,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-18,2,dominicks,140736,11.85464107,1,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-25,2,tropicana,35200,10.46880136,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-25,2,dominicks,960,6.866933285,1,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-04-25,2,minute.maid,8576,9.056722882999999,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-02,2,dominicks,1216,7.103322062999999,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-02,2,minute.maid,15104,9.622714887999999,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-02,2,tropicana,23936,10.08313888,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-09,2,tropicana,7104,8.868413285,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-09,2,minute.maid,76480,11.24478455,1,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-09,2,dominicks,1664,7.416979621,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-16,2,dominicks,4992,8.51559191,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-16,2,minute.maid,5056,8.528330936,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-16,2,tropicana,24512,10.10691807,1,2.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-23,2,tropicana,6336,8.754002933999999,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-23,2,minute.maid,4736,8.462948177000001,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-23,2,dominicks,27968,10.23881628,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-30,2,dominicks,12160,9.405907156,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-30,2,minute.maid,4480,8.407378325,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-05-30,2,tropicana,6080,8.712759975,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-06,2,tropicana,33536,10.42037477,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-06,2,minute.maid,4032,8.30201781,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-06,2,dominicks,2240,7.714231145,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-13,2,dominicks,5504,8.61323038,1,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-13,2,minute.maid,14784,9.601300794,1,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-13,2,tropicana,13248,9.491601877,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-20,2,tropicana,6208,8.733594062,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-20,2,dominicks,8832,9.086136769,0,1.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-20,2,minute.maid,12096,9.400630097999999,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-27,2,dominicks,2624,7.87245515,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-27,2,minute.maid,41792,10.64046021,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-06-27,2,tropicana,10624,9.270870872,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-04,2,tropicana,44672,10.70710219,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-04,2,minute.maid,10560,9.264828557000001,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-04,2,dominicks,10432,9.252633284,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-18,2,tropicana,20096,9.908276069,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-18,2,dominicks,8320,9.026417534,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-18,2,minute.maid,4224,8.348537825,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-25,2,dominicks,6784,8.822322178,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-25,2,minute.maid,2880,7.965545572999999,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-07-25,2,tropicana,9152,9.121727714,1,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-01,2,tropicana,21952,9.996613531,0,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-01,2,minute.maid,3968,8.286017467999999,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-01,2,dominicks,60544,11.01112565,1,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-08,2,dominicks,20608,9.933434629,0,0.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-08,2,minute.maid,3712,8.219326094,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-08,2,tropicana,13568,9.515469357999999,0,2.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-29,2,tropicana,4160,8.333270353,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-29,2,minute.maid,2816,7.943072717000001,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-08-29,2,dominicks,16064,9.684336023,0,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-05,2,tropicana,39424,10.58213005,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-05,2,minute.maid,4288,8.363575702999999,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-05,2,dominicks,12480,9.431882642,0,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-12,2,tropicana,5632,8.636219898,0,3.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-12,2,minute.maid,18240,9.811372264,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-12,2,dominicks,17024,9.742379392,0,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-19,2,dominicks,13440,9.505990614,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-19,2,minute.maid,7360,8.903815212,0,1.95,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-19,2,tropicana,9024,9.107642974,1,2.68,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-26,2,tropicana,6016,8.702177866,0,3.44,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-26,2,minute.maid,7808,8.962904128,0,1.83,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-09-26,2,dominicks,10112,9.221478116,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-03,2,dominicks,9088,9.114710141,0,1.56,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-03,2,minute.maid,13504,9.510741217,0,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-03,2,tropicana,7744,8.954673629,0,3.14,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-10,2,tropicana,6784,8.822322178,0,3.07,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-10,2,dominicks,22848,10.03661887,1,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-10,2,minute.maid,10048,9.215128888999999,0,1.91,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-17,2,dominicks,6976,8.850230966,0,1.65,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-17,2,minute.maid,135936,11.81993947,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-17,2,tropicana,6784,8.822322178,0,3.07,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-24,2,tropicana,6272,8.743850562,0,3.07,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-24,2,minute.maid,5056,8.528330936,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-24,2,dominicks,4160,8.333270353,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-31,2,tropicana,5312,8.577723691000001,0,3.07,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-31,2,minute.maid,27968,10.23881628,0,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-10-31,2,dominicks,3328,8.110126802,0,1.83,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-07,2,tropicana,9216,9.128696383,0,3.11,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-07,2,minute.maid,4736,8.462948177000001,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-07,2,dominicks,12096,9.400630097999999,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-14,2,tropicana,7296,8.895081532,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-14,2,minute.maid,7808,8.962904128,0,2.14,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-14,2,dominicks,6208,8.733594062,0,1.76,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-21,2,tropicana,34240,10.44114983,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-21,2,minute.maid,12480,9.431882642,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-21,2,dominicks,3008,8.009030685,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-28,2,dominicks,19456,9.875910785,1,1.5,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-28,2,minute.maid,9664,9.17616292,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-11-28,2,tropicana,7168,8.877381955,0,2.64,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-05,2,minute.maid,7168,8.877381955,0,2.06,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-05,2,dominicks,16768,9.727227587,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-05,2,tropicana,6080,8.712759975,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-12,2,dominicks,13568,9.515469357999999,1,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-12,2,minute.maid,4480,8.407378325,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-12,2,tropicana,5120,8.540909718,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-19,2,tropicana,8320,9.026417534,0,2.74,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-19,2,minute.maid,5952,8.691482577,0,2.22,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-19,2,dominicks,6080,8.712759975,0,1.61,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-26,2,dominicks,10432,9.252633284,1,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-26,2,minute.maid,21696,9.984883191,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1991-12-26,2,tropicana,17728,9.78290059,0,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-02,2,minute.maid,12032,9.395325046,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-02,2,dominicks,11712,9.368369236,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-02,2,tropicana,13120,9.481893063,0,2.35,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-09,2,dominicks,4032,8.30201781,0,1.76,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-09,2,minute.maid,7040,8.859363449,0,2.12,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-09,2,tropicana,13120,9.481893063,0,2.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-16,2,dominicks,6336,8.754002933999999,0,1.82,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-16,2,tropicana,9792,9.189321005,0,2.43,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-16,2,minute.maid,10240,9.234056899,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-23,2,tropicana,3520,8.166216269,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-23,2,minute.maid,6848,8.831711918,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-23,2,dominicks,13632,9.520175249,0,1.47,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-30,2,tropicana,5504,8.61323038,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-30,2,minute.maid,3968,8.286017467999999,0,2.61,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-01-30,2,dominicks,45120,10.71708089,0,1.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-06,2,tropicana,6720,8.812843434,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-06,2,minute.maid,5888,8.68067166,0,2.26,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-06,2,dominicks,9984,9.208739091,0,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-13,2,tropicana,20224,9.914625297,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-13,2,dominicks,4800,8.476371197,0,1.82,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-13,2,minute.maid,6208,8.733594062,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-20,2,dominicks,11776,9.373818841,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-20,2,minute.maid,72256,11.18797065,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-20,2,tropicana,5056,8.528330936,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-27,2,tropicana,43584,10.68244539,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-27,2,minute.maid,11520,9.351839934,0,2.11,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-02-27,2,dominicks,11584,9.357380115,0,1.54,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-05,2,tropicana,25728,10.15533517,0,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-05,2,minute.maid,5824,8.66974259,0,2.35,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-05,2,dominicks,51264,10.84474403,1,1.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-12,2,tropicana,31808,10.36747311,0,1.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-12,2,minute.maid,19392,9.872615889,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-12,2,dominicks,14976,9.614204199,0,1.44,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-19,2,tropicana,20736,9.939626599,0,1.91,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-19,2,minute.maid,9536,9.162829389,0,2.1,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-19,2,dominicks,30784,10.33475035,0,1.59,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-26,2,tropicana,15168,9.626943225,0,2.81,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-26,2,minute.maid,5312,8.577723691000001,0,2.28,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-03-26,2,dominicks,12480,9.431882642,0,1.6,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-02,2,tropicana,28096,10.2433825,1,2.5,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-02,2,dominicks,3264,8.090708716,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-02,2,minute.maid,14528,9.583833101,1,1.9,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-09,2,dominicks,8768,9.078864009,0,1.48,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-09,2,minute.maid,12416,9.426741242,0,2.12,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-09,2,tropicana,12416,9.426741242,0,2.58,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-16,2,tropicana,5376,8.589699882,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-16,2,minute.maid,5376,8.589699882,0,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-16,2,dominicks,70848,11.16829202,1,1.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-23,2,tropicana,9792,9.189321005,0,2.67,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-23,2,minute.maid,19008,9.852615222,1,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-23,2,dominicks,18560,9.828764006,0,1.42,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-30,2,tropicana,16960,9.738612909,1,2.39,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-30,2,minute.maid,3904,8.269756948,0,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-04-30,2,dominicks,9152,9.121727714,0,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-07,2,tropicana,8320,9.026417534,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-07,2,minute.maid,6336,8.754002933999999,0,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-07,2,dominicks,9600,9.169518378,0,2.0,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-14,2,tropicana,6912,8.841014311,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-14,2,minute.maid,5440,8.60153434,0,2.79,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-14,2,dominicks,4800,8.476371197,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-21,2,tropicana,6976,8.850230966,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-21,2,minute.maid,22400,10.01681624,1,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-21,2,dominicks,9664,9.17616292,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-28,2,minute.maid,3968,8.286017467999999,0,2.84,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-28,2,tropicana,7232,8.886270902,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-05-28,2,dominicks,45568,10.726961,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-04,2,tropicana,51520,10.84972536,1,2.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-04,2,minute.maid,3264,8.090708716,0,2.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-04,2,dominicks,20992,9.951896692,0,1.74,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-11,2,minute.maid,4352,8.378390789,0,2.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-11,2,tropicana,22272,10.01108556,0,2.21,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-11,2,dominicks,6592,8.793612072,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-18,2,dominicks,4992,8.51559191,0,2.05,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-18,2,minute.maid,4480,8.407378325,0,2.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-18,2,tropicana,46144,10.73952222,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-25,2,tropicana,4352,8.378390789,1,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-25,2,minute.maid,3840,8.253227646000001,0,2.52,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-06-25,2,dominicks,8064,8.99516499,0,1.24,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-02,2,tropicana,17280,9.757305042,0,2.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-02,2,minute.maid,13312,9.496421162999999,1,2.0,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-02,2,dominicks,7360,8.903815212,0,1.61,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-09,2,tropicana,5696,8.647519453,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-09,2,minute.maid,3776,8.236420527,1,2.33,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-09,2,dominicks,10048,9.215128888999999,0,1.4,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-16,2,tropicana,6848,8.831711918,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-16,2,dominicks,10112,9.221478116,0,1.91,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-16,2,minute.maid,4800,8.476371197,0,2.89,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-23,2,dominicks,9152,9.121727714,0,1.69,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-23,2,minute.maid,24960,10.12502982,1,2.29,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-23,2,tropicana,4416,8.392989587999999,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-30,2,tropicana,4672,8.449342525,0,3.16,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-30,2,minute.maid,4544,8.42156296,0,2.86,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-07-30,2,dominicks,36288,10.49924239,1,1.49,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-06,2,tropicana,7168,8.877381955,1,3.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-06,2,minute.maid,3968,8.286017467999999,1,2.81,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-06,2,dominicks,3776,8.236420527,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-13,2,tropicana,5056,8.528330936,0,3.19,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-13,2,dominicks,3328,8.110126802,0,1.97,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-13,2,minute.maid,49600,10.81174611,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1992-08-20,2,dominicks,13824,9.534161491,0,1.36,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997
-1990-06-14,5,dominicks,1792,7.491087594,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-06-14,5,minute.maid,4224,8.348537825,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-06-14,5,tropicana,5888,8.68067166,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-06-28,5,minute.maid,4352,8.378390789,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-06-28,5,dominicks,2496,7.82244473,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-06-28,5,tropicana,6976,8.850230966,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-05,5,dominicks,2944,7.98752448,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-05,5,minute.maid,4928,8.502688505,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-05,5,tropicana,6528,8.783855897,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-12,5,dominicks,1024,6.931471806,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-12,5,minute.maid,31168,10.34714721,1,2.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-12,5,tropicana,4928,8.502688505,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-26,5,dominicks,4224,8.348537825,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-26,5,minute.maid,10048,9.215128888999999,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-07-26,5,tropicana,5312,8.577723691000001,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-02,5,minute.maid,21760,9.987828701,1,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-02,5,tropicana,5120,8.540909718,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-02,5,dominicks,4544,8.42156296,1,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-09,5,dominicks,1728,7.454719948999999,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-09,5,minute.maid,4544,8.42156296,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-09,5,tropicana,7936,8.979164649,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-16,5,tropicana,6080,8.712759975,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-16,5,minute.maid,52224,10.86329744,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-16,5,dominicks,1216,7.103322062999999,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-23,5,dominicks,1152,7.049254841000001,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-23,5,minute.maid,3584,8.184234774,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-23,5,tropicana,4160,8.333270353,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-30,5,minute.maid,5120,8.540909718,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-30,5,tropicana,5888,8.68067166,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-08-30,5,dominicks,30144,10.31374118,1,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-06,5,dominicks,8960,9.100525506,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-06,5,minute.maid,4416,8.392989587999999,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-06,5,tropicana,9536,9.162829389,0,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-13,5,tropicana,8320,9.026417534,0,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-13,5,dominicks,8192,9.010913347,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-13,5,minute.maid,30208,10.31586207,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-20,5,dominicks,6528,8.783855897,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-20,5,minute.maid,4160,8.333270353,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-20,5,tropicana,8000,8.987196821,0,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-27,5,dominicks,34688,10.45414909,1,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-27,5,minute.maid,4992,8.51559191,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-09-27,5,tropicana,5824,8.66974259,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-04,5,dominicks,4672,8.449342525,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-04,5,minute.maid,13952,9.543378146,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-04,5,tropicana,10624,9.270870872,1,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-11,5,tropicana,6656,8.803273982999999,0,3.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-11,5,dominicks,1088,6.992096427000001,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-11,5,minute.maid,47680,10.772267300000001,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-18,5,tropicana,5184,8.553332238,0,3.51,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-18,5,minute.maid,7616,8.938006577000001,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-18,5,dominicks,69440,11.14821835,1,1.24,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-25,5,tropicana,4928,8.502688505,0,3.51,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-25,5,minute.maid,8896,9.093357017,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-10-25,5,dominicks,1280,7.154615357000001,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-01,5,tropicana,5888,8.68067166,0,3.51,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-01,5,minute.maid,28544,10.25920204,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-01,5,dominicks,35456,10.47604777,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-08,5,tropicana,5312,8.577723691000001,0,3.51,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-08,5,dominicks,13824,9.534161491,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-08,5,minute.maid,5440,8.60153434,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-15,5,tropicana,9984,9.208739091,0,3.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-15,5,minute.maid,52416,10.86696717,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-15,5,dominicks,14208,9.561560465,0,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-22,5,tropicana,8448,9.041685006,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-22,5,dominicks,29312,10.28575227,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-22,5,minute.maid,11712,9.368369236,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-29,5,tropicana,10880,9.29468152,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-29,5,minute.maid,13952,9.543378146,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-11-29,5,dominicks,52992,10.87789624,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-06,5,dominicks,15680,9.660141293999999,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-06,5,minute.maid,36160,10.49570882,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-06,5,tropicana,5696,8.647519453,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-13,5,tropicana,5696,8.647519453,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-13,5,minute.maid,12864,9.462187991,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-13,5,dominicks,43520,10.68097588,1,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-20,5,tropicana,32384,10.38541975,0,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-20,5,minute.maid,22208,10.00820786,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-20,5,dominicks,3904,8.269756948,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-27,5,tropicana,10752,9.282847063,0,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-27,5,minute.maid,9984,9.208739091,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-12-27,5,dominicks,896,6.797940412999999,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-03,5,tropicana,6912,8.841014311,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-03,5,minute.maid,14016,9.547954812999999,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-03,5,dominicks,2240,7.714231145,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-10,5,tropicana,13440,9.505990614,0,2.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-10,5,minute.maid,6080,8.712759975,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-10,5,dominicks,125760,11.74213061,1,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-17,5,tropicana,7808,8.962904128,0,2.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-17,5,minute.maid,7808,8.962904128,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-17,5,dominicks,1408,7.249925537,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-24,5,tropicana,5248,8.565602331000001,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-24,5,minute.maid,40896,10.61878754,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-24,5,dominicks,7232,8.886270902,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-31,5,tropicana,6208,8.733594062,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-31,5,minute.maid,6272,8.743850562,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-01-31,5,dominicks,41216,10.62658181,1,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-07,5,tropicana,21440,9.973013615,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-07,5,minute.maid,7872,8.971067439,0,2.41,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-07,5,dominicks,9024,9.107642974,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-14,5,dominicks,1600,7.377758908,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-14,5,tropicana,7360,8.903815212,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-14,5,minute.maid,6144,8.723231275,0,2.41,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-21,5,tropicana,6720,8.812843434,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-21,5,minute.maid,8448,9.041685006,0,2.41,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-21,5,dominicks,2496,7.82244473,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-28,5,tropicana,6656,8.803273982999999,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-28,5,minute.maid,18688,9.835636886,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-02-28,5,dominicks,6336,8.754002933999999,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-07,5,tropicana,6016,8.702177866,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-07,5,minute.maid,6272,8.743850562,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-07,5,dominicks,56384,10.93994071,1,1.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-14,5,tropicana,6144,8.723231275,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-14,5,minute.maid,12096,9.400630097999999,0,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-14,5,dominicks,1600,7.377758908,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-21,5,tropicana,4928,8.502688505,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-21,5,minute.maid,73216,11.20116926,1,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-21,5,dominicks,2944,7.98752448,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-28,5,tropicana,67712,11.1230187,1,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-28,5,minute.maid,18944,9.849242538,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-03-28,5,dominicks,13504,9.510741217,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-04,5,dominicks,5376,8.589699882,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-04,5,tropicana,8640,9.064157862,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-04,5,minute.maid,6400,8.764053269,1,2.46,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-11,5,tropicana,35520,10.477851199999998,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-11,5,minute.maid,8640,9.064157862,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-11,5,dominicks,6656,8.803273982999999,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-18,5,tropicana,9664,9.17616292,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-18,5,minute.maid,7296,8.895081532,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-18,5,dominicks,95680,11.46876457,1,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-25,5,tropicana,49088,10.80136989,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-25,5,minute.maid,12480,9.431882642,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-04-25,5,dominicks,896,6.797940412999999,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-02,5,dominicks,1728,7.454719948999999,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-02,5,minute.maid,14144,9.557045785,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-02,5,tropicana,14912,9.609921537,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-09,5,minute.maid,88256,11.38799696,1,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-09,5,tropicana,6464,8.774003599999999,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-09,5,dominicks,1280,7.154615357000001,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-16,5,dominicks,5696,8.647519453,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-16,5,minute.maid,6848,8.831711918,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-16,5,tropicana,25024,10.12759064,1,2.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-23,5,minute.maid,7808,8.962904128,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-23,5,tropicana,6272,8.743850562,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-23,5,dominicks,28288,10.25019297,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-30,5,dominicks,4864,8.489616424,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-30,5,minute.maid,6272,8.743850562,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-05-30,5,tropicana,5056,8.528330936,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-06,5,minute.maid,6144,8.723231275,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-06,5,tropicana,47616,10.77092412,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-06,5,dominicks,2880,7.965545572999999,0,2.09,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-13,5,dominicks,5760,8.658692754,1,1.41,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-13,5,minute.maid,27776,10.23192762,1,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-13,5,tropicana,13888,9.538780437,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-20,5,tropicana,6144,8.723231275,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-20,5,minute.maid,20800,9.942708266,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-20,5,dominicks,15040,9.618468598,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-27,5,dominicks,5120,8.540909718,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-27,5,minute.maid,45696,10.72976605,1,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-06-27,5,tropicana,9344,9.142489705,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-04,5,minute.maid,14336,9.570529135,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-04,5,tropicana,32896,10.40110635,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-04,5,dominicks,3264,8.090708716,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-11,5,dominicks,9536,9.162829389,1,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-11,5,minute.maid,4928,8.502688505,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-11,5,tropicana,21056,9.954940834,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-18,5,tropicana,15360,9.639522007,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-18,5,minute.maid,4608,8.435549202,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-18,5,dominicks,6208,8.733594062,0,1.59,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-25,5,dominicks,6592,8.793612072,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-25,5,tropicana,8000,8.987196821,1,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-07-25,5,minute.maid,5248,8.565602331000001,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-01,5,tropicana,21120,9.957975738,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-01,5,dominicks,63552,11.05961375,1,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-01,5,minute.maid,4224,8.348537825,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-08,5,dominicks,27968,10.23881628,0,0.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-08,5,minute.maid,4288,8.363575702999999,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-08,5,tropicana,11904,9.384629757,0,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-15,5,minute.maid,16896,9.734832187,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-15,5,tropicana,5056,8.528330936,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-15,5,dominicks,21760,9.987828701,1,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-22,5,dominicks,2688,7.896552702,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-22,5,minute.maid,77184,11.25394746,1,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-22,5,tropicana,4608,8.435549202,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-29,5,tropicana,6016,8.702177866,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-29,5,minute.maid,5184,8.553332238,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-08-29,5,dominicks,10432,9.252633284,0,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-05,5,tropicana,50752,10.83470631,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-05,5,minute.maid,5248,8.565602331000001,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-05,5,dominicks,9792,9.189321005,0,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-12,5,minute.maid,20672,9.936535407000001,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-12,5,tropicana,5632,8.636219898,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-12,5,dominicks,8448,9.041685006,0,1.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-26,5,tropicana,6400,8.764053269,0,3.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-26,5,dominicks,6912,8.841014311,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-09-26,5,minute.maid,12352,9.421573272,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-03,5,dominicks,8256,9.018695487999999,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-03,5,minute.maid,12032,9.395325046,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-03,5,tropicana,5440,8.60153434,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-10,5,minute.maid,13440,9.505990614,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-10,5,dominicks,28672,10.26367632,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-10,5,tropicana,8128,9.00307017,0,2.94,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-24,5,tropicana,7232,8.886270902,0,2.94,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-24,5,minute.maid,5824,8.66974259,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-24,5,dominicks,4416,8.392989587999999,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-31,5,tropicana,7168,8.877381955,0,2.94,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-31,5,minute.maid,50112,10.82201578,0,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-10-31,5,dominicks,1856,7.526178913,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-07,5,minute.maid,5184,8.553332238,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-07,5,tropicana,7872,8.971067439,0,2.94,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-07,5,dominicks,6528,8.783855897,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-14,5,tropicana,7552,8.929567707999999,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-14,5,minute.maid,8384,9.034080407000001,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-14,5,dominicks,6080,8.712759975,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-21,5,tropicana,69504,11.14913958,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-21,5,dominicks,3456,8.14786713,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-21,5,minute.maid,10112,9.221478116,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-28,5,dominicks,25856,10.16029796,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-28,5,minute.maid,8384,9.034080407000001,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-11-28,5,tropicana,8960,9.100525506,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-05,5,tropicana,6912,8.841014311,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-05,5,dominicks,25728,10.15533517,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-05,5,minute.maid,11456,9.346268889,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-12,5,dominicks,23552,10.06696602,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-12,5,minute.maid,5952,8.691482577,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-12,5,tropicana,6656,8.803273982999999,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-19,5,tropicana,8192,9.010913347,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-19,5,dominicks,2944,7.98752448,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-19,5,minute.maid,8512,9.049232212,0,2.26,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-26,5,dominicks,5888,8.68067166,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-26,5,minute.maid,27968,10.23881628,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1991-12-26,5,tropicana,13440,9.505990614,0,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-02,5,tropicana,12160,9.405907156,0,2.39,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-02,5,dominicks,6848,8.831711918,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-02,5,minute.maid,24000,10.08580911,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-09,5,dominicks,1792,7.491087594,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-09,5,minute.maid,6848,8.831711918,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-09,5,tropicana,11840,9.379238908,0,2.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-16,5,tropicana,8640,9.064157862,0,2.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-16,5,dominicks,5248,8.565602331000001,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-16,5,minute.maid,15104,9.622714887999999,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-23,5,tropicana,5888,8.68067166,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-23,5,minute.maid,11392,9.340666634,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-23,5,dominicks,16768,9.727227587,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-30,5,tropicana,7424,8.912473275,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-30,5,minute.maid,5824,8.66974259,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-01-30,5,dominicks,52160,10.8620712,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-06,5,tropicana,5632,8.636219898,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-06,5,minute.maid,7488,8.921057017999999,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-06,5,dominicks,16640,9.719564714,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-13,5,tropicana,33600,10.42228135,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-13,5,minute.maid,8320,9.026417534,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-13,5,dominicks,1344,7.2034055210000005,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-20,5,dominicks,4608,8.435549202,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-20,5,tropicana,5376,8.589699882,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-20,5,minute.maid,99904,11.511965,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-27,5,tropicana,54272,10.90176372,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-27,5,minute.maid,6976,8.850230966,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-02-27,5,dominicks,12672,9.447150114,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-05,5,tropicana,33600,10.42228135,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-05,5,minute.maid,9984,9.208739091,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-05,5,dominicks,48640,10.79220152,1,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-12,5,tropicana,24448,10.10430369,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-12,5,minute.maid,32832,10.39915893,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-12,5,dominicks,13248,9.491601877,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-19,5,tropicana,22784,10.03381381,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-19,5,minute.maid,8128,9.00307017,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-19,5,dominicks,29248,10.28356647,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-26,5,tropicana,19008,9.852615222,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-26,5,minute.maid,6464,8.774003599999999,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-03-26,5,dominicks,4608,8.435549202,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-02,5,tropicana,15808,9.66827142,1,2.5,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-02,5,minute.maid,36800,10.51325312,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-02,5,dominicks,3136,8.050703382,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-09,5,dominicks,13184,9.486759252,0,1.58,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-09,5,tropicana,14144,9.557045785,0,2.5,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-09,5,minute.maid,12928,9.467150781,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-16,5,tropicana,9600,9.169518378,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-16,5,minute.maid,7424,8.912473275,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-16,5,dominicks,67712,11.1230187,1,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-23,5,tropicana,10112,9.221478116,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-23,5,minute.maid,34176,10.43927892,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-23,5,dominicks,18880,9.84585844,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-30,5,minute.maid,4160,8.333270353,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-30,5,tropicana,31872,10.36948316,1,2.24,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-04-30,5,dominicks,6208,8.733594062,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-07,5,tropicana,9280,9.135616826,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-07,5,minute.maid,5952,8.691482577,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-07,5,dominicks,5952,8.691482577,0,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-14,5,tropicana,7680,8.946374826,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-14,5,minute.maid,6528,8.783855897,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-14,5,dominicks,4160,8.333270353,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-21,5,tropicana,8704,9.071537969,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-21,5,minute.maid,30656,10.33058368,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-21,5,dominicks,23488,10.06424493,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-28,5,tropicana,9920,9.2023082,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-28,5,dominicks,60480,11.01006801,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-05-28,5,minute.maid,6656,8.803273982999999,0,2.66,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-04,5,tropicana,91968,11.42919597,1,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-04,5,minute.maid,4416,8.392989587999999,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-04,5,dominicks,20416,9.924074186,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-11,5,tropicana,44096,10.69412435,0,2.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-11,5,dominicks,6336,8.754002933999999,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-11,5,minute.maid,5696,8.647519453,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-25,5,minute.maid,5696,8.647519453,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-25,5,tropicana,7296,8.895081532,1,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-06-25,5,dominicks,1408,7.249925537,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-02,5,tropicana,12928,9.467150781,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-02,5,minute.maid,39680,10.58860256,1,2.01,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-02,5,dominicks,4672,8.449342525,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-09,5,tropicana,6848,8.831711918,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-09,5,minute.maid,6208,8.733594062,1,2.19,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-09,5,dominicks,19520,9.87919486,0,1.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-16,5,tropicana,8064,8.99516499,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-16,5,minute.maid,7872,8.971067439,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-16,5,dominicks,7872,8.971067439,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-23,5,dominicks,5184,8.553332238,0,1.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-23,5,tropicana,4992,8.51559191,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-23,5,minute.maid,54528,10.90646961,1,2.29,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-30,5,tropicana,7360,8.903815212,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-30,5,minute.maid,6400,8.764053269,0,2.69,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-07-30,5,dominicks,42240,10.65112292,1,1.49,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-06,5,tropicana,8384,9.034080407000001,1,2.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-06,5,minute.maid,5888,8.68067166,1,2.65,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-06,5,dominicks,6592,8.793612072,1,1.89,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-13,5,tropicana,8832,9.086136769,0,2.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-13,5,minute.maid,56384,10.93994071,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-13,5,dominicks,2112,7.655390645,0,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1992-08-20,5,dominicks,21248,9.964018052,0,1.79,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837
-1990-06-14,8,dominicks,14336,9.570529135,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-06-14,8,minute.maid,6080,8.712759975,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-06-14,8,tropicana,8896,9.093357017,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-06-21,8,dominicks,6400,8.764053269,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-06-21,8,minute.maid,51968,10.85838342,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-06-21,8,tropicana,7296,8.895081532,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-06-28,8,tropicana,10368,9.246479419,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-06-28,8,minute.maid,4928,8.502688505,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-06-28,8,dominicks,3968,8.286017467999999,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-05,8,dominicks,4352,8.378390789,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-05,8,minute.maid,5312,8.577723691000001,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-05,8,tropicana,6976,8.850230966,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-12,8,tropicana,6464,8.774003599999999,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-12,8,dominicks,3520,8.166216269,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-12,8,minute.maid,39424,10.58213005,1,2.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-19,8,tropicana,8192,9.010913347,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-19,8,dominicks,6464,8.774003599999999,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-19,8,minute.maid,5568,8.624791202,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-26,8,dominicks,5952,8.691482577,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-26,8,minute.maid,14592,9.588228712000001,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-07-26,8,tropicana,7936,8.979164649,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-02,8,tropicana,6656,8.803273982999999,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-02,8,minute.maid,22208,10.00820786,1,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-02,8,dominicks,8832,9.086136769,1,2.09,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-09,8,dominicks,7232,8.886270902,0,2.09,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-09,8,minute.maid,5760,8.658692754,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-09,8,tropicana,8256,9.018695487999999,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-16,8,tropicana,5568,8.624791202,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-16,8,minute.maid,54016,10.89703558,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-16,8,dominicks,5504,8.61323038,0,2.09,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-23,8,dominicks,4800,8.476371197,0,2.09,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-23,8,minute.maid,5824,8.66974259,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-23,8,tropicana,7488,8.921057017999999,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-30,8,tropicana,6144,8.723231275,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-30,8,minute.maid,6528,8.783855897,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-08-30,8,dominicks,52672,10.87183928,1,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-06,8,dominicks,16448,9.707959168,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-06,8,minute.maid,5440,8.60153434,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-06,8,tropicana,11008,9.30637756,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-13,8,minute.maid,36544,10.50627229,1,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-13,8,dominicks,19072,9.85597657,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-13,8,tropicana,5760,8.658692754,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-20,8,dominicks,13376,9.501217335,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-20,8,minute.maid,3776,8.236420527,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-20,8,tropicana,10112,9.221478116,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-27,8,tropicana,8448,9.041685006,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-27,8,minute.maid,5504,8.61323038,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-09-27,8,dominicks,61440,11.02581637,1,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-04,8,tropicana,8448,9.041685006,1,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-04,8,dominicks,13760,9.529521112000001,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-04,8,minute.maid,12416,9.426741242,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-11,8,minute.maid,53696,10.89109379,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-11,8,dominicks,3136,8.050703382,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-11,8,tropicana,7424,8.912473275,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-18,8,tropicana,5824,8.66974259,0,3.04,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-18,8,minute.maid,5696,8.647519453,0,2.51,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-18,8,dominicks,186176,12.13444774,1,1.14,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-25,8,tropicana,6656,8.803273982999999,0,3.04,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-25,8,minute.maid,4864,8.489616424,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-10-25,8,dominicks,3712,8.219326094,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-01,8,tropicana,6272,8.743850562,0,3.04,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-01,8,minute.maid,37184,10.52363384,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-01,8,dominicks,35776,10.48503256,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-08,8,tropicana,6912,8.841014311,0,3.04,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-08,8,minute.maid,5504,8.61323038,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-08,8,dominicks,26880,10.1991378,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-15,8,tropicana,10496,9.258749511,0,3.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-15,8,minute.maid,51008,10.83973776,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-15,8,dominicks,71680,11.17996705,0,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-22,8,tropicana,11840,9.379238908,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-22,8,minute.maid,11072,9.312174678,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-22,8,dominicks,25088,10.13014492,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-29,8,tropicana,9664,9.17616292,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-29,8,minute.maid,12160,9.405907156,0,2.62,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-11-29,8,dominicks,91456,11.42361326,1,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-06,8,minute.maid,30528,10.32639957,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-06,8,dominicks,23808,10.07777694,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-06,8,tropicana,6272,8.743850562,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-13,8,dominicks,89856,11.40596367,1,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-13,8,minute.maid,12096,9.400630097999999,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-13,8,tropicana,7168,8.877381955,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-20,8,minute.maid,16448,9.707959168,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-20,8,dominicks,12224,9.411156511,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-20,8,tropicana,29504,10.29228113,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-27,8,minute.maid,9344,9.142489705,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-27,8,dominicks,3776,8.236420527,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1990-12-27,8,tropicana,8704,9.071537969,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-03,8,tropicana,9280,9.135616826,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-03,8,minute.maid,16128,9.688312171,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-03,8,dominicks,13824,9.534161491,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-10,8,minute.maid,5376,8.589699882,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-10,8,dominicks,251072,12.43349503,1,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-10,8,tropicana,12224,9.411156511,0,2.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-17,8,minute.maid,6656,8.803273982999999,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-17,8,tropicana,10368,9.246479419,0,2.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-17,8,dominicks,4864,8.489616424,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-24,8,minute.maid,59712,10.99728828,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-24,8,dominicks,10176,9.227787286,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-24,8,tropicana,8128,9.00307017,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-31,8,tropicana,5952,8.691482577,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-31,8,minute.maid,9856,9.195835686,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-01-31,8,dominicks,105344,11.56498647,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-07,8,minute.maid,6720,8.812843434,0,2.12,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-07,8,dominicks,33600,10.42228135,0,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-07,8,tropicana,21696,9.984883191,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-14,8,dominicks,4736,8.462948177000001,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-14,8,minute.maid,4224,8.348537825,0,2.12,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-14,8,tropicana,7808,8.962904128,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-21,8,tropicana,8128,9.00307017,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-21,8,minute.maid,9728,9.182763604,0,2.12,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-21,8,dominicks,10304,9.240287448,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-28,8,tropicana,7424,8.912473275,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-28,8,minute.maid,40320,10.604602900000001,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-02-28,8,dominicks,5056,8.528330936,1,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-07,8,dominicks,179968,12.10053434,1,0.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-07,8,tropicana,5952,8.691482577,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-07,8,minute.maid,5120,8.540909718,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-14,8,minute.maid,19264,9.865993348,0,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-14,8,dominicks,4992,8.51559191,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-14,8,tropicana,7616,8.938006577000001,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-21,8,tropicana,5312,8.577723691000001,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-21,8,minute.maid,170432,12.04609167,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-21,8,dominicks,6400,8.764053269,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-28,8,minute.maid,39680,10.58860256,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-28,8,dominicks,14912,9.609921537,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-03-28,8,tropicana,161792,11.99406684,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-04,8,dominicks,34624,10.45230236,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-04,8,minute.maid,8128,9.00307017,1,2.17,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-04,8,tropicana,17280,9.757305042,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-11,8,tropicana,47040,10.75875358,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-11,8,minute.maid,9088,9.114710141,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-11,8,dominicks,10368,9.246479419,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-18,8,tropicana,14464,9.579418083,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-18,8,minute.maid,6720,8.812843434,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-18,8,dominicks,194880,12.18013926,1,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-25,8,tropicana,52928,10.87668778,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-25,8,dominicks,5696,8.647519453,1,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-04-25,8,minute.maid,7552,8.929567707999999,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-02,8,dominicks,7168,8.877381955,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-02,8,minute.maid,24768,10.11730778,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-02,8,tropicana,21184,9.961001459,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-09,8,tropicana,7360,8.903815212,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-09,8,minute.maid,183296,12.11885761,1,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-09,8,dominicks,2880,7.965545572999999,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-16,8,dominicks,12288,9.416378455,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-16,8,minute.maid,8896,9.093357017,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-05-16,8,tropicana,15744,9.664214619,1,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-06,8,dominicks,9280,9.135616826,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-06,8,tropicana,46912,10.75602879,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-06,8,minute.maid,6656,8.803273982999999,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-13,8,tropicana,18240,9.811372264,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-13,8,dominicks,25856,10.16029796,1,1.26,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-13,8,minute.maid,35456,10.47604777,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-20,8,dominicks,19264,9.865993348,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-20,8,minute.maid,17408,9.76468515,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-20,8,tropicana,6464,8.774003599999999,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-27,8,dominicks,6848,8.831711918,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-27,8,minute.maid,75520,11.2321528,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-06-27,8,tropicana,8512,9.049232212,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-04,8,tropicana,28416,10.25470765,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-04,8,minute.maid,21632,9.981928979,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-04,8,dominicks,12928,9.467150781,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-11,8,dominicks,44032,10.69267192,1,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-11,8,minute.maid,8384,9.034080407000001,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-11,8,tropicana,16960,9.738612909,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-18,8,minute.maid,9920,9.2023082,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-18,8,dominicks,25408,10.14281936,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-18,8,tropicana,8320,9.026417534,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-25,8,dominicks,38336,10.55414468,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-25,8,minute.maid,6592,8.793612072,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-07-25,8,tropicana,11136,9.317938383,1,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-01,8,tropicana,27712,10.22962081,0,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-01,8,minute.maid,7168,8.877381955,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-01,8,dominicks,152384,11.93415893,1,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-08,8,dominicks,54464,10.90529521,0,0.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-08,8,minute.maid,6208,8.733594062,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-08,8,tropicana,7744,8.954673629,0,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-15,8,minute.maid,30528,10.32639957,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-15,8,dominicks,47680,10.772267300000001,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-15,8,tropicana,5184,8.553332238,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-22,8,dominicks,14720,9.596962392,0,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-22,8,minute.maid,155840,11.95658512,1,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-22,8,tropicana,6272,8.743850562,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-29,8,tropicana,7744,8.954673629,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-29,8,dominicks,53248,10.88271552,0,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-08-29,8,minute.maid,10752,9.282847063,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-05,8,tropicana,53184,10.88151288,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-05,8,minute.maid,6976,8.850230966,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-05,8,dominicks,40576,10.61093204,0,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-12,8,dominicks,25856,10.16029796,0,1.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-12,8,tropicana,6784,8.822322178,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-12,8,minute.maid,31872,10.36948316,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-19,8,dominicks,24064,10.08847223,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-19,8,minute.maid,5312,8.577723691000001,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-19,8,tropicana,8000,8.987196821,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-26,8,tropicana,6592,8.793612072,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-26,8,minute.maid,33344,10.41463313,0,1.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-09-26,8,dominicks,15680,9.660141293999999,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-03,8,minute.maid,13504,9.510741217,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-03,8,dominicks,16576,9.715711145,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-03,8,tropicana,5248,8.565602331000001,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-10,8,dominicks,49664,10.8130356,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-10,8,tropicana,6592,8.793612072,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-10,8,minute.maid,13504,9.510741217,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-17,8,dominicks,10752,9.282847063,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-17,8,minute.maid,335808,12.72429485,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-17,8,tropicana,5888,8.68067166,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-24,8,tropicana,6336,8.754002933999999,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-24,8,dominicks,9792,9.189321005,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-24,8,minute.maid,13120,9.481893063,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-31,8,tropicana,5888,8.68067166,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-31,8,minute.maid,49664,10.8130356,0,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-10-31,8,dominicks,7104,8.868413285,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-07,8,dominicks,9216,9.128696383,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-07,8,tropicana,6080,8.712759975,0,2.94,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-07,8,minute.maid,10880,9.29468152,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-14,8,tropicana,6848,8.831711918,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-14,8,minute.maid,9984,9.208739091,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-14,8,dominicks,12608,9.442086812000001,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-21,8,tropicana,54016,10.89703558,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-21,8,minute.maid,9216,9.128696383,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-21,8,dominicks,16448,9.707959168,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-28,8,tropicana,10368,9.246479419,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-28,8,dominicks,27968,10.23881628,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-11-28,8,minute.maid,7680,8.946374826,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-05,8,minute.maid,7296,8.895081532,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-05,8,dominicks,37824,10.5406991,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-05,8,tropicana,5568,8.624791202,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-12,8,dominicks,33664,10.4241843,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-12,8,minute.maid,8192,9.010913347,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-12,8,tropicana,4864,8.489616424,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-19,8,tropicana,7232,8.886270902,0,2.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-19,8,minute.maid,6080,8.712759975,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-19,8,dominicks,17728,9.78290059,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-26,8,tropicana,15232,9.631153757,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-26,8,dominicks,25088,10.13014492,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1991-12-26,8,minute.maid,15040,9.618468598,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-02,8,minute.maid,9472,9.156095357,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-02,8,dominicks,13184,9.486759252,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-02,8,tropicana,47040,10.75875358,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-09,8,dominicks,3136,8.050703382,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-09,8,minute.maid,5888,8.68067166,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-09,8,tropicana,9280,9.135616826,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-16,8,tropicana,6720,8.812843434,0,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-16,8,minute.maid,14336,9.570529135,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-16,8,dominicks,5696,8.647519453,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-23,8,minute.maid,11712,9.368369236,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-23,8,dominicks,19008,9.852615222,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-23,8,tropicana,5056,8.528330936,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-30,8,minute.maid,7936,8.979164649,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-30,8,dominicks,121664,11.70901843,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-01-30,8,tropicana,6080,8.712759975,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-06,8,tropicana,10496,9.258749511,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-06,8,minute.maid,5184,8.553332238,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-06,8,dominicks,38848,10.56741187,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-13,8,minute.maid,7168,8.877381955,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-13,8,dominicks,6144,8.723231275,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-13,8,tropicana,39040,10.57234204,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-20,8,dominicks,13632,9.520175249,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-20,8,minute.maid,216064,12.28332994,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-20,8,tropicana,4480,8.407378325,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-27,8,tropicana,61760,11.03101119,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-27,8,minute.maid,15040,9.618468598,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-02-27,8,dominicks,9792,9.189321005,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-05,8,tropicana,15360,9.639522007,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-05,8,minute.maid,11840,9.379238908,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-05,8,dominicks,86912,11.37265139,1,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-12,8,minute.maid,25472,10.14533509,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-12,8,dominicks,24512,10.10691807,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-12,8,tropicana,54976,10.91465201,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-19,8,minute.maid,16384,9.704060528,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-19,8,dominicks,58048,10.96902553,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-19,8,tropicana,34368,10.44488118,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-26,8,tropicana,10752,9.282847063,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-26,8,minute.maid,20480,9.927204079,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-03-26,8,dominicks,13952,9.543378146,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-02,8,minute.maid,34688,10.45414909,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-02,8,dominicks,15168,9.626943225,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-02,8,tropicana,20096,9.908276069,1,2.5,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-09,8,dominicks,14592,9.588228712000001,0,1.58,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-09,8,minute.maid,22400,10.01681624,0,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-09,8,tropicana,16192,9.692272572,0,2.5,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-16,8,tropicana,6528,8.783855897,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-16,8,minute.maid,7808,8.962904128,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-16,8,dominicks,145088,11.88509573,1,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-23,8,tropicana,8320,9.026417534,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-23,8,minute.maid,48064,10.78028874,1,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-23,8,dominicks,43712,10.68537794,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-30,8,tropicana,30784,10.33475035,1,2.16,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-30,8,minute.maid,7360,8.903815212,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-04-30,8,dominicks,20608,9.933434629,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-07,8,tropicana,18048,9.800790154,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-07,8,minute.maid,6272,8.743850562,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-07,8,dominicks,18752,9.839055692,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-14,8,tropicana,12864,9.462187991,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-14,8,minute.maid,6400,8.764053269,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-14,8,dominicks,20160,9.911455722000001,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-21,8,tropicana,7168,8.877381955,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-21,8,minute.maid,54592,10.90764263,1,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-21,8,dominicks,18688,9.835636886,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-28,8,minute.maid,8128,9.00307017,0,2.39,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-28,8,tropicana,9024,9.107642974,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-05-28,8,dominicks,133824,11.80428078,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-04,8,tropicana,84992,11.35031241,1,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-04,8,minute.maid,4928,8.502688505,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-04,8,dominicks,63488,11.05860619,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-11,8,minute.maid,5440,8.60153434,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-11,8,tropicana,14144,9.557045785,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-11,8,dominicks,71040,11.17099838,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-25,8,tropicana,7488,8.921057017999999,1,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-25,8,minute.maid,5888,8.68067166,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-06-25,8,dominicks,15360,9.639522007,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-02,8,minute.maid,23872,10.0804615,1,2.02,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-02,8,dominicks,17728,9.78290059,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-02,8,tropicana,12352,9.421573272,0,2.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-09,8,tropicana,5696,8.647519453,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-09,8,minute.maid,6848,8.831711918,1,2.19,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-09,8,dominicks,24256,10.09641929,0,1.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-16,8,minute.maid,8192,9.010913347,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-16,8,dominicks,19968,9.901886271,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-16,8,tropicana,7680,8.946374826,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-23,8,dominicks,15936,9.67633598,0,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-23,8,minute.maid,55040,10.91581547,1,2.29,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-23,8,tropicana,5440,8.60153434,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-30,8,tropicana,5632,8.636219898,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-30,8,minute.maid,6528,8.783855897,0,2.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-07-30,8,dominicks,76352,11.24310951,1,1.49,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-06,8,tropicana,8960,9.100525506,1,2.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-06,8,minute.maid,6208,8.733594062,1,2.45,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-06,8,dominicks,17408,9.76468515,1,1.69,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-13,8,minute.maid,94720,11.45868045,1,1.99,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-13,8,tropicana,6080,8.712759975,0,2.89,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-13,8,dominicks,17536,9.77201119,0,1.79,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947
-1992-08-20,8,dominicks,31232,10.34919849,0,1.59,0.252394035,0.095173274,10.59700966,0.054227156,0.131749698,0.035243328,0.283074736,2.636332801,1.5,2.905384316,0.641015947

