Merge pull request #930 from Azure/release_update/Release-44

update samples from Release-44 as a part of  SDK release

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
@@ -13,11 +13,11 @@ 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 [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).

configuration.ipynb

@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.85 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

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

@@ -117,7 +117,7 @@ jupyter notebook
     - Simple example of using automated ML for regression
     - Uses azure compute for training
 
-- [auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb](regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb)
+- [auto-ml-regression-explanation-featurization.ipynb](regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb)
     - Dataset: Hardware Performance Dataset
     - Shows featurization and excplanation
     - Uses azure compute for training
@@ -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)
@@ -152,7 +152,7 @@ jupyter notebook
     - Beer Production Forecasting
 
 - [auto-ml-continuous-retraining.ipynb](continuous-retraining/auto-ml-continuous-retraining.ipynb)
-    - Continous retraining using Pipelines and Time-Series TabularDataset
+    - Continuous retraining using Pipelines and Time-Series TabularDataset
 
 - [auto-ml-classification-text-dnn.ipynb](classification-text-dnn/auto-ml-classification-text-dnn.ipynb)
     - Classification with text data using deep learning in AutoML

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

@@ -4,6 +4,7 @@ dependencies:
   # Currently Azure ML only supports 3.5.2 and later.
 - 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
@@ -12,7 +13,7 @@ dependencies:
 - scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
-- py-xgboost<=0.80
+- py-xgboost<=0.90
 - fbprophet==0.5
 - pytorch=1.1.0
 - cudatoolkit=9.0
@@ -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:
+- anaconda
 - conda-forge
 - pytorch

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

@@ -5,6 +5,7 @@ dependencies:
 - 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:
+- anaconda
 - conda-forge
 - pytorch

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

@@ -92,6 +92,23 @@
         "from azureml.explain.model._internal.explanation_client import ExplanationClient"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -132,7 +149,6 @@
         "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",
@@ -160,35 +176,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-4\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpu-cluster-4\"\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",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -320,7 +323,6 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**training_data**|Input dataset, containing both features and label column.|\n",
         "|**label_column_name**|The name of the label column.|\n",
-        "|**model_explainability**|Indicate to explain each trained pipeline or not.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -352,7 +354,6 @@
         "                             training_data = train_data,\n",
         "                             label_column_name = label,\n",
         "                             validation_data = validation_dataset,\n",
-        "                             model_explainability=True,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -396,8 +397,6 @@
       "outputs": [],
       "source": [
         "#from azureml.train.automl.run import AutoMLRun\n",
-        "#experiment_name = 'automl-classification-bmarketing'\n",
-        "#experiment = Experiment(ws, experiment_name)\n",
         "#remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
         "#remote_run"
       ]
@@ -500,11 +499,11 @@
       "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

@@ -5,7 +5,4 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - interpret
   - onnxruntime==1.0.0
-  - azureml-explain-model
-  - azureml-contrib-interpret

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

@@ -80,6 +80,23 @@
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -94,7 +111,6 @@
         "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",
@@ -122,35 +138,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your AmlCompute cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-1\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "cpu_cluster_name = \"cpu-cluster-1\"\n",
         "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'cpu-cluster-1':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_DS12_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -212,7 +215,7 @@
         "    \"primary_metric\": 'average_precision_score_weighted',\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",
@@ -282,7 +285,11 @@
     {
       "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

@@ -5,5 +5,3 @@ dependencies:
   - 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

@@ -47,8 +47,8 @@
         "Notebook synopsis:\n",
         "1. Creating an Experiment in an existing Workspace\n",
         "2. Configuration and remote run of AutoML for a text dataset (20 Newsgroups dataset from scikit-learn) for classification\n",
-        "3. Evaluating the final model on a test set\n",
-        "4. Deploying the model on ACI"
+        "3. Registering the best model for future use\n",
+        "4. Evaluating the final model on a test set"
       ]
     },
     {
@@ -84,6 +84,23 @@
         "from sklearn.datasets import fetch_20newsgroups"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -105,7 +122,6 @@
         "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 Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -121,9 +137,9 @@
       "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."
       ]
     },
     {
@@ -132,34 +148,25 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
+        "\n",
         "# 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",
-        "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",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_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 = 1)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\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",
-        "                                                                # or similar GPU option\n",
-        "                                                                # available in your workspace\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()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -218,7 +225,7 @@
       "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"
       ]
     },
     {
@@ -347,15 +354,34 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#best_run, fitted_model = automl_run.get_output()"
+        "best_run, fitted_model = automl_run.get_output()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploying the model\n",
-        "We now use the best fitted model from the AutoML Run to make predictions on the test set.  "
+        "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Registering the best model\n",
+        "We now register the best fitted model from the AutoML Run for use in future deployments.  "
       ]
     },
     {

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

@@ -6,3 +6,8 @@ dependencies:
   - 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

@@ -20,7 +20,7 @@
       "metadata": {},
       "source": [
         "# Automated Machine Learning \n",
-        "**Continous retraining using Pipelines and Time-Series TabularDataset**\n",
+        "**Continuous retraining using Pipelines and Time-Series TabularDataset**\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
         "2. [Setup](#Setup)\n",
@@ -75,6 +75,23 @@
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -112,7 +129,6 @@
         "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",
@@ -143,33 +159,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-42\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"cont-cluster\"\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 = 4)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=4)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_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 = 0, timeout_in_minutes = 10)\n",
-        "    \n",
-        "     # For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -197,7 +202,7 @@
         "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 +282,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "data_pipeline_run.wait_for_completion()"
+        "data_pipeline_run.wait_for_completion(show_output=False)"
       ]
     },
     {
@@ -343,11 +348,11 @@
       "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",
         "    \"max_concurrent_iterations\": 3,\n",

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

@@ -101,6 +101,23 @@
         "from azureml.train.estimator import Estimator"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -128,7 +145,6 @@
         "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",
@@ -163,7 +179,7 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster\"\n",
+        "cpu_cluster_name = \"beer-cluster\"\n",
         "\n",
         "# Verify that cluster does not exist already\n",
         "try:\n",
@@ -218,19 +234,18 @@
         "import pandas as pd\n",
         "from pandas import DataFrame\n",
         "from pandas import Grouper\n",
-        "from matplotlib import pyplot\n",
         "from pandas import concat\n",
-        "from matplotlib import pyplot\n",
         "from pandas.plotting import register_matplotlib_converters\n",
+        "\n",
         "register_matplotlib_converters()\n",
-        "plt.tight_layout()\n",
         "plt.figure(figsize=(20, 10))\n",
+        "plt.tight_layout()\n",
         "\n",
         "plt.subplot(2, 1, 1)\n",
         "plt.title('Beer Production By Year')\n",
         "df = pd.read_csv(\"Beer_no_valid_split_train.csv\", parse_dates=True, index_col= 'DATE').drop(columns='grain')\n",
         "test_df = pd.read_csv(\"Beer_no_valid_split_test.csv\", parse_dates=True, index_col= 'DATE').drop(columns='grain')\n",
-        "pyplot.plot(df)\n",
+        "plt.plot(df)\n",
         "\n",
         "plt.subplot(2, 1, 2)\n",
         "plt.title('Beer Production By Month')\n",
@@ -239,7 +254,8 @@
         "months = DataFrame(months)\n",
         "months.columns = range(1,13)\n",
         "months.boxplot()\n",
-        "pyplot.show()\n"
+        "\n",
+        "plt.show()"
       ]
     },
     {
@@ -538,7 +554,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "compute_target = ws.compute_targets['cpu-cluster']\n",
+        "compute_target = ws.compute_targets['beer-cluster']\n",
         "test_experiment = Experiment(ws, experiment_name + \"_test\")"
       ]
     },

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

@@ -1,9 +1,10 @@
 name: auto-ml-forecasting-beer-remote
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
+- py-xgboost<=0.90
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
+  - pandas==0.23.4
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -74,6 +74,23 @@
         "from datetime import datetime"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -95,7 +112,6 @@
         "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['SKU'] = ws.sku\n",
@@ -124,35 +140,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-bike\"\n",
+        "amlcompute_cluster_name = \"bike-cluster\"\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 = 4)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=4)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {

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

@@ -1,9 +1,10 @@
 name: auto-ml-forecasting-bike-share
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
+- py-xgboost<=0.90
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
+  - pandas==0.23.4
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -28,7 +28,6 @@
         "1. [Setup](#Setup)\n",
         "1. [Data and Forecasting Configurations](#Data)\n",
         "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
         "\n",
         "Advanced Forecasting\n",
         "1. [Advanced Training](#advanced_training)\n",
@@ -85,6 +84,23 @@
         "from datetime import datetime"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -109,7 +125,6 @@
         "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",
@@ -140,35 +155,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"aml-compute\"\n",
+        "amlcompute_cluster_name = \"energy-cluster\"\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",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_DS12_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_DS12_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
-        "\n",
-        "    # Create the cluster.\\n\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -463,7 +465,7 @@
       "metadata": {},
       "source": [
         "### Forecast Function\n",
-        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see notebook on [high frequency forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb)."
+        "For forecasting, we will use the forecast function instead of the predict function. Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use. Forecast function also can handle more complicated scenarios, see notebook on [high frequency forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/auto-ml-forecasting-function.ipynb)."
       ]
     },
     {

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

@@ -2,9 +2,8 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
+  - pandas==0.23.4
   - 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/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

@@ -1,64 +0,0 @@
-import argparse
-import json
-
-from azureml.core import Run, Model
-from azureml.core.model import InferenceConfig
-from azureml.core.environment import Environment
-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
-
-myenv = Environment.from_conda_specification(name="env", file_path=conda_env_file_name)
-
-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, environment=myenv)
-
-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

@@ -68,6 +68,7 @@
         "import logging\n",
         "import warnings\n",
         "\n",
+        "import azureml.core\n",
         "from azureml.core.dataset import Dataset\n",
         "from pandas.tseries.frequencies import to_offset\n",
         "from azureml.core.compute import AmlCompute\n",
@@ -81,13 +82,29 @@
         "np.set_printoptions(precision=4, suppress=True, linewidth=120)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "import azureml.core\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.train.automl import AutoMLConfig\n",
@@ -100,7 +117,6 @@
         "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['SKU'] = ws.sku\n",
@@ -258,29 +274,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "amlcompute_cluster_name = \"cpu-cluster-fcfn\"\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",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\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",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"fcfn-cluster\"\n",
         "\n",
-        "    # Create the cluster.\\n\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_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)"
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -459,8 +468,8 @@
         "# 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"
       ]
     },
     {
@@ -701,7 +710,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright, nirovins"
+        "name": "erwright"
       }
     ],
     "category": "tutorial",

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

@@ -1,9 +1,10 @@
-name: automl-forecasting-function
+name: auto-ml-forecasting-function
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
+- py-xgboost<=0.90
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
+  - pandas==0.23.4
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -68,6 +68,23 @@
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -89,7 +106,6 @@
         "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['SKU'] = ws.sku\n",
@@ -118,35 +134,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-oj\"\n",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"oj-cluster\"\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",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=6)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {

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

@@ -1,9 +1,10 @@
 name: auto-ml-forecasting-orange-juice-sales
 dependencies:
-- fbprophet==0.5
-- py-xgboost<=0.80
+- py-xgboost<=0.90
 - pip:
   - azureml-sdk
+  - numpy==1.16.2
+  - 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

