update samples from Release-37 as a part of SDK release

Merge pull request #779 from Azure/release_update/Release-36
update samples from Release-36 as a part of SDK release
2025-12-23 02:52:39 -05:00 · 2020-02-08 04:49:27 +00:00 · 2020-02-07 21:40:57 -07:00 · 2020-02-08 04:35:14 +00:00 · 2020-02-05 22:41:22 -08:00 · 2020-02-05 20:03:41 -05:00
55 changed files with 263 additions and 5446 deletions
--- a/README.md
+++ b/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
@@ -17,7 +17,7 @@ This [index](.index.md) should assist in navigating the Azure Machine Learning n
 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).
--- a/configuration.ipynb
+++ b/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 1.1.0rc0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
- pip
+- pip<=19.3.1
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -2,7 +2,7 @@ name: azure_automl
 dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
- pip
+- pip<=19.3.1
 - nomkl
 - python>=3.5.2,<3.6.8
 - nb_conda
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
@@ -282,7 +282,11 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "metadata": {},
+      "metadata": {
        "tags": [
          "widget-rundetails-sample"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb
+++ b/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
 }
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.yml
@@ -1,8 +0,0 @@
 name: auto-ml-forecasting-grouping
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - azureml-pipeline
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/build.py
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/data/dominicks_OJ_2_5_8_test.csv
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/data/dominicks_OJ_2_5_8_train.csv
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/deploy.py
+++ b/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)
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/myenv.yml
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/deploy/score.py
+++ b/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
--- a/how-to-use-azureml/automated-machine-learning/forecasting-grouping/register/register.py
+++ b/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')
--- a/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb
@@ -717,10 +717,10 @@
      "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 +761,7 @@
        "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 +769,8 @@
        "                                                     \"method\" : \"local_explanation\"}, \n",
        "                                               description='Get local explanations for Machine test data')\n",
        "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   entry_script=\"score_explain.py\",\n",
+        "inference_config = InferenceConfig(entry_script=\"score_explain.py\", environment=myenv)\n",
        "                                   conda_file=\"myenv.yml\")\n",
        "\n",
        "# Use configs and models generated above\n",
        "service = Model.deploy(ws, 'model-scoring', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
--- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb
+++ b/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",
+        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "                                   entry_script=\"score_remote_explain.py\",\n",
+        "inference_config = InferenceConfig(entry_script=\"score_remote_explain.py\", environment=myenv)\n",
        "                                   conda_file=\"myenv.yml\",\n",
        "                                   extra_docker_file_steps=\"dockerfile\")\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",
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/README.md
+++ b/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)
--- a/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb
+++ b/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()"
      ]
    },
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb
@@ -24,9 +24,9 @@
        "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
        "\n",
        "> **Note**\n",
-        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook.\n",
+        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook. Pandas is used to display job results.\n",
        "```\n",
-        "pip install azureml-contrib-pipeline-steps\n",
+        "pip install azureml-contrib-pipeline-steps pandas\n",
        "```\n",
        "> **Tip**\n",
        "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
--- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb
@@ -24,9 +24,9 @@
        "In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.\n",
        "\n",
        "> **Note**\n",
-        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook.\n",
+        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook. Pandas is used to display job results.\n",
        "```\n",
-        "pip install azureml-contrib-pipeline-steps\n",
+        "pip install azureml-contrib-pipeline-steps pandas\n",
        "```\n",
        "> **Tip**\n",
        "If your system requires low-latency processing (to process a single document or small set of documents quickly), use [real-time scoring](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-consume-web-service) instead of batch prediction.\n",
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/batch_scoring.py
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/batch_scoring.py
@@ -1,119 +0,0 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import os
 import argparse
 import datetime
 import time
 import tensorflow as tf
 from math import ceil
 import numpy as np
 import shutil
 from tensorflow.contrib.slim.python.slim.nets import inception_v3
 from azureml.core.model import Model
 slim = tf.contrib.slim
 parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