how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/deploy.py

@@ -1,66 +0,0 @@
-import argparse
-import json
-
-from azureml.core import Run, Model, Workspace
-from azureml.core.conda_dependencies import CondaDependencies
-from azureml.core.model import InferenceConfig
-from azureml.core.webservice import AciWebservice
-
-
-script_file_name = 'score.py'
-conda_env_file_name = 'myenv.yml'
-
-print("In deploy.py")
-parser = argparse.ArgumentParser()
-parser.add_argument("--time_column_name", type=str, help="time column name")
-parser.add_argument("--group_column_names", type=str, help="group column names")
-parser.add_argument("--model_names", type=str, help="model names")
-parser.add_argument("--service_name", type=str, help="service name")
-
-args = parser.parse_args()
-
-# replace the group column names in scoring script to the ones set by user
-print("Update group_column_names")
-print(args.group_column_names)
-
-with open(script_file_name, 'r') as cefr:
-    content = cefr.read()
-with open(script_file_name, 'w') as cefw:
-    content = content.replace('<<groups>>', args.group_column_names.rstrip())
-    cefw.write(content.replace('<<time_colname>>', args.time_column_name.rstrip()))
-
-with open(script_file_name, 'r') as cefr1:
-    content1 = cefr1.read()
-print(content1)
-
-model_list = json.loads(args.model_names)
-print(model_list)
-
-run = Run.get_context()
-ws = run.experiment.workspace
-
-deployment_config = AciWebservice.deploy_configuration(
-    cpu_cores=1,
-    memory_gb=2,
-    tags={"method": "grouping"},
-    description='grouping demo aci deployment'
-)
-
-inference_config = InferenceConfig(
-    entry_script=script_file_name,
-    runtime='python',
-    conda_file=conda_env_file_name
-)
-
-models = []
-for model_name in model_list:
-    models.append(Model(ws, name=model_name))
-
-service = Model.deploy(
-    ws,
-    name=args.service_name,
-    models=models,
-    inference_config=inference_config,
-    deployment_config=deployment_config
-)
-service.wait_for_deployment(True)

how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/myenv.yml

@@ -1,11 +0,0 @@
-name: automl_grouping_env
-dependencies:
-  # The python interpreter version.
-
-  # Currently Azure ML only supports 3.5.2 and later.
-
-- python=3.6.2
-- numpy>=1.16.0,<=1.16.2
-- scikit-learn>=0.19.0,<=0.20.3
-- conda-forge::fbprophet==0.5
-

how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/score.py

@@ -1,55 +0,0 @@
-import json
-import pickle
-import re
-
-import numpy as np
-import pandas as pd
-from sklearn.externals import joblib
-from sklearn.linear_model import Ridge
-
-from azureml.core.model import Model
-import azureml.train.automl
-
-
-def init():
-    global models
-    models = {}
-    global group_columns_str
-    group_columns_str = "<<groups>>"
-    global time_column_name
-    time_column_name = "<<time_colname>>"
-
-    global group_columns
-    group_columns = group_columns_str.split("#####")
-    global valid_chars
-    valid_chars = re.compile('[^a-zA-Z0-9-]')
-
-
-def run(raw_data):
-    try:
-        data = pd.read_json(raw_data)
-        # Make sure we have correct time points.
-        data[time_column_name] = pd.to_datetime(data[time_column_name], unit='ms')
-        dfs = []
-        for grain, df_one in data.groupby(group_columns):
-            if isinstance(grain, int):
-                cur_group = str(grain)
-            elif isinstance(grain, str):
-                cur_group = grain
-            else:
-                cur_group = "#####".join(list(grain))
-            cur_group = valid_chars.sub('', cur_group)
-            print("Query model for group {}".format(cur_group))
-            if cur_group not in models:
-                model_path = Model.get_model_path(cur_group)
-                model = joblib.load(model_path)
-                models[cur_group] = model
-            _, xtrans = models[cur_group].forecast(df_one)
-            dfs.append(xtrans)
-        df_ret = pd.concat(dfs)
-        df_ret.reset_index(drop=False, inplace=True)
-        return json.dumps({'predictions': df_ret.to_json()})
-
-    except Exception as e:
-        error = str(e)
-        return error

how-to-use-azureml/automated-machine-learning/forecasting-grouping/register/register.py