@@ -49,7 +49,9 @@
         "2. Configure AutoML using `AutoMLConfig`.\n",
         "3. Train the model.\n",
         "4. Explore the results.\n",
-        "5. Test the fitted model."
+        "5. Visualization model's feature importance in azure portal\n",
+        "6. Explore any model's explanation and explore feature importance in azure portal\n",
+        "7. Test the fitted model."
       ]
     },
     {
@@ -71,13 +73,30 @@
         "\n",
         "from matplotlib import pyplot as plt\n",
         "import pandas as pd\n",
-        "import os\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
         "from azureml.core.dataset import Dataset\n",
-        "from azureml.train.automl import AutoMLConfig"
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },
     {
@@ -94,7 +113,6 @@
         "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",
@@ -155,7 +173,7 @@
         "automl_settings = {\n",
         "    \"n_cross_validations\": 3,\n",
         "    \"primary_metric\": 'average_precision_score_weighted',\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",
@@ -262,6 +280,133 @@
         "The fitted_model is a python object and you can read the different properties of the object.\n"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Best Model 's explanation\n",
+        "Retrieve the explanation from the best_run which includes explanations for engineered features and raw features.\n",
+        "\n",
+        "#### Download engineered feature importance from artifact store\n",
+        "You can use ExplanationClient to download the engineered feature explanations from the artifact store of the best_run. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "client = ExplanationClient.from_run(best_run)\n",
+        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + best_run.get_portal_url())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Explanations\n",
+        "In this section, we will show how to compute model explanations and visualize the explanations using azureml-explain-model package. Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different test data. The following steps will allow you to compute and visualize engineered feature importance based on your test data."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Retrieve any other AutoML model from training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_run, fitted_model = local_run.get_output(metric='accuracy')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Setup the model explanations for AutoML models\n",
+        "The fitted_model can generate the following which will be used for getting the engineered explanations using automl_setup_model_explanations:-\n",
+        "\n",
+        "1. Featurized data from train samples/test samples\n",
+        "2. Gather engineered name lists\n",
+        "3. Find the classes in your labeled column in classification scenarios\n",
+        "\n",
+        "The automl_explainer_setup_obj contains all the structures from above list."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train = training_data.drop_columns(columns=[label_column_name])\n",
+        "y_train = training_data.keep_columns(columns=[label_column_name], validate=True)\n",
+        "X_test = validation_data.drop_columns(columns=[label_column_name])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.train.automl.runtime.automl_explain_utilities import automl_setup_model_explanations\n",
+        "\n",
+        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
+        "                                                             X_test=X_test, y=y_train, \n",
+        "                                                             task='classification')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Initialize the Mimic Explainer for feature importance\n",
+        "For explaining the AutoML models, use the MimicWrapper from azureml.explain.model package. The MimicWrapper can be initialized with fields in automl_explainer_setup_obj, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (fitted_model here). The MimicWrapper also takes the automl_run object where engineered explanations will be uploaded."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
+        "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
+        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,\n",
+        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
+        "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
+        "                         classes=automl_explainer_setup_obj.classes)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
+        "The explain() method in MimicWrapper can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
+        "print(engineered_explanations.get_feature_importance_dict())\n",
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -358,7 +503,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "tzvikei"
+        "name": "anumamah"
       }
     ],
     "category": "tutorial",

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

@@ -5,5 +5,3 @@ dependencies:
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - interpret
-  - azureml-explain-model

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

@@ -51,8 +51,8 @@
         "4. Explore the results and featurization transparency options\n",
         "5. Setup remote compute for computing the model explanations for a given AutoML model.\n",
         "6. Start an AzureML experiment on your remote compute to compute explanations for an AutoML model.\n",
-        "7. Download the feature importance for engineered features and visualize the explanations for engineered features. \n",
-        "8. Download the feature importance for raw features and visualize the explanations for raw features. \n"
+        "7. Download the feature importance for engineered features and visualize the explanations for engineered features on azure portal. \n",
+        "8. Download the feature importance for raw features and visualize the explanations for raw features on azure portal. \n"
       ]
     },
     {
@@ -85,6 +85,23 @@
         "from azureml.core.dataset import Dataset"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -98,7 +115,6 @@
         "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 Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
@@ -127,35 +143,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"cpu-cluster-5\"\n",
+        "amlcompute_cluster_name = \"hardware-cluster\"\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",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=4)\n",
+        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\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 = 4)\n",
-        "\n",
-        "    # Create the cluster.\\n\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "compute_target.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -262,7 +265,7 @@
       "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",
@@ -320,8 +323,6 @@
       "outputs": [],
       "source": [
         "#from azureml.train.automl.run import AutoMLRun\n",
-        "#experiment_name = 'automl-regression-hardware'\n",
-        "#experiment = Experiment(ws, experiment_name)\n",
         "#remote_run = AutoMLRun(experiment=experiment, run_id='<run_ID_goes_here')\n",
         "#remote_run"
       ]
@@ -514,7 +515,7 @@
         "    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 +533,7 @@
       "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 +552,9 @@
         "# 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"
       ]
     },
     {
@@ -603,38 +599,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Feature importance  and  explanation dashboard\n",
-        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Setup for visualizing the model explanation results\n",
-        "For visualizing the explanation results for the *fitted_model* we need to perform the following steps:-\n",
-        "1. Featurize test data samples.\n",
-        "\n",
-        "The *automl_explainer_setup_obj* contains all the structures from above list. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_test = test_data.drop_columns([label]).to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
-        "explainer_setup_class = automl_setup_model_explanations(fitted_model, 'regression', X_test=X_test)"
+        "### Feature importance  and  visualizing explanation dashboard\n",
+        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model on the azure portal."
       ]
     },
     {
@@ -642,7 +608,7 @@
       "metadata": {},
       "source": [
         "#### Download engineered feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the engineered features."
+        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use azure portal url to view the dash board visualization of the feature importance values of the engineered features."
       ]
     },
     {
@@ -652,11 +618,10 @@
       "outputs": [],
       "source": [
         "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
-        "from interpret_community.widget import ExplanationDashboard\n",
         "client = ExplanationClient.from_run(automl_run)\n",
-        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "engineered_explanations = client.download_model_explanation(raw=False, comment='engineered explanations')\n",
         "print(engineered_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, datasetX=explainer_setup_class.X_test_transform)"
+        "print(\"You can visualize the engineered explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
       ]
     },
     {
@@ -664,7 +629,7 @@
       "metadata": {},
       "source": [
         "#### Download raw feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the raw features."
+        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use azure portal url to view the dash board visualization of the feature importance values of the raw features."
       ]
     },
     {
@@ -673,9 +638,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "raw_explanations = client.download_model_explanation(raw=True)\n",
+        "raw_explanations = client.download_model_explanation(raw=True, comment='raw explanations')\n",
         "print(raw_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, datasetX=explainer_setup_class.X_test_raw)"
+        "print(\"You can visualize the raw explanations under the 'Explanations (preview)' tab in the AutoML run at:-\\n\" + automl_run.get_portal_url())"
       ]
     },
     {
@@ -717,10 +682,10 @@
       "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 +726,7 @@
         "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 +734,8 @@
         "                                                     \"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",
@@ -808,6 +773,7 @@
       "outputs": [],
       "source": [
         "if service.state == 'Healthy':\n",
+        "    X_test = test_data.drop_columns([label]).to_pandas_dataframe()\n",
         "    # Serialize the first row of the test data into json\n",
         "    X_test_json = X_test[:1].to_json(orient='records')\n",
         "    print(X_test_json)\n",

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

@@ -1,8 +1,7 @@
-name: auto-ml-forecasting-grouping
+name: auto-ml-regression-explanation-featurization
 dependencies:
 - pip:
   - azureml-sdk
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
-  - azureml-pipeline

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

@@ -11,6 +11,7 @@ from azureml.train.automl.runtime.automl_explain_utilities import AutoMLExplaine
 from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
 from azureml.explain.model.mimic_wrapper import MimicWrapper
 from automl.client.core.common.constants import MODEL_PATH
+from azureml.automl.core.shared.constants import MODEL_EXPLANATION_TAG
 from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save
 
 
@@ -22,7 +23,7 @@ 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
@@ -60,16 +61,18 @@ explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMEx
                          classes=automl_explainer_setup_obj.classes)
 
 # Compute the engineered explanations
-engineered_explanations = explainer.explain(['local', 'global'],
+engineered_explanations = explainer.explain(['local', 'global'], tag='engineered explanations',
                                             eval_dataset=automl_explainer_setup_obj.X_test_transform)
 
 # Compute the raw explanations
-raw_explanations = explainer.explain(['local', 'global'], get_raw=True,
+raw_explanations = explainer.explain(['local', 'global'], get_raw=True, tag='raw explanations',
                                      raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
                                      eval_dataset=automl_explainer_setup_obj.X_test_transform)
 
-print("Engineered and raw explanations computed successfully")
+# Set tag that explanations completed
+automl_run.tag(MODEL_EXPLANATION_TAG, 'True')
 
+print("Engineered and raw explanations computed successfully")
 
 # Initialize the ScoringExplainer
 scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])

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

@@ -1,11 +0,0 @@
-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/auto-ml-regression.ipynb

@@ -79,6 +79,23 @@
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This sample notebook may use features that are not available in previous versions of the Azure ML SDK."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"This notebook was created using version 1.3.0 of the Azure ML SDK\")\n",
+        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -93,7 +110,6 @@
         "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",
@@ -122,7 +138,7 @@
         "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
         "# Choose a name for your CPU cluster\n",
-        "cpu_cluster_name = \"cpu-cluster-2\"\n",
+        "cpu_cluster_name = \"reg-cluster\"\n",
         "\n",
         "# Verify that cluster does not exist already\n",
         "try:\n",

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

@@ -2,6 +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/automl/automl-databricks-local-with-deployment.ipynb