 parser.add_argument('--model_name', dest="model_name", required=True)
 parser.add_argument('--label_dir', dest="label_dir", required=True)
 parser.add_argument('--dataset_path', dest="dataset_path", required=True)
 parser.add_argument('--output_dir', dest="output_dir", required=True)
 parser.add_argument('--batch_size', dest="batch_size", type=int, required=True)
 args = parser.parse_args()
 image_size = 299
 num_channel = 3
 # create output directory if it does not exist
 os.makedirs(args.output_dir, exist_ok=True)
 def get_class_label_dict(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label
 class DataIterator:
    def __init__(self, data_dir):
        self.file_paths = []
        image_list = os.listdir(data_dir)
        # total_size = len(image_list)
        self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list]
        self.labels = [1 for file_name in self.file_paths]
    @property
    def size(self):
        return len(self.labels)
    def input_pipeline(self, batch_size):
        images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)
        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)
        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)
        labels = input_queue[1]
        images_content = tf.read_file(input_queue[0])
        image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name="jpeg_reader")
        float_caster = tf.cast(image_reader, tf.float32)
        new_size = tf.constant([image_size, image_size], dtype=tf.int32)
        images = tf.image.resize_images(float_caster, new_size)
        images = tf.divide(tf.subtract(images, [0]), [255])
        image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)
        return image_batch
 def main(_):
    # start_time = datetime.datetime.now()
    label_file_name = os.path.join(args.label_dir, "labels.txt")
    label_dict = get_class_label_dict(label_file_name)
    classes_num = len(label_dict)
    test_feeder = DataIterator(data_dir=args.dataset_path)
    total_size = len(test_feeder.labels)
    count = 0
    # get model from model registry
    model_path = Model.get_model_path(args.model_name)
    with tf.Session() as sess:
        test_images = test_feeder.input_pipeline(batch_size=args.batch_size)
        with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
            input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])
            logits, _ = inception_v3.inception_v3(input_images,
                                                  num_classes=classes_num,
                                                  is_training=False)
            probabilities = tf.argmax(logits, 1)
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)
        saver = tf.train.Saver()
        saver.restore(sess, model_path)
        out_filename = os.path.join(args.output_dir, "result-labels.txt")
        with open(out_filename, "w") as result_file:
            i = 0
            while count < total_size and not coord.should_stop():
                test_images_batch = sess.run(test_images)
                file_names_batch = test_feeder.file_paths[i * args.batch_size:
                                                          min(test_feeder.size, (i + 1) * args.batch_size)]
                results = sess.run(probabilities, feed_dict={input_images: test_images_batch})
                new_add = min(args.batch_size, total_size - count)
                count += new_add
                i += 1
                for j in range(new_add):
                    result_file.write(os.path.basename(file_names_batch[j]) + ": " + label_dict[results[j]] + "\n")
                result_file.flush()
            coord.request_stop()
            coord.join(threads)
        # copy the file to artifacts
        shutil.copy(out_filename, "./outputs/")
        # Move the processed data out of the blob so that the next run can process the data.
 if __name__ == "__main__":
    tf.app.run()
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb
@@ -1,630 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../../../contrib/batch_inferencing/) for examples on how to get started."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Using Azure Machine Learning Pipelines for batch prediction\n",
        "\n",
        "In this notebook we will demonstrate how to run a batch scoring job using Azure Machine Learning pipelines. Our example job will be to take an already-trained image classification model, and run that model on some unlabeled images. The image classification model that we'll use is the __[Inception-V3 model](https://arxiv.org/abs/1512.00567)__  and we'll run this model on unlabeled images from the __[ImageNet](http://image-net.org/)__ dataset. \n",
        "\n",
        "The outline of this notebook is as follows:\n",
        "\n",
        "- Register the pretrained inception model into the model registry. \n",
        "- Store the dataset images in a blob container.\n",
        "- Use the registered model to do batch scoring on the images in the data blob container."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first if you haven't. This sets you up with a working config file that has information on your workspace, subscription id, etc. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Experiment\n",
        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
        "from azureml.core.datastore import Datastore\n",
        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.pipeline.steps import PythonScriptStep"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set up machine learning resources"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Set up datastores\n",
        "First, let\u00e2\u20ac\u2122s access the datastore that has the model, labels, and images. \n",
        "\n",
        "### Create a datastore that points to a blob container containing sample images\n",