@@ -1,22 +0,0 @@
-import argparse
-
-from azureml.core import Run, Model
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--model_name")
-parser.add_argument("--model_path")
-
-args = parser.parse_args()
-
-run = Run.get_context()
-ws = run.experiment.workspace
-print('retrieved ws: {}'.format(ws))
-
-print('begin register model')
-model = Model.register(
-    workspace=ws,
-    model_path=args.model_path,
-    model_name=args.model_name
-)
-print('model registered: {}'.format(model))
-print('complete')

how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-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"
       ]
     },
     {

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

@@ -0,0 +1,9 @@
+name: auto-ml-forecasting-function
+dependencies:
+- fbprophet==0.5
+- py-xgboost<=0.80
+- pip:
+  - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib

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

@@ -1,4 +0,0 @@
-name: automl-forecasting-function
-dependencies:
-- pip:
-  - azureml-sdk

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

@@ -631,9 +631,7 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "# The request data frame needs to have y_query column which corresponds to query.\n",
         "X_query = X_test.copy()\n",
-        "X_query['y_query'] = np.NaN\n",
         "# We have to convert datetime to string, because Timestamps cannot be serialized to JSON.\n",
         "X_query[time_column_name] = X_query[time_column_name].astype(str)\n",
         "# The Service object accept the complex dictionary, which is internally converted to JSON string.\n",

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

@@ -1,4 +1,10 @@
 name: auto-ml-forecasting-orange-juice-sales
 dependencies:
+- fbprophet==0.5
+- py-xgboost<=0.80
 - pip:
   - azureml-sdk
+  - pandas==0.23.4
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib

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

@@ -155,8 +155,7 @@
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'average_precision_score_weighted',\n",
-        "    \"preprocess\": True,\n",
-        "    \"experiment_timeout_hours\": 0.2, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ability to find the best model possible\n",
+        "    \"experiment_timeout_hours\": 0.25, # This is a time limit for testing purposes, remove it for real use cases, this will drastically limit ability to find the best model possible\n",
         "    \"verbosity\": logging.INFO,\n",
         "    \"enable_stack_ensemble\": False\n",
         "}\n",

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

@@ -2,3 +2,8 @@ name: auto-ml-classification-credit-card-fraud-local
 dependencies:
 - pip:
   - azureml-sdk
+  - 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

@@ -208,7 +208,7 @@
         "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
         "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n",
         "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n",
-        "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Note: If the input data is sparse, featurization cannot be turned on.|\n",
+        "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*. Note: If the input data is sparse, featurization cannot be turned on.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|"
@@ -244,7 +244,7 @@
       "source": [
         "featurization_config = FeaturizationConfig()\n",
         "featurization_config.blocked_transformers = ['LabelEncoder']\n",
-        "#featurization_config.drop_columns = ['ERP', 'MMIN']\n",
+        "#featurization_config.drop_columns = ['MMIN']\n",
         "featurization_config.add_column_purpose('MYCT', 'Numeric')\n",
         "featurization_config.add_column_purpose('VendorName', 'CategoricalHash')\n",
         "#default strategy mean, add transformer param for for 3 columns\n",
@@ -262,7 +262,7 @@
       "source": [
         "automl_settings = {\n",
         "    \"enable_early_stopping\": True, \n",
-        "    \"experiment_timeout_hours\" : 0.2,\n",
+        "    \"experiment_timeout_hours\" : 0.25,\n",
         "    \"max_concurrent_iterations\": 4,\n",
         "    \"max_cores_per_iteration\": -1,\n",
         "    \"n_cross_validations\": 5,\n",
@@ -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",
@@ -532,8 +532,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Create conda configuration for model explanations experiment\n",
-        "We need `azureml-explain-model`, `azureml-train-automl` and `azureml-core` packages for computing model explanations for your AutoML model on remote compute."
+        "#### Create conda configuration for model explanations experiment from automl_run object"
       ]
     },
     {
@@ -552,13 +551,9 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-train-automl', 'azureml-core', 'azureml-explain-model'\n",
-        "]\n",
         "\n",
         "# specify CondaDependencies obj\n",
-        "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
-        "    pip_packages=azureml_pip_packages)"
+        "conda_run_config.environment.python.conda_dependencies = automl_run.get_environment().python.conda_dependencies"
       ]
     },
     {
@@ -717,10 +712,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "myenv = automl_run.get_environment().python.conda_dependencies\n",
+        "conda_dep = automl_run.get_environment().python.conda_dependencies\n",
         "\n",
         "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())\n",
+        "    f.write(conda_dep.serialize_to_string())\n",
         "\n",
         "with open(\"myenv.yml\",\"r\") as f:\n",
         "    print(f.read())"
@@ -761,6 +756,7 @@
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",
@@ -768,9 +764,8 @@
         "                                                     \"method\" : \"local_explanation\"}, \n",
         "                                               description='Get local explanations for Machine test data')\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"score_explain.py\",\n",
-        "                                   conda_file=\"myenv.yml\")\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score_explain.py\", environment=myenv)\n",
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",

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

@@ -2,3 +2,10 @@ name: auto-ml-regression-hardware-performance-explanation-and-featurization
 dependencies:
 - pip:
   - azureml-sdk
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib
+  - interpret
+  - azureml-explain-model
+  - azureml-explain-model
+  - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/train_explainer.py

@@ -22,7 +22,7 @@ run = Run.get_context()
 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

how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb

@@ -198,7 +198,6 @@
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'r2_score',\n",
-        "    \"preprocess\": True,\n",
         "    \"enable_early_stopping\": True, \n",
         "    \"experiment_timeout_hours\": 0.3, #for real scenarios we reccommend a timeout of at least one hour \n",
         "    \"max_concurrent_iterations\": 4,\n",

how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.yml

@@ -2,3 +2,7 @@ name: auto-ml-regression
 dependencies:
 - pip:
   - azureml-sdk
+  - pandas==0.23.4
+  - azureml-train-automl
+  - azureml-widgets
+  - matplotlib

how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb

@@ -11,6 +11,13 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register Azure Databricks trained model and deploy it to ACI\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -265,6 +272,15 @@
         "myservice.delete()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Deploying to other types of computes\n",
+        "\n",
+        "In order to learn how to deploy to other types of compute targets, such as AKS, please take a look at the set of notebooks in the [deployment](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/deployment) folder."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-05.ipynb

@@ -1,312 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
-        "\n",
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "This notebook uses image from ACI notebook for deploying to AKS."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "\n",
-        "# Check core SDK version number\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Set auth to be used by workspace related APIs.\n",
-        "# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
-        "# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
-        "auth = None"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config(auth = auth)\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Register the model\n",
-        "import os\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "model_name = \"AdultCensus_runHistory_aks.mml\" # \n",
-        "model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
-        "\n",
-        "print(\"copy model from dbfs to local\")\n",
-        "model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
-        "dbutils.fs.cp(model_name, model_local, True)\n",
-        "\n",
-        "mymodel = Model.register(model_path = model_name, # this points to a local file\n",
-        "                       model_name = model_name, # this is the name the model is registered as, am using same name for both path and name.                 \n",
-        "                       description = \"ADB trained model by Parashar\",\n",
-        "                       workspace = ws)\n",
-        "\n",
-        "print(mymodel.name, mymodel.description, mymodel.version)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#%%writefile score_sparkml.py\n",
-        "score_sparkml = \"\"\"\n",
-        " \n",
-        "import json\n",
-        " \n",
-        "def init():\n",
-        "    # One-time initialization of PySpark and predictive model\n",
-        "    import pyspark\n",
-        "    from azureml.core.model import Model\n",
-        "    from pyspark.ml import PipelineModel\n",
-        " \n",
-        "    global trainedModel\n",
-        "    global spark\n",
-        " \n",
-        "    spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
-        "    model_name = \"{model_name}\" #interpolated\n",
-        "    model_path = Model.get_model_path(model_name)\n",
-        "    trainedModel = PipelineModel.load(model_path)\n",
-        "    \n",
-        "def run(input_json):\n",
-        "    if isinstance(trainedModel, Exception):\n",
-        "        return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
-        "      \n",
-        "    try:\n",
-        "        sc = spark.sparkContext\n",
-        "        input_list = json.loads(input_json)\n",
-        "        input_rdd = sc.parallelize(input_list)\n",
-        "        input_df = spark.read.json(input_rdd)\n",
-        "    \n",
-        "        # Compute prediction\n",
-        "        prediction = trainedModel.transform(input_df)\n",
-        "        #result = prediction.first().prediction\n",
-        "        predictions = prediction.collect()\n",
-        " \n",
-        "        #Get each scored result\n",
-        "        preds = [str(x['prediction']) for x in predictions]\n",
-        "        result = \",\".join(preds)\n",
-        "        # you can return any data type as long as it is JSON-serializable\n",
-        "        return result.tolist()\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return result\n",
-        "    \n",
-        "\"\"\".format(model_name=model_name)\n",
-        " \n",
-        "exec(score_sparkml)\n",
-        " \n",
-        "with open(\"score_sparkml.py\", \"w\") as file:\n",
-        "    file.write(score_sparkml)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
-        "\n",
-        "with open(\"mydeployenv.yml\",\"w\") as f:\n",
-        "    f.write(myacienv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#create AKS compute\n",
-        "#it may take 20-25 minutes to create a new cluster\n",
-        "\n",
-        "from azureml.core.compute import AksCompute, ComputeTarget\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "aks_name = 'ps-aks-demo2' \n",
-        "\n",
-        "try:\n",
-        "    aks_target = ComputeTarget(workspace=ws, name=aks_name)\n",
-        "    print('Found existing cluster, use it.')\n",
-        "except ComputeTargetException:\n",
-        "    # Use the default configuration (can also provide parameters to customize)\n",
-        "    prov_config = AksCompute.provisioning_configuration()\n",
-        "    \n",
-        "    # Create the cluster\n",
-        "    aks_target = ComputeTarget.create(workspace = ws, \n",
-        "                                  name = aks_name, \n",
-        "                                  provisioning_configuration = prov_config)\n",
-        "\n",
-        "aks_target.wait_for_completion(show_output = True)\n",
-        "\n",
-        "print(aks_target.provisioning_state)\n",
-        "print(aks_target.provisioning_errors)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#deploy to AKS\n",
-        "from azureml.core.webservice import AksWebservice, Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
-        "from azureml.core.model import InferenceConfig\n",
-        "\n",
-        "aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)\n",
-        "\n",
-        "service_name = 'ps-aks-service'\n",
-        "\n",
-        "# Remove any existing service under the same name.\n",
-        "try:\n",
-        "    Webservice(ws, service_name).delete()\n",
-        "except WebserviceException:\n",
-        "    pass\n",
-        "\n",
-        "inference_config = InferenceConfig(runtime = 'spark-py', \n",
-        "                                   entry_script ='score_sparkml.py',\n",
-        "                                   conda_file ='mydeployenv.yml')\n",
-        "\n",
-        "aks_service = Model.deploy(ws, service_name, [mymodel], inference_config, aks_config, aks_target)\n",
-        "aks_service.wait_for_deployment(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aks_service.deployment_status"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#for using the Web HTTP API \n",
-        "print(aks_service.scoring_uri)\n",
-        "print(aks_service.get_keys())"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "#get the some sample data\n",
-        "test_data_path = \"AdultCensusIncomeTest\"\n",
-        "test = spark.read.parquet(test_data_path).limit(5)\n",
-        "\n",
-        "test_json = json.dumps(test.toJSON().collect())\n",
-        "\n",
-        "print(test_json)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#using data defined above predict if income is >50K (1) or <=50K (0)\n",
-        "aks_service.run(input_data=test_json)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#comment to not delete the web service\n",
-        "aks_service.delete()\n",
-        "#model.delete()\n",
-        "aks_target.delete() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-existingimage-05.png)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "pasha"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.8"
-    },
-    "name": "deploy-to-aks-existingimage-05",
-    "notebookId": 1030695628045968
-  },
-  "nbformat": 4,
-  "nbformat_minor": 1
-}

how-to-use-azureml/azureml-sdk-for-r/README.md

@@ -0,0 +1,36 @@
+## Examples to get started with Azure Machine Learning SDK for R
+
+Learn how to use Azure Machine Learning SDK for R for experimentation and model management.
+
+As a pre-requisite, go through the [Installation](vignettes/installation.Rmd) and [Configuration](vignettes/configuration.Rmd) vignettes to first install the package and set up your Azure Machine Learning Workspace unless you are running these examples on an Azure Machine Learning compute instance. Azure Machine Learning compute instances have the Azure Machine Learning SDK pre-installed and your workspace details pre-configured.
+
+
+Samples
+* Deployment
+	* [deploy-to-aci](./samples/deployment/deploy-to-aci): Deploy a model as a web service to Azure Container Instances (ACI).
+	* [deploy-to-local](./samples/deployment/deploy-to-local): Deploy a model as a web service locally.
+* Training
+	* [train-on-amlcompute](./samples/training/train-on-amlcompute): Train a model on a remote AmlCompute cluster.
+	* [train-on-local](./samples/training/train-on-local): Train a model locally with Docker.
+
+Vignettes
+* [deploy-to-aks](./vignettes/deploy-to-aks): Production deploy a model as a web service to Azure Kubernetes Service (AKS).
+* [hyperparameter-tune-with-keras](./vignettes/hyperparameter-tune-with-keras): Hyperparameter tune a Keras model using HyperDrive, Azure ML's hyperparameter tuning functionality.
+* [train-and-deploy-to-aci](./vignettes/train-and-deploy-to-aci): Train a caret model and deploy as a web service to Azure Container Instances (ACI).
+* [train-with-tensorflow](./vignettes/train-with-tensorflow): Train a deep learning TensorFlow model with Azure ML.
+
+Find more information on the [official documentation site for Azure Machine Learning SDK for R](https://azure.github.io/azureml-sdk-for-r/).
+
+
+### Troubleshooting
+
+- If the following error occurs when submitting an experiment using RStudio:
+   ```R
+    Error in py_call_impl(callable, dots$args, dots$keywords) : 
+     PermissionError: [Errno 13] Permission denied
+   ```
+  Move the files for your project into a subdirectory and reset the working directory to that directory before re-submitting.
+  
+  In order to submit an experiment, the Azure ML SDK must create a .zip file of the project directory to send to the service. However,
+  the SDK does not have permission to write into the .Rproj.user subdirectory that is automatically created during an RStudio
+  session. For this reason, the recommended best practice is to isolate project files into their own directory.

how-to-use-azureml/azureml-sdk-for-r/samples/README.md

@@ -0,0 +1,11 @@
+## Azure Machine Learning samples
+These samples are short code examples for using Azure Machine Learning SDK for R. If you are new to the R SDK, we recommend that you first take a look at the more detailed end-to-end [vignettes](../vignettes).
+
+Before running a sample in RStudio, set the working directory to the folder that contains the sample script in RStudio using `setwd(dirname)` or Session -> Set Working Directory -> To Source File Location. Each vignette assumes that the data and scripts are in the current working directory.
+
+1. [train-on-amlcompute](training/train-on-amlcompute): Train a model on a remote AmlCompute cluster.
+2. [train-on-local](training/train-on-local): Train a model locally with Docker.
+2. [deploy-to-aci](deployment/deploy-to-aci): Deploy a model as a web service to Azure Container Instances (ACI).
+3. [deploy-to-local](deployment/deploy-to-local): Deploy a model as a web service locally.
+
+> Before you run these samples, make sure you have an Azure Machine Learning workspace. You can follow the [configuration vignette](../vignettes/configuration.Rmd) to set up a workspace. (You do not need to do this if you are running these examples on an Azure Machine Learning compute instance).

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-aci/deploy-to-aci.R

@@ -0,0 +1,59 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+library(azuremlsdk)
+library(jsonlite)
+
+ws <- load_workspace_from_config()
+
+# Register the model
+model <- register_model(ws, model_path = "project_files/model.rds",
+                        model_name = "model.rds")
+
+# Create environment
+r_env <- r_environment(name = "r_env")
+
+# Create inference config
+inference_config <- inference_config(
+  entry_script = "score.R",
+  source_directory = "project_files",
+  environment = r_env)
+
+# Create ACI deployment config
+deployment_config <- aci_webservice_deployment_config(cpu_cores = 1,
+                                                      memory_gb = 1)
+
+# Deploy the web service
+service <- deploy_model(ws,
+                        'rservice',
+                        list(model),
+                        inference_config,
+                        deployment_config)
+wait_for_deployment(service, show_output = TRUE)
+
+# If you encounter any issue in deploying the webservice, please visit
+# https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment
+
+# Inferencing
+# versicolor
+plant <- data.frame(Sepal.Length = 6.4,
+                    Sepal.Width = 2.8,
+                    Petal.Length = 4.6,
+                    Petal.Width = 1.8)
+# setosa
+plant <- data.frame(Sepal.Length = 5.1,
+                    Sepal.Width = 3.5,
+                    Petal.Length = 1.4,
+                    Petal.Width = 0.2)
+# virginica
+plant <- data.frame(Sepal.Length = 6.7,
+                    Sepal.Width = 3.3,
+                    Petal.Length = 5.2,
+                    Petal.Width = 2.3)
+
+# Test the web service
+predicted_val <- invoke_webservice(service, toJSON(plant))
+predicted_val
+
+# Delete the web service
+delete_webservice(service)

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-aci/project_files/score.R

@@ -0,0 +1,17 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+library(jsonlite)
+
+init <- function() {
+    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
+    model <- readRDS(file.path(model_path, "model.rds"))
+    message("model is loaded")
+
+    function(data) {
+        plant <- as.data.frame(fromJSON(data))
+        prediction <- predict(model, plant)
+        result <- as.character(prediction)
+        toJSON(result)
+    }
+}

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/deploy-to-local.R

@@ -0,0 +1,112 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+# Register model and deploy locally
+# This example shows how to deploy a web service in step-by-step fashion:
+#   
+# 1) Register model
+# 2) Deploy the model as a web service in a local Docker container.
+# 3) Invoke web service with SDK or call web service with raw HTTP call.
+# 4) Quickly test changes to your entry script by reloading the local service.
+# 5) Optionally, you can also make changes to model and update the local service.
+
+library(azuremlsdk)
+library(jsonlite)
+
+ws <- load_workspace_from_config()
+
+# Register the model
+model <- register_model(ws, model_path = "project_files/model.rds",
+                        model_name = "model.rds")
+
+# Create environment
+r_env <- r_environment(name = "r_env")
+
+# Create inference config
+inference_config <- inference_config(
+  entry_script = "score.R",
+  source_directory = "project_files",
+  environment = r_env)
+
+# Create local deployment config
+local_deployment_config <- local_webservice_deployment_config()
+
+# Deploy the web service
+# NOTE:
+# The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:
+# # PowerShell command to switch to Linux engine
+# & 'C:\Program Files\Docker\Docker\DockerCli.exe' -SwitchLinuxEngine
+service <- deploy_model(ws,
+                        'rservice-local',
+                        list(model),
+                        inference_config,
+                        local_deployment_config)
+# Wait for deployment
+wait_for_deployment(service, show_output = TRUE)
+
+# Show the port of local service
+message(service$port)
+
+# If you encounter any issue in deploying the webservice, please visit
+# https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment
+
+# Inferencing
+# versicolor
+# plant <- data.frame(Sepal.Length = 6.4,
+#                     Sepal.Width = 2.8,
+#                     Petal.Length = 4.6,
+#                     Petal.Width = 1.8)
+# setosa
+plant <- data.frame(Sepal.Length = 5.1,
+                    Sepal.Width = 3.5,
+                    Petal.Length = 1.4,
+                    Petal.Width = 0.2)
+# # virginica
+# plant <- data.frame(Sepal.Length = 6.7,
+#                     Sepal.Width = 3.3,
+#                     Petal.Length = 5.2,
+#                     Petal.Width = 2.3)
+
+#Test the web service
+invoke_webservice(service, toJSON(plant))
+
+## The last few lines of the logs should have the correct prediction and should display -> R[write to console]: "setosa" 
+cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
+
+## Test the web service with a HTTP Raw request
+# 
+# NOTE:
+# To test the service locally use the https://localhost:<local_service$port> URL
+
+# Import the request library
+library(httr)
+# Get the service scoring URL from the service object, its URL is for testing locally
+local_service_url <- service$scoring_uri #Same as https://localhost:<local_service$port>
+
+#POST request to web service
+resp <- POST(local_service_url, body = plant, encode = "json", verbose())
+
+## The last few lines of the logs should have the correct prediction and should display -> R[write to console]: "setosa" 
+cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
+
+
+# Optional, use a new scoring script
+inference_config <- inference_config(
+  entry_script = "score_new.R",
+  source_directory = "project_files",
+  environment = r_env)
+
+## Then reload the service to see the changes made
+reload_local_webservice_assets(service)
+
+## Check reloaded service, you will see the last line will say "this is a new scoring script! I was reloaded"
+invoke_webservice(service, toJSON(plant))
+cat(gsub(pattern = "\n", replacement = " \n", x = get_webservice_logs(service)))
+
+# Update service
+# If you want to change your model(s), environment, or deployment configuration, call update() to rebuild the Docker image.
+
+# update_local_webservice(service, models = [NewModelObject], deployment_config = deployment_config, wait = FALSE, inference_config = inference_config)
+
+# Delete service
+delete_local_webservice(service)

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/project_files/score.R

@@ -0,0 +1,18 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+library(jsonlite)
+
+init <- function() {
+    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
+    model <- readRDS(file.path(model_path, "model.rds"))
+    message("model is loaded")
+
+    function(data) {
+        plant <- as.data.frame(fromJSON(data))
+        prediction <- predict(model, plant)
+        result <- as.character(prediction)
+        message(result)
+        toJSON(result)
+    }
+}

how-to-use-azureml/azureml-sdk-for-r/samples/deployment/deploy-to-local/project_files/score_new.R

@@ -0,0 +1,19 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+library(jsonlite)
+
+init <- function() {
+    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
+    model <- readRDS(file.path(model_path, "model.rds"))
+    message("model is loaded")
+
+    function(data) {
+        plant <- as.data.frame(fromJSON(data))
+        prediction <- predict(model, plant)
+        result <- as.character(prediction)
+        message(result)
+        message("this is a new scoring script! I was reloaded")
+        toJSON(result)
+    }
+}

how-to-use-azureml/azureml-sdk-for-r/samples/training/train-on-amlcompute/scripts/train.R

@@ -0,0 +1,34 @@
+# This script loads a dataset of which the last column is supposed to be the
+# class and logs the accuracy
+
+library(azuremlsdk)
+library(caret)
+library(optparse)
+library(datasets)
+
+
+iris_data <- data(iris)
+summary(iris_data)
+
+in_train <- createDataPartition(y = iris_data$Species, p = .8, list = FALSE)
+train_data <- iris_data[in_train,]
+test_data <- iris_data[-in_train,]
+
+# Run algorithms using 10-fold cross validation
+control <- trainControl(method = "cv", number = 10)
+metric <- "Accuracy"
+
+set.seed(7)
+model <- train(Species ~ .,
+               data = train_data,
+               method = "lda",
+               metric = metric,
+               trControl = control)
+predictions <- predict(model, test_data)
+conf_matrix <- confusionMatrix(predictions, test_data$Species)
+message(conf_matrix)
+
+log_metric_to_run(metric, conf_matrix$overall["Accuracy"])
+
+saveRDS(model, file = "./outputs/model.rds")
+message("Model saved")

how-to-use-azureml/azureml-sdk-for-r/samples/training/train-on-amlcompute/train-on-amlcompute.R