@@ -512,9 +512,11 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Retrieve the Best Model after the above run is complete \n",
+        "## Deploy\n",
         "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
+        "### Retrieve the Best Model\n",
+        "\n",
+        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
       ]
     },
     {
@@ -523,17 +525,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "best_run, fitted_model = local_run.get_output()\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
+        "best_run, fitted_model = local_run.get_output()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
-        "Show the run and the model that has the smallest `log_loss` value:"
+        "### Download the conda environment file\n",
+        "From the *best_run* download the conda environment file that was used to train the AutoML model."
       ]
     },
     {
@@ -542,10 +542,34 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "lookup_metric = \"log_loss\"\n",
-        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
-        "print(best_run)\n",
-        "print(fitted_model)"
+        "from automl.client.core.common import constants\n",
+        "conda_env_file_name = 'conda_env.yml'\n",
+        "best_run.download_file(name=\"outputs/conda_env_v_1_0_0.yml\", output_file_path=conda_env_file_name)\n",
+        "with open(conda_env_file_name, \"r\") as conda_file:\n",
+        "    conda_file_contents = conda_file.read()\n",
+        "    print(conda_file_contents)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Download the model scoring file\n",
+        "From the *best_run* download the scoring file to get the predictions from the AutoML model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from automl.client.core.common import constants\n",
+        "script_file_name = 'scoring_file.py'\n",
+        "best_run.download_file(name=\"outputs/scoring_file_v_1_0_0.py\", output_file_path=script_file_name)\n",
+        "with open(script_file_name, \"r\") as scoring_file:\n",
+        "    scoring_file_contents = scoring_file.read()\n",
+        "    print(scoring_file_contents)"
       ]
     },
     {
@@ -572,8 +596,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Create Scoring Script\n",
-        "Replace model_id with name of model from output of above register cell"
+        "### Deploy the model as a Web Service on Azure Container Instance\n",
+        "\n",
+        "Create the configuration needed for deploying the model as a web service service."
       ]
     },
     {
@@ -582,123 +607,17 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "%%writefile score.py\n",
-        "import pickle\n",
-        "import json\n",
-        "import numpy as np\n",
-        "import azureml.train.automl\n",
-        "from sklearn.externals import joblib\n",
-        "from azureml.core.model import Model\n",
-        "import pandas as pd\n",
-        "\n",
-        "def init():\n",
-        "    global model\n",
-        "    model_path = Model.get_model_path(model_name = '<<model_id>>') # this name is model.id of model that we want to deploy\n",
-        "    # deserialize the model file back into a sklearn model\n",
-        "    model = joblib.load(model_path)\n",
-        "\n",
-        "def run(raw_data):\n",
-        "    try:\n",
-        "        data = (pd.DataFrame(np.array(json.loads(raw_data)['data']), columns=[str(i) for i in range(0,64)]))\n",
-        "        result = model.predict(data)\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return json.dumps({\"error\": result})\n",
-        "    return json.dumps({\"result\":result.tolist()})"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "#Replace <<model_id>>\n",
-        "content = \"\"\n",
-        "with open(\"score.py\", \"r\") as fo:\n",
-        "    content = fo.read()\n",
-        "\n",
-        "new_content = content.replace(\"<<model_id>>\", local_run.model_id)\n",
-        "with open(\"score.py\", \"w\") as fw:\n",
-        "    fw.write(new_content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create a YAML File for the Environment"
-      ]
-    },
-    {
-      "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'], pip_packages=['azureml-defaults', 'azureml-sdk[automl]'])\n",
-        "\n",
-        "conda_env_file_name = 'myenv.yml'\n",
-        "myenv.save_to_file('.', conda_env_file_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy the model as a Web Service on Azure Container Instance\n",
-        "Replace servicename with any meaningful name of service"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# this will take 10-15 minutes to finish\n",
-        "\n",
-        "from azureml.core.webservice import AciWebservice, Webservice\n",
-        "from azureml.exceptions import WebserviceException\n",
         "from azureml.core.model import InferenceConfig\n",
-        "from azureml.core.model import Model\n",
+        "from azureml.core.webservice import AciWebservice\n",
         "from azureml.core.environment import Environment\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import uuid\n",
         "\n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=conda_env_file_name)\n",
+        "inference_config = InferenceConfig(entry_script=script_file_name, environment=myenv)\n",
         "\n",
-        "myaci_config = AciWebservice.deploy_configuration(\n",
-        "    cpu_cores = 2, \n",
-        "    memory_gb = 2, \n",
-        "    tags = {'name':'Databricks Azure ML ACI'}, \n",
-        "    description = 'This is for ADB and AutoML example.')\n",
-        "\n",
-        "myenv = Environment.get(ws, name='AzureML-PySpark-MmlSpark-0.15')\n",
-        "# we need to add extra packages to procured environment\n",
-        "# in order to deploy amended environment we need to rename it\n",
-        "myenv.name = 'myenv'\n",
-        "model_dependencies = CondaDependencies('myenv.yml')\n",
-        "for pip_dep in model_dependencies.pip_packages:\n",
-        "    myenv.python.conda_dependencies.add_pip_package(pip_dep)\n",
-        "for conda_dep in model_dependencies.conda_packages:\n",
-        "    myenv.python.conda_dependencies.add_conda_package(conda_dep)\n",
-        "inference_config = InferenceConfig(entry_script='score_sparkml.py', environment=myenv)\n",
-        "\n",
-        "guid = str(uuid.uuid4()).split(\"-\")[0]\n",
-        "service_name = \"myservice-{}\".format(guid)\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",
-        "print(\"Creating service with name: {}\".format(service_name))\n",
-        "\n",
-        "myservice = Model.deploy(ws, service_name, [model], inference_config, myaci_config)\n",
-        "myservice.wait_for_deployment(show_output=True)"
+        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+        "                                               memory_gb = 1, \n",
+        "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
+        "                                               description = 'sample service for Automl Classification')"
       ]
     },
     {
@@ -707,8 +626,14 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "#for using the Web HTTP API \n",
-        "print(myservice.scoring_uri)"
+        "from azureml.core.webservice import Webservice\n",
+        "from azureml.core.model import Model\n",
+        "\n",
+        "aci_service_name = 'automl-databricks-local'\n",
+        "print(aci_service_name)\n",
+        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "aci_service.wait_for_deployment(True)\n",
+        "print(aci_service.state)"
       ]
     },
     {
@@ -752,7 +677,7 @@
         "for index in np.random.choice(len(y_test), 2, replace = False):\n",
         "    print(index)\n",
         "    test_sample = json.dumps({'data':X_test[index:index + 1].values.tolist()})\n",
-        "    predicted = myservice.run(input_data = test_sample)\n",
+        "    predicted = aci_service.run(input_data = test_sample)\n",
         "    label = y_test.values[index]\n",
         "    predictedDict = json.loads(predicted)\n",
         "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])    \n",

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-multi-model/multi-model-register-and-deploy.ipynb

@@ -285,7 +285,7 @@
         "from azureml.exceptions import WebserviceException\n",
         "\n",
         "deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
-        "aci_service_name = 'aciservice1'\n",
+        "aci_service_name = 'aciservice-multimodel'\n",
         "\n",
         "try:\n",
         "    # if you want to get existing service below is the command\n",

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

@@ -341,9 +341,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import json\n",
-        "\n",
-        "\n",
         "input_payload = json.dumps({\n",
         "    'data': [\n",
         "        [ 0.03807591,  0.05068012,  0.06169621, 0.02187235, -0.0442235,\n",
@@ -376,16 +373,118 @@
       "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": "markdown",
+      "metadata": {},
+      "source": [
+        "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n",
+        "You can try get the dataset first to see if it's already registered."
+      ]
+    },
+    {
+      "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",
+        "dataset_name='diabetes_sample_request_data'\n",
+        "\n",
+        "dataset_registered = False\n",
+        "try:\n",
+        "    sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n",
+        "    dataset_registered = True\n",
+        "except:\n",
+        "    print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n",
+        "\n",
+        "if not dataset_registered:\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 = \"{}.txt\".format(dataset_name)\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=dataset_name,\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()"
       ]
     },
     {
@@ -430,7 +529,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "category": "deployment",

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/onnx/onnx-model-register-and-deploy.ipynb

@@ -202,7 +202,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "kernelspec": {

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": "vaidyas"
+      }
+    ],
+    "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 +198,124 @@
         "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": "markdown",
+      "metadata": {},
+      "source": [
+        "You may want to register datasets using the register() method to your workspace so they can be shared with others, reused and referred to by name in your script.\n",
+        "You can try get the dataset first to see if it's already registered."
+      ]
+    },
+    {
+      "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",
+        "dataset_name='sample_request_data'\n",
+        "\n",
+        "dataset_registered = False\n",
+        "try:\n",
+        "    sample_request_data = Dataset.get_by_name(workspace = ws, name = dataset_name)\n",
+        "    dataset_registered = True\n",
+        "except:\n",
+        "    print(\"The dataset {} is not registered in workspace yet.\".format(dataset_name))\n",
+        "\n",
+        "if not dataset_registered:\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 = \"{}.txt\".format(dataset_name)\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=dataset_name,\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": {},
@@ -460,7 +578,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "kernelspec": {

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

@@ -302,7 +302,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "category": "deployment",

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

@@ -234,7 +234,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "aashishb"
+        "name": "vaidyas"
       }
     ],
     "kernelspec": {

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

@@ -687,12 +687,12 @@
       "source": [
         "## Next\n",
         "Learn about other use cases of the explain package on a:\n",
-        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
-        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n",
         "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n",
         "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
         "1. Inferencing time: deploy a classification model and explainer:\n",
         "    1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",

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

@@ -2,8 +2,9 @@ name: explain-model-on-amlcompute
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-interpret
+  - interpret-community[visualization]
+  - matplotlib
   - azureml-contrib-interpret
   - sklearn-pandas
   - azureml-dataprep

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

@@ -582,12 +582,12 @@
       "source": [
         "## Next\n",
         "Learn about other use cases of the explain package on a:\n",
-        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
-        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n",
         "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n",
         "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
         "1. Inferencing time: deploy a classification model and explainer:\n",
         "    1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",

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

@@ -2,7 +2,8 @@ name: save-retrieve-explanations-run-history
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-interpret
+  - interpret-community[visualization]
+  - matplotlib
   - azureml-contrib-interpret
   - ipywidgets

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

@@ -445,12 +445,12 @@
       "source": [
         "## Next\n",
         "Learn about other use cases of the explain package on a:\n",
-        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
-        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n",
         "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n",
         "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
         "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
         "1. [Inferencing time: deploy a remotely-trained model and explainer](./train-explain-model-on-amlcompute-and-deploy.ipynb)"

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

@@ -2,8 +2,9 @@ name: train-explain-model-locally-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-interpret
+  - interpret-community[visualization]
+  - matplotlib
   - azureml-contrib-interpret
   - sklearn-pandas
   - ipywidgets

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",
@@ -493,16 +483,15 @@
       "source": [
         "## Next\n",
         "Learn about other use cases of the explain package on a:\n",
-        "1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb)       \n",
-        "1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
-        "1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
+        "1. [Training time: regression problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-regression-local.ipynb)       \n",
+        "1. [Training time: binary classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-binary-classification-local.ipynb)\n",
+        "1. [Training time: multiclass classification problem](https://github.com/interpretml/interpret-community/blob/master/notebooks/explain-multiclass-classification-local.ipynb)\n",
         "1. Explain models with engineered features:\n",
-        "    1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
-        "    1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Simple feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/simple-feature-transformations-explain-local.ipynb)\n",
+        "    1. [Advanced feature transformations](https://github.com/interpretml/interpret-community/blob/master/notebooks/advanced-feature-transformations-explain-local.ipynb)\n",
         "1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
         "1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
-        "1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)\n",
-        "   "
+        "1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)"
       ]
     },
     {

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

@@ -2,8 +2,9 @@ name: train-explain-model-on-amlcompute-and-deploy
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-interpret
+  - interpret-community[visualization]
+  - matplotlib
   - azureml-contrib-interpret
   - sklearn-pandas
   - azureml-dataprep