        "\n",
        "We have created a public blob container `sampledata` on an account named `pipelinedata`, containing images from the ImageNet evaluation set. In the next step, we create a datastore with the name `images_datastore`, which points to this container. In the call to `register_azure_blob_container` below, setting the `overwrite` flag to `True` overwrites any datastore that was created previously with that name. \n",
        "\n",
        "This step can be changed to point to your blob container by providing your own `datastore_name`, `container_name`, and `account_name`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "account_name = \"pipelinedata\"\n",
        "datastore_name=\"images_datastore\"\n",
        "container_name=\"sampledata\"\n",
        "\n",
        "batchscore_blob = Datastore.register_azure_blob_container(ws, \n",
        "                      datastore_name=datastore_name, \n",
        "                      container_name= container_name, \n",
        "                      account_name=account_name, \n",
        "                      overwrite=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, let\u00e2\u20ac\u2122s specify the default datastore for the outputs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def_data_store = ws.get_default_datastore()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Configure data references\n",
        "Now you need to add references to the data, as inputs to the appropriate pipeline steps in your pipeline. A data source in a pipeline is represented by a DataReference object. The DataReference object points to data that lives in, or is accessible from, a datastore. We need DataReference objects corresponding to the following: the directory containing the input images, the directory in which the pretrained model is stored, the directory containing the labels, and the output directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "input_images = DataReference(datastore=batchscore_blob, \n",
        "                             data_reference_name=\"input_images\",\n",
        "                             path_on_datastore=\"batchscoring/images\",\n",
        "                             mode=\"download\"\n",
        "                            )\n",
        "model_dir = DataReference(datastore=batchscore_blob, \n",
        "                          data_reference_name=\"input_model\",\n",
        "                          path_on_datastore=\"batchscoring/models\",\n",
        "                          mode=\"download\"                          \n",
        "                         )\n",
        "label_dir = DataReference(datastore=batchscore_blob, \n",
        "                          data_reference_name=\"input_labels\",\n",
        "                          path_on_datastore=\"batchscoring/labels\",\n",
        "                          mode=\"download\"                          \n",
        "                         )\n",
        "output_dir = PipelineData(name=\"scores\", \n",
        "                          datastore=def_data_store, \n",
        "                          output_path_on_compute=\"batchscoring/results\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create and attach Compute targets\n",
        "Use the below code to create and attach Compute targets. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# choose a name for your cluster\n",
        "aml_compute_name = os.environ.get(\"AML_COMPUTE_NAME\", \"gpu-cluster\")\n",
        "cluster_min_nodes = os.environ.get(\"AML_COMPUTE_MIN_NODES\", 0)\n",
        "cluster_max_nodes = os.environ.get(\"AML_COMPUTE_MAX_NODES\", 1)\n",
        "vm_size = os.environ.get(\"AML_COMPUTE_SKU\", \"STANDARD_NC6\")\n",
        "\n",
        "\n",
        "if aml_compute_name in ws.compute_targets:\n",
        "    compute_target = ws.compute_targets[aml_compute_name]\n",
        "    if compute_target and type(compute_target) is AmlCompute:\n",
        "        print('found compute target. just use it. ' + aml_compute_name)\n",
        "else:\n",
        "    print('creating a new compute target...')\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size, # NC6 is GPU-enabled\n",
        "                                                                vm_priority = 'lowpriority', # optional\n",
        "                                                                min_nodes = cluster_min_nodes, \n",
        "                                                                max_nodes = cluster_max_nodes)\n",
        "\n",
        "    # create the cluster\n",
        "    compute_target = ComputeTarget.create(ws, aml_compute_name, provisioning_config)\n",
        "    \n",
        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
        "    # if no min node count is provided it will use the scale settings for the cluster\n",
        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
        "    \n",
        "     # For a more detailed view of current Azure Machine Learning Compute  status, use get_status()\n",
        "    print(compute_target.get_status().serialize())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prepare the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Download the Model\n",
        "\n",
        "Download and extract the model from http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz to `\"models\"`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create directory for model\n",
        "model_dir = 'models'\n",
        "if not os.path.isdir(model_dir):\n",
        "    os.mkdir(model_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import tarfile\n",
        "import urllib.request\n",
        "\n",
        "url=\"http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz\"\n",
        "response = urllib.request.urlretrieve(url, \"model.tar.gz\")\n",