@@ -0,0 +1,41 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+# Reminder: set working directory to current file location prior to running this script
+
+library(azuremlsdk)
+
+ws <- load_workspace_from_config()
+
+# Create AmlCompute cluster
+cluster_name <- "r-cluster"
+compute_target <- get_compute(ws, cluster_name = cluster_name)
+if (is.null(compute_target)) {
+    vm_size <- "STANDARD_D2_V2"
+    compute_target <- create_aml_compute(workspace = ws,
+                                       cluster_name = cluster_name,
+                                       vm_size = vm_size,
+                                       max_nodes = 1)
+
+    wait_for_provisioning_completion(compute_target, show_output = TRUE)
+}
+
+# Define estimator
+est <- estimator(source_directory = "scripts",
+                 entry_script = "train.R",
+                 compute_target = compute_target)
+
+experiment_name <- "train-r-script-on-amlcompute"
+exp <- experiment(ws, experiment_name)
+
+# Submit job and display the run details
+run <- submit_experiment(exp, est)
+view_run_details(run)
+wait_for_run_completion(run, show_output = TRUE)
+
+# Get the run metrics
+metrics <- get_run_metrics(run)
+metrics
+
+# Delete cluster
+delete_compute(compute_target)

how-to-use-azureml/azureml-sdk-for-r/samples/training/train-on-local/scripts/train.R

@@ -0,0 +1,28 @@
+# This script loads a dataset of which the last column is supposed to be the
+# class and logs the accuracy
+
+library(azuremlsdk)
+library(caret)
+library(datasets)
+
+iris_data <- data(iris)
+summary(iris_data)
+
+in_train <- createDataPartition(y = iris_data$Species, p = .8, list = FALSE)
+train_data <- iris_data[in_train,]
+test_data <- iris_data[-in_train,]
+# Run algorithms using 10-fold cross validation
+control <- trainControl(method = "cv", number = 10)
+metric <- "Accuracy"
+
+set.seed(7)
+model <- train(Species ~ .,
+               data = train_data,
+               method = "lda",
+               metric = metric,
+               trControl = control)
+predictions <- predict(model, test_data)
+conf_matrix <- confusionMatrix(predictions, test_data$Species)
+message(conf_matrix)
+
+log_metric_to_run(metric, conf_matrix$overall["Accuracy"])

how-to-use-azureml/azureml-sdk-for-r/samples/training/train-on-local/train-on-local.R

@@ -0,0 +1,26 @@
+# Copyright(c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+# Reminder: set working directory to current file location prior to running this script
+
+library(azuremlsdk)
+
+ws <- load_workspace_from_config()
+
+# Define estimator
+est <- estimator(source_directory = "scripts",
+                 entry_script = "train.R",
+                 compute_target = "local")
+
+# Initialize experiment
+experiment_name <- "train-r-script-on-local"
+exp <- experiment(ws, experiment_name)
+
+# Submit job and display the run details
+run <- submit_experiment(exp, est)
+view_run_details(run)
+wait_for_run_completion(run, show_output = TRUE)
+
+# Get the run metrics
+metrics <- get_run_metrics(run)
+metrics

how-to-use-azureml/azureml-sdk-for-r/vignettes/README.md

@@ -0,0 +1,17 @@
+## Azure Machine Learning vignettes
+
+These vignettes are end-to-end tutorials for using Azure Machine Learning SDK for R.
+
+Before running a vignette in RStudio, set the working directory to the folder that contains the vignette file (.Rmd file) in RStudio using `setwd(dirname)` or Session -> Set Working Directory -> To Source File Location. Each vignette assumes that the data and scripts are in the current working directory.
+
+The following vignettes are included:
+1. [installation](installation.Rmd): Install the Azure ML SDK for R.
+2. [configuration](configuration.Rmd): Set up an Azure ML workspace.
+3. [train-and-deploy-to-aci](train-and-deploy-to-aci): Train a caret model and deploy as a web service to Azure Container Instances (ACI).
+4. [train-with-tensorflow](train-with-tensorflow/): Train a deep learning TensorFlow model with Azure ML.
+5. [hyperparameter-tune-with-keras](hyperparameter-tune-with-keras/): Hyperparameter tune a Keras model using HyperDrive, Azure ML's hyperparameter tuning functionality.
+6. [deploy-to-aks](deploy-to-aks/): Production deploy a model as a web service to Azure Kubernetes Service (AKS).
+
+> Before you run these samples, make sure you have an Azure Machine Learning workspace. You can follow the [configuration vignette](../vignettes/configuration.Rmd) to set up a workspace. (You do not need to do this if you are running these examples on an Azure Machine Learning compute instance).
+
+For additional examples on using the R SDK, see the [samples](../samples) folder.

how-to-use-azureml/azureml-sdk-for-r/vignettes/configuration.Rmd

@@ -0,0 +1,108 @@
+---
+title: "Set up an Azure ML workspace"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Set up an Azure ML workspace}
+  %\VignetteEngine{knitr::rmarkdown}
+  \use_package{UTF-8}
+---
+
+This tutorial gets you started with the Azure Machine Learning service by walking through the requirements and instructions for setting up a workspace, the top-level resource for Azure ML.
+
+You do not need run this if you are working on an Azure Machine Learning Compute Instance, as the compute instance is already associated with an existing workspace.
+
+## What is an Azure ML workspace?
+The workspace is the top-level resource for Azure ML, providing a centralized place to work with all the artifacts you create when you use Azure ML. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts.
+
+When you create a new workspace, it automatically creates several Azure resources that are used by the workspace:
+
+* Azure Container Registry: Registers docker containers that you use during training and when you deploy a model. To minimize costs, ACR is lazy-loaded until deployment images are created.
+* Azure Storage account: Used as the default datastore for the workspace.
+* Azure Application Insights: Stores monitoring information about your models.
+* Azure Key Vault: Stores secrets that are used by compute targets and other sensitive information that's needed by the workspace.
+
+## Setup
+This section describes the steps required before you can access any Azure ML service functionality.
+
+### Azure subscription
+In order to create an Azure ML workspace, first you need access to an Azure subscription. An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud. You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com/). Later in this tutorial you will need information such as your subscription ID in order to create and access workspaces.
+
+### Azure ML SDK installation
+Follow the [installation guide](https://azure.github.io/azureml-sdk-for-r/articles/installation.html) to install **azuremlsdk** on your machine.
+
+## Configure your workspace
+### Workspace parameters
+To use an Azure ML workspace, you will need to supply the following information:
+
+* Your subscription ID
+* A resource group name
+* (Optional) The region that will host your workspace
+* A name for your workspace
+
+You can get your subscription ID from the [Azure portal](https://portal.azure.com/).
+
+You will also need access to a [resource group](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group. You can see what resource groups to which you have access, or create a new one in the Azure portal. If you don't have a resource group, the `create_workspace()` method will create one for you using the name you provide.
+
+The region to host your workspace will be used if you are creating a new workspace. You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service). You should pick a region that is close to your location or that contains your data.
+
+The name for your workspace is unique within the subscription and should be descriptive enough to discern among other workspaces. The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.
+
+The following code chunk allows you to specify your workspace parameters. It uses `Sys.getenv` to read values from environment variables, which is useful for automation. If no environment variable exists, the parameters will be set to the specified default values. Replace the default values in the code below with your default parameter values.
+
+``` {r configure_parameters, eval=FALSE}
+subscription_id <- Sys.getenv("SUBSCRIPTION_ID", unset = "<my-subscription-id>")
+resource_group <- Sys.getenv("RESOURCE_GROUP", default="<my-resource-group>")
+workspace_name <- Sys.getenv("WORKSPACE_NAME", default="<my-workspace-name>")
+workspace_region <- Sys.getenv("WORKSPACE_REGION", default="eastus2")
+``` 
+
+### Create a new workspace
+If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace. If you don't have a resource group, `create_workspace()` will create one for you using the name you provide. If you don't want it to do so, set the `create_resource_group = FALSE` parameter.
+
+Note: As with other Azure services, there are limits on certain resources (e.g. AmlCompute quota) associated with the Azure ML service. Please read this [article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.
+
+This cell will create an Azure ML workspace for you in a subscription, provided you have the correct permissions.
+
+This will fail if:
+
+* You do not have permission to create a workspace in the resource group.
+* You do not have permission to create a resource group if it does not exist.
+* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.
+
+If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.
+
+There are additional parameters that are not shown below that can be configured when creating a workspace. Please see [`create_workspace()`](https://azure.github.io/azureml-sdk-for-r/reference/create_workspace.html) for more details.
+
+``` {r create_workspace, eval=FALSE}
+library(azuremlsdk)
+
+ws <- create_workspace(name = workspace_name,
+                       subscription_id = subscription_id,
+                       resource_group = resource_group,
+                       location = workspace_region,
+                       exist_ok = TRUE)
+```
+
+You can out write out the workspace ARM properties to a config file with [`write_workspace_config()`](https://azure.github.io/azureml-sdk-for-r/reference/write_workspace_config.html). The method provides a simple way of reusing the same workspace across multiple files or projects. Users can save the workspace details with `write_workspace_config()`, and use [`load_workspace_from_config()`](https://azure.github.io/azureml-sdk-for-r/reference/load_workspace_from_config.html) to load the same workspace in different files or projects without retyping the workspace ARM properties. The method defaults to writing out the config file to the current working directory with "config.json" as the file name. To specify a different path or file name, set the `path` and `file_name` parameters.
+
+``` {r write_config, eval=FALSE}
+write_workspace_config(ws)
+```
+
+### Access an existing workspace
+You can access an existing workspace in a couple of ways. If your workspace properties were previously saved to a config file, you can load the workspace as follows:
+
+``` {r load_config, eval=FALSE}
+ws <- load_workspace_from_config()
+```
+
+If Azure ML cannot find the config file, specify the path to the config file with the `path` parameter. The method defaults to starting the search in the current directory.
+
+You can also initialize a workspace using the [`get_workspace()`](https://azure.github.io/azureml-sdk-for-r/reference/get_workspace.html) method.
+
+``` {r get_workspace, eval=FALSE}
+ws <- get_workspace(name = workspace_name,
+                    subscription_id = subscription_id,
+                    resource_group = resource_group)
+```

how-to-use-azureml/azureml-sdk-for-r/vignettes/deploy-to-aks/deploy-to-aks.Rmd

@@ -0,0 +1,188 @@
+---
+title: "Deploy a web service to Azure Kubernetes Service"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Deploy a web service to Azure Kubernetes Service}
+  %\VignetteEngine{knitr::rmarkdown}
+  \use_package{UTF-8}
+---
+
+This tutorial demonstrates how to deploy a model as a web service on [Azure Kubernetes Service](https://azure.microsoft.com/en-us/services/kubernetes-service/) (AKS). AKS is good for high-scale production deployments; use it if you need one or more of the following capabilities:
+
+* Fast response time
+* Autoscaling of the deployed service
+* Hardware acceleration options such as GPU
+
+You will learn to:
+
+* Set up your testing environment
+* Register a model
+* Provision an AKS cluster
+* Deploy the model to AKS
+* Test the deployed service
+
+## Prerequisites
+If you don<6F>t have access to an Azure ML workspace, follow the [setup tutorial](https://azure.github.io/azureml-sdk-for-r/articles/configuration.html) to configure and create a workspace.
+
+## Set up your testing environment
+Start by setting up your environment. This includes importing the **azuremlsdk** package and connecting to your workspace.
+
+### Import package
+```{r import_package, eval=FALSE}
+library(azuremlsdk)
+```
+
+### Load your workspace
+Instantiate a workspace object from your existing workspace. The following code will load the workspace details from a **config.json** file if you previously wrote one out with `write_workspace_config()`. 
+```{r load_workspace, eval=FALSE}
+ws <- load_workspace_from_config()
+```
+
+Or, you can retrieve a workspace by directly specifying your workspace details:
+```{r get_workspace, eval=FALSE}
+ws <- get_workspace("<your workspace name>", "<your subscription ID>", "<your resource group>")
+```
+
+## Register the model
+In this tutorial we will deploy a model that was trained in one of the [samples](https://github.com/Azure/azureml-sdk-for-r/blob/master/samples/training/train-on-amlcompute/train-on-amlcompute.R). The model was trained with the Iris dataset and can be used to determine if a flower is one of three Iris flower species (setosa, versicolor, virginica). We have provided the model file (`model.rds`) for the tutorial; it is located in the "project_files" directory of this vignette.
+
+First, register the model to your workspace with [`register_model()`](https://azure.github.io/azureml-sdk-for-r/reference/register_model.html). A registered model can be any collection of files, but in this case the R model file is sufficient. Azure ML will use the registered model for deployment.
+
+```{r register_model, eval=FALSE}
+model <- register_model(ws, 
+                        model_path = "project_files/model.rds", 
+                        model_name = "iris_model",
+                        description = "Predict an Iris flower type")
+```
+
+## Provision an AKS cluster
+When deploying a web service to AKS, you deploy to an AKS cluster that is connected to your workspace. There are two ways to connect an AKS cluster to your workspace:
+
+* Create the AKS cluster. The process automatically connects the cluster to the workspace. 
+* Attach an existing AKS cluster to your workspace. You can attach a cluster with the [`attach_aks_compute()`](https://azure.github.io/azureml-sdk-for-r/reference/attach_aks_compute.html) method.
+
+Creating or attaching an AKS cluster is a one-time process for your workspace. You can reuse this cluster for multiple deployments. If you delete the cluster or the resource group that contains it, you must create a new cluster the next time you need to deploy.
+
+In this tutorial, we will go with the first method of provisioning a new cluster. See the [`create_aks_compute()`](https://azure.github.io/azureml-sdk-for-r/reference/create_aks_compute.html) reference for the full set of configurable parameters. If you pick custom values for the `agent_count` and `vm_size` parameters, you need to make sure `agent_count` multiplied by `vm_size` is greater than or equal to `12` virtual CPUs.
+
+``` {r provision_cluster, eval=FALSE}
+aks_target <- create_aks_compute(ws, cluster_name = 'myakscluster')
+
+wait_for_provisioning_completion(aks_target, show_output = TRUE)
+```
+
+The Azure ML SDK does not provide support for scaling an AKS cluster. To scale the nodes in the cluster, use the UI for your AKS cluster in the Azure portal. You can only change the node count, not the VM size of the cluster.
+
+## Deploy as a web service
+### Define the inference dependencies
+To deploy a model, you need an **inference configuration**, which describes the environment needed to host the model and web service. To create an inference config, you will first need a scoring script and an Azure ML environment.
+
+The scoring script (`entry_script`) is an R script that will take as input variable values (in JSON format) and output a prediction from your model. For this tutorial, use the provided scoring file `score.R`. The scoring script must contain an `init()` method that loads your model and returns a function that uses the model to make a prediction based on the input data. See the [documentation](https://azure.github.io/azureml-sdk-for-r/reference/inference_config.html#details) for more details.
+
+Next, define an Azure ML **environment** for your script<70>s package dependencies. With an environment, you specify R packages (from CRAN or elsewhere) that are needed for your script to run. You can also provide the values of environment variables that your script can reference to modify its behavior. 
+
+By default Azure ML will build a default Docker image that includes R, the Azure ML SDK, and additional required dependencies for deployment. See the documentation here for the full list of dependencies that will be installed in the default container. You can also specify additional packages to be installed at runtime, or even a custom Docker image to be used instead of the base image that will be built, using the other available parameters to [`r_environment()`](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html).
+
+```{r create_env, eval=FALSE}
+r_env <- r_environment(name = "deploy_env")
+```
+
+Now you have everything you need to create an inference config for encapsulating your scoring script and environment dependencies.
+
+``` {r create_inference_config, eval=FALSE}
+inference_config <- inference_config(
+  entry_script = "score.R",
+  source_directory = "project_files",
+  environment = r_env)
+```
+
+### Deploy to AKS
+Now, define the deployment configuration that describes the compute resources needed, for example, the number of cores and memory. See the [`aks_webservice_deployment_config()`](https://azure.github.io/azureml-sdk-for-r/reference/aks_webservice_deployment_config.html) for the full set of configurable parameters.
+
+``` {r deploy_config, eval=FALSE}
+aks_config <- aks_webservice_deployment_config(cpu_cores = 1, memory_gb = 1)
+```
+
+Now, deploy your model as a web service to the AKS cluster you created earlier.
+
+```{r deploy_service, eval=FALSE}
+aks_service <- deploy_model(ws, 
+                            'my-new-aksservice', 
+                            models = list(model), 
+                            inference_config = inference_config, 
+                            deployment_config = aks_config,
+                            deployment_target = aks_target)
+
+wait_for_deployment(aks_service, show_output = TRUE)
+```
+
+To inspect the logs from the deployment:
+```{r get_logs, eval=FALSE}
+get_webservice_logs(aks_service)
+```
+
+If you encounter any issue in deploying the web service, please visit the [troubleshooting guide](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment).
+
+## Test the deployed service
+Now that your model is deployed as a service, you can test the service from R using [`invoke_webservice()`](https://azure.github.io/azureml-sdk-for-r/reference/invoke_webservice.html). Provide a new set of data to predict from, convert it to JSON, and send it to the service.
+
+``` {r test_service, eval=FALSE}
+library(jsonlite)
+# versicolor
+plant <- data.frame(Sepal.Length = 6.4,
+                    Sepal.Width = 2.8,
+                    Petal.Length = 4.6,
+                    Petal.Width = 1.8)
+
+# setosa
+# plant <- data.frame(Sepal.Length = 5.1,
+#                    Sepal.Width = 3.5,
+#                    Petal.Length = 1.4,
+#                    Petal.Width = 0.2)
+
+# virginica
+# plant <- data.frame(Sepal.Length = 6.7,
+#                    Sepal.Width = 3.3,
+#                    Petal.Length = 5.2,
+#                    Petal.Width = 2.3)
+
+predicted_val <- invoke_webservice(aks_service, toJSON(plant))
+message(predicted_val)
+```
+
+You can also get the web service<63>s HTTP endpoint, which accepts REST client calls. You can share this endpoint with anyone who wants to test the web service or integrate it into an application.
+
+``` {r eval=FALSE}
+aks_service$scoring_uri
+```
+
+## Web service authentication
+When deploying to AKS, key-based authentication is enabled by default. You can also enable token-based authentication. Token-based authentication requires clients to use an Azure Active Directory account to request an authentication token, which is used to make requests to the deployed service.
+
+To disable key-based auth, set the `auth_enabled = FALSE` parameter when creating the deployment configuration with [`aks_webservice_deployment_config()`](https://azure.github.io/azureml-sdk-for-r/reference/aks_webservice_deployment_config.html). 
+To enable token-based auth, set `token_auth_enabled = TRUE` when creating the deployment config.
+
+### Key-based authentication
+If key authentication is enabled, you can use the [`get_webservice_keys()`](https://azure.github.io/azureml-sdk-for-r/reference/get_webservice_keys.html) method to retrieve a primary and secondary authentication key. To generate a new key, use [`generate_new_webservice_key()`](https://azure.github.io/azureml-sdk-for-r/reference/generate_new_webservice_key.html).
+
+### Token-based authentication
+If token authentication is enabled, you can use the [`get_webservice_token()`](https://azure.github.io/azureml-sdk-for-r/reference/get_webservice_token.html) method to retrieve a JWT token and that token's expiration time. Make sure to request a new token after the token's expiration time.
+
+## Clean up resources
+Delete the resources once you no longer need them. Do not delete any resource you plan on still using.
+
+Delete the web service:
+```{r delete_service, eval=FALSE}
+delete_webservice(aks_service)
+```
+
+Delete the registered model:
+```{r delete_model, eval=FALSE}
+delete_model(model)
+```
+
+Delete the AKS cluster:
+```{r delete_cluster, eval=FALSE}
+delete_compute(aks_target)
+```

how-to-use-azureml/azureml-sdk-for-r/vignettes/deploy-to-aks/project_files/score.R

@@ -0,0 +1,17 @@
+#' Copyright(c) Microsoft Corporation.
+#' Licensed under the MIT license.
+
+library(jsonlite)
+
+init <- function() {
+    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
+    model <- readRDS(file.path(model_path, "model.rds"))
+    message("model is loaded")
+
+    function(data) {
+        plant <- as.data.frame(fromJSON(data))
+        prediction <- predict(model, plant)
+        result <- as.character(prediction)
+        toJSON(result)
+    }
+}

how-to-use-azureml/azureml-sdk-for-r/vignettes/hyperparameter-tune-with-keras/hyperparameter-tune-with-keras.Rmd