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-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)"
@@ -105,7 +105,7 @@
       "metadata": {},
       "source": [
         "## Create an Azure ML experiment\n",
-        "Let's create an experiment named \"automl-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
+        "Let's create an experiment named \"automlstep-classification\" and a folder to hold the training scripts. The script runs will be recorded under the experiment in Azure.\n",
         "\n",
         "The best practice is to use separate folders for scripts and its dependent files for each step and specify that folder as the `source_directory` for the step. This helps reduce the size of the snapshot created for the step (only the specific folder is snapshotted). Since changes in any files in the `source_directory` would trigger a re-upload of the snapshot, this helps keep the reuse of the step when there are no changes in the `source_directory` of the step."
       ]
@@ -165,25 +165,6 @@
         "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "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",
-        "conda_run_config.environment.python.conda_dependencies = cd\n",
-        "\n",
-        "print('run config is ready')"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -197,19 +178,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
-        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
-        "dataset.to_pandas_dataframe().describe()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "dataset.take(5).to_pandas_dataframe()"
+        "# Try to load the dataset from the Workspace. Otherwise, create it from the file\n",
+        "found = False\n",
+        "key = \"Crime-Dataset\"\n",
+        "description_text = \"Crime Dataset (used in the the aml-pipelines-with-automated-machine-learning-step.ipynb notebook)\"\n",
+        "\n",
+        "if key in ws.datasets.keys(): \n",
+        "        found = True\n",
+        "        dataset = ws.datasets[key] \n",
+        "\n",
+        "if not found:\n",
+        "        # Create AML Dataset and register it into Workspace\n",
+        "        # The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
+        "        example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
+        "        dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
+        "        dataset = dataset.drop_columns(['FBI Code'])\n",
+        "        \n",
+        "        #Register Dataset in Workspace\n",
+        "        dataset = dataset.register(workspace=ws,\n",
+        "                                   name=key,\n",
+        "                                   description=description_text)\n",
+        "\n",
+        "\n",
+        "df = dataset.to_pandas_dataframe()\n",
+        "df.describe()"
       ]
     },
     {
@@ -229,9 +221,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "X = dataset.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
-        "y = dataset.keep_columns(columns=['Primary Type'], validate=True)\n",
-        "print('X and y are ready!')"
+        "dataset.take(5).to_pandas_dataframe()"
       ]
     },
     {
@@ -249,19 +239,18 @@
       "outputs": [],
       "source": [
         "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\" : 5,\n",
-        "    \"iterations\" : 2,\n",
-        "    \"primary_metric\" : 'AUC_weighted',\n",
-        "    \"preprocess\" : True,\n",
-        "    \"verbosity\" : logging.INFO\n",
+        "    \"experiment_timeout_minutes\": 20,\n",
+        "    \"max_concurrent_iterations\": 4,\n",
+        "    \"primary_metric\" : 'AUC_weighted'\n",
         "}\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
+        "automl_config = AutoMLConfig(compute_target=compute_target,\n",
+        "                             task = \"classification\",\n",
+        "                             training_data=dataset,\n",
+        "                             label_column_name=\"Primary Type\",   \n",
         "                             path = project_folder,\n",
-        "                             compute_target=compute_target,\n",
-        "                             run_configuration=conda_run_config,\n",
-        "                             X = X,\n",
-        "                             y = y,\n",
+        "                             enable_early_stopping= True,\n",
+        "                             featurization= 'auto',\n",
+        "                             debug_log = \"automl_errors.log\",\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -270,6 +259,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
+        "#### Create Pipeline and AutoMLStep\n",
+        "\n",
         "You can define outputs for the AutoMLStep using TrainingOutput."
       ]
     },
@@ -305,7 +296,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "automlstep-remarks-sample1"
+        ]
+      },
       "outputs": [],
       "source": [
         "automl_step = AutoMLStep(\n",
@@ -318,7 +313,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "tags": [
+          "automlstep-remarks-sample2"
+        ]
+      },
       "outputs": [],
       "source": [
         "from azureml.pipeline.core import Pipeline\n",
@@ -383,8 +382,8 @@
       "outputs": [],
       "source": [
         "import json\n",
-        "with open(metrics_output._path_on_datastore) as f:  \n",
-        "   metrics_output_result = f.read()\n",
+        "with open(metrics_output._path_on_datastore) as f:\n",
+        "    metrics_output_result = f.read()\n",
         "    \n",
         "deserialized_metrics_output = json.loads(metrics_output_result)\n",
         "df = pd.DataFrame(deserialized_metrics_output)\n",
@@ -404,6 +403,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# Retrieve best model from Pipeline Run\n",
         "best_model_output = pipeline_run.get_pipeline_output(best_model_output_name)\n",
         "num_file_downloaded = best_model_output.download('.', show_progress=True)"
       ]
@@ -421,6 +421,15 @@
         "best_model"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "best_model.steps"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -436,11 +445,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dataset = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
+        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
         "df_test = dataset_test.to_pandas_dataframe()\n",
-        "df_test = df_test[pd.notnull(df['Primary Type'])]\n",
+        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
         "\n",
-        "y_test = df_test[['Primary Type']]\n",
+        "y_test = df_test['Primary Type']\n",
         "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
       ]
     },
@@ -459,15 +468,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from pandas_ml import ConfusionMatrix\n",
-        "\n",
+        "from sklearn.metrics import confusion_matrix\n",
         "ypred = best_model.predict(X_test)\n",
-        "\n",
-        "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
-        "\n",
-        "print(cm)\n",
-        "\n",
-        "cm.plot()"
+        "cm = confusion_matrix(y_test, ypred)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Visualize the confusion matrix\n",
+        "pd.DataFrame(cm).style.background_gradient(cmap='Blues', low=0, high=0.9)"
       ]
     }
   ],

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",
@@ -354,6 +352,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.widgets import RunDetails\n",
         "RunDetails(pipeline_run1).show()"
       ]
     },