        "tar = tarfile.open(\"model.tar.gz\", \"r:gz\")\n",
        "tar.extractall(model_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register the model with Workspace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import shutil\n",
        "from azureml.core.model import Model\n",
        "\n",
        "# register downloaded model \n",
        "model = Model.register(model_path = \"models/inception_v3.ckpt\",\n",
        "                       model_name = \"inception\", # this is the name the model is registered as\n",
        "                       tags = {'pretrained': \"inception\"},\n",
        "                       description = \"Imagenet trained tensorflow inception\",\n",
        "                       workspace = ws)\n",
        "# remove the downloaded dir after registration if you wish\n",
        "shutil.rmtree(\"models\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Write your scoring script"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To do the scoring, we use a batch scoring script `batch_scoring.py`, which is located in the same directory that this notebook is in. You can take a look at this script to see how you might modify it for your custom batch scoring task.\n",
        "\n",
        "The python script `batch_scoring.py` takes input images, applies the image classification model to these images, and outputs a classification result to a results file.\n",
        "\n",
        "The script `batch_scoring.py` takes the following parameters:\n",
        "\n",
        "- `--model_name`: the name of the model being used, which is expected to be in the `model_dir` directory\n",
        "- `--label_dir` : the directory holding the `labels.txt` file \n",
        "- `--dataset_path`: the directory containing the input images\n",
        "- `--output_dir` : the script will run the model on the data and output a `results-label.txt` to this directory\n",
        "- `--batch_size` : the batch size used in running the model.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Build and run the batch scoring pipeline\n",
        "You have everything you need to build the pipeline. Let\u00e2\u20ac\u2122s put all these together."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "###  Specify the environment to run the script\n",
        "Specify the conda dependencies for your script. You will need this object when you create the pipeline step later on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
        "\n",
        "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.13.1\", \"azureml-defaults\"])\n",
        "\n",
        "# Runconfig\n",
        "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
        "amlcompute_run_config.environment.docker.enabled = True\n",
        "amlcompute_run_config.environment.docker.base_image = DEFAULT_GPU_IMAGE\n",
        "amlcompute_run_config.environment.spark.precache_packages = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Specify the parameters for your pipeline\n",
        "A subset of the parameters to the python script can be given as input when we re-run a `PublishedPipeline`. In the current example, we define `batch_size` taken by the script as such parameter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.pipeline.core.graph import PipelineParameter\n",
        "batch_size_param = PipelineParameter(name=\"param_batch_size\", default_value=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create the pipeline step\n",
        "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use PythonScriptStep to create the pipeline step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "inception_model_name = \"inception_v3.ckpt\"\n",
        "\n",
        "batch_score_step = PythonScriptStep(\n",
        "    name=\"batch_scoring\",\n",
        "    script_name=\"batch_scoring.py\",\n",
        "    arguments=[\"--dataset_path\", input_images, \n",
        "               \"--model_name\", \"inception\",\n",
        "               \"--label_dir\", label_dir, \n",
        "               \"--output_dir\", output_dir, \n",
        "               \"--batch_size\", batch_size_param],\n",
        "    compute_target=compute_target,\n",
        "    inputs=[input_images, label_dir],\n",
        "    outputs=[output_dir],\n",
        "    runconfig=amlcompute_run_config\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Run the pipeline\n",
        "At this point you can run the pipeline and examine the output it produced. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "pipelineparameterssample"
        ]
      },
      "outputs": [],
      "source": [
        "pipeline = Pipeline(workspace=ws, steps=[batch_score_step])\n",
        "pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline, pipeline_parameters={\"param_batch_size\": 20})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Monitor the run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(pipeline_run).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "pipeline_run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Download and review output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "step_run = list(pipeline_run.get_children())[0]\n",
        "step_run.download_file(\"./outputs/result-labels.txt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"result-labels.txt\", delimiter=\":\", header=None)\n",
        "df.columns = [\"Filename\", \"Prediction\"]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Publish a pipeline and rerun using a REST call"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a published pipeline\n",
        "Once you are satisfied with the outcome of the run, you can publish the pipeline to run it with different input values later. When you publish a pipeline, you will get a REST endpoint that accepts invoking of the pipeline with the set of parameters you have already incorporated above using PipelineParameter."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "published_pipeline = pipeline_run.publish_pipeline(\n",