@@ -0,0 +1,242 @@
+---
+title: "Hyperparameter tune a Keras model"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Hyperparameter tune a Keras model}
+  %\VignetteEngine{knitr::rmarkdown}
+  \use_package{UTF-8}
+---
+
+This tutorial demonstrates how you can efficiently tune hyperparameters for a model using HyperDrive, Azure ML's hyperparameter tuning functionality. You will train a Keras model on the CIFAR10 dataset, automate hyperparameter exploration, launch parallel jobs, log your results, and find the best run.
+
+### What are hyperparameters?
+
+Hyperparameters are variable parameters chosen to train a model. Learning rate, number of epochs, and batch size are all examples of hyperparameters.
+
+Using brute-force methods to find the optimal values for parameters can be time-consuming, and poor-performing runs can result in wasted money. To avoid this, HyperDrive automates hyperparameter exploration in a time-saving and cost-effective manner by launching several parallel runs with different configurations and finding the configuration that results in best performance on your primary metric.
+
+Let's get started with the example to see how it works!
+
+## Prerequisites
+
+If you don<6F>t have access to an Azure ML workspace, follow the [setup tutorial](https://azure.github.io/azureml-sdk-for-r/articles/configuration.html) to configure and create a workspace.
+
+## Set up development environment
+The setup for your development work in this tutorial includes the following actions:
+
+* Import required packages
+* Connect to a workspace
+* Create an experiment to track your runs
+* Create a remote compute target to use for training
+
+### Import **azuremlsdk** package
+```{r eval=FALSE}
+library(azuremlsdk)
+```
+
+### Load your workspace
+Instantiate a workspace object from your existing workspace. The following code will load the workspace details from a **config.json** file if you previously wrote one out with [`write_workspace_config()`](https://azure.github.io/azureml-sdk-for-r/reference/write_workspace_config.html).
+```{r load_workpace, eval=FALSE}
+ws <- load_workspace_from_config()
+```
+
+Or, you can retrieve a workspace by directly specifying your workspace details:
+```{r get_workpace, eval=FALSE}
+ws <- get_workspace("<your workspace name>", "<your subscription ID>", "<your resource group>")
+```
+
+### Create an experiment
+An Azure ML **experiment** tracks a grouping of runs, typically from the same training script. Create an experiment to track hyperparameter tuning runs for the Keras model.
+
+```{r create_experiment, eval=FALSE}
+exp <- experiment(workspace = ws, name = 'hyperdrive-cifar10')
+```
+
+If you would like to track your runs in an existing experiment, simply specify that experiment's name to the `name` parameter of `experiment()`.
+
+### Create a compute target
+By using Azure Machine Learning Compute (AmlCompute), a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. In this tutorial, you create a GPU-enabled cluster as your training environment. The code below creates the compute cluster for you if it doesn't already exist in your workspace.
+
+You may need to wait a few minutes for your compute cluster to be provisioned if it doesn't already exist.
+
+```{r create_cluster, eval=FALSE}
+cluster_name <- "gpucluster"
+
+compute_target <- get_compute(ws, cluster_name = cluster_name)
+if (is.null(compute_target))
+{
+  vm_size <- "STANDARD_NC6"
+  compute_target <- create_aml_compute(workspace = ws, 
+                                       cluster_name = cluster_name,
+                                       vm_size = vm_size, 
+                                       max_nodes = 4)
+  
+  wait_for_provisioning_completion(compute_target, show_output = TRUE)
+}
+```
+
+## Prepare the training script
+A training script called `cifar10_cnn.R` has been provided for you in the "project_files" directory of this tutorial.
+
+In order to leverage HyperDrive, the training script for your model must log the relevant metrics during model training. When you configure the hyperparameter tuning run, you specify the primary metric to use for evaluating run performance. You must log this metric so it is available to the hyperparameter tuning process.
+
+In order to log the required metrics, you need to do the following **inside the training script**:
+
+* Import the **azuremlsdk** package
+```
+library(azuremlsdk)
+```
+
+* Take the hyperparameters as command-line arguments to the script. This is necessary so that when HyperDrive carries out the hyperparameter sweep, it can run the training script with different values to the hyperparameters as defined by the search space.
+
+* Use the [`log_metric_to_run()`](https://azure.github.io/azureml-sdk-for-r/reference/log_metric_to_run.html) function to log the hyperparameters and the primary metric.
+```
+log_metric_to_run("batch_size", batch_size)
+...
+log_metric_to_run("epochs", epochs)
+...
+log_metric_to_run("lr", lr)
+...
+log_metric_to_run("decay", decay)
+...
+log_metric_to_run("Loss", results[[1]])
+```
+
+## Create an estimator
+
+An Azure ML **estimator** encapsulates the run configuration information needed for executing a training script on the compute target. Azure ML runs are run as containerized jobs on the specified compute target. By default, the Docker image built for your training job will include R, the Azure ML SDK, and a set of commonly used R packages. See the full list of default packages included [here](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html). The estimator is used to define the configuration for each of the child runs that the parent HyperDrive run will kick off.
+
+To create the estimator, define the following:
+
+* The directory that contains your scripts needed for training (`source_directory`). All the files in this directory are uploaded to the cluster node(s) for execution. The directory must contain your training script and any additional scripts required.
+* The training script that will be executed (`entry_script`).
+* The compute target (`compute_target`), in this case the AmlCompute cluster you created earlier.
+* Any environment dependencies required for training. Since the training script requires the Keras package, which is not included in the image by default, pass the package name to the `cran_packages` parameter to have it installed in the Docker container where the job will run. See the [`estimator()`](https://azure.github.io/azureml-sdk-for-r/reference/estimator.html) reference for the full set of configurable options.
+* Set the `use_gpu = TRUE` flag so the default base GPU Docker image will be built, since the job will be run on a GPU cluster.
+
+```{r create_estimator, eval=FALSE}
+est <- estimator(source_directory = "project_files",
+                 entry_script = "cifar10_cnn.R",
+                 compute_target = compute_target,
+                 cran_packages = c("keras"),
+                 use_gpu = TRUE)
+```
+
+## Configure the HyperDrive run
+To kick off hyperparameter tuning in Azure ML, you will need to configure a HyperDrive run, which will in turn launch individual children runs of the training scripts with the corresponding hyperparameter values.
+
+### Define search space
+
+In this experiment, we will use four hyperparameters: batch size, number of epochs, learning rate, and decay. In order to begin tuning, we must define the range of values we would like to explore from and how they will be distributed. This is called a parameter space definition and can be created with discrete or continuous ranges.
+
+__Discrete hyperparameters__ are specified as a choice among discrete values represented as a list.
+
+Advanced discrete hyperparameters can also be specified using a distribution. The following distributions are supported:
+
+ * `quniform(low, high, q)`
+ * `qloguniform(low, high, q)`
+ * `qnormal(mu, sigma, q)`
+ * `qlognormal(mu, sigma, q)`
+
+__Continuous hyperparameters__ are specified as a distribution over a continuous range of values. The following distributions are supported:
+
+ * `uniform(low, high)`
+ * `loguniform(low, high)`
+ * `normal(mu, sigma)`
+ * `lognormal(mu, sigma)`
+
+Here, we will use the [`random_parameter_sampling()`](https://azure.github.io/azureml-sdk-for-r/reference/random_parameter_sampling.html) function to define the search space for each hyperparameter. `batch_size` and `epochs` will be chosen from discrete sets while `lr` and `decay` will be drawn from continuous distributions.
+
+Other available sampling function options are:
+
+ * [`grid_parameter_sampling()`](https://azure.github.io/azureml-sdk-for-r/reference/grid_parameter_sampling.html)
+ * [`bayesian_parameter_sampling()`](https://azure.github.io/azureml-sdk-for-r/reference/bayesian_parameter_sampling.html)
+
+```{r search_space, eval=FALSE}
+sampling <- random_parameter_sampling(list(batch_size = choice(c(16, 32, 64)),
+                                           epochs = choice(c(200, 350, 500)),
+                                           lr = normal(0.0001, 0.005),
+                                           decay = uniform(1e-6, 3e-6)))
+```
+
+### Define termination policy
+
+To prevent resource waste, Azure ML can detect and terminate poorly performing runs. HyperDrive will do this automatically if you specify an early termination policy.
+
+Here, you will use the [`bandit_policy()`](https://azure.github.io/azureml-sdk-for-r/reference/bandit_policy.html), which terminates any runs where the primary metric is not within the specified slack factor with respect to the best performing training run.
+
+```{r termination_policy, eval=FALSE}
+policy <- bandit_policy(slack_factor = 0.15)
+```
+
+Other termination policy options are:
+
+ * [`median_stopping_policy()`](https://azure.github.io/azureml-sdk-for-r/reference/median_stopping_policy.html)
+ * [`truncation_selection_policy()`](https://azure.github.io/azureml-sdk-for-r/reference/truncation_selection_policy.html)
+ 
+If no policy is provided, all runs will continue to completion regardless of performance.
+
+### Finalize configuration
+
+Now, you can create a `HyperDriveConfig` object to define your HyperDrive run. Along with the sampling and policy definitions, you need to specify the name of the primary metric that you want to track and whether we want to maximize it or minimize it. The `primary_metric_name` must correspond with the name of the primary metric you logged in your training script. `max_total_runs` specifies the total number of child runs to launch. See the [hyperdrive_config()](https://azure.github.io/azureml-sdk-for-r/reference/hyperdrive_config.html) reference for the full set of configurable parameters.
+
+```{r create_config, eval=FALSE}
+hyperdrive_config <- hyperdrive_config(hyperparameter_sampling = sampling,
+                                       primary_metric_goal("MINIMIZE"),
+                                       primary_metric_name = "Loss",
+                                       max_total_runs = 4,
+                                       policy = policy,
+                                       estimator = est)
+```
+
+## Submit the HyperDrive run
+
+Finally submit the experiment to run on your cluster. The parent HyperDrive run will launch the individual child runs. `submit_experiment()` will return a `HyperDriveRun` object that you will use to interface with the run. In this tutorial, since the cluster we created scales to a max of `4` nodes, all 4 child runs will be launched in parallel.
+
+```{r submit_run, eval=FALSE}
+hyperdrive_run <- submit_experiment(exp, hyperdrive_config)
+```
+
+You can view the HyperDrive run<75>s details as a table. Clicking the <20>Web View<65> link provided will bring you to Azure Machine Learning studio, where you can monitor the run in the UI.
+
+```{r eval=FALSE}
+view_run_details(hyperdrive_run)
+```
+
+Wait until hyperparameter tuning is complete before you run more code.
+
+```{r eval=FALSE}
+wait_for_run_completion(hyperdrive_run, show_output = TRUE)
+```
+
+## Analyse runs by performance
+
+Finally, you can view and compare the metrics collected during all of the child runs!
+
+```{r analyse_runs, eval=FALSE}
+# Get the metrics of all the child runs
+child_run_metrics <- get_child_run_metrics(hyperdrive_run)
+child_run_metrics
+
+# Get the child run objects sorted in descending order by the best primary metric
+child_runs <- get_child_runs_sorted_by_primary_metric(hyperdrive_run)
+child_runs
+
+# Directly get the run object of the best performing run
+best_run <- get_best_run_by_primary_metric(hyperdrive_run)
+
+# Get the metrics of the best performing run
+metrics <- get_run_metrics(best_run)
+metrics
+```
+
+The `metrics` variable will include the values of the hyperparameters that resulted in the best performing run.
+
+## Clean up resources
+Delete the resources once you no longer need them. Don't delete any resource you plan to still use. 
+
+Delete the compute cluster:
+```{r delete_compute, eval=FALSE}
+delete_compute(compute_target)
+```

how-to-use-azureml/azureml-sdk-for-r/vignettes/hyperparameter-tune-with-keras/project_files/cifar10_cnn.R

@@ -0,0 +1,124 @@
+#' Modified from: "https://github.com/rstudio/keras/blob/master/vignettes/
+#' examples/cifar10_cnn.R"
+#' 
+#' Train a simple deep CNN on the CIFAR10 small images dataset.
+#'  
+#' It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50
+#' epochs, though it is still underfitting at that point.
+
+library(keras)
+install_keras()
+
+library(azuremlsdk)
+
+# Parameters --------------------------------------------------------------
+
+args <- commandArgs(trailingOnly = TRUE)
+
+batch_size <- as.numeric(args[2])
+log_metric_to_run("batch_size", batch_size)
+
+epochs <- as.numeric(args[4])
+log_metric_to_run("epochs", epochs)
+
+lr <- as.numeric(args[6])
+log_metric_to_run("lr", lr)
+
+decay <- as.numeric(args[8])
+log_metric_to_run("decay", decay)
+
+data_augmentation <- TRUE
+
+
+# Data Preparation --------------------------------------------------------
+
+# See ?dataset_cifar10 for more info
+cifar10 <- dataset_cifar10()
+
+# Feature scale RGB values in test and train inputs  
+x_train <- cifar10$train$x / 255
+x_test <- cifar10$test$x / 255
+y_train <- to_categorical(cifar10$train$y, num_classes = 10)
+y_test <- to_categorical(cifar10$test$y, num_classes = 10)
+
+
+# Defining Model ----------------------------------------------------------
+
+# Initialize sequential model
+model <- keras_model_sequential()
+
+model %>%
+
+# Start with hidden 2D convolutional layer being fed 32x32 pixel images
+layer_conv_2d(
+    filter = 32, kernel_size = c(3, 3), padding = "same",
+    input_shape = c(32, 32, 3)
+  ) %>%
+  layer_activation("relu") %>%
+
+  # Second hidden layer
+  layer_conv_2d(filter = 32, kernel_size = c(3, 3)) %>%
+  layer_activation("relu") %>%
+
+  # Use max pooling
+  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
+  layer_dropout(0.25) %>%
+
+  # 2 additional hidden 2D convolutional layers
+  layer_conv_2d(filter = 32, kernel_size = c(3, 3), padding = "same") %>%
+  layer_activation("relu") %>%
+  layer_conv_2d(filter = 32, kernel_size = c(3, 3)) %>%
+  layer_activation("relu") %>%
+
+  # Use max pooling once more
+  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
+  layer_dropout(0.25) %>%
+
+  # Flatten max filtered output into feature vector 
+  # and feed into dense layer
+  layer_flatten() %>%
+  layer_dense(512) %>%
+  layer_activation("relu") %>%
+  layer_dropout(0.5) %>%
+
+  # Outputs from dense layer are projected onto 10 unit output layer
+  layer_dense(10) %>%
+  layer_activation("softmax")
+
+opt <- optimizer_rmsprop(lr, decay)
+
+model %>%
+    compile(loss = "categorical_crossentropy",
+          optimizer = opt,
+          metrics = "accuracy"
+)
+
+
+# Training ----------------------------------------------------------------
+
+if (!data_augmentation) {
+
+    model %>%
+    fit(x_train,
+        y_train,
+        batch_size = batch_size,
+        epochs = epochs,
+        validation_data = list(x_test, y_test),
+        shuffle = TRUE
+  )
+
+} else {
+
+    datagen <- image_data_generator(rotation_range = 20,
+                                  width_shift_range = 0.2,
+                                  height_shift_range = 0.2,
+                                  horizontal_flip = TRUE
+  )
+
+    datagen %>% fit_image_data_generator(x_train)
+
+    results <- evaluate(model, x_train, y_train, batch_size)
+    log_metric_to_run("Loss", results[[1]])
+    cat("Loss: ", results[[1]], "\n")
+    cat("Accuracy: ", results[[2]], "\n")
+}

how-to-use-azureml/azureml-sdk-for-r/vignettes/installation.Rmd

@@ -0,0 +1,100 @@
+---
+title: "Install the Azure ML SDK for R"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Install the Azure ML SDK for R}
+  %\VignetteEngine{knitr::rmarkdown}
+  \use_package{UTF-8}
+---
+
+This article covers the step-by-step instructions for installing the Azure ML SDK for R.
+
+You do not need run this if you are working on an Azure Machine Learning Compute Instance, as the compute instance already has the Azure ML SDK preinstalled.
+
+## Install Conda
+
+If you do not have Conda already installed on your machine, you will first need to install it, since the Azure ML R SDK uses **reticulate** to bind to the Python SDK. We recommend installing [Miniconda](https://docs.conda.io/en/latest/miniconda.html), which is a smaller, lightweight version of Anaconda. Choose the 64-bit binary for Python 3.5 or later.
+
+## Install the **azuremlsdk** R package
+You will need **remotes** to install **azuremlsdk** from the GitHub repo.
+``` {r install_remotes, eval=FALSE}
+install.packages('remotes')
+``` 
+
+Then, you can use the `install_github` function to install the package.
+``` {r install_azuremlsdk, eval=FALSE}
+remotes::install_cran('azuremlsdk', repos = 'https://cloud.r-project.org/')
+```
+
+If you are using R installed from CRAN, which comes with 32-bit and 64-bit binaries, you may need to specify the parameter `INSTALL_opts=c("--no-multiarch")` to only build for the current 64-bit architecture.
+``` {r eval=FALSE}
+remotes::install_cran('azuremlsdk', repos = 'https://cloud.r-project.org/', INSTALL_opts=c("--no-multiarch"))
+```
+
+## Install the Azure ML Python SDK
+Lastly, use the **azuremlsdk** R library to install the Python SDK. By default, `azuremlsdk::install_azureml()` will install the [latest version of the Python SDK](https://pypi.org/project/azureml-sdk/) in a conda environment called `r-azureml` if reticulate < 1.14 or `r-reticulate` if reticulate ≥ 1.14.
+``` {r install_pythonsdk, eval=FALSE}
+azuremlsdk::install_azureml()
+```
+
+If you would like to override the default version, environment name, or Python version, you can pass in those arguments. If you would like to restart the R session after installation or delete the conda environment if it already exists and create a new environment, you can also do so: 
+``` {r eval=FALSE}
+azuremlsdk::install_azureml(version = NULL, 
+                            custom_envname = "<your conda environment name>",
+                            conda_python_version = "<desired python version>",
+                            restart_session = TRUE,
+                            remove_existing_env = TRUE)
+```
+
+## Test installation
+You can confirm your installation worked by loading the library and successfully retrieving a run.
+``` {r test_installation, eval=FALSE}
+library(azuremlsdk)
+get_current_run()
+```
+
+## Troubleshooting
+-   In step 3 of the installation, if you get ssl errors on windows, it is due to an
+outdated openssl binary. Install the latest openssl binaries from
+[here](https://wiki.openssl.org/index.php/Binaries).
+
+- If installation fails due to this error:
+
+  ```R
+  Error in strptime(xx, f, tz = tz) : 
+    (converted from warning) unable to identify current timezone 'C':
+  please set environment variable 'TZ'
+  In R CMD INSTALL
+  Error in i.p(...) : 
+    (converted from warning) installation of package ‘C:/.../azureml_0.4.0.tar.gz’ had non-zero exit
+    status
+  ```
+  
+  You will need to set your time zone environment variable to GMT and restart the installation process.
+  
+  ```R
+  Sys.setenv(TZ='GMT')
+  ```
+
+- If the following permission error occurs while installing in RStudio,
+  change your RStudio session to administrator mode, and re-run the installation command.
+
+  ```R
+  Downloading GitHub repo Azure/azureml-sdk-for-r@master
+  Skipping 2 packages ahead of CRAN: reticulate, rlang
+  Running `R CMD build`...
+  
+  Error: (converted from warning) invalid package
+  'C:/.../file2b441bf23631'
+  In R CMD INSTALL
+  Error in i.p(...) : 
+    (converted from warning) installation of package
+    ‘C:/.../file2b441bf23631’ had non-zero exit status
+  In addition: Warning messages:
+  1: In file(con, "r") :
+    cannot open file 'C:...\file2b44144a540f': Permission denied
+  2: In file(con, "r") :
+    cannot open file 'C:...\file2b4463c21577': Permission denied
+  ```
+  

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-and-deploy-to-aci/project_files/accident_predict.R

@@ -0,0 +1,16 @@
+#' Copyright(c) Microsoft Corporation.
+#' Licensed under the MIT license.
+
+library(jsonlite)
+
+init <- function() {
+    model_path <- Sys.getenv("AZUREML_MODEL_DIR")
+    model <- readRDS(file.path(model_path, "model.rds"))
+    message("logistic regression model loaded")
+
+    function(data) {
+        vars <- as.data.frame(fromJSON(data))
+        prediction <- as.numeric(predict(model, vars, type = "response") * 100)
+        toJSON(prediction)
+    }
+}

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-and-deploy-to-aci/project_files/accidents.R

@@ -0,0 +1,33 @@
+#' Copyright(c) Microsoft Corporation.
+#' Licensed under the MIT license.
+
+library(azuremlsdk)
+library(optparse)
+library(caret)
+
+options <- list(
+  make_option(c("-d", "--data_folder"))
+)
+
+opt_parser <- OptionParser(option_list = options)
+opt <- parse_args(opt_parser)
+
+paste(opt$data_folder)
+
+accidents <- readRDS(file.path(opt$data_folder, "accidents.Rd"))
+summary(accidents)
+
+mod <- glm(dead ~ dvcat + seatbelt + frontal + sex + ageOFocc + yearVeh + airbag + occRole, family = binomial, data = accidents)
+summary(mod)
+predictions <- factor(ifelse(predict(mod) > 0.1, "dead", "alive"))
+conf_matrix <- confusionMatrix(predictions, accidents$dead)
+message(conf_matrix)
+
+log_metric_to_run("Accuracy", conf_matrix$overall["Accuracy"])
+
+output_dir = "outputs"
+if (!dir.exists(output_dir)) {
+    dir.create(output_dir)
+}
+saveRDS(mod, file = "./outputs/model.rds")
+message("Model saved")

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-and-deploy-to-aci/train-and-deploy-to-aci.Rmd

@@ -0,0 +1,326 @@
+---
+title: "Train and deploy your first model with Azure ML"
+author: "David Smith"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Train and deploy your first model with Azure ML}
+  %\VignetteEngine{knitr::rmarkdown}
+  \use_package{UTF-8}
+---
+
+In this tutorial, you learn the foundational design patterns in Azure Machine Learning.  You'll train and deploy a **caret** model to predict the likelihood of a fatality in an automobile accident. After completing this tutorial, you'll have the practical knowledge of the R SDK to scale up to developing more-complex experiments and workflows.
+
+In this tutorial, you learn the following tasks:
+
+* Connect your workspace
+* Load data and prepare for training
+* Upload data to the datastore so it is available for remote training
+* Create a compute resource
+* Train a caret model to predict probability of fatality
+* Deploy a prediction endpoint
+* Test the model from R
+
+## Prerequisites
+
+If you don't have access to an Azure ML workspace, follow the [setup tutorial](https://azure.github.io/azureml-sdk-for-r/articles/configuration.html) to configure and create a workspace.
+
+## Set up your development environment
+The setup for your development work in this tutorial includes the following actions:
+
+* Install required packages
+* Connect to a workspace, so that your local computer can communicate with remote resources
+* Create an experiment to track your runs
+* Create a remote compute target to use for training
+
+### Install required packages
+This tutorial assumes you already have the Azure ML SDK installed. Go ahead and import the **azuremlsdk** package.
+
+```{r eval=FALSE}
+library(azuremlsdk)
+```
+
+The tutorial uses data from the [**DAAG** package](https://cran.r-project.org/package=DAAG). Install the package if you don't have it.
+
+```{r eval=FALSE}
+install.packages("DAAG")
+```
+
+The training and scoring scripts (`accidents.R` and `accident_predict.R`) have some additional dependencies. If you plan on running those scripts locally, make sure you have those required packages as well.
+
+### Load your workspace
+Instantiate a workspace object from your existing workspace. The following code will load the workspace details from the **config.json** file. You can also retrieve a workspace using [`get_workspace()`](https://azure.github.io/azureml-sdk-for-r/reference/get_workspace.html).
+
+```{r load_workpace, eval=FALSE}
+ws <- load_workspace_from_config()
+```
+
+### Create an experiment
+An Azure ML experiment tracks a grouping of runs, typically from the same training script. Create an experiment to track the runs for training the caret model on the accidents data.
+
+```{r create_experiment, eval=FALSE}
+experiment_name <- "accident-logreg"
+exp <- experiment(ws, experiment_name)
+```
+
+### Create a compute target
+By using Azure Machine Learning Compute (AmlCompute), a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create a single-node AmlCompute cluster as your training environment. The code below creates the compute cluster for you if it doesn't already exist in your workspace.
+
+You may need to wait a few minutes for your compute cluster to be provisioned if it doesn't already exist.
+
+```{r create_cluster, eval=FALSE}
+cluster_name <- "rcluster"
+compute_target <- get_compute(ws, cluster_name = cluster_name)
+if (is.null(compute_target)) {
+  vm_size <- "STANDARD_D2_V2" 
+  compute_target <- create_aml_compute(workspace = ws,
+                                       cluster_name = cluster_name,
+                                       vm_size = vm_size,
+                                       max_nodes = 1)
+  
+  wait_for_provisioning_completion(compute_target, show_output = TRUE)
+}
+```
+
+## Prepare data for training
+This tutorial uses data from the **DAAG** package. This dataset includes data from over 25,000 car crashes in the US, with variables you can use to predict the likelihood of a fatality. First, import the data into R and transform it into a new dataframe `accidents` for analysis, and export it to an `Rdata` file.
+
+```{r load_data, eval=FALSE}
+library(DAAG)
+data(nassCDS)
+
+accidents <- na.omit(nassCDS[,c("dead","dvcat","seatbelt","frontal","sex","ageOFocc","yearVeh","airbag","occRole")])
+accidents$frontal <- factor(accidents$frontal, labels=c("notfrontal","frontal"))
+accidents$occRole <- factor(accidents$occRole)
+
+saveRDS(accidents, file="accidents.Rd")
+```
+
+### Upload data to the datastore
+Upload data to the cloud so that it can be access by your remote training environment. Each Azure ML workspace comes with a default datastore that stores the connection information to the Azure blob container that is provisioned in the storage account attached to the workspace. The following code will upload the accidents data you created above to that datastore.
+
+```{r upload_data, eval=FALSE}
+ds <- get_default_datastore(ws)
+
+target_path <- "accidentdata"
+upload_files_to_datastore(ds,
+                          list("./project_files/accidents.Rd"),
+                          target_path = target_path,
+                          overwrite = TRUE)
+```
+
+
+## Train a model
+
+For this tutorial, fit a logistic regression model on your uploaded data using your remote compute cluster. To submit a job, you need to:
+
+* Prepare the training script
+* Create an estimator
+* Submit the job
+
+### Prepare the training script
+A training script called `accidents.R` has been provided for you in the "project_files" directory of this tutorial. Notice the following details **inside the training script** that have been done to leverage the Azure ML service for training:
+
+* The training script takes an argument `-d` to find the directory that contains the training data. When you define and submit your job later, you point to the datastore for this argument. Azure ML will mount the storage folder to the remote cluster for the training job.
+* The training script logs the final accuracy as a metric to the run record in Azure ML using `log_metric_to_run()`. The Azure ML SDK provides a set of logging APIs for logging various metrics during training runs. These metrics are recorded and persisted in the experiment run record. The metrics can then be accessed at any time or viewed in the run details page in [Azure Machine Learning studio](http://ml.azure.com). See the [reference](https://azure.github.io/azureml-sdk-for-r/reference/index.html#section-training-experimentation) for the full set of logging methods `log_*()`.
+* The training script saves your model into a directory named **outputs**. The `./outputs` folder receives special treatment by Azure ML. During training, files written to `./outputs` are automatically uploaded to your run record by Azure ML and persisted as artifacts. By saving the trained model to `./outputs`, you'll be able to access and retrieve your model file even after the run is over and you no longer have access to your remote training environment.
+
+### Create an estimator
+
+An Azure ML estimator encapsulates the run configuration information needed for executing a training script on the compute target. Azure ML runs are run as containerized jobs on the specified compute target. By default, the Docker image built for your training job will include R, the Azure ML SDK, and a set of commonly used R packages. See the full list of default packages included [here](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html).
+
+To create the estimator, define:
+
+* The directory that contains your scripts needed for training (`source_directory`). All the files in this directory are uploaded to the cluster node(s) for execution. The directory must contain your training script and any additional scripts required.
+* The training script that will be executed (`entry_script`).
+* The compute target (`compute_target`), in this case the AmlCompute cluster you created earlier.
+* The parameters required from the training script (`script_params`). Azure ML will run your training script as a command-line script with `Rscript`. In this tutorial you specify one argument to the script, the data directory mounting point, which you can access with `ds$path(target_path)`.
+* Any environment dependencies required for training. The default Docker image built for training already contains the three packages (`caret`, `e1071`, and `optparse`) needed in the training script.  So you don't need to specify additional information. If you are using R packages that are not included by default, use the estimator's `cran_packages` parameter to add additional CRAN packages. See the [`estimator()`](https://azure.github.io/azureml-sdk-for-r/reference/estimator.html) reference for the full set of configurable options.
+
+```{r create_estimator, eval=FALSE}
+est <- estimator(source_directory = "project_files",
+                 entry_script = "accidents.R",
+                 script_params = list("--data_folder" = ds$path(target_path)),
+                 compute_target = compute_target
+                 )
+```
+
+### Submit the job on the remote cluster
+
+Finally submit the job to run on your cluster. `submit_experiment()` returns a Run object that you then use to interface with the run. In total, the first run takes **about 10 minutes**. But for later runs, the same Docker image is reused as long as the script dependencies don't change.  In this case, the image is cached and the container startup time is much faster.
+
+```{r submit_job, eval=FALSE}
+run <- submit_experiment(exp, est)
+```
+
+You can view a table of the run's details. Clicking the "Web View" link provided will bring you to Azure Machine Learning studio, where you can monitor the run in the UI.
+
+```{r view_run, eval=FALSE}
+view_run_details(run)
+```
+
+Model training happens in the background. Wait until the model has finished training before you run more code.
+
+```{r wait_run, eval=FALSE}
+wait_for_run_completion(run, show_output = TRUE)
+```
+
+You -- and colleagues with access to the workspace -- can submit multiple experiments in parallel, and Azure ML will take of scheduling the tasks on the compute cluster. You can even configure the cluster to automatically scale up to multiple nodes, and scale back when there are no more compute tasks in the queue. This configuration is a cost-effective way for teams to share compute resources.
+
+## Retrieve training results
+Once your model has finished training, you can access the artifacts of your job that were persisted to the run record, including any metrics logged and the final trained model.
+
+### Get the logged metrics
+In the training script `accidents.R`, you logged a metric from your model: the accuracy of the predictions in the training data. You can see metrics in the [studio](https://ml.azure.com), or extract them to the local session as an R list as follows:
+
+```{r metrics, eval=FALSE}
+metrics <- get_run_metrics(run)
+metrics
+```
+
+If you've run multiple experiments (say, using differing variables, algorithms, or hyperparamers), you can use the metrics from each run to compare and choose the model you'll use in production.
+
+### Get the trained model
+You can retrieve the trained model and look at the results in your local R session. The following code will download the contents of the `./outputs` directory, which includes the model file.
+
+```{r retrieve_model, eval=FALSE}
+download_files_from_run(run, prefix="outputs/")
+accident_model <- readRDS("project_files/outputs/model.rds")
+summary(accident_model)
+```
+
+You see some factors that contribute to an increase in the estimated probability of death:
+
+* higher impact speed 
+* male driver
+* older occupant
+* passenger
+
+You see lower probabilities of death with:
+
+* presence of airbags
+* presence seatbelts
+* frontal collision 
+
+The vehicle year of manufacture does not have a significant effect.
+
+You can use this model to make new predictions:
+
+```{r manual_predict, eval=FALSE}
+newdata <- data.frame( # valid values shown below
+ dvcat="10-24",        # "1-9km/h" "10-24"   "25-39"   "40-54"   "55+"  
+ seatbelt="none",      # "none"   "belted"  
+ frontal="frontal",    # "notfrontal" "frontal"
+ sex="f",              # "f" "m"
+ ageOFocc=16,          # age in years, 16-97
+ yearVeh=2002,         # year of vehicle, 1955-2003
+ airbag="none",        # "none"   "airbag"   
+ occRole="pass"        # "driver" "pass"
+ )
+
+## predicted probability of death for these variables, as a percentage
+as.numeric(predict(accident_model,newdata, type="response")*100)
+```
+
+## Deploy as a web service
+
+With your model, you can predict the danger of death from a collision. Use Azure ML to deploy your model as a prediction service. In this tutorial, you will deploy the web service in [Azure Container Instances](https://docs.microsoft.com/en-us/azure/container-instances/) (ACI).
+
+### Register the model
+
+First, register the model you downloaded to your workspace with [`register_model()`](https://azure.github.io/azureml-sdk-for-r/reference/register_model.html). A registered model can be any collection of files, but in this case the R model object is sufficient. Azure ML will use the registered model for deployment.
+
+```{r register_model, eval=FALSE}
+model <- register_model(ws, 
+                        model_path = "project_files/outputs/model.rds", 
+                        model_name = "accidents_model",
+                        description = "Predict probablity of auto accident")
+```
+
+### Define the inference dependencies
+To create a web service for your model, you first need to create a scoring script (`entry_script`), an R script that will take as input variable values (in JSON format) and output a prediction from your model. For this tutorial, use the provided scoring file `accident_predict.R`. The scoring script must contain an `init()` method that loads your model and returns a function that uses the model to make a prediction based on the input data. See the [documentation](https://azure.github.io/azureml-sdk-for-r/reference/inference_config.html#details) for more details.
+
+Next, define an Azure ML **environment** for your script's package dependencies. With an environment, you specify R packages (from CRAN or elsewhere) that are needed for your script to run. You can also provide the values of environment variables that your script can reference to modify its behavior. By default, Azure ML will build the same default Docker image used with the estimator for training. Since the tutorial has no special requirements, create an environment with no special attributes.
+
+```{r create_environment, eval=FALSE}
+r_env <- r_environment(name = "basic_env")
+```
+
+If you want to use your own Docker image for deployment instead, specify the `custom_docker_image` parameter. See the [`r_environment()`](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html) reference for the full set of configurable options for defining an environment.
+
+Now you have everything you need to create an **inference config** for encapsulating your scoring script and environment dependencies.
+
+``` {r create_inference_config, eval=FALSE}
+inference_config <- inference_config(
+  entry_script = "accident_predict.R",
+  source_directory = "project_files",
+  environment = r_env)
+```
+
+### Deploy to ACI
+In this tutorial, you will deploy your service to ACI. This code provisions a single container to respond to inbound requests, which is suitable for testing and light loads. See [`aci_webservice_deployment_config()`](https://azure.github.io/azureml-sdk-for-r/reference/aci_webservice_deployment_config.html) for additional configurable options. (For production-scale deployments, you can also [deploy to Azure Kubernetes Service](https://azure.github.io/azureml-sdk-for-r/articles/deploy-to-aks/deploy-to-aks.html).)
+
+``` {r create_aci_config, eval=FALSE}
+aci_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 0.5)
+```
+
+Now you deploy your model as a web service. Deployment **can take several minutes**. 
+
+```{r deploy_service, eval=FALSE}
+aci_service <- deploy_model(ws, 
+                            'accident-pred', 
+                            list(model), 
+                            inference_config, 
+                            aci_config)
+
+wait_for_deployment(aci_service, show_output = TRUE)
+```
+
+If you encounter any issue in deploying the web service, please visit the [troubleshooting guide](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-troubleshoot-deployment).
+
+## Test the deployed service
+
+Now that your model is deployed as a service, you can test the service from R using [`invoke_webservice()`](https://azure.github.io/azureml-sdk-for-r/reference/invoke_webservice.html).  Provide a new set of data to predict from, convert it to JSON, and send it to the service.
+
+```{r test_deployment, eval=FALSE}
+library(jsonlite)
+
+newdata <- data.frame( # valid values shown below
+ dvcat="10-24",        # "1-9km/h" "10-24"   "25-39"   "40-54"   "55+"  
+ seatbelt="none",      # "none"   "belted"  
+ frontal="frontal",    # "notfrontal" "frontal"
+ sex="f",              # "f" "m"
+ ageOFocc=22,          # age in years, 16-97
+ yearVeh=2002,         # year of vehicle, 1955-2003
+ airbag="none",        # "none"   "airbag"   
+ occRole="pass"        # "driver" "pass"
+ )
+
+prob <- invoke_webservice(aci_service, toJSON(newdata))
+prob
+```
+
+You can also get the web service's HTTP endpoint, which accepts REST client calls. You can share this endpoint with anyone who wants to test the web service or integrate it into an application.
+
+```{r get_endpoint, eval=FALSE}
+aci_service$scoring_uri
+```
+
+## Clean up resources
+
+Delete the resources once you no longer need them. Don't delete any resource you plan to still use. 
+
+Delete the web service:
+```{r delete_service, eval=FALSE}
+delete_webservice(aci_service)
+```
+
+Delete the registered model:
+```{r delete_model, eval=FALSE}
+delete_model(model)
+```
+
+Delete the compute cluster:
+```{r delete_compute, eval=FALSE}
+delete_compute(compute_target)
+```