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb

@@ -16,16 +16,12 @@
         "\n",
         "You can combine the two part tutorial into one using AzureML Pipelines as Pipelines provide a way to stitch together various steps involved (like data preparation and training in this case) in a machine learning workflow.\n",
         "\n",
-        "In this notebook, you learn how to prepare data for regression modeling by using the [Azure Machine Learning Data Prep SDK](https://aka.ms/data-prep-sdk) for Python. You run various transformations to filter and combine two different NYC taxi data sets. Once you prepare the NYC taxi data for regression modeling, then you will use [AutoMLStep](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.automlstep?view=azure-ml-py) available with [Azure Machine Learning Pipelines](https://aka.ms/aml-pipelines) to define your machine learning goals and constraints as well as to launch the automated machine learning process. The automated machine learning technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n",
+        "In this notebook, you learn how to prepare data for regression modeling by using open source library [pandas](https://pandas.pydata.org/). You run various transformations to filter and combine two different NYC taxi datasets. Once you prepare the NYC taxi data for regression modeling, then you will use [AutoMLStep](https://docs.microsoft.com/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automl_step.automlstep?view=azure-ml-py) available with [Azure Machine Learning Pipelines](https://aka.ms/aml-pipelines) to define your machine learning goals and constraints as well as to launch the automated machine learning process. The automated machine learning technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n",
         "\n",
         "After you complete building the model, you can predict the cost of a taxi trip by training a model on data features. These features include the pickup day and time, the number of passengers, and the pickup location.\n",
         "\n",
         "## Prerequisite\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.\n",
-        "\n",
-        "We will run various transformations to filter and combine two different NYC taxi data sets. We will use DataPrep SDK for this preparing data. \n",
-        "\n",
-        "Perform `pip install azureml-dataprep` if you have't already done so."
+        "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."
       ]
     },
     {
@@ -108,7 +104,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "import azureml.dataprep as dprep\n",
         "from IPython.display import display\n",
         "\n",
         "display(green_df_raw.head(5))\n",
@@ -144,8 +139,8 @@
         "if not os.path.exists(yelloDir):\n",
         "    os.mkdir(yelloDir)\n",
         "    \n",
-        "greenTaxiData = greenDir + \"/part-00000\"\n",
-        "yellowTaxiData = yelloDir + \"/part-00000\"\n",
+        "greenTaxiData = greenDir + \"/unprepared.parquet\"\n",
+        "yellowTaxiData = yelloDir + \"/unprepared.parquet\"\n",
         "\n",
         "green_df_raw.to_csv(greenTaxiData, index=False)\n",
         "yellow_df_raw.to_csv(yellowTaxiData, index=False)\n",
@@ -169,17 +164,54 @@
         "\n",
         "default_store.upload_files([greenTaxiData], \n",
         "                           target_path = 'green', \n",
-        "                           overwrite = False, \n",
+        "                           overwrite = True, \n",
         "                           show_progress = True)\n",
         "\n",
         "default_store.upload_files([yellowTaxiData], \n",
         "                           target_path = 'yellow', \n",
-        "                           overwrite = False, \n",
+        "                           overwrite = True, \n",
         "                           show_progress = True)\n",
         "\n",
         "print(\"Upload calls completed.\")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Create and register datasets\n",
+        "\n",
+        "By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. You can learn more about the what subsetting capabilities are supported by referring to [our documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset?view=azure-ml-py#remarks). The data remains in its existing location, so no extra storage cost is incurred."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core import Dataset\n",
+        "green_taxi_data = Dataset.Tabular.from_delimited_files(default_store.path('green/unprepared.parquet'))\n",
+        "yellow_taxi_data = Dataset.Tabular.from_delimited_files(default_store.path('yellow/unprepared.parquet'))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Register the taxi datasets with the workspace so that you can reuse them in other experiments or share with your colleagues who have access to your workspace."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "green_taxi_data = green_taxi_data.register(ws, 'green_taxi_data')\n",
+        "yellow_taxi_data = yellow_taxi_data.register(ws, 'yellow_taxi_data')"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -194,20 +226,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
+        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+        "from azureml.core.compute_target import ComputeTargetException\n",
         "\n",
-        "aml_compute = ws.get_default_compute_target(\"CPU\")\n",
+        "# Choose a name for your CPU cluster\n",
+        "amlcompute_cluster_name = \"cpu-cluster\"\n",
         "\n",
-        "if aml_compute is None:\n",
-        "    amlcompute_cluster_name = \"cpu-cluster\"\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\",\n",
-        "                                                                max_nodes = 4)\n",
+        "# Verify that cluster does not exist already\n",
+        "try:\n",
+        "    aml_compute = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n",
+        "    print('Found existing cluster, use it.')\n",
+        "except ComputeTargetException:\n",
+        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
+        "                                                           max_nodes=4)\n",
+        "    aml_compute = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n",
         "\n",
-        "    aml_compute = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    aml_compute.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "aml_compute"
+        "aml_compute.wait_for_completion(show_output=True)"
       ]
     },
     {
@@ -215,7 +249,7 @@
       "metadata": {},
       "source": [
         "#### Define RunConfig for the compute\n",
-        "We need `azureml-dataprep` SDK for all the steps below. We will also use `pandas`, `scikit-learn` and `automl` for the training step. Defining the `runconfig` for that."
+        "We will also use `pandas`, `scikit-learn` and `automl`, `pyarrow` for the pipeline steps. Defining the `runconfig` for that."
       ]
     },
     {
@@ -242,13 +276,10 @@
         "# Use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
         "aml_run_config.environment.python.user_managed_dependencies = False\n",
         "\n",
-        "# Auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
-        "aml_run_config.auto_prepare_environment = True\n",
-        "\n",
         "# Specify CondaDependencies obj, add necessary packages\n",
         "aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
         "    conda_packages=['pandas','scikit-learn'], \n",
-        "    pip_packages=['azureml-sdk', 'azureml-dataprep', 'azureml-train-automl'], \n",
+        "    pip_packages=['azureml-sdk[automl,explain]', 'pyarrow'], \n",
         "    pin_sdk_version=False)\n",
         "\n",
         "print (\"Run configuration created.\")"
@@ -259,7 +290,7 @@
       "metadata": {},
       "source": [
         "### Prepare data\n",
-        "Now we will prepare for regression modeling by using the `Azure Machine Learning Data Prep SDK for Python`. We run various transformations to filter and combine two different NYC taxi data sets.\n",
+        "Now we will prepare for regression modeling by using `pandas`. We run various transformations to filter and combine two different NYC taxi datasets.\n",
         "\n",
         "We achieve this by creating a separate step for each transformation as this allows us to reuse the steps and saves us from running all over again in case of any change. We will keep data preparation scripts in one subfolder and training scripts in another.\n",
         "\n",
@@ -270,7 +301,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Define Useful Colums\n",
+        "#### Define Useful Columns\n",
         "Here we are defining a set of \"useful\" columns for both Green and Yellow taxi data."
       ]
     },
@@ -304,18 +335,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.data.data_reference import DataReference \n",
         "from azureml.pipeline.core import PipelineData\n",
         "from azureml.pipeline.steps import PythonScriptStep\n",
         "\n",
         "# python scripts folder\n",
         "prepare_data_folder = './scripts/prepdata'\n",
         "\n",
-        "blob_green_data = DataReference(\n",
-        "    datastore=default_store,\n",
-        "    data_reference_name=\"green_taxi_data\",\n",
-        "    path_on_datastore=\"green/part-00000\")\n",
-        "\n",
         "# rename columns as per Azure Machine Learning NYC Taxi tutorial\n",
         "green_columns = str({ \n",
         "    \"vendorID\": \"vendor\",\n",
@@ -332,7 +357,7 @@
         "}).replace(\",\", \";\")\n",
         "\n",
         "# Define output after cleansing step\n",
-        "cleansed_green_data = PipelineData(\"green_taxi_data\", datastore=default_store)\n",
+        "cleansed_green_data = PipelineData(\"cleansed_green_data\", datastore=default_store).as_dataset()\n",
         "\n",
         "print('Cleanse script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n",
         "\n",
@@ -341,11 +366,10 @@
         "cleansingStepGreen = PythonScriptStep(\n",
         "    name=\"Cleanse Green Taxi Data\",\n",
         "    script_name=\"cleanse.py\", \n",
-        "    arguments=[\"--input_cleanse\", blob_green_data, \n",
-        "               \"--useful_columns\", useful_columns,\n",
+        "    arguments=[\"--useful_columns\", useful_columns,\n",
         "               \"--columns\", green_columns,\n",
         "               \"--output_cleanse\", cleansed_green_data],\n",
-        "    inputs=[blob_green_data],\n",
+        "    inputs=[green_taxi_data.as_named_input('raw_data')],\n",
         "    outputs=[cleansed_green_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig=aml_run_config,\n",
@@ -369,11 +393,6 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "blob_yellow_data = DataReference(\n",
-        "    datastore=default_store,\n",
-        "    data_reference_name=\"yellow_taxi_data\",\n",
-        "    path_on_datastore=\"yellow/part-00000\")\n",
-        "\n",
         "yellow_columns = str({\n",
         "    \"vendorID\": \"vendor\",\n",
         "    \"tpepPickupDateTime\": \"pickup_datetime\",\n",
@@ -389,7 +408,7 @@
         "}).replace(\",\", \";\")\n",
         "\n",
         "# Define output after cleansing step\n",
-        "cleansed_yellow_data = PipelineData(\"yellow_taxi_data\", datastore=default_store)\n",
+        "cleansed_yellow_data = PipelineData(\"cleansed_yellow_data\", datastore=default_store).as_dataset()\n",
         "\n",
         "print('Cleanse script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n",
         "\n",
@@ -398,11 +417,10 @@
         "cleansingStepYellow = PythonScriptStep(\n",
         "    name=\"Cleanse Yellow Taxi Data\",\n",
         "    script_name=\"cleanse.py\", \n",
-        "    arguments=[\"--input_cleanse\", blob_yellow_data, \n",
-        "               \"--useful_columns\", useful_columns,\n",
+        "    arguments=[\"--useful_columns\", useful_columns,\n",
         "               \"--columns\", yellow_columns,\n",
         "               \"--output_cleanse\", cleansed_yellow_data],\n",
-        "    inputs=[blob_yellow_data],\n",
+        "    inputs=[yellow_taxi_data.as_named_input('raw_data')],\n",
         "    outputs=[cleansed_yellow_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig=aml_run_config,\n",
@@ -428,7 +446,7 @@
       "outputs": [],
       "source": [
         "# Define output after merging step\n",
-        "merged_data = PipelineData(\"merged_data\", datastore=default_store)\n",
+        "merged_data = PipelineData(\"merged_data\", datastore=default_store).as_dataset()\n",
         "\n",
         "print('Merge script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n",
         "\n",
@@ -437,10 +455,9 @@
         "mergingStep = PythonScriptStep(\n",
         "    name=\"Merge Taxi Data\",\n",
         "    script_name=\"merge.py\", \n",
-        "    arguments=[\"--input_green_merge\", cleansed_green_data, \n",
-        "               \"--input_yellow_merge\", cleansed_yellow_data,\n",
-        "               \"--output_merge\", merged_data],\n",
-        "    inputs=[cleansed_green_data, cleansed_yellow_data],\n",
+        "    arguments=[\"--output_merge\", merged_data],\n",
+        "    inputs=[cleansed_green_data.parse_parquet_files(file_extension=None),\n",
+        "            cleansed_yellow_data.parse_parquet_files(file_extension=None)],\n",
         "    outputs=[merged_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig=aml_run_config,\n",
@@ -466,7 +483,7 @@
       "outputs": [],
       "source": [
         "# Define output after merging step\n",
-        "filtered_data = PipelineData(\"filtered_data\", datastore=default_store)\n",
+        "filtered_data = PipelineData(\"filtered_data\", datastore=default_store).as_dataset()\n",
         "\n",
         "print('Filter script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n",
         "\n",
@@ -475,9 +492,8 @@
         "filterStep = PythonScriptStep(\n",
         "    name=\"Filter Taxi Data\",\n",
         "    script_name=\"filter.py\", \n",
-        "    arguments=[\"--input_filter\", merged_data, \n",
-        "               \"--output_filter\", filtered_data],\n",
-        "    inputs=[merged_data],\n",
+        "    arguments=[\"--output_filter\", filtered_data],\n",
+        "    inputs=[merged_data.parse_parquet_files(file_extension=None)],\n",
         "    outputs=[filtered_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -503,7 +519,7 @@
       "outputs": [],
       "source": [
         "# Define output after normalize step\n",
-        "normalized_data = PipelineData(\"normalized_data\", datastore=default_store)\n",
+        "normalized_data = PipelineData(\"normalized_data\", datastore=default_store).as_dataset()\n",
         "\n",
         "print('Normalize script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n",
         "\n",
@@ -512,9 +528,8 @@
         "normalizeStep = PythonScriptStep(\n",
         "    name=\"Normalize Taxi Data\",\n",
         "    script_name=\"normalize.py\", \n",
-        "    arguments=[\"--input_normalize\", filtered_data, \n",
-        "               \"--output_normalize\", normalized_data],\n",
-        "    inputs=[filtered_data],\n",
+        "    arguments=[\"--output_normalize\", normalized_data],\n",
+        "    inputs=[filtered_data.parse_parquet_files(file_extension=None)],\n",
         "    outputs=[normalized_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -544,8 +559,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Define output after transforme step\n",
-        "transformed_data = PipelineData(\"transformed_data\", datastore=default_store)\n",
+        "# Define output after transform step\n",
+        "transformed_data = PipelineData(\"transformed_data\", datastore=default_store).as_dataset()\n",
         "\n",
         "print('Transform script is in {}.'.format(os.path.realpath(prepare_data_folder)))\n",
         "\n",
@@ -554,9 +569,8 @@
         "transformStep = PythonScriptStep(\n",
         "    name=\"Transform Taxi Data\",\n",
         "    script_name=\"transform.py\", \n",
-        "    arguments=[\"--input_transform\", normalized_data,\n",
-        "               \"--output_transform\", transformed_data],\n",
-        "    inputs=[normalized_data],\n",
+        "    arguments=[\"--output_transform\", transformed_data],\n",
+        "    inputs=[normalized_data.parse_parquet_files(file_extension=None)],\n",
         "    outputs=[transformed_data],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
@@ -571,8 +585,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Extract features\n",
-        "Add the following columns to be features for our model creation. The prediction value will be *cost*."
+        "### Split the data into train and test sets\n",
+        "This function segregates the data into dataset for model training and dataset for testing."
       ]
     },
     {
@@ -581,92 +595,11 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "feature_columns = str(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor']).replace(\",\", \";\")\n",
-        "\n",
         "train_model_folder = './scripts/trainmodel'\n",
         "\n",
-        "print('Extract script is in {}.'.format(os.path.realpath(train_model_folder)))\n",
-        "\n",
-        "# features data after transform step\n",
-        "features_data = PipelineData(\"features_data\", datastore=default_store)\n",
-        "\n",
-        "# featurization step creation\n",
-        "# See the featurization.py for details about input and output\n",
-        "featurizationStep = PythonScriptStep(\n",
-        "    name=\"Extract Features\",\n",
-        "    script_name=\"featurization.py\", \n",
-        "    arguments=[\"--input_featurization\", transformed_data, \n",
-        "               \"--useful_columns\", feature_columns,\n",
-        "               \"--output_featurization\", features_data],\n",
-        "    inputs=[transformed_data],\n",
-        "    outputs=[features_data],\n",
-        "    compute_target=aml_compute,\n",
-        "    runconfig = aml_run_config,\n",
-        "    source_directory=train_model_folder,\n",
-        "    allow_reuse=True\n",
-        ")\n",
-        "\n",
-        "print(\"featurizationStep created.\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Extract label"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "label_columns = str(['cost']).replace(\",\", \";\")\n",
-        "\n",
-        "# label data after transform step\n",
-        "label_data = PipelineData(\"label_data\", datastore=default_store)\n",
-        "\n",
-        "print('Extract script is in {}.'.format(os.path.realpath(train_model_folder)))\n",
-        "\n",
-        "# label step creation\n",
-        "# See the featurization.py for details about input and output\n",
-        "labelStep = PythonScriptStep(\n",
-        "    name=\"Extract Labels\",\n",
-        "    script_name=\"featurization.py\", \n",
-        "    arguments=[\"--input_featurization\", transformed_data, \n",
-        "               \"--useful_columns\", label_columns,\n",
-        "               \"--output_featurization\", label_data],\n",
-        "    inputs=[transformed_data],\n",
-        "    outputs=[label_data],\n",
-        "    compute_target=aml_compute,\n",
-        "    runconfig = aml_run_config,\n",
-        "    source_directory=train_model_folder,\n",
-        "    allow_reuse=True\n",
-        ")\n",
-        "\n",
-        "print(\"labelStep created.\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Split the data into train and test sets\n",
-        "This function segregates the data into the **x**, features, dataset for model training and **y**, values to predict, dataset for testing."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
         "# train and test splits output\n",
-        "output_split_train_x = PipelineData(\"output_split_train_x\", datastore=default_store)\n",
-        "output_split_train_y = PipelineData(\"output_split_train_y\", datastore=default_store)\n",
-        "output_split_test_x = PipelineData(\"output_split_test_x\", datastore=default_store)\n",
-        "output_split_test_y = PipelineData(\"output_split_test_y\", datastore=default_store)\n",
+        "output_split_train = PipelineData(\"output_split_train\", datastore=default_store).as_dataset()\n",
+        "output_split_test = PipelineData(\"output_split_test\", datastore=default_store).as_dataset()\n",
         "\n",
         "print('Data spilt script is in {}.'.format(os.path.realpath(train_model_folder)))\n",
         "\n",
@@ -675,14 +608,10 @@
         "testTrainSplitStep = PythonScriptStep(\n",
         "    name=\"Train Test Data Split\",\n",
         "    script_name=\"train_test_split.py\", \n",
-        "    arguments=[\"--input_split_features\", features_data, \n",
-        "               \"--input_split_labels\", label_data,\n",
-        "               \"--output_split_train_x\", output_split_train_x,\n",
-        "               \"--output_split_train_y\", output_split_train_y,\n",
-        "               \"--output_split_test_x\", output_split_test_x,\n",
-        "               \"--output_split_test_y\", output_split_test_y],\n",
-        "    inputs=[features_data, label_data],\n",
-        "    outputs=[output_split_train_x, output_split_train_y, output_split_test_x, output_split_test_y],\n",
+        "    arguments=[\"--output_split_train\", output_split_train,\n",
+        "               \"--output_split_test\", output_split_test],\n",
+        "    inputs=[transformed_data.parse_parquet_files(file_extension=None)],\n",
+        "    outputs=[output_split_train, output_split_test],\n",
         "    compute_target=aml_compute,\n",
         "    runconfig = aml_run_config,\n",
         "    source_directory=train_model_folder,\n",
@@ -697,7 +626,7 @@
       "metadata": {},
       "source": [
         "## Use automated machine learning to build regression model\n",
-        "Now we will use **automated machine learning** to build the regression model. We will use [AutoMLStep](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.automlstep?view=azure-ml-py) in AML Pipelines for this part. These functions use various features from the data set and allow an automated model to build relationships between the features and the price of a taxi trip."
+        "Now we will use **automated machine learning** to build the regression model. We will use [AutoMLStep](https://docs.microsoft.com/python/api/azureml-train-automl-runtime/azureml.train.automl.runtime.automl_step.automlstep?view=azure-ml-py) in AML Pipelines for this part. Perform `pip install azureml-sdk[automl]`to get the automated machine learning package. These functions use various features from the data set and allow an automated model to build relationships between the features and the price of a taxi trip."
       ]
     },
     {
@@ -727,52 +656,13 @@
         "print(\"Experiment created\")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create get_data script\n",
-        "\n",
-        "A script with `get_data()` function is necessary to fetch training features(X) and labels(Y) on remote compute, from input data. Here we use mounted path of `train_test_split` step to get the x and y train values. They are added as environment variable on compute machine by default\n",
-        "\n",
-        "Note: Every DataReference are added as environment variable on compute machine since the defualt mode is mount"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print('get_data.py will be written to {}.'.format(os.path.realpath(train_model_folder)))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile $train_model_folder/get_data.py\n",
-        "import os\n",
-        "import pandas as pd\n",
-        "\n",
-        "def get_data():\n",
-        "    print(\"In get_data\")\n",
-        "    print(os.environ['AZUREML_DATAREFERENCE_output_split_train_x'])\n",
-        "    X_train  = pd.read_csv(os.environ['AZUREML_DATAREFERENCE_output_split_train_x'] + \"/part-00000\", header=0)\n",
-        "    y_train  = pd.read_csv(os.environ['AZUREML_DATAREFERENCE_output_split_train_y'] + \"/part-00000\", header=0)\n",
-        "    \n",
-        "    return { \"X\" : X_train.values, \"y\" : y_train.values.flatten() }"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "#### Define settings for autogeneration and tuning\n",
         "\n",
-        "Here we define the experiment parameter and model settings for autogeneration and tuning. We can specify automl_settings as **kwargs as well. Also note that we have to use a get_data() function for remote excutions. See get_data script for more details.\n",
+        "Here we define the experiment parameter and model settings for autogeneration and tuning. We can specify automl_settings as **kwargs as well.\n",
         "\n",
         "Use your defined training settings as a parameter to an `AutoMLConfig` object. Additionally, specify your training data and the type of model, which is `regression` in this case.\n",
         "\n",
@@ -793,17 +683,20 @@
         "    \"iteration_timeout_minutes\" : 10,\n",
         "    \"iterations\" : 2,\n",
         "    \"primary_metric\" : 'spearman_correlation',\n",
-        "    \"preprocess\" : True,\n",
-        "    \"verbosity\" : logging.INFO,\n",
         "    \"n_cross_validations\": 5\n",
         "}\n",
         "\n",
+        "train_X = output_split_train.parse_parquet_files(file_extension=None).keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor'])\n",
+        "train_y = output_split_train.parse_parquet_files(file_extension=None).keep_columns('cost')\n",
+        "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
         "                             debug_log = 'automated_ml_errors.log',\n",
         "                             path = train_model_folder,\n",
-        "                             compute_target=aml_compute,\n",
-        "                             run_configuration=aml_run_config,\n",
-        "                             data_script = train_model_folder + \"/get_data.py\",\n",
+        "                             compute_target = aml_compute,\n",
+        "                             run_configuration = aml_run_config,\n",
+        "                             featurization = 'auto',\n",
+        "                             X = train_X,\n",
+        "                             y = train_y,\n",
         "                             **automl_settings)\n",
         "                             \n",
         "print(\"AutoML config created.\")"
@@ -822,15 +715,12 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl.runtime import AutoMLStep\n",
-        "\n",
-        "trainWithAutomlStep = AutoMLStep(\n",
-        "    name='AutoML_Regression',\n",
-        "    automl_config=automl_config,\n",
-        "    inputs=[output_split_train_x, output_split_train_y],\n",
-        "    allow_reuse=True,\n",
-        "    hash_paths=[os.path.realpath(train_model_folder)])\n",
+        "from azureml.pipeline.steps import AutoMLStep\n",
         "\n",
+        "trainWithAutomlStep = AutoMLStep(name='AutoML_Regression',\n",
+        "                                 automl_config=automl_config,\n",
+        "                                 passthru_automl_config=False,\n",
+        "                                 allow_reuse=True)\n",
         "print(\"trainWithAutomlStep created.\")"
       ]
     },
@@ -892,12 +782,11 @@
         "    return path\n",
         "\n",
         "def fetch_df(step, output_name):\n",
-        "    output_data = step.get_output_data(output_name)\n",
-        "    \n",
+        "    output_data = step.get_output_data(output_name)    \n",
         "    download_path = './outputs/' + output_name\n",
-        "    output_data.download(download_path)\n",
-        "    df_path = get_download_path(download_path, output_name) + '/part-00000'\n",
-        "    return dprep.auto_read_file(path=df_path)"
+        "    output_data.download(download_path, overwrite=True)\n",
+        "    df_path = get_download_path(download_path, output_name) + '/processed.parquet'\n",
+        "    return pd.read_parquet(df_path)"
       ]
     },
     {
@@ -939,7 +828,7 @@
         "merge_step = pipeline_run.find_step_run(mergingStep.name)[0]\n",
         "combined_df = fetch_df(merge_step, merged_data.name)\n",
         "\n",
-        "display(combined_df.get_profile())"
+        "display(combined_df.describe())"
       ]
     },
     {
@@ -958,7 +847,7 @@
         "filter_step = pipeline_run.find_step_run(filterStep.name)[0]\n",
         "filtered_df = fetch_df(filter_step, filtered_data.name)\n",
         "\n",
-        "display(filtered_df.get_profile())"
+        "display(filtered_df.describe())"
       ]
     },
     {
@@ -996,7 +885,7 @@
         "transform_step = pipeline_run.find_step_run(transformStep.name)[0]\n",
         "transformed_df = fetch_df(transform_step, transformed_data.name)\n",
         "\n",
-        "display(transformed_df.get_profile())\n",
+        "display(transformed_df.describe())\n",
         "display(transformed_df.head(5))"
       ]
     },
@@ -1014,16 +903,10 @@
       "outputs": [],
       "source": [
         "split_step = pipeline_run.find_step_run(testTrainSplitStep.name)[0]\n",
-        "train_split_x = fetch_df(split_step, output_split_train_x.name)\n",
-        "train_split_y = fetch_df(split_step, output_split_train_y.name)\n",
+        "train_split = fetch_df(split_step, output_split_train.name)\n",
         "\n",
-        "display_x_train = train_split_x.keep_columns(columns=[\"vendor\", \"pickup_weekday\", \"pickup_hour\", \"passengers\", \"distance\"])\n",
-        "display_y_train = train_split_y.rename_columns(column_pairs={\"Column1\": \"cost\"})\n",
-        "\n",
-        "display(display_x_train.get_profile())\n",
-        "display(display_x_train.head(5))\n",
-        "display(display_y_train.get_profile())\n",
-        "display(display_y_train.head(5))"
+        "display(train_split.describe())\n",
+        "display(train_split.head(5))"
       ]
     },
     {
@@ -1125,14 +1008,11 @@
       "source": [
         "# split_step = pipeline_run.find_step_run(testTrainSplitStep.name)[0]\n",
         "\n",
-        "# x_test = fetch_df(split_step, output_split_test_x.name)\n",
-        "# y_test = fetch_df(split_step, output_split_test_y.name)\n",
+        "# x_test = fetch_df(split_step, output_split_test.name)[['distance','passengers', 'vendor','pickup_weekday','pickup_hour']]\n",
+        "# y_test = fetch_df(split_step, output_split_test.name)[['cost']]\n",
         "\n",
-        "# display(x_test.keep_columns(columns=[\"vendor\", \"pickup_weekday\", \"pickup_hour\", \"passengers\", \"distance\"]).head(5))\n",
-        "# display(y_test.rename_columns(column_pairs={\"Column1\": \"cost\"}).head(5))\n",
-        "\n",
-        "# x_test = x_test.to_pandas_dataframe()\n",
-        "# y_test = y_test.to_pandas_dataframe()"
+        "# display(x_test.head(5))\n",
+        "# display(y_test.head(5))"
       ]
     },
     {
@@ -1150,9 +1030,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# y_predict = fitted_model.predict(x_test.values)\n",
+        "# y_predict = fitted_model.predict(x_test)\n",
         "\n",
-        "# y_actual =  y_test.iloc[:,0].values.tolist()\n",
+        "# y_actual =  y_test.values.tolist()\n",
         "\n",
         "# display(pd.DataFrame({'Actual':y_actual, 'Predicted':y_predict}).head(5))"
       ]
@@ -1168,7 +1048,7 @@
         "# fig = plt.figure(figsize=(14, 10))\n",
         "# ax1 = fig.add_subplot(111)\n",
         "\n",
-        "# distance_vals = [x[4] for x in x_test.values]\n",
+        "# distance_vals = [x[0] for x in x_test.values]\n",
         "\n",
         "# ax1.scatter(distance_vals[:100], y_predict[:100], s=18, c='b', marker=\"s\", label='Predicted')\n",
         "# ax1.scatter(distance_vals[:100], y_actual[:100], s=18, c='r', marker=\"o\", label='Actual')\n",
@@ -1204,7 +1084,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.9"
     }
   },
   "nbformat": 4,