        "    name=\"Inception_v3_scoring\", description=\"Batch scoring using Inception v3 model\", version=\"1.0\")\n",
        "\n",
        "published_pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Get published pipeline\n",
        "\n",
        "You can get the published pipeline using **pipeline id**.\n",
        "\n",
        "To get all the published pipelines for a given workspace(ws): \n",
        "```css\n",
        "all_pub_pipelines = PublishedPipeline.get_all(ws)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.pipeline.core import PublishedPipeline\n",
        "\n",
        "pipeline_id = published_pipeline.id # use your published pipeline id\n",
        "published_pipeline = PublishedPipeline.get(ws, pipeline_id)\n",
        "\n",
        "published_pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Rerun the pipeline using the REST endpoint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Get AAD token\n",
        "[This notebook](https://aka.ms/pl-restep-auth) shows how to authenticate to AML workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
        "import requests\n",
        "\n",
        "auth = InteractiveLoginAuthentication()\n",
        "aad_token = auth.get_authentication_header()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Run published pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "rest_endpoint = published_pipeline.endpoint\n",
        "# specify batch size when running the pipeline\n",
        "response = requests.post(rest_endpoint, \n",
        "                         headers=aad_token, \n",
        "                         json={\"ExperimentName\": \"batch_scoring\",\n",
        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "try:\n",
        "    response.raise_for_status()\n",
        "except Exception:    \n",
        "    raise Exception('Received bad response from the endpoint: {}\\n'\n",
        "                    'Response Code: {}\\n'\n",
        "                    'Headers: {}\\n'\n",
        "                    'Content: {}'.format(rest_endpoint, response.status_code, response.headers, response.content))\n",
        "\n",
        "run_id = response.json().get('Id')\n",
        "print('Submitted pipeline run: ', run_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Monitor the new run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.pipeline.core.run import PipelineRun\n",
        "published_pipeline_run = PipelineRun(ws.experiments[\"batch_scoring\"], run_id)\n",
        "\n",
        "RunDetails(published_pipeline_run).show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "sanpil"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.yml
+++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.yml
@@ -1,7 +0,0 @@
 name: pipeline-batch-scoring
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-widgets
  - pandas
  - requests
--- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
+++ b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
@@ -100,7 +100,7 @@
        "\n",
        "# Check core SDK version number\n",
        "\n",
-        "print(\"This notebook was created using SDK version 1.0.85, you are currently running version\", azureml.core.VERSION)"
+        "print(\"This notebook was created using SDK version 1.1.0rc0, you are currently running version\", azureml.core.VERSION)"
      ]
    },
    {
--- a/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.yml
+++ b/how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.yml
@@ -2,11 +2,9 @@ name: train-on-remote-vm
 dependencies:
 - matplotlib
 - tqdm
- scikit-learn
+- scikit-learn==0.22.1
 - numpy==1.18.1
 - pip:
  - azureml-sdk
  - azureml-widgets
-  - azureml-dataprep
+  - azureml-dataprep[fuse,pandas]
  - pandas
  - fuse
  - scikit-learn
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.ipynb
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.ipynb
@@ -1,403 +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/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Introduction to labeled datasets\n",
        "\n",
        "Labeled datasets are output from Azure Machine Learning [labeling projects](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-create-labeling-projects). It captures the reference to the data (e.g. image files) and its labels. \n",
        "\n",
        "This tutorial introduces the capabilities of labeled datasets and how to use it in training.\n",
        "\n",
        "Learn how-to:\n",
        "\n",
        "> * Set up your development environment\n",
        "> * Explore labeled datasets\n",
        "> * Train a simple deep learning neural network on a remote cluster\n",
        "\n",
        "## Prerequisite:\n",
        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
        "* Go through Azure Machine Learning [labeling projects](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-labeling-projects) and export the labels as an Azure Machine Learning dataset\n",
        "* Go through the [configuration notebook](../../../configuration.ipynb) to:\n",
        "    * install the latest version of azureml-sdk\n",
        "    * install the latest version of azureml-contrib-dataset\n",
        "    * install [PyTorch](https://pytorch.org/)\n",
        "    * create a workspace and its configuration file (`config.json`)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set up your development environment\n",
        "\n",
        "All the setup for your development work can be accomplished in a Python notebook.  Setup includes:\n",
        "\n",
        "* Importing Python packages\n",
        "* Connecting to a workspace to enable communication between your local computer and remote resources\n",
        "* Creating an experiment to track all your runs\n",