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-with-tensorflow/project_files/tf_mnist.R

@@ -0,0 +1,62 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2016 RStudio, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+
+library(tensorflow)
+install_tensorflow(version = "1.13.2-gpu")
+
+library(azuremlsdk)
+
+# Create the model
+x <- tf$placeholder(tf$float32, shape(NULL, 784L))
+W <- tf$Variable(tf$zeros(shape(784L, 10L)))
+b <- tf$Variable(tf$zeros(shape(10L)))
+
+y <- tf$nn$softmax(tf$matmul(x, W) + b)
+
+# Define loss and optimizer
+y_ <- tf$placeholder(tf$float32, shape(NULL, 10L))
+cross_entropy <- tf$reduce_mean(-tf$reduce_sum(y_ * log(y),
+                                               reduction_indices = 1L))
+train_step <- tf$train$GradientDescentOptimizer(0.5)$minimize(cross_entropy)
+
+# Create session and initialize  variables
+sess <- tf$Session()
+sess$run(tf$global_variables_initializer())
+
+# Load mnist data    )
+datasets <- tf$contrib$learn$datasets
+mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE)
+
+# Train
+for (i in 1:1000) {
+    batches <- mnist$train$next_batch(100L)
+    batch_xs <- batches[[1]]
+    batch_ys <- batches[[2]]
+    sess$run(train_step,
+           feed_dict = dict(x = batch_xs, y_ = batch_ys))
+}
+
+# Test trained model
+correct_prediction <- tf$equal(tf$argmax(y, 1L), tf$argmax(y_, 1L))
+accuracy <- tf$reduce_mean(tf$cast(correct_prediction, tf$float32))
+cat("Accuracy: ", sess$run(accuracy,
+                           feed_dict = dict(x = mnist$test$images,
+                                            y_ = mnist$test$labels)))
+
+log_metric_to_run("accuracy",
+                  sess$run(accuracy, feed_dict = dict(x = mnist$test$images,
+                                                      y_ = mnist$test$labels)))

how-to-use-azureml/azureml-sdk-for-r/vignettes/train-with-tensorflow/train-with-tensorflow.Rmd

@@ -0,0 +1,143 @@
+---
+title: "Train a TensorFlow model"
+date: "`r Sys.Date()`"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Train a TensorFlow model}
+  %\VignetteEngine{knitr::rmarkdown}
+  \use_package{UTF-8}
+---
+
+This tutorial demonstrates how run a TensorFlow job at scale using Azure ML. You will train a TensorFlow model to classify handwritten digits (MNIST) using a deep neural network (DNN) and log your results to the Azure ML service.
+
+## Prerequisites
+If you don<6F>t have access to an Azure ML workspace, follow the [setup tutorial](https://azure.github.io/azureml-sdk-for-r/articles/configuration.html) to configure and create a workspace.
+
+## Set up development environment
+The setup for your development work in this tutorial includes the following actions:
+
+* Import required packages
+* Connect to a workspace
+* Create an experiment to track your runs
+* Create a remote compute target to use for training
+
+### Import **azuremlsdk** package
+```{r eval=FALSE}
+library(azuremlsdk)
+```
+
+### Load your workspace
+Instantiate a workspace object from your existing workspace. The following code will load the workspace details from a **config.json** file if you previously wrote one out with [`write_workspace_config()`](https://azure.github.io/azureml-sdk-for-r/reference/write_workspace_config.html).
+```{r load_workpace, eval=FALSE}
+ws <- load_workspace_from_config()
+```
+
+Or, you can retrieve a workspace by directly specifying your workspace details:
+```{r get_workpace, eval=FALSE}
+ws <- get_workspace("<your workspace name>", "<your subscription ID>", "<your resource group>")
+```
+
+### Create an experiment
+An Azure ML **experiment** tracks a grouping of runs, typically from the same training script. Create an experiment to track the runs for training the TensorFlow model on the MNIST data.
+
+```{r create_experiment, eval=FALSE}
+exp <- experiment(workspace = ws, name = "tf-mnist")
+```
+
+If you would like to track your runs in an existing experiment, simply specify that experiment's name to the `name` parameter of `experiment()`.
+
+### Create a compute target
+By using Azure Machine Learning Compute (AmlCompute), a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. In this tutorial, you create a GPU-enabled cluster as your training environment. The code below creates the compute cluster for you if it doesn't already exist in your workspace.
+
+You may need to wait a few minutes for your compute cluster to be provisioned if it doesn't already exist.
+
+```{r create_cluster, eval=FALSE}
+cluster_name <- "gpucluster"
+compute_target <- get_compute(ws, cluster_name = cluster_name)
+if (is.null(compute_target))
+{
+  vm_size <- "STANDARD_NC6"
+  compute_target <- create_aml_compute(workspace = ws, 
+                                       cluster_name = cluster_name,
+                                       vm_size = vm_size, 
+                                       max_nodes = 4)
+  
+  wait_for_provisioning_completion(compute_target, show_output = TRUE)
+}
+```
+
+## Prepare the training script
+
+A training script called `tf_mnist.R` has been provided for you in the "project_files" directory of this tutorial. The Azure ML SDK provides a set of logging APIs for logging various metrics during training runs. These metrics are recorded and persisted in the experiment run record, and can be be accessed at any time or viewed in the run details page in [Azure Machine Learning studio](http://ml.azure.com/).
+
+In order to collect and upload run metrics, you need to do the following **inside the training script**:
+
+* Import the **azuremlsdk** package
+```
+library(azuremlsdk)
+```
+
+* Add the [`log_metric_to_run()`](https://azure.github.io/azureml-sdk-for-r/reference/log_metric_to_run.html) function to track our primary metric,  "accuracy", for this experiment. If you have your own training script with several important metrics, simply create a logging call for each one within the script.
+```
+log_metric_to_run("accuracy",
+                  sess$run(accuracy,
+                  feed_dict = dict(x = mnist$test$images, y_ = mnist$test$labels)))
+```
+
+See the [reference](https://azure.github.io/azureml-sdk-for-r/reference/index.html#section-training-experimentation) for the full set of logging methods `log_*()` available from the R SDK.
+
+## Create an estimator
+
+An Azure ML **estimator** encapsulates the run configuration information needed for executing a training script on the compute target. Azure ML runs are run as containerized jobs on the specified compute target. By default, the Docker image built for your training job will include R, the Azure ML SDK, and a set of commonly used R packages. See the full list of default packages included [here](https://azure.github.io/azureml-sdk-for-r/reference/r_environment.html).
+
+To create the estimator, define the following:
+
+* The directory that contains your scripts needed for training (`source_directory`). All the files in this directory are uploaded to the cluster node(s) for execution. The directory must contain your training script and any additional scripts required.
+* The training script that will be executed (`entry_script`).
+* The compute target (`compute_target`), in this case the AmlCompute cluster you created earlier.
+* Any environment dependencies required for training. Since the training script requires the TensorFlow package, which is not included in the image by default, pass the package name to the `cran_packages` parameter to have it installed in the Docker container where the job will run. See the [`estimator()`](https://azure.github.io/azureml-sdk-for-r/reference/estimator.html) reference for the full set of configurable options.
+* Set the `use_gpu = TRUE` flag so the default base GPU Docker image will be built, since the job will be run on a GPU cluster.
+
+```{r create_estimator, eval=FALSE}
+est <- estimator(source_directory = "project_files",
+                 entry_script = "tf_mnist.R",
+                 compute_target = compute_target,
+                 cran_packages = c("tensorflow"),
+                 use_gpu = TRUE)
+```
+
+## Submit the job
+
+Finally submit the job to run on your cluster. [`submit_experiment()`](https://azure.github.io/azureml-sdk-for-r/reference/submit_experiment.html) returns a `Run` object that you can then use to interface with the run.
+
+```{r submit_job, eval=FALSE}
+run <- submit_experiment(exp, est)
+```
+
+You can view the run<75>s details as a table. Clicking the <20>Web View<65> link provided will bring you to Azure Machine Learning studio, where you can monitor the run in the UI.
+
+```{r eval=FALSE}
+view_run_details(run)
+```
+
+Model training happens in the background. Wait until the model has finished training before you run more code.
+
+```{r eval=FALSE}
+wait_for_run_completion(run, show_output = TRUE)
+```
+
+## View run metrics
+Once your job has finished, you can view the metrics collected during your TensorFlow run.
+
+```{r get_metrics, eval=FALSE}
+metrics <- get_run_metrics(run)
+metrics
+```
+
+## Clean up resources
+Delete the resources once you no longer need them. Don't delete any resource you plan to still use. 
+
+Delete the compute cluster:
+```{r delete_compute, eval=FALSE}
+delete_compute(compute_target)
+```

how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb

@@ -20,7 +20,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Register model and deploy as webservice\n",
+        "# Register model and deploy as webservice in ACI\n",
         "\n",
         "Following this notebook, you will:\n",
         "\n",
@@ -45,6 +45,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
+        "\n",
         "# Check core SDK version number.\n",
         "print('SDK version:', azureml.core.VERSION)"
       ]
@@ -70,6 +71,7 @@
       "source": [
         "from azureml.core import Workspace\n",
         "\n",
+        "\n",
         "ws = Workspace.from_config()\n",
         "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
       ]
@@ -91,6 +93,7 @@
       "source": [
         "from azureml.core import Dataset\n",
         "\n",
+        "\n",
         "datastore = ws.get_default_datastore()\n",
         "datastore.upload_files(files=['./features.csv', './labels.csv'],\n",
         "                       target_path='sklearn_regression/',\n",
@@ -125,6 +128,7 @@
         "from azureml.core import Model\n",
         "from azureml.core.resource_configuration import ResourceConfiguration\n",
         "\n",
+        "\n",
         "model = Model.register(workspace=ws,\n",
         "                       model_name='my-sklearn-model',                # Name of the registered model in your workspace.\n",
         "                       model_path='./sklearn_regression_model.pkl',  # Local file to upload and register as a model.\n",
@@ -159,6 +163,8 @@
         "\n",
         "The Azure Machine Learning service provides a default environment for supported model frameworks, including scikit-learn, based on the metadata you provided when registering your model. This is the easiest way to deploy your model.\n",
         "\n",
+        "Even when you deploy your model to ACI with a default environment you can still customize the deploy configuration (i.e. the number of cores and amount of memory made available for the deployment) using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--). Look at the \"Use a custom environment\" section of this notebook for more information on deploy configuration.\n",
+        "\n",
         "**Note**: This step can take several minutes."
       ]
     },
@@ -171,6 +177,7 @@
         "from azureml.core import Webservice\n",
         "from azureml.exceptions import WebserviceException\n",
         "\n",
+        "\n",
         "service_name = 'my-sklearn-service'\n",
         "\n",
         "# Remove any existing service under the same name.\n",
@@ -198,6 +205,7 @@
       "source": [
         "import json\n",
         "\n",
+        "\n",
         "input_payload = json.dumps({\n",
         "    'data': [\n",
         "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
@@ -231,9 +239,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Use a custom environment (for all models)\n",
+        "### Use a custom environment\n",
         "\n",
-        "If you want more control over how your model is run, if it uses another framework, or if it has special runtime requirements, you can instead specify your own environment and scoring method.\n",
+        "If you want more control over how your model is run, if it uses another framework, or if it has special runtime requirements, you can instead specify your own environment and scoring method. Custom environments can be used for any model you want to deploy.\n",
         "\n",
         "Specify the model's runtime environment by creating an [Environment](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment%28class%29?view=azure-ml-py) object and providing the [CondaDependencies](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.conda_dependencies.condadependencies?view=azure-ml-py) needed by your model."
       ]
@@ -247,6 +255,7 @@
         "from azureml.core import Environment\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
+        "\n",
         "environment = Environment('my-sklearn-environment')\n",
         "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
         "    'azureml-defaults',\n",
@@ -278,7 +287,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Deploy your model in the custom environment by providing an [InferenceConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-).\n",
+        "Deploy your model in the custom environment by providing an [InferenceConfig](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py) object to [Model.deploy()](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py#deploy-workspace--name--models--inference-config--deployment-config-none--deployment-target-none-). In this case we are also using the [AciWebservice.deploy_configuration()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--) method to generate a custom deploy configuration.\n",
         "\n",
         "**Note**: This step can take several minutes."
       ]
@@ -288,15 +297,18 @@
       "execution_count": null,
       "metadata": {
         "tags": [
-          "azuremlexception-remarks-sample"
+          "azuremlexception-remarks-sample",
+          "sample-aciwebservice-deploy-config"
         ]
       },
       "outputs": [],
       "source": [
         "from azureml.core import Webservice\n",
         "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import AciWebservice\n",
         "from azureml.exceptions import WebserviceException\n",
         "\n",
+        "\n",
         "service_name = 'my-custom-env-service'\n",
         "\n",
         "# Remove any existing service under the same name.\n",
@@ -305,11 +317,14 @@
         "except WebserviceException:\n",
         "    pass\n",
         "\n",
-        "inference_config = InferenceConfig(entry_script='score.py',\n",
-        "                                   source_directory='.',\n",
-        "                                   environment=environment)\n",
+        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
+        "aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
         "\n",
-        "service = Model.deploy(ws, service_name, [model], inference_config)\n",
+        "service = Model.deploy(workspace=ws,\n",
+        "                       name=service_name,\n",
+        "                       models=[model],\n",
+        "                       inference_config=inference_config,\n",
+        "                       deployment_config=aci_config)\n",
         "service.wait_for_deployment(show_output=True)"
       ]
     },
@@ -326,8 +341,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import json\n",
-        "\n",
         "input_payload = json.dumps({\n",
         "    'data': [\n",
         "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
@@ -360,16 +373,101 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Model profiling\n",
+        "### Model Profiling\n",
         "\n",
-        "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
+        "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
         "\n",
-        "```python\n",
-        "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
-        "profile.wait_for_profiling(True)\n",
-        "profiling_results = profile.get_results()\n",
-        "print(profiling_results)\n",
-        "```"
+        "In order to profile your model you will need:\n",
+        "- a registered model\n",
+        "- an entry script\n",
+        "- an inference configuration\n",
+        "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
+        "\n",
+        "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
+        "\n",
+        "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Datastore\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.data import dataset_type_definitions\n",
+        "\n",
+        "\n",
+        "# create a string that can be utf-8 encoded and\n",
+        "# put in the body of the request\n",
+        "serialized_input_json = json.dumps({\n",
+        "    'data': [\n",
+        "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
+        "         -0.03482076, -0.04340085, -0.00259226, 0.01990842, -0.01764613]\n",
+        "    ]\n",
+        "})\n",
+        "dataset_content = []\n",
+        "for i in range(100):\n",
+        "    dataset_content.append(serialized_input_json)\n",
+        "dataset_content = '\\n'.join(dataset_content)\n",
+        "file_name = 'sample_request_data.txt'\n",
+        "f = open(file_name, 'w')\n",
+        "f.write(dataset_content)\n",
+        "f.close()\n",
+        "\n",
+        "# upload the txt file created above to the Datastore and create a dataset from it\n",
+        "data_store = Datastore.get_default(ws)\n",
+        "data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n",
+        "datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n",
+        "sample_request_data = Dataset.Tabular.from_delimited_files(\n",
+        "    datastore_path,\n",
+        "    separator='\\n',\n",
+        "    infer_column_types=True,\n",
+        "    header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
+        "sample_request_data = sample_request_data.register(workspace=ws,\n",
+        "                                                   name='diabetes_sample_request_data',\n",
+        "                                                   create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "\n",
+        "\n",
+        "environment = Environment('my-sklearn-environment')\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "    'azureml-defaults',\n",
+        "    'inference-schema[numpy-support]',\n",
+        "    'joblib',\n",
+        "    'numpy',\n",
+        "    'scikit-learn'\n",
+        "])\n",
+        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
+        "# if cpu and memory_in_gb parameters are not provided\n",
+        "# the model will be profiled on default configuration of\n",
+        "# 3.5CPU and 15GB memory\n",
+        "profile = Model.profile(ws,\n",
+        "            'rgrsn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
+        "            [model],\n",
+        "            inference_config,\n",
+        "            input_dataset=sample_request_data,\n",
+        "            cpu=1.0,\n",
+        "            memory_in_gb=0.5)\n",
+        "\n",
+        "profile.wait_for_completion(True)\n",
+        "details = profile.get_details()"
       ]
     },
     {

how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb

@@ -145,6 +145,110 @@
         "                                   environment=environment)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model Profiling\n",
+        "\n",
+        "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
+        "\n",
+        "In order to profile your model you will need:\n",
+        "- a registered model\n",
+        "- an entry script\n",
+        "- an inference configuration\n",
+        "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
+        "\n",
+        "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
+        "\n",
+        "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "from azureml.core import Datastore\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.data import dataset_type_definitions\n",
+        "\n",
+        "\n",
+        "# create a string that can be put in the body of the request\n",
+        "serialized_input_json = json.dumps({\n",
+        "    'data': [\n",
+        "        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+        "        [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]\n",
+        "    ]\n",
+        "})\n",
+        "dataset_content = []\n",
+        "for i in range(100):\n",
+        "    dataset_content.append(serialized_input_json)\n",
+        "dataset_content = '\\n'.join(dataset_content)\n",
+        "file_name = 'sample_request_data_diabetes.txt'\n",
+        "f = open(file_name, 'w')\n",
+        "f.write(dataset_content)\n",
+        "f.close()\n",
+        "\n",
+        "# upload the txt file created above to the Datastore and create a dataset from it\n",
+        "data_store = Datastore.get_default(ws)\n",
+        "data_store.upload_files(['./' + file_name], target_path='sample_request_data_diabetes')\n",
+        "datastore_path = [(data_store, 'sample_request_data_diabetes' +'/' + file_name)]\n",
+        "sample_request_data_diabetes = Dataset.Tabular.from_delimited_files(\n",
+        "    datastore_path,\n",
+        "    separator='\\n',\n",
+        "    infer_column_types=True,\n",
+        "    header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
+        "sample_request_data_diabetes = sample_request_data_diabetes.register(workspace=ws,\n",
+        "                                                   name='sample_request_data_diabetes',\n",
+        "                                                   create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "from azureml.core import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.model import Model, InferenceConfig\n",
+        "\n",
+        "\n",
+        "environment = Environment('my-sklearn-environment')\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "    'azureml-defaults',\n",
+        "    'inference-schema[numpy-support]',\n",
+        "    'joblib',\n",
+        "    'numpy',\n",
+        "    'scikit-learn'\n",
+        "])\n",
+        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
+        "# if cpu and memory_in_gb parameters are not provided\n",
+        "# the model will be profiled on default configuration of\n",
+        "# 3.5CPU and 15GB memory\n",
+        "profile = Model.profile(ws,\n",
+        "            'profile-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
+        "            [model],\n",
+        "            inference_config,\n",
+        "            input_dataset=sample_request_data_diabetes,\n",
+        "            cpu=1.0,\n",
+        "            memory_in_gb=0.5)\n",
+        "\n",
+        "profile.wait_for_completion(True)\n",
+        "details = profile.get_details()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb