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.yml

@@ -4,6 +4,7 @@ dependencies:
   - azureml-sdk
   - azureml-widgets
   - azureml-opendatasets
-  - azureml-dataprep
   - azureml-train-automl
   - matplotlib
+  - pandas
+  - pyarrow

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/cleanse.py

@@ -3,15 +3,14 @@
 
 import argparse
 import os
-import pandas as pd
-import azureml.dataprep as dprep
+from azureml.core import Run
 
 
 def get_dict(dict_str):
     pairs = dict_str.strip("{}").split("\;")
     new_dict = {}
     for pair in pairs:
-        key, value = pair.strip('\\').split(":")
+        key, value = pair.strip().split(":")
         new_dict[key.strip().strip("'")] = value.strip().strip("'")
 
     return new_dict
@@ -19,40 +18,37 @@ def get_dict(dict_str):
 
 print("Cleans the input data")
 
+# Get the input green_taxi_data. To learn more about how to access dataset in your script, please
+# see https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-with-datasets.
+run = Run.get_context()
+raw_data = run.input_datasets["raw_data"]
+
+
 parser = argparse.ArgumentParser("cleanse")
-parser.add_argument("--input_cleanse", type=str, help="raw taxi data")
 parser.add_argument("--output_cleanse", type=str, help="cleaned taxi data directory")
 parser.add_argument("--useful_columns", type=str, help="useful columns to keep")
 parser.add_argument("--columns", type=str, help="rename column pattern")
 
 args = parser.parse_args()
 