        "* Creating a remote compute target to use for training\n",
        "\n",
        "### Import packages\n",
        "\n",
        "Import Python packages you need in this session. Also display the Azure Machine Learning SDK version."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import azureml.core\n",
        "import azureml.contrib.dataset\n",
        "from azureml.core import Dataset, Workspace, Experiment\n",
        "from azureml.contrib.dataset import FileHandlingOption\n",
        "\n",
        "# check core SDK version number\n",
        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)\n",
        "print(\"Azure ML Contrib Version\", azureml.contrib.dataset.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Connect to workspace\n",
        "\n",
        "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `workspace`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# load workspace\n",
        "workspace = Workspace.from_config()\n",
        "print('Workspace name: ' + workspace.name, \n",
        "      'Azure region: ' + workspace.location, \n",
        "      'Subscription id: ' + workspace.subscription_id, \n",
        "      'Resource group: ' + workspace.resource_group, sep='\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create experiment and a directory\n",
        "\n",
        "Create an experiment to track the runs in your workspace and a directory to deliver the necessary code from your computer to the remote resource."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create an ML experiment\n",
        "exp = Experiment(workspace=workspace, name='labeled-datasets')\n",
        "\n",
        "# create a directory\n",
        "script_folder = './labeled-datasets'\n",
        "os.makedirs(script_folder, exist_ok=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create or Attach existing compute resource\n",
        "By using Azure Machine Learning Compute, 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 will create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
        "\n",
        "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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",
        "cluster_name = \"openhack\"\n",
        "\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=workspace, name=cluster_name)\n",
        "    print('Found existing compute target')\n",
        "except ComputeTargetException:\n",
        "    print('Creating a new compute target...')\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
        "                                                           max_nodes=4)\n",
        "\n",
        "    # create the cluster\n",
        "    compute_target = ComputeTarget.create(workspace, cluster_name, compute_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 uses 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",
        "# use get_status() to get a detailed status for the current cluster. \n",
        "print(compute_target.get_status().serialize())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explore labeled datasets\n",
        "\n",
        "**Note**: How to create labeled datasets is not covered in this tutorial. To create labeled datasets, you can go through [labeling projects](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-labeling-projects) and export the output labels as Azure Machine Lerning datasets. \n",
        "\n",
        "`animal_labels` used in this tutorial section is the output from a labeling project, with the task type of \"Object Identification\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get animal_labels dataset from the workspace\n",
        "animal_labels = Dataset.get_by_name(workspace, 'animal_labels')\n",
        "animal_labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can load labeled datasets into pandas DataFrame. There are 3 file handling option that you can choose to load the data files referenced by the labeled datasets:\n",
        "* Streaming: The default option to load data files.\n",
        "* Download: Download your data files to a local path.\n",
        "* Mount: Mount your data files to a mount point. Mount only works for Linux-based compute, including Azure Machine Learning notebook VM and Azure Machine Learning Compute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "animal_pd = animal_labels.to_pandas_dataframe(file_handling_option=FileHandlingOption.DOWNLOAD, target_path='./download/', overwrite_download=True)\n",
        "animal_pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "\n",
        "# read images from downloaded path\n",
        "img = mpimg.imread(animal_pd.loc[0,'image_url'])\n",
        "imgplot = plt.imshow(img)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can also load labeled datasets into [torchvision datasets](https://pytorch.org/docs/stable/torchvision/datasets.html), so that you can leverage on the open source libraries provided by PyTorch for image transformation and training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from torchvision.transforms import functional as F\n",
        "\n",
        "# load animal_labels dataset into torchvision dataset\n",
        "pytorch_dataset = animal_labels.to_torchvision()\n",
        "img = pytorch_dataset[0][0]\n",
        "print(type(img))\n",
        "\n",
        "# use methods from torchvision to transform the img into grayscale\n",
        "pil_image = F.to_pil_image(img)\n",
        "gray_image = F.to_grayscale(pil_image, num_output_channels=3)\n",
        "\n",
        "imgplot = plt.imshow(gray_image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train an image classification model\n",
        "\n",
        " `crack_labels` dataset used in this tutorial section is the output from a labeling project, with the task type of \"Image Classification Multi-class\". We will use this dataset to train an image classification model that classify whether an image has cracks or not."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get crack_labels dataset from the workspace\n",