@@ -0,0 +1,314 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
+        "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
+        "We then test and delete the service, image and model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import azureml.core\n",
+        "print(azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Get workspace\n",
+        "Load existing workspace from the config file info."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.workspace import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register the model\n",
+        "Register an existing trained model, add descirption and tags. Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. You can download a [pretrained resnet50](http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v1_fp32_savedmodel_NCHW_jpg.tar.gz) and unpack it to that directory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#Register the model\n",
+        "from azureml.core.model import Model\n",
+        "model = Model.register(model_path = \"resnet50\", # this points to a local file\n",
+        "                       model_name = \"resnet50\", # this is the name the model is registered as\n",
+        "                       tags = {'area': \"Image classification\", 'type': \"classification\"},\n",
+        "                       description = \"Image classification trained on Imagenet Dataset\",\n",
+        "                       workspace = ws)\n",
+        "\n",
+        "print(model.name, model.description, model.version)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Provision the AKS Cluster\n",
+        "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.compute import ComputeTarget, AksCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
+        "# Choose a name for your GPU cluster\n",
+        "gpu_cluster_name = \"aks-gpu-cluster\"\n",
+        "\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
+        "    print(\"Found existing gpu cluster\")\n",
+        "except ComputeTargetException:\n",
+        "    print(\"Creating new gpu-cluster\")\n",
+        "    \n",
+        "    # Specify the configuration for the new cluster\n",
+        "    compute_config = AksCompute.provisioning_configuration(cluster_purpose=AksCompute.ClusterPurpose.DEV_TEST,\n",
+        "                                                           agent_count=1,\n",
+        "                                                           vm_size=\"Standard_NV6\")\n",
+        "    # Create the cluster with the specified name and configuration\n",
+        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
+        "\n",
+        "    # Wait for the cluster to complete, show the output log\n",
+        "    gpu_cluster.wait_for_completion(show_output=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Deploy the model as a web service to AKS\n",
+        "\n",
+        "First create a scoring script"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%writefile score.py\n",
+        "import tensorflow as tf\n",
+        "import numpy as np\n",
+        "import json\n",
+        "import os\n",
+        "from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
+        "from azureml.contrib.services.aml_response import AMLResponse\n",
+        "\n",
+        "def init():\n",
+        "    global session\n",
+        "    global input_name\n",
+        "    global output_name\n",
+        "    \n",
+        "    session = tf.Session()\n",
+        "\n",
+        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'resnet50')\n",
+        "    model = tf.saved_model.loader.load(session, ['serve'], model_path)\n",
+        "    if len(model.signature_def['serving_default'].inputs) > 1:\n",
+        "        raise ValueError(\"This score.py only supports one input\")\n",
+        "    input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n",
+        "    output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()]\n",
+        "    \n",
+        "\n",
+        "@rawhttp\n",
+        "def run(request):\n",
+        "    if request.method == 'POST':\n",
+        "        reqBody = request.get_data(False)\n",
+        "        resp = score(reqBody)\n",
+        "        return AMLResponse(resp, 200)\n",
+        "    if request.method == 'GET':\n",
+        "        respBody = str.encode(\"GET is not supported\")\n",
+        "        return AMLResponse(respBody, 405)\n",
+        "    return AMLResponse(\"bad request\", 500)\n",
+        "\n",
+        "def score(data):\n",
+        "    result = session.run(output_name, {input_name: [data]})\n",
+        "    return json.dumps(result[1].tolist())\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    init()\n",
+        "    with open(\"test_image.jpg\", 'rb') as f:\n",
+        "        content = f.read()\n",
+        "        print(score(content))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now create the deployment configuration objects and deploy the model as a webservice."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Set the web service configuration (using default here)\n",
+        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.webservice import AksWebservice\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.environment import Environment, DEFAULT_GPU_IMAGE\n",
+        "\n",
+        "env = Environment('deploytocloudenv')\n",
+        "# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n",
+        "# for open-sourced GPU base images.\n",
+        "env.docker.base_image = DEFAULT_GPU_IMAGE\n",
+        "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n",
+        "                                 pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n",
+        "aks_config = AksWebservice.deploy_configuration()\n",
+        "\n",
+        "# # Enable token auth and disable (key) auth on the webservice\n",
+        "# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service_name ='gpu-rn50'\n",
+        "\n",
+        "aks_service = Model.deploy(workspace=ws,\n",
+        "                           name=aks_service_name,\n",
+        "                           models=[model],\n",
+        "                           inference_config=inference_config,\n",
+        "                           deployment_config=aks_config,\n",
+        "                           deployment_target=gpu_cluster)\n",
+        "\n",
+        "aks_service.wait_for_deployment(show_output = True)\n",
+        "print(aks_service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Test the web service\n",
+        "We test the web sevice by passing the test images content."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "import requests\n",
+        "\n",
+        "# if (key) auth is enabled, fetch keys and include in the request\n",
+        "key1, key2 = aks_service.get_keys()\n",
+        "\n",
+        "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
+        "\n",
+        "# # if token auth is enabled, fetch token and include in the request\n",
+        "# access_token, fetch_after = aks_service.get_token()\n",
+        "# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
+        "\n",
+        "test_sample = open('snowleopardgaze.jpg', 'rb').read()\n",
+        "resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Clean up\n",
+        "Delete the service, image, model and compute target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "%%time\n",
+        "aks_service.delete()\n",
+        "model.delete()\n",
+        "gpu_cluster.delete()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "aashishb"
+      }
+    ],
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.yml

@@ -0,0 +1,5 @@
+name: production-deploy-to-aks-gpu
+dependencies:
+- pip:
+  - azureml-sdk
+  - tensorflow

how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb

@@ -198,6 +198,106 @@
         "inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Model Profiling\n",
+        "\n",
+        "Profile your model to understand how much CPU and memory the service, created as a result of its deployment, will need. Profiling returns information such as CPU usage, memory usage, and response latency. It also provides a CPU and memory recommendation based on the resource usage. You can profile your model (or more precisely the service built based on your model) on any CPU and/or memory combination where 0.1 <= CPU <= 3.5 and 0.1GB <= memory <= 15GB. If you do not provide a CPU and/or memory requirement, we will test it on the default configuration of 3.5 CPU and 15GB memory.\n",
+        "\n",
+        "In order to profile your model you will need:\n",
+        "- a registered model\n",
+        "- an entry script\n",
+        "- an inference configuration\n",
+        "- a single column tabular dataset, where each row contains a string representing sample request data sent to the service.\n",
+        "\n",
+        "At this point we only support profiling of services that expect their request data to be a string, for example: string serialized json, text, string serialized image, etc. The content of each row of the dataset (string) will be put into the body of the HTTP request and sent to the service encapsulating the model for scoring.\n",
+        "\n",
+        "Below is an example of how you can construct an input dataset to profile a service which expects its incoming requests to contain serialized json. In this case we created a dataset based one hundred instances of the same request data. In real world scenarios however, we suggest that you use larger datasets with various inputs, especially if your model resource usage/behavior is input dependent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "from azureml.core import Datastore\n",
+        "from azureml.core.dataset import Dataset\n",
+        "from azureml.data import dataset_type_definitions\n",
+        "\n",
+        "input_json = {'data': [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+        "                       [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\n",
+        "# create a string that can be put in the body of the request\n",
+        "serialized_input_json = json.dumps(input_json)\n",
+        "dataset_content = []\n",
+        "for i in range(100):\n",
+        "    dataset_content.append(serialized_input_json)\n",
+        "sample_request_data = '\\n'.join(dataset_content)\n",
+        "file_name = 'sample_request_data.txt'\n",
+        "f = open(file_name, 'w')\n",
+        "f.write(sample_request_data)\n",
+        "f.close()\n",
+        "\n",
+        "# upload the txt file created above to the Datastore and create a dataset from it\n",
+        "data_store = Datastore.get_default(ws)\n",
+        "data_store.upload_files(['./' + file_name], target_path='sample_request_data')\n",
+        "datastore_path = [(data_store, 'sample_request_data' +'/' + file_name)]\n",
+        "sample_request_data = Dataset.Tabular.from_delimited_files(\n",
+        "    datastore_path,\n",
+        "    separator='\\n',\n",
+        "    infer_column_types=True,\n",
+        "    header=dataset_type_definitions.PromoteHeadersBehavior.NO_HEADERS)\n",
+        "sample_request_data = sample_request_data.register(workspace=ws,\n",
+        "                                                   name='sample_request_data',\n",
+        "                                                   create_new_version=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now that we have an input dataset we are ready to go ahead with profiling. In this case we are testing the previously introduced sklearn regression model on 1 CPU and 0.5 GB memory. The memory usage and recommendation presented in the result is measured in Gigabytes. The CPU usage and recommendation is measured in CPU cores."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from datetime import datetime\n",
+        "from azureml.core import Environment\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.model import Model, InferenceConfig\n",
+        "\n",
+        "\n",
+        "environment = Environment('my-sklearn-environment')\n",
+        "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n",
+        "    'azureml-defaults',\n",
+        "    'inference-schema[numpy-support]',\n",
+        "    'joblib',\n",
+        "    'numpy',\n",
+        "    'scikit-learn'\n",
+        "])\n",
+        "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n",
+        "# if cpu and memory_in_gb parameters are not provided\n",
+        "# the model will be profiled on default configuration of\n",
+        "# 3.5CPU and 15GB memory\n",
+        "profile = Model.profile(ws,\n",
+        "            'sklearn-%s' % datetime.now().strftime('%m%d%Y-%H%M%S'),\n",
+        "            [model],\n",
+        "            inference_config,\n",
+        "            input_dataset=sample_request_data,\n",
+        "            cpu=1.0,\n",
+        "            memory_in_gb=0.5)\n",
+        "\n",
+        "profile.wait_for_completion(True)\n",
+        "details = profile.get_details()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -318,7 +418,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-deploy-to-aks"
+        ]
+      },
       "outputs": [],
       "source": [
         "# Set the web service configuration (using default here)\n",
@@ -331,7 +435,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "sample-deploy-to-aks"
+        ]
+      },
       "outputs": [],
       "source": [
         "%%time\n",

how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb

@@ -1,457 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Register Model, Create Image and Deploy Service\n",
-        "\n",
-        "This example shows how to deploy a web service in step-by-step fashion:\n",
-        "\n",
-        " 1. Register model\n",
-        " 2. Query versions of models and select one to deploy\n",
-        " 3. Create Docker image\n",
-        " 4. Query versions of images\n",
-        " 5. Deploy the image as web service\n",
-        " \n",
-        "**IMPORTANT**:\n",
-        " * This notebook requires you to first complete [train-within-notebook](../../training/train-within-notebook/train-within-notebook.ipynb) example\n",
-        " \n",
-        "The train-within-notebook example taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize Workspace\n",
-        "\n",
-        "Initialize a workspace object from persisted configuration."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create workspace"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
-        "\n",
-        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "import sklearn\n",
-        "\n",
-        "library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
-        "\n",
-        "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
-        "                       model_name = \"sklearn_regression_model.pkl\",\n",
-        "                       tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
-        "                       description = \"Ridge regression model to predict diabetes\",\n",
-        "                       workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "register model from file"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "regression_models = Model.list(workspace=ws, tags=['area'])\n",
-        "for m in regression_models:\n",
-        "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "You can pick a specific model to deploy"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(model.name, model.description, model.version, sep = '\\t')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create Docker Image"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import os\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy\n",
-        "from sklearn.externals import joblib\n",
-        "from sklearn.linear_model import Ridge\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-        "    model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_regression_model.pkl')\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "# note you can pass in multiple rows for scoring\n",
-        "def run(raw_data):\n",
-        "    try:\n",
-        "        data = json.loads(raw_data)['data']\n",
-        "        data = numpy.array(data)\n",
-        "        result = model.predict(data)\n",
-        "        # you can return any datatype as long as it is JSON-serializable\n",
-        "        return result.tolist()\n",
-        "    except Exception as e:\n",
-        "        error = str(e)\n",
-        "        return error"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note that following command can take few minutes. \n",
-        "\n",
-        "You can add tags and descriptions to images. Also, an image can contain multiple models."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image",
-          "sample-image-create"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.image import Image, ContainerImage\n",
-        "\n",
-        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                 execution_script=\"score.py\",\n",
-        "                                 conda_file=\"myenv.yml\",\n",
-        "                                 tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
-        "                                 description = \"Image with ridge regression model\")\n",
-        "\n",
-        "image = Image.create(name = \"myimage1\",\n",
-        "                     # this is the model object. note you can pass in 0-n models via this list-type parameter\n",
-        "                     # in case you need to reference multiple models, or none at all, in your scoring script.\n",
-        "                     models = [model],\n",
-        "                     image_config = image_config, \n",
-        "                     workspace = ws)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "image.wait_for_creation(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Use a custom Docker image\n",
-        "\n",
-        "You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
-        "\n",
-        "Only Supported for `ContainerImage`(from azureml.core.image) with `python` runtime.\n",
-        "```python\n",
-        "# use an image available in public Container Registry without authentication\n",
-        "image_config.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
-        "\n",
-        "# or, use an image available in a private Container Registry\n",
-        "image_config.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
-        "image_config.base_image_registry.address = \"myregistry.azurecr.io\"\n",
-        "image_config.base_image_registry.username = \"username\"\n",
-        "image_config.base_image_registry.password = \"password\"\n",
-        "\n",
-        "# or, use an image built during training.\n",
-        "image_config.base_image = run.properties[\"AzureML.DerivedImageName\"]\n",
-        "```\n",
-        "You can get the address of training image from the properties of a Run object. Only new runs submitted with azureml-sdk>=1.0.22 to AMLCompute targets will have the 'AzureML.DerivedImageName' property. Instructions on how to get a Run can be found in [manage-runs](../../training/manage-runs/manage-runs.ipynb). \n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "List images by tag and find out the detailed build log for debugging."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "create image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
-        "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy image as web service on Azure Container Instance\n",
-        "\n",
-        "Note that the service creation can take few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci",
-          "sample-aciwebservice-deploy-config"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
-        "                                               description = 'Predict diabetes using regression model')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci",
-          "sample-aciwebservice-deploy-from-image"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "\n",
-        "aci_service_name = 'my-aci-service-2'\n",
-        "print(aci_service_name)\n",
-        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
-        "                                           image = image,\n",
-        "                                           name = aci_service_name,\n",
-        "                                           workspace = ws)\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Test web service"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the web service with some dummy input data to get a prediction."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "test_sample = json.dumps({'data': [\n",
-        "    [1,2,3,4,5,6,7,8,9,10], \n",
-        "    [10,9,8,7,6,5,4,3,2,1]\n",
-        "]})\n",
-        "test_sample = bytes(test_sample,encoding = 'utf8')\n",
-        "\n",
-        "prediction = aci_service.run(input_data=test_sample)\n",
-        "print(prediction)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Delete ACI to clean up"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "deploy service",
-          "aci"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "aci_service.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "aashishb"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.yml

@@ -1,8 +0,0 @@
-name: register-model-create-image-deploy-service
-dependencies:
-- pip:
-  - azureml-sdk
-  - matplotlib
-  - tqdm
-  - scipy
-  - sklearn

how-to-use-azureml/deployment/spark/iris.model/metadata/part-00000

@@ -0,0 +1 @@
+{"class":"org.apache.spark.ml.classification.LogisticRegressionModel","timestamp":1570147252329,"sparkVersion":"2.4.0","uid":"LogisticRegression_5df3978caaf3","paramMap":{"regParam":0.01},"defaultParamMap":{"aggregationDepth":2,"threshold":0.5,"rawPredictionCol":"rawPrediction","featuresCol":"features","labelCol":"label","predictionCol":"prediction","family":"auto","regParam":0.0,"tol":1.0E-6,"probabilityCol":"probability","standardization":true,"elasticNetParam":0.0,"maxIter":100,"fitIntercept":true}}

how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb

@@ -0,0 +1,343 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Register Spark Model and deploy as Webservice\n",
+        "\n",
+        "This example shows how to deploy a Webservice in step-by-step fashion:\n",
+        "\n",
+        " 1. Register Spark Model\n",
+        " 2. Deploy Spark Model as Webservice"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration](../../../configuration.ipynb) Notebook first if you haven't."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check core SDK version number\n",
+        "import azureml.core\n",
+        "\n",
+        "print(\"SDK version:\", azureml.core.VERSION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Initialize Workspace\n",
+        "\n",
+        "Initialize a workspace object from persisted configuration."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create workspace"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core import Workspace\n",
+        "\n",
+        "ws = Workspace.from_config()\n",
+        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Register Model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can add tags and descriptions to your Models. Note you need to have a `iris.model` file in the current directory. This model file is generated using [train in spark](../training/train-in-spark/train-in-spark.ipynb) notebook. The below call registers that file as a Model with the same name `iris.model` in the workspace.\n",
+        "\n",
+        "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "register model from file"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import Model\n",
+        "\n",
+        "model = Model.register(model_path=\"iris.model\",\n",
+        "                       model_name=\"iris.model\",\n",
+        "                       tags={'type': \"regression\"},\n",
+        "                       description=\"Logistic regression model to predict iris species\",\n",
+        "                       workspace=ws)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Fetch Environment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment.\n",
+        "\n",
+        "In this notebook, we will be using 'AzureML-PySpark-MmlSpark-0.15', a curated environment.\n",
+        "\n",
+        "More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Environment\n",
+        "\n",
+        "env = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create Inference Configuration\n",
+        "\n",
+        "There is now support for a source directory, you can upload an entire folder from your local machine as dependencies for the Webservice.\n",
+        "Note: in that case, your entry_script is relative path to the source_directory path.\n",
+        "\n",
+        "Sample code for using a source directory:\n",
+        "\n",
+        "```python\n",
+        "inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
+        "                                   entry_script=\"x/y/score.py\",\n",
+        "                                   environment=environment)\n",
+        "```\n",
+        "\n",
+        " - source_directory = holds source path as string, this entire folder gets added in image so its really easy to access any files within this folder or subfolder\n",
+        " - entry_script = contains logic specific to initializing your model and running predictions\n",
+        " - environment = An environment object to use for the deployment. Doesn't have to be registered"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "create image"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.model import InferenceConfig\n",
+        "\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy Model as Webservice on Azure Container Instance\n",
+        "\n",
+        "Note that the service creation can take few minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "azuremlexception-remarks-sample"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice, Webservice\n",
+        "from azureml.exceptions import WebserviceException\n",
+        "\n",
+        "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
+        "aci_service_name = 'aciservice1'\n",
+        "\n",
+        "try:\n",
+        "    # if you want to get existing service below is the command\n",
+        "    # since aci name needs to be unique in subscription deleting existing aci if any\n",
+        "    # we use aci_service_name to create azure aci\n",
+        "    service = Webservice(ws, name=aci_service_name)\n",
+        "    if service:\n",
+        "        service.delete()\n",
+        "except WebserviceException as e:\n",
+        "    print()\n",
+        "\n",
+        "service = Model.deploy(ws, aci_service_name, [model], inference_config, deployment_config)\n",
+        "\n",
+        "service.wait_for_deployment(True)\n",
+        "print(service.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Test web service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import json\n",
+        "test_sample = json.dumps({'features':{'type':1,'values':[4.3,3.0,1.1,0.1]},'label':2.0})\n",
+        "\n",
+        "test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
+        "prediction = service.run(input_data=test_sample_encoded)\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Delete ACI to clean up"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "tags": [
+          "deploy service",
+          "aci"
+        ]
+      },
+      "outputs": [],
+      "source": [
+        "service.delete()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Model Profiling\n",
+        "\n",
+        "You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
+        "\n",
+        "```python\n",
+        "profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
+        "profile.wait_for_profiling(True)\n",
+        "profiling_results = profile.get_results()\n",
+        "print(profiling_results)\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Model Packaging\n",
+        "\n",
+        "If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
+        "\n",
+        "You must include an Environment object in your inference configuration to use `Model.package()`.\n",
+        "\n",
+        "```python\n",
+        "package = Model.package(ws, [model], inference_config)\n",
+        "package.wait_for_creation(show_output=True)  # Or show_output=False to hide the Docker build logs.\n",
+        "package.pull()\n",
+        "```\n",
+        "\n",
+        "Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n",
+        "\n",
+        "```python\n",
+        "package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n",
+        "package.wait_for_creation(show_output=True)\n",
+        "package.save(\"./local_context_dir\")\n",
+        "```"
+      ]
+    }
+  ],
+  "metadata": {
+    "authors": [
+      {
+        "name": "aashishb"
+      }
+    ],
+    "category": "deployment",
+    "compute": [
+      "None"
+    ],
+    "datasets": [
+      "Iris"
+    ],
+    "deployment": [
+      "Azure Container Instance"
+    ],
+    "exclude_from_index": false,
+    "framework": [
+      "PySpark"
+    ],
+    "friendly_name": "Register Spark model and deploy as webservice",
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.2"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}

how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.yml

@@ -0,0 +1,4 @@
+name: model-register-and-deploy-spark
+dependencies:
+- pip:
+  - azureml-sdk

how-to-use-azureml/deployment/spark/score.py

@@ -0,0 +1,37 @@
+import traceback
+from pyspark.ml.linalg import VectorUDT
+from azureml.core.model import Model
+from pyspark.ml.classification import LogisticRegressionModel
+from pyspark.sql.types import StructType, StructField
+from pyspark.sql.types import DoubleType
+from pyspark.sql import SQLContext
+from pyspark import SparkContext
+
+sc = SparkContext.getOrCreate()
+sqlContext = SQLContext(sc)
+spark = sqlContext.sparkSession
+
+input_schema = StructType([StructField("features", VectorUDT()), StructField("label", DoubleType())])
+reader = spark.read
+reader.schema(input_schema)
+
+
+def init():
+    global model
+    # note here "iris.model" is the name of the model registered under the workspace
+    # this call should return the path to the model.pkl file on the local disk.
+    model_path = Model.get_model_path('iris.model')
+    # Load the model file back into a LogisticRegression model
+    model = LogisticRegressionModel.load(model_path)
+
+
+def run(data):
+    try:
+        input_df = reader.json(sc.parallelize([data]))
+        result = model.transform(input_df)
+        # you can return any datatype as long as it is JSON-serializable
+        return result.collect()[0]['prediction']
+    except Exception as e:
+        traceback.print_exc()
+        error = str(e)
+        return error

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

@@ -409,16 +409,6 @@
         "    print(f.read())"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile dockerfile\n",
-        "RUN apt-get update && apt-get install -y g++ "
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -439,6 +429,8 @@
         "from azureml.core.model import InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.model import Model\n",
+        "from azureml.core.environment import Environment\n",
+        "\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",
@@ -446,10 +438,8 @@
         "                                                     \"method\" : \"local_explanation\"}, \n",
         "                                               description='Get local explanations for IBM Employee Attrition data')\n",
         "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"score_remote_explain.py\",\n",
-        "                                   conda_file=\"myenv.yml\",\n",
-        "                                   extra_docker_file_steps=\"dockerfile\")\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score_remote_explain.py\", environment=myenv)\n",
         "\n",
         "# Use configs and models generated above\n",
         "service = Model.deploy(ws, 'model-scoring-service', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",

how-to-use-azureml/machine-learning-pipelines/README.md

@@ -42,6 +42,8 @@ In this directory, there are two types of notebooks:
 
 1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score): This notebook demonstrates how to run a batch scoring job using Azure Machine Learning pipelines.
 2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans): This notebook demonstrates a multi-step pipeline that uses GPU compute. This sample also showcases how to use conda dependencies using runconfig when using Pipelines.
-3. [nyc-taxi-data-regression-model-building.ipynb](https://aka.ms/pl-nyctaxi-tutorial): This notebook is an AzureML Pipelines version of the previously published two part sample. 
+3. [nyc-taxi-data-regression-model-building.ipynb](https://aka.ms/pl-nyctaxi-tutorial): This notebook is an AzureML Pipelines version of the previously published two part sample.
+4. [file-dataset-image-inference-mnist.ipynb](https://aka.ms/pl-pr-filedata): This notebook demonstrates how to use ParallelRunStep to process unstructured data (file dataset).
+5. [tabular-dataset-inference-iris.ipynb](https://aka.ms/pl-pr-tabulardata): This notebook demonstrates how to use ParallelRunStep to process structured data (tabular dataset).
 