-print("Argument 1(input taxi data path): %s" % args.input_cleanse)
-print("Argument 2(columns to keep): %s" % str(args.useful_columns.strip("[]").split("\;")))
-print("Argument 3(columns renaming mapping): %s" % str(args.columns.strip("{}").split("\;")))
-print("Argument 4(output cleansed taxi data path): %s" % args.output_cleanse)
+print("Argument 1(columns to keep): %s" % str(args.useful_columns.strip("[]").split("\;")))
+print("Argument 2(columns renaming mapping): %s" % str(args.columns.strip("{}").split("\;")))
+print("Argument 3(output cleansed taxi data path): %s" % args.output_cleanse)
 
-raw_df = dprep.read_csv(path=args.input_cleanse, header=dprep.PromoteHeadersMode.GROUPED)
-
-# These functions ensure that null data is removed from the data set,
+# These functions ensure that null data is removed from the dataset,
 # which will help increase machine learning model accuracy.
-# Visit https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep
-# for more details
 
 useful_columns = [s.strip().strip("'") for s in args.useful_columns.strip("[]").split("\;")]
 columns = get_dict(args.columns)
 
-all_columns = dprep.ColumnSelector(term=".*", use_regex=True)
-drop_if_all_null = [all_columns, dprep.ColumnRelationship(dprep.ColumnRelationship.ALL)]
+new_df = (raw_data.to_pandas_dataframe()
+          .dropna(how='all')
+          .rename(columns=columns))[useful_columns]
 
-new_df = (raw_df
-          .replace_na(columns=all_columns)
-          .drop_nulls(*drop_if_all_null)
-          .rename_columns(column_pairs=columns)
-          .keep_columns(columns=useful_columns))
+new_df.reset_index(inplace=True, drop=True)
 
 if not (args.output_cleanse is None):
     os.makedirs(args.output_cleanse, exist_ok=True)
     print("%s created" % args.output_cleanse)
-    write_df = new_df.write_to_csv(directory_path=dprep.LocalFileOutput(args.output_cleanse))
-    write_df.run_local()
+    path = args.output_cleanse + "/processed.parquet"
+    write_df = new_df.to_parquet(path)

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/filter.py

@@ -1,55 +1,47 @@
 import argparse
 import os
-import azureml.dataprep as dprep
+from azureml.core import Run
 
 print("Filters out coordinates for locations that are outside the city border.",
       "Chain the column filter commands within the filter() function",
       "and define the minimum and maximum bounds for each field.")
 
+run = Run.get_context()
+
+# To learn more about how to access dataset in your script, please
+# see https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-with-datasets.
+merged_data = run.input_datasets["merged_data"]
+combined_df = merged_data.to_pandas_dataframe()
+
 parser = argparse.ArgumentParser("filter")
-parser.add_argument("--input_filter", type=str, help="merged taxi data directory")
 parser.add_argument("--output_filter", type=str, help="filter out out of city locations")
 
 args = parser.parse_args()
 
-print("Argument 1(input taxi data path): %s" % args.input_filter)
-print("Argument 2(output filtered taxi data path): %s" % args.output_filter)
-
-combined_df = dprep.read_csv(args.input_filter + '/part-*')
+print("Argument (output filtered taxi data path): %s" % args.output_filter)
 
 # These functions filter out coordinates for locations that are outside the city border.
-# Visit https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep for more details
-
-# Create a condensed view of the dataflow to just show the lat/long fields,
-# which makes it easier to evaluate missing or out-of-scope coordinates
-decimal_type = dprep.TypeConverter(data_type=dprep.FieldType.DECIMAL)
-combined_df = combined_df.set_column_types(type_conversions={
-    "pickup_longitude": decimal_type,
-    "pickup_latitude": decimal_type,
-    "dropoff_longitude": decimal_type,
-    "dropoff_latitude": decimal_type
-})
 
 # Filter out coordinates for locations that are outside the city border.
 # Chain the column filter commands within the filter() function
 # and define the minimum and maximum bounds for each field
-latlong_filtered_df = (combined_df
-                       .drop_nulls(columns=["pickup_longitude",
-                                            "pickup_latitude",
-                                            "dropoff_longitude",
-                                            "dropoff_latitude"],
-                                   column_relationship=dprep.ColumnRelationship(dprep.ColumnRelationship.ANY))
-                       .filter(dprep.f_and(dprep.col("pickup_longitude") <= -73.72,
-                                           dprep.col("pickup_longitude") >= -74.09,
-                                           dprep.col("pickup_latitude") <= 40.88,
-                                           dprep.col("pickup_latitude") >= 40.53,
-                                           dprep.col("dropoff_longitude") <= -73.72,
-                                           dprep.col("dropoff_longitude") >= -74.09,
-                                           dprep.col("dropoff_latitude") <= 40.88,
-                                           dprep.col("dropoff_latitude") >= 40.53)))
+
+combined_df = combined_df.astype({"pickup_longitude": 'float64', "pickup_latitude": 'float64',
+                                  "dropoff_longitude": 'float64', "dropoff_latitude": 'float64'})
+
+latlong_filtered_df = combined_df[(combined_df.pickup_longitude <= -73.72) &
+                                  (combined_df.pickup_longitude >= -74.09) &
+                                  (combined_df.pickup_latitude <= 40.88) &
+                                  (combined_df.pickup_latitude >= 40.53) &
+                                  (combined_df.dropoff_longitude <= -73.72) &
+                                  (combined_df.dropoff_longitude >= -74.72) &
+                                  (combined_df.dropoff_latitude <= 40.88) &
+                                  (combined_df.dropoff_latitude >= 40.53)]
+
+latlong_filtered_df.reset_index(inplace=True, drop=True)
 
 if not (args.output_filter is None):
     os.makedirs(args.output_filter, exist_ok=True)
     print("%s created" % args.output_filter)
-    write_df = latlong_filtered_df.write_to_csv(directory_path=dprep.LocalFileOutput(args.output_filter))
-    write_df.run_local()
+    path = args.output_filter + "/processed.parquet"
+    write_df = latlong_filtered_df.to_parquet(path)

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/merge.py

@@ -1,29 +1,30 @@
-
 import argparse
 import os
-import azureml.dataprep as dprep
+from azureml.core import Run
 
 print("Merge Green and Yellow taxi data")
 
+run = Run.get_context()
+
+# To learn more about how to access dataset in your script, please
+# see https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-with-datasets.
+cleansed_green_data = run.input_datasets["cleansed_green_data"]
+cleansed_yellow_data = run.input_datasets["cleansed_yellow_data"]
+green_df = cleansed_green_data.to_pandas_dataframe()
+yellow_df = cleansed_yellow_data.to_pandas_dataframe()
+
 parser = argparse.ArgumentParser("merge")
-parser.add_argument("--input_green_merge", type=str, help="cleaned green taxi data directory")
-parser.add_argument("--input_yellow_merge", type=str, help="cleaned yellow taxi data directory")
 parser.add_argument("--output_merge", type=str, help="green and yellow taxi data merged")
 
 args = parser.parse_args()
-
-print("Argument 1(input green taxi data path): %s" % args.input_green_merge)
-print("Argument 2(input yellow taxi data path): %s" % args.input_yellow_merge)
-print("Argument 3(output merge taxi data path): %s" % args.output_merge)
-
-green_df = dprep.read_csv(args.input_green_merge + '/part-*')
-yellow_df = dprep.read_csv(args.input_yellow_merge + '/part-*')
+print("Argument (output merge taxi data path): %s" % args.output_merge)
 
 # Appending yellow data to green data
-combined_df = green_df.append_rows([yellow_df])
+combined_df = green_df.append(yellow_df, ignore_index=True)
+combined_df.reset_index(inplace=True, drop=True)
 
 if not (args.output_merge is None):
     os.makedirs(args.output_merge, exist_ok=True)
     print("%s created" % args.output_merge)
-    write_df = combined_df.write_to_csv(directory_path=dprep.LocalFileOutput(args.output_merge))
-    write_df.run_local()
+    path = args.output_merge + "/processed.parquet"
+    write_df = combined_df.to_parquet(path)

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/normalize.py

@@ -1,47 +1,48 @@
 import argparse
 import os
-import azureml.dataprep as dprep
+import pandas as pd
+from azureml.core import Run
 
 print("Replace undefined values to relavant values and rename columns to meaningful names")
 
+run = Run.get_context()
+
+# To learn more about how to access dataset in your script, please
+# see https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-with-datasets.
+filtered_data = run.input_datasets['filtered_data']
+combined_converted_df = filtered_data.to_pandas_dataframe()
+
 parser = argparse.ArgumentParser("normalize")
-parser.add_argument("--input_normalize", type=str, help="combined and converted taxi data")
 parser.add_argument("--output_normalize", type=str, help="replaced undefined values and renamed columns")
 
 args = parser.parse_args()
 
-print("Argument 1(input taxi data path): %s" % args.input_normalize)
-print("Argument 2(output normalized taxi data path): %s" % args.output_normalize)
-
-combined_converted_df = dprep.read_csv(args.input_normalize + '/part-*')
+print("Argument (output normalized taxi data path): %s" % args.output_normalize)
 
 # These functions replace undefined values and rename to use meaningful names.
-# Visit https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep for more details
+replaced_stfor_vals_df = (combined_converted_df.replace({"store_forward": "0"}, {"store_forward": "N"})
+                          .fillna({"store_forward": "N"}))
 
-replaced_stfor_vals_df = combined_converted_df.replace(columns="store_forward",
-                                                       find="0",
-                                                       replace_with="N").fill_nulls("store_forward", "N")
+replaced_distance_vals_df = (replaced_stfor_vals_df.replace({"distance": ".00"}, {"distance": 0})
+                             .fillna({"distance": 0}))
 
-replaced_distance_vals_df = replaced_stfor_vals_df.replace(columns="distance",
-                                                           find=".00",
-                                                           replace_with=0).fill_nulls("distance", 0)
+normalized_df = replaced_distance_vals_df.astype({"distance": 'float64'})
 
-replaced_distance_vals_df = replaced_distance_vals_df.to_number(["distance"])
+temp = pd.DatetimeIndex(normalized_df["pickup_datetime"])
+normalized_df["pickup_date"] = temp.date
+normalized_df["pickup_time"] = temp.time
 
-time_split_df = (replaced_distance_vals_df
-                 .split_column_by_example(source_column="pickup_datetime")
-                 .split_column_by_example(source_column="dropoff_datetime"))
+temp = pd.DatetimeIndex(normalized_df["dropoff_datetime"])
+normalized_df["dropoff_date"] = temp.date
+normalized_df["dropoff_time"] = temp.time
 
-# Split the pickup and dropoff datetime values into the respective date and time columns
-renamed_col_df = (time_split_df
-                  .rename_columns(column_pairs={
-                      "pickup_datetime_1": "pickup_date",
-                      "pickup_datetime_2": "pickup_time",
-                      "dropoff_datetime_1": "dropoff_date",
-                      "dropoff_datetime_2": "dropoff_time"}))
+del normalized_df["pickup_datetime"]
+del normalized_df["dropoff_datetime"]
+
+normalized_df.reset_index(inplace=True, drop=True)
 
 if not (args.output_normalize is None):
     os.makedirs(args.output_normalize, exist_ok=True)
     print("%s created" % args.output_normalize)
-    write_df = renamed_col_df.write_to_csv(directory_path=dprep.LocalFileOutput(args.output_normalize))
-    write_df.run_local()
+    path = args.output_normalize + "/processed.parquet"
+    write_df = normalized_df.to_parquet(path)

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/prepdata/transform.py

@@ -1,22 +1,24 @@
 import argparse
 import os
-import azureml.dataprep as dprep
+from azureml.core import Run
 
 print("Transforms the renamed taxi data to the required format")
 
+run = Run.get_context()
+
+# To learn more about how to access dataset in your script, please
+# see https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-with-datasets.
+normalized_data = run.input_datasets['normalized_data']
+normalized_df = normalized_data.to_pandas_dataframe()
+
 parser = argparse.ArgumentParser("transform")
-parser.add_argument("--input_transform", type=str, help="renamed taxi data")
 parser.add_argument("--output_transform", type=str, help="transformed taxi data")
 
 args = parser.parse_args()
 
-print("Argument 1(input taxi data path): %s" % args.input_transform)
 print("Argument 2(output final transformed taxi data): %s" % args.output_transform)
 
-renamed_df = dprep.read_csv(args.input_transform + '/part-*')
-
 # These functions transform the renamed data to be used finally for training.
-# Visit https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep for more details
 
 # Split the pickup and dropoff date further into the day of the week, day of the month, and month values.
 # To get the day of the week value, use the derive_column_by_example() function.
@@ -27,62 +29,46 @@ renamed_df = dprep.read_csv(args.input_transform + '/part-*')
 # use the drop_columns() function to delete the original fields as the newly generated features are preferred.
 # Rename the rest of the fields to use meaningful descriptions.
 