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/README.png)

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md

@@ -18,5 +18,6 @@ These notebooks below are designed to go in sequence.
 13. [aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb](https://aka.ms/pl-datapath): This notebook showcases how to use DataPath and PipelineParameter in AML Pipeline.
 14. [aml-pipelines-how-to-use-pipeline-drafts.ipynb](http://aka.ms/pl-pl-draft): This notebook shows how to use Pipeline Drafts. Pipeline Drafts are mutable pipelines which can be used to submit runs and create Published Pipelines.
 15. [aml-pipelines-hot-to-use-modulestep.ipynb](https://aka.ms/pl-modulestep): This notebook shows how to define Module, ModuleVersion and how to use them in an AML Pipeline using ModuleStep.
+16. [aml-pipelines-with-notebook-runner-step.ipynb](https://aka.ms/pl-nbrstep): This notebook shows how you can run another notebook as a step in Azure Machine Learning Pipeline.
 
  ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.png)

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-parameter-tuning-with-hyperdrive.ipynb

@@ -620,14 +620,13 @@
       "outputs": [],
       "source": [
         "%%time\n",
-        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.environment import Environment\n",
+        "from azureml.core.model import Model, InferenceConfig\n",
         "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
-        "                                   entry_script = \"score.py\",\n",
-        "                                   conda_file = \"myenv.yml\")\n",
+        "\n",
+        "myenv = Environment.from_conda_specification(name=\"env\", file_path=\"myenv.yml\")\n",
+        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
         "                                               memory_gb=1, \n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb

@@ -378,7 +378,8 @@
         "### Create a schedule for the pipeline using a Datastore\n",
         "This schedule will run when additions or modifications are made to Blobs in the Datastore.\n",
         "By default, the Datastore container is monitored for changes. Use the path_on_datastore parameter to instead specify a path on the Datastore to monitor for changes. Note: the path_on_datastore will be under the container for the datastore, so the actual path monitored will be container/path_on_datastore. Changes made to subfolders in the container/path will not trigger the schedule.\n",
-        "Note: Only Blob Datastores are supported."
+        "Note: Only Blob Datastores are supported.\n",
+        "Note: Not supported for CMK workspaces. Please review these [instructions](https://docs.microsoft.com/azure/machine-learning/how-to-trigger-published-pipeline) in order to setup a blob trigger submission schedule with CMK enabled. Also see those instructions to bring your own LogicApp to avoid the schedule triggers per month limit."
       ]
     },
     {

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb

@@ -76,7 +76,7 @@
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
-        "from azureml.train.automl.runtime import AutoMLStep\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
         "# Check core SDK version number\n",
         "print(\"SDK version:\", azureml.core.VERSION)"
@@ -173,12 +173,7 @@
       "source": [
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "conda_run_config.environment.docker.enabled = True\n",
-        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], \n",
-        "                              conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
         "print('run config is ready')"

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb

@@ -70,8 +70,6 @@
         "from azureml.pipeline.core import PipelineData\n",
         "from azureml.core.datastore import Datastore\n",
         "\n",
-        "from azureml.widgets import RunDetails\n",
-        "\n",
         "from azureml.core import Workspace, Experiment\n",
         "from azureml.contrib.notebook import NotebookRunConfig, AzureMLNotebookHandler\n",
         "\n",
@@ -149,7 +147,7 @@
       "metadata": {},
       "source": [
         "### Create or Attach an AmlCompute cluster\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
+        "You will need to create a [compute target](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.computetarget?view=azure-ml-py) for your remote run. In this tutorial, you get the default `AmlCompute` as your training compute resource."
       ]
     },
     {
@@ -205,7 +203,7 @@
         "conda_run_config.environment.docker.enabled = True\n",
         "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(pip_packages=['azureml-sdk'], pin_sdk_version=False)\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd\n",
         "\n",
         "print('run config is ready')"
@@ -298,7 +296,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.pipeline.core import PipelineParameter, TrainingOutput\n",
+        "from azureml.pipeline.core import PipelineParameter\n",
         "\n",
         "output_from_notebook = PipelineData(name=\"notebook_processed_data\",\n",
         "                                    datastore=Datastore.get(ws, \"workspaceblobstore\"))\n",
@@ -326,7 +324,7 @@
         "\n",
         "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py). By deafult, all these steps will run in **parallel** once we submit the pipeline for run.\n",
         "\n",
-        "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment(class)?view=azure-ml-py#submit-config--tags-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow."
+        "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py#submit-experiment-name--pipeline-parameters-none--continue-on-step-failure-false--regenerate-outputs-false--parent-run-id-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow."
       ]
     },
     {
@@ -336,9 +334,7 @@
       "outputs": [],
       "source": [
         "pipeline1 = Pipeline(workspace=ws, steps=[notebook_runner_step])\n",
-        "\n",
-        "pipeline1.validate()\n",
-        "print(\"Pipeline validation complete\")"
+        "print(\"Pipeline creation complete\")"
       ]
     },
     {
@@ -356,6 +352,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.widgets import RunDetails\n",
         "RunDetails(pipeline_run1).show()"
       ]
     },
@@ -375,8 +372,7 @@
       "outputs": [],
       "source": [
         "pipeline_run1.wait_for_completion()\n",
-        " Retrieve the step runs by name `train.py`\n",
-        "train_step = pipeline_run1.find_step_run('training_notebook_step')\n",
+        "train_step = pipeline_run1.find_step_run('training_notebook_step') # Retrieve the step runs by name `train.py`\n",
         "\n",
         "if train_step:\n",
         "    train_step_obj = train_step[0] # since we have only one step by name `training_notebook_step`\n",
@@ -420,7 +416,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.7.3"
+      "version": "3.6.7"
     },
     "order_index": 12,
     "star_tag": [

how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md

@@ -71,7 +71,7 @@ base_image_registry.password = "password"
   - **models**: zero or more model names already registered in Azure Machine Learning model registry.
   - **parallel_run_config**: ParallelRunConfig as defined above.
   - **inputs**: one or more Dataset objects.
-  - **output**: this should be a PipelineData object encapsulating an Azure BLOB container path. 
+  - **output**: this should be a PipelineData object encapsulating an Azure BLOB container path.
   - **arguments**: list of custom arguments passed to scoring script (optional)
   - **allow_reuse**: optional, default value is True. If the inputs remain the same as a previous run, it will make the previous run results immediately available (skips re-computing the step).
 
@@ -121,7 +121,8 @@ pipeline_run.wait_for_completion(show_output=True)
 
 # Sample notebooks
 
--  [file-dataset-image-inference-mnist.ipynb](./file-dataset-image-inference-mnist.ipynb) demonstrates how to run batch inference on an MNIST dataset.
--  [tabular-dataset-inference-iris.ipynb](./tabular-dataset-inference-iris.ipynb) demonstrates how to run batch inference on an IRIS dataset.
+-  [file-dataset-image-inference-mnist.ipynb](./file-dataset-image-inference-mnist.ipynb) demonstrates how to run batch inference on an MNIST dataset using FileDataset.
+-  [tabular-dataset-inference-iris.ipynb](./tabular-dataset-inference-iris.ipynb) demonstrates how to run batch inference on an IRIS dataset using TabularDataset.
+-  [pipeline-style-transfer.ipynb](../pipeline-style-transfer/pipeline-style-transfer.ipynb) demonstrates using ParallelRunStep in multi-step pipeline and using output from one step as input to ParallelRunStep.
 
 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.png)

how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb

@@ -24,9 +24,9 @@
         "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
         "\n",
         "> **Note**\n",
-        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook.\n",
+        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook. Pandas is used to display job results.\n",
         "```\n",
-        "pip install azureml-contrib-pipeline-steps\n",
+        "pip install azureml-contrib-pipeline-steps pandas\n",
         "```\n",
         "> **Tip**\n",
         "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",

how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.yml

@@ -4,3 +4,4 @@ dependencies:
   - azureml-sdk
   - azureml-contrib-pipeline-steps
   - azureml-widgets
+  - pandas

how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb

@@ -24,9 +24,9 @@
         "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
         "\n",
         "> **Note**\n",
-        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook.\n",
+        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook. Pandas is used to display job results.\n",
         "```\n",
-        "pip install azureml-contrib-pipeline-steps\n",
+        "pip install azureml-contrib-pipeline-steps pandas\n",
         "```\n",
         "> **Tip**\n",
         "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",

how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.yml

@@ -4,3 +4,4 @@ dependencies:
   - azureml-sdk
   - azureml-contrib-pipeline-steps
   - azureml-widgets
+  - pandas

how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/batch_scoring.py

@@ -1,119 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-import os
-import argparse
-import datetime
-import time
-import tensorflow as tf
-from math import ceil
-import numpy as np
-import shutil
-from tensorflow.contrib.slim.python.slim.nets import inception_v3
-from azureml.core.model import Model
-
-slim = tf.contrib.slim
-
-parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
-parser.add_argument('--model_name', dest="model_name", required=True)
-parser.add_argument('--label_dir', dest="label_dir", required=True)
-parser.add_argument('--dataset_path', dest="dataset_path", required=True)
-parser.add_argument('--output_dir', dest="output_dir", required=True)
-parser.add_argument('--batch_size', dest="batch_size", type=int, required=True)
-
-args = parser.parse_args()
-
-image_size = 299
-num_channel = 3
-
-# create output directory if it does not exist
-os.makedirs(args.output_dir, exist_ok=True)
-
-
-def get_class_label_dict(label_file):
-    label = []
-    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
-    for l in proto_as_ascii_lines:
-        label.append(l.rstrip())
-    return label
-
-
-class DataIterator:
-    def __init__(self, data_dir):
-        self.file_paths = []
-        image_list = os.listdir(data_dir)
-        # total_size = len(image_list)
-        self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list]
-
-        self.labels = [1 for file_name in self.file_paths]
-
-    @property
-    def size(self):
-        return len(self.labels)
-
-    def input_pipeline(self, batch_size):
-        images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)
-        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)
-        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)
-        labels = input_queue[1]
-        images_content = tf.read_file(input_queue[0])
-
-        image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name="jpeg_reader")
-        float_caster = tf.cast(image_reader, tf.float32)
-        new_size = tf.constant([image_size, image_size], dtype=tf.int32)
-        images = tf.image.resize_images(float_caster, new_size)
-        images = tf.divide(tf.subtract(images, [0]), [255])
-
-        image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)
-        return image_batch
-
-
-def main(_):
-    # start_time = datetime.datetime.now()
-    label_file_name = os.path.join(args.label_dir, "labels.txt")
-    label_dict = get_class_label_dict(label_file_name)
-    classes_num = len(label_dict)
-    test_feeder = DataIterator(data_dir=args.dataset_path)
-    total_size = len(test_feeder.labels)
-    count = 0
-    # get model from model registry
-    model_path = Model.get_model_path(args.model_name)
-    with tf.Session() as sess:
-        test_images = test_feeder.input_pipeline(batch_size=args.batch_size)
-        with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
-            input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])
-            logits, _ = inception_v3.inception_v3(input_images,
-                                                  num_classes=classes_num,
-                                                  is_training=False)
-            probabilities = tf.argmax(logits, 1)
-
-        sess.run(tf.global_variables_initializer())
-        sess.run(tf.local_variables_initializer())
-        coord = tf.train.Coordinator()
-        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
-        saver = tf.train.Saver()
-        saver.restore(sess, model_path)
-        out_filename = os.path.join(args.output_dir, "result-labels.txt")
-        with open(out_filename, "w") as result_file:
-            i = 0
-            while count < total_size and not coord.should_stop():
-                test_images_batch = sess.run(test_images)
-                file_names_batch = test_feeder.file_paths[i * args.batch_size:
-                                                          min(test_feeder.size, (i + 1) * args.batch_size)]
-                results = sess.run(probabilities, feed_dict={input_images: test_images_batch})
-                new_add = min(args.batch_size, total_size - count)
-                count += new_add
-                i += 1
-                for j in range(new_add):
-                    result_file.write(os.path.basename(file_names_batch[j]) + ": " + label_dict[results[j]] + "\n")
-                result_file.flush()
-            coord.request_stop()
-            coord.join(threads)
-
-        # copy the file to artifacts
-        shutil.copy(out_filename, "./outputs/")
-        # Move the processed data out of the blob so that the next run can process the data.
-
-
-if __name__ == "__main__":
-    tf.app.run()

how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb

@@ -1,630 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../../../contrib/batch_inferencing/) for examples on how to get started."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Using Azure Machine Learning Pipelines for batch prediction\n",
-        "\n",
-        "In this notebook we will demonstrate how to run a batch scoring job using Azure Machine Learning pipelines. Our example job will be to take an already-trained image classification model, and run that model on some unlabeled images. The image classification model that we'll use is the __[Inception-V3 model](https://arxiv.org/abs/1512.00567)__  and we'll run this model on unlabeled images from the __[ImageNet](http://image-net.org/)__ dataset. \n",
-        "\n",
-        "The outline of this notebook is as follows:\n",
-        "\n",
-        "- Register the pretrained inception model into the model registry. \n",
-        "- Store the dataset images in a blob container.\n",
-        "- Use the registered model to do batch scoring on the images in the data blob container."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
-        "from azureml.core.datastore import Datastore\n",
-        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
-        "from azureml.data.data_reference import DataReference\n",
-        "from azureml.pipeline.core import Pipeline, PipelineData\n",
-        "from azureml.pipeline.steps import PythonScriptStep"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Set up machine learning resources"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Set up datastores\n",
-        "First, let\u00e2\u20ac\u2122s access the datastore that has the model, labels, and images. \n",
-        "\n",
-        "### Create a datastore that points to a blob container containing sample images\n",
-        "\n",
-        "We have created a public blob container `sampledata` on an account named `pipelinedata`, containing images from the ImageNet evaluation set. In the next step, we create a datastore with the name `images_datastore`, which points to this container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n",
-        "\n",
-        "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "account_name = \"pipelinedata\"\n",
-        "datastore_name=\"images_datastore\"\n",
-        "container_name=\"sampledata\"\n",
-        "\n",
-        "batchscore_blob = Datastore.register_azure_blob_container(ws, \n",
-        "                      datastore_name=datastore_name, \n",
-        "                      container_name= container_name, \n",
-        "                      account_name=account_name, \n",
-        "                      overwrite=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Next, let\u00e2\u20ac\u2122s specify the default datastore for the outputs."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def_data_store = ws.get_default_datastore()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure data references\n",
-        "Now you need to add references to the data, as inputs to the appropriate pipeline steps in your pipeline. A data source in a pipeline is represented by a DataReference object. The DataReference object points to data that lives in, or is accessible from, a datastore. We need DataReference objects corresponding to the following: the directory containing the input images, the directory in which the pretrained model is stored, the directory containing the labels, and the output directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "input_images = DataReference(datastore=batchscore_blob, \n",
-        "                             data_reference_name=\"input_images\",\n",
-        "                             path_on_datastore=\"batchscoring/images\",\n",
-        "                             mode=\"download\"\n",
-        "                            )\n",
-        "model_dir = DataReference(datastore=batchscore_blob, \n",
-        "                          data_reference_name=\"input_model\",\n",
-        "                          path_on_datastore=\"batchscoring/models\",\n",
-        "                          mode=\"download\"                          \n",
-        "                         )\n",
-        "label_dir = DataReference(datastore=batchscore_blob, \n",
-        "                          data_reference_name=\"input_labels\",\n",
-        "                          path_on_datastore=\"batchscoring/labels\",\n",
-        "                          mode=\"download\"                          \n",
-        "                         )\n",
-        "output_dir = PipelineData(name=\"scores\", \n",
-        "                          datastore=def_data_store, \n",
-        "                          output_path_on_compute=\"batchscoring/results\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create and attach Compute targets\n",
-        "Use the below code to create and attach Compute targets. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# choose a name for your cluster\n",
-        "aml_compute_name = os.environ.get(\"AML_COMPUTE_NAME\", \"gpu-cluster\")\n",
-        "cluster_min_nodes = os.environ.get(\"AML_COMPUTE_MIN_NODES\", 0)\n",
-        "cluster_max_nodes = os.environ.get(\"AML_COMPUTE_MAX_NODES\", 1)\n",
-        "vm_size = os.environ.get(\"AML_COMPUTE_SKU\", \"STANDARD_NC6\")\n",
-        "\n",
-        "\n",
-        "if aml_compute_name in ws.compute_targets:\n",
-        "    compute_target = ws.compute_targets[aml_compute_name]\n",
-        "    if compute_target and type(compute_target) is AmlCompute:\n",
-        "        print('found compute target. just use it. ' + aml_compute_name)\n",
-        "else:\n",
-        "    print('creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size, # NC6 is GPU-enabled\n",
-        "                                                                vm_priority = 'lowpriority', # optional\n",
-        "                                                                min_nodes = cluster_min_nodes, \n",
-        "                                                                max_nodes = cluster_max_nodes)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, aml_compute_name, provisioning_config)\n",
-        "    \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 Azure Machine Learning Compute  status, use get_status()\n",
-        "    print(compute_target.get_status().serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prepare the Model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Download the Model\n",
-        "\n",
-        "Download and extract the model from http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz to `\"models\"`"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# create directory for model\n",
-        "model_dir = 'models'\n",
-        "if not os.path.isdir(model_dir):\n",
-        "    os.mkdir(model_dir)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import tarfile\n",
-        "import urllib.request\n",
-        "\n",
-        "url=\"http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz\"\n",
-        "response = urllib.request.urlretrieve(url, \"model.tar.gz\")\n",
-        "tar = tarfile.open(\"model.tar.gz\", \"r:gz\")\n",
-        "tar.extractall(model_dir)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register the model with Workspace"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "# register downloaded model \n",
-        "model = Model.register(model_path = \"models/inception_v3.ckpt\",\n",
-        "                       model_name = \"inception\", # this is the name the model is registered as\n",
-        "                       tags = {'pretrained': \"inception\"},\n",
-        "                       description = \"Imagenet trained tensorflow inception\",\n",
-        "                       workspace = ws)\n",
-        "# remove the downloaded dir after registration if you wish\n",
-        "shutil.rmtree(\"models\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Write your scoring script"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "To do the scoring, we use a batch scoring script `batch_scoring.py`, which is located in the same directory that this notebook is in. You can take a look at this script to see how you might modify it for your custom batch scoring task.\n",
-        "\n",
-        "The python script `batch_scoring.py` takes input images, applies the image classification model to these images, and outputs a classification result to a results file.\n",
-        "\n",
-        "The script `batch_scoring.py` takes the following parameters:\n",
-        "\n",
-        "- `--model_name`: the name of the model being used, which is expected to be in the `model_dir` directory\n",
-        "- `--label_dir` : the directory holding the `labels.txt` file \n",
-        "- `--dataset_path`: the directory containing the input images\n",
-        "- `--output_dir` : the script will run the model on the data and output a `results-label.txt` to this directory\n",
-        "- `--batch_size` : the batch size used in running the model.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Build and run the batch scoring pipeline\n",
-        "You have everything you need to build the pipeline. Let\u00e2\u20ac\u2122s put all these together."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "###  Specify the environment to run the script\n",
-        "Specify the conda dependencies for your script. You will need this object when you create the pipeline step later on."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
-        "\n",
-        "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.13.1\", \"azureml-defaults\"])\n",
-        "\n",
-        "# Runconfig\n",
-        "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
-        "amlcompute_run_config.environment.docker.enabled = True\n",
-        "amlcompute_run_config.environment.docker.base_image = DEFAULT_GPU_IMAGE\n",
-        "amlcompute_run_config.environment.spark.precache_packages = False"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Specify the parameters for your pipeline\n",
-        "A subset of the parameters to the python script can be given as input when we re-run a `PublishedPipeline`. In the current example, we define `batch_size` taken by the script as such parameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core.graph import PipelineParameter\n",
-        "batch_size_param = PipelineParameter(name=\"param_batch_size\", default_value=20)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create the pipeline step\n",
-        "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use PythonScriptStep to create the pipeline step."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "inception_model_name = \"inception_v3.ckpt\"\n",
-        "\n",
-        "batch_score_step = PythonScriptStep(\n",
-        "    name=\"batch_scoring\",\n",
-        "    script_name=\"batch_scoring.py\",\n",
-        "    arguments=[\"--dataset_path\", input_images, \n",
-        "               \"--model_name\", \"inception\",\n",
-        "               \"--label_dir\", label_dir, \n",
-        "               \"--output_dir\", output_dir, \n",
-        "               \"--batch_size\", batch_size_param],\n",
-        "    compute_target=compute_target,\n",
-        "    inputs=[input_images, label_dir],\n",
-        "    outputs=[output_dir],\n",
-        "    runconfig=amlcompute_run_config\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Run the pipeline\n",
-        "At this point you can run the pipeline and examine the output it produced. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "pipelineparameterssample"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "pipeline = Pipeline(workspace=ws, steps=[batch_score_step])\n",
-        "pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline, pipeline_parameters={\"param_batch_size\": 20})"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Monitor the run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(pipeline_run).show()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pipeline_run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Download and review output"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "step_run = list(pipeline_run.get_children())[0]\n",
-        "step_run.download_file(\"./outputs/result-labels.txt\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "df = pd.read_csv(\"result-labels.txt\", delimiter=\":\", header=None)\n",
-        "df.columns = [\"Filename\", \"Prediction\"]\n",
-        "df.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Publish a pipeline and rerun using a REST call"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a published pipeline\n",
-        "Once you are satisfied with the outcome of the run, you can publish the pipeline to run it with different input values later. When you publish a pipeline, you will get a REST endpoint that accepts invoking of the pipeline with the set of parameters you have already incorporated above using PipelineParameter."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "published_pipeline = pipeline_run.publish_pipeline(\n",
-        "    name=\"Inception_v3_scoring\", description=\"Batch scoring using Inception v3 model\", version=\"1.0\")\n",
-        "\n",
-        "published_pipeline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Get published pipeline\n",
-        "\n",
-        "You can get the published pipeline using **pipeline id**.\n",
-        "\n",
-        "To get all the published pipelines for a given workspace(ws): \n",
-        "```css\n",
-        "all_pub_pipelines = PublishedPipeline.get_all(ws)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core import PublishedPipeline\n",
-        "\n",
-        "pipeline_id = published_pipeline.id # use your published pipeline id\n",
-        "published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n",
-        "\n",
-        "published_pipeline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Rerun the pipeline using the REST endpoint"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Get AAD token\n",
-        "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
-        "import requests\n",
-        "\n",
-        "auth = InteractiveLoginAuthentication()\n",
-        "aad_token = auth.get_authentication_header()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Run published pipeline"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "rest_endpoint = published_pipeline.endpoint\n",
-        "# specify batch size when running the pipeline\n",
-        "response = requests.post(rest_endpoint, \n",
-        "                         headers=aad_token, \n",
-        "                         json={\"ExperimentName\": \"batch_scoring\",\n",
-        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "try:\n",
-        "    response.raise_for_status()\n",
-        "except Exception:    \n",
-        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
-        "                    'Response Code: {}\\n'\n",
-        "                    'Headers: {}\\n'\n",
-        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
-        "\n",
-        "run_id = response.json().get('Id')\n",
-        "print('Submitted pipeline run: ', run_id)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Monitor the new run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.pipeline.core.run import PipelineRun\n",
-        "published_pipeline_run = PipelineRun(ws.experiments[\"batch_scoring\"], run_id)\n",
-        "\n",
-        "RunDetails(published_pipeline_run).show()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "sanpil"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.yml

@@ -1,7 +0,0 @@
-name: pipeline-batch-scoring
-dependencies:
-- pip:
-  - azureml-sdk
-  - azureml-widgets
-  - pandas
-  - requests