-transformed_features_df = (renamed_df
-                           .derive_column_by_example(
-                               source_columns="pickup_date",
-                               new_column_name="pickup_weekday",
-                               example_data=[("2009-01-04", "Sunday"), ("2013-08-22", "Thursday")])
-                           .derive_column_by_example(
-                               source_columns="dropoff_date",
-                               new_column_name="dropoff_weekday",
-                               example_data=[("2013-08-22", "Thursday"), ("2013-11-03", "Sunday")])
+normalized_df = normalized_df.astype({"pickup_date": 'datetime64', "dropoff_date": 'datetime64',
+                                      "pickup_time": 'datetime64', "dropoff_time": 'datetime64',
+                                      "distance": 'float64', "cost": 'float64'})
 
-                           .split_column_by_example(source_column="pickup_time")
-                           .split_column_by_example(source_column="dropoff_time")
+normalized_df["pickup_weekday"] = normalized_df["pickup_date"].dt.dayofweek
+normalized_df["pickup_month"] = normalized_df["pickup_date"].dt.month
+normalized_df["pickup_monthday"] = normalized_df["pickup_date"].dt.day
 
-                           .split_column_by_example(source_column="pickup_time_1")
-                           .split_column_by_example(source_column="dropoff_time_1")
-                           .drop_columns(columns=[
-                               "pickup_date", "pickup_time", "dropoff_date", "dropoff_time",
-                               "pickup_date_1", "dropoff_date_1", "pickup_time_1", "dropoff_time_1"])
+normalized_df["dropoff_weekday"] = normalized_df["dropoff_date"].dt.dayofweek
+normalized_df["dropoff_month"] = normalized_df["dropoff_date"].dt.month
+normalized_df["dropoff_monthday"] = normalized_df["dropoff_date"].dt.day
 
-                           .rename_columns(column_pairs={
-                               "pickup_date_2": "pickup_month",
-                               "pickup_date_3": "pickup_monthday",
-                               "pickup_time_1_1": "pickup_hour",
-                               "pickup_time_1_2": "pickup_minute",
-                               "pickup_time_2": "pickup_second",
-                               "dropoff_date_2": "dropoff_month",
-                               "dropoff_date_3": "dropoff_monthday",
-                               "dropoff_time_1_1": "dropoff_hour",
-                               "dropoff_time_1_2": "dropoff_minute",
-                               "dropoff_time_2": "dropoff_second"}))
+normalized_df["pickup_hour"] = normalized_df["pickup_time"].dt.hour
+normalized_df["pickup_minute"] = normalized_df["pickup_time"].dt.minute
+normalized_df["pickup_second"] = normalized_df["pickup_time"].dt.second
 
-# Drop the pickup_datetime and dropoff_datetime columns because they're
+normalized_df["dropoff_hour"] = normalized_df["dropoff_time"].dt.hour
+normalized_df["dropoff_minute"] = normalized_df["dropoff_time"].dt.minute
+normalized_df["dropoff_second"] = normalized_df["dropoff_time"].dt.second
+
+# Drop the pickup_date, dropoff_date, pickup_time, dropoff_time columns because they're
 # no longer needed (granular time features like hour,
 # minute and second are more useful for model training).
-processed_df = transformed_features_df.drop_columns(columns=["pickup_datetime", "dropoff_datetime"])
+del normalized_df["pickup_date"]
+del normalized_df["dropoff_date"]
+del normalized_df["pickup_time"]
+del normalized_df["dropoff_time"]
 
-# Use the type inference functionality to automatically check the data type of each field,
-# and display the inference results.
-type_infer = processed_df.builders.set_column_types()
-type_infer.learn()
-
-# The inference results look correct based on the data. Now apply the type conversions to the dataflow.
-type_converted_df = type_infer.to_dataflow()
-
-# Before you package the dataflow, run two final filters on the data set.
+# Before you package the dataset, run two final filters on the dataset.
 # To eliminate incorrectly captured data points,
-# filter the dataflow on records where both the cost and distance variable values are greater than zero.
+# filter the dataset on records where both the cost and distance variable values are greater than zero.
 # This step will significantly improve machine learning model accuracy,
 # because data points with a zero cost or distance represent major outliers that throw off prediction accuracy.
 
-final_df = type_converted_df.filter(dprep.col("distance") > 0)
-final_df = final_df.filter(dprep.col("cost") > 0)
+final_df = normalized_df[(normalized_df.distance > 0) & (normalized_df.cost > 0)]
+final_df.reset_index(inplace=True, drop=True)
 
 # Writing the final dataframe to use for training in the following steps
 if not (args.output_transform is None):
     os.makedirs(args.output_transform, exist_ok=True)
     print("%s created" % args.output_transform)
-    write_df = final_df.write_to_csv(directory_path=dprep.LocalFileOutput(args.output_transform))
-    write_df.run_local()
+    path = args.output_transform + "/processed.parquet"
+    write_df = final_df.to_parquet(path)

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/trainmodel/featurization.py

@@ -1,31 +0,0 @@
-import argparse
-import os
-import azureml.dataprep as dprep
-import azureml.core
-
-print("Extracts important features from prepared data")
-
-parser = argparse.ArgumentParser("featurization")
-parser.add_argument("--input_featurization", type=str, help="input featurization")
-parser.add_argument("--useful_columns", type=str, help="columns to use")
-parser.add_argument("--output_featurization", type=str, help="output featurization")
-
-args = parser.parse_args()
-
-print("Argument 1(input training data path): %s" % args.input_featurization)
-print("Argument 2(column features to use): %s" % str(args.useful_columns.strip("[]").split("\;")))
-print("Argument 3:(output featurized training data path) %s" % args.output_featurization)
-
-dflow_prepared = dprep.read_csv(args.input_featurization + '/part-*')
-
-# These functions extracts useful features for training
-# Visit https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-auto-train-models for more detail
-
-useful_columns = [s.strip().strip("'") for s in args.useful_columns.strip("[]").split("\;")]
-dflow = dflow_prepared.keep_columns(useful_columns)
-
-if not (args.output_featurization is None):
-    os.makedirs(args.output_featurization, exist_ok=True)
-    print("%s created" % args.output_featurization)
-    write_df = dflow.write_to_csv(directory_path=dprep.LocalFileOutput(args.output_featurization))
-    write_df.run_local()

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/trainmodel/get_data.py

@@ -1,12 +0,0 @@
-
-import os
-import pandas as pd
-
-
-def get_data():
-    print("In get_data")
-    print(os.environ['AZUREML_DATAREFERENCE_output_split_train_x'])
-    X_train = pd.read_csv(os.environ['AZUREML_DATAREFERENCE_output_split_train_x'] + "/part-00000", header=0)
-    y_train = pd.read_csv(os.environ['AZUREML_DATAREFERENCE_output_split_train_y'] + "/part-00000", header=0)
-
-    return {"X": X_train.values, "y": y_train.values.flatten()}

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/scripts/trainmodel/train_test_split.py

@@ -1,48 +1,38 @@
 import argparse
 import os
-import azureml.dataprep as dprep
 import azureml.core
+from azureml.core import Run
 from sklearn.model_selection import train_test_split
 
 
 def write_output(df, path):
     os.makedirs(path, exist_ok=True)
     print("%s created" % path)
-    df.to_csv(path + "/part-00000", index=False)
+    df.to_parquet(path + "/processed.parquet")
 
 
 print("Split the data into train and test")
+run = Run.get_context()
+transformed_data = run.input_datasets['transformed_data']
+transformed_df = transformed_data.to_pandas_dataframe()
 
 parser = argparse.ArgumentParser("split")
-parser.add_argument("--input_split_features", type=str, help="input split features")
-parser.add_argument("--input_split_labels", type=str, help="input split labels")
-parser.add_argument("--output_split_train_x", type=str, help="output split train features")
-parser.add_argument("--output_split_train_y", type=str, help="output split train labels")
-parser.add_argument("--output_split_test_x", type=str, help="output split test features")
-parser.add_argument("--output_split_test_y", type=str, help="output split test labels")
+parser.add_argument("--output_split_train", type=str, help="output split train data")
+parser.add_argument("--output_split_test", type=str, help="output split test data")
 
 args = parser.parse_args()
 
-print("Argument 1(input taxi data features path): %s" % args.input_split_features)
-print("Argument 2(input taxi data labels path): %s" % args.input_split_labels)
-print("Argument 3(output training features split path): %s" % args.output_split_train_x)
-print("Argument 4(output training labels split path): %s" % args.output_split_train_y)
-print("Argument 5(output test features split path): %s" % args.output_split_test_x)
-print("Argument 6(output test labels split path): %s" % args.output_split_test_y)
-
-x_df = dprep.read_csv(path=args.input_split_features, header=dprep.PromoteHeadersMode.GROUPED).to_pandas_dataframe()
-y_df = dprep.read_csv(path=args.input_split_labels, header=dprep.PromoteHeadersMode.GROUPED).to_pandas_dataframe()
+print("Argument 1(output training data split path): %s" % args.output_split_train)
+print("Argument 2(output test data split path): %s" % args.output_split_test)
 
 # These functions splits the input features and labels into test and train data
 # Visit https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-auto-train-models for more detail
 
-x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)
+output_split_train, output_split_test = train_test_split(transformed_df, test_size=0.2, random_state=223)
+output_split_train.reset_index(inplace=True, drop=True)
+output_split_test.reset_index(inplace=True, drop=True)
 
-if not (args.output_split_train_x is None and
-        args.output_split_test_x is None and
-        args.output_split_train_y is None and
-        args.output_split_test_y is None):
-    write_output(x_train, args.output_split_train_x)
-    write_output(y_train, args.output_split_train_y)
-    write_output(x_test, args.output_split_test_x)
-    write_output(y_test, args.output_split_test_y)
+if not (args.output_split_train is None and
+        args.output_split_test is None):
+    write_output(output_split_train, args.output_split_train)
+    write_output(output_split_test, args.output_split_test)