        "crack_labels = Dataset.get_by_name(workspace, 'crack_labels')\n",
        "crack_labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Configure Estimator for training\n",
        "\n",
        "You can ask the system to build a conda environment based on your dependency specification. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Environment\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "conda_env = Environment('conda-env')\n",
        "conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-sdk',\n",
        "                                                                             'azureml-contrib-dataset',\n",
        "                                                                             'torch','torchvision',\n",
        "                                                                             'azureml-dataprep[pandas]'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create a generic estimator for by specifying\n",
        "\n",
        "* The name of the estimator object, `est`\n",
        "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
        "* The training script name, train.py\n",
        "* The input dataset for training\n",
        "* The compute target. In this case you will use the AmlCompute you created\n",
        "* The environment definition for the experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.estimator import Estimator\n",
        "\n",
        "est = Estimator(source_directory=script_folder, \n",
        "                entry_script='train.py',\n",
        "                inputs=[crack_labels.as_named_input('crack_labels')],\n",
        "                compute_target=compute_target,\n",
        "                environment_definition= conda_env)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit job to run\n",
        "\n",
        "Submit the estimator to the Azure ML experiment to kick off the execution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run = exp.submit(est)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.wait_for_completion(show_output=True)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "sihhu"
      }
    ],
    "category": "tutorial",
    "compute": [
      "Remote"
    ],
    "deployment": [
      "None"
    ],
    "exclude_from_index": false,
    "framework": [
      "Azure ML"
    ],
    "friendly_name": "Introduction to labeled datasets",
    "index_order": 1,
    "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": "3.6.9"
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    },
    "star_tag": [
      "featured"
    ],
    "tags": [
      "Dataset",
      "label",
      "Estimator"
    ],
    "task": "Train"
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets/train.py
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets/train.py
@@ -1,106 +0,0 @@
 import os
 import torchvision
 import torchvision.transforms as transforms
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 from azureml.core import Dataset, Run
 import azureml.contrib.dataset
 from azureml.contrib.dataset import FileHandlingOption, LabeledDatasetTask
 run = Run.get_context()
 # get input dataset by name
 labeled_dataset = run.input_datasets['crack_labels']
 pytorch_dataset = labeled_dataset.to_torchvision()
 indices = torch.randperm(len(pytorch_dataset)).tolist()
 dataset_train = torch.utils.data.Subset(pytorch_dataset, indices[:40])
 dataset_test = torch.utils.data.Subset(pytorch_dataset, indices[-10:])
 trainloader = torch.utils.data.DataLoader(dataset_train, batch_size=4,
                                          shuffle=True, num_workers=0)
 testloader = torch.utils.data.DataLoader(dataset_test, batch_size=4,
                                         shuffle=True, num_workers=0)
 class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 71 * 71, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(x.size(0), 16 * 71 * 71)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
 net = Net()
 criterion = nn.CrossEntropyLoss()
 optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
 for epoch in range(2):  # loop over the dataset multiple times
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data
        # zero the parameter gradients
        optimizer.zero_grad()
        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        # print statistics
        running_loss += loss.item()
        if i % 5 == 4:    # print every 5 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 5))
            running_loss = 0.0
 print('Finished Training')
 classes = trainloader.dataset.dataset.labels
 PATH = './cifar_net.pth'
 torch.save(net.state_dict(), PATH)
 dataiter = iter(testloader)
 images, labels = dataiter.next()
 net = Net()
 net.load_state_dict(torch.load(PATH))
 outputs = net(images)
 _, predicted = torch.max(outputs, 1)
 correct = 0
 total = 0
 with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
 print('Accuracy of the network on the 10 test images: %d %%' % (100 * correct / total))
 pass
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/prepare.py
@@ -1,35 +0,0 @@
 import os
 def convert(imgf, labelf, outf, n):
    f = open(imgf, "rb")
    l = open(labelf, "rb")
    o = open(outf, "w")
    f.read(16)
    l.read(8)
    images = []
    for i in range(n):
        image = [ord(l.read(1))]
        for j in range(28 * 28):
            image.append(ord(f.read(1)))
        images.append(image)
    for image in images:
        o.write(",".join(str(pix) for pix in image) + "\n")
    f.close()
    o.close()
    l.close()
 mounted_input_path = os.environ['fashion_ds']
 mounted_output_path = os.environ['AZUREML_DATAREFERENCE_prepared_fashion_ds']
 os.makedirs(mounted_output_path, exist_ok=True)
 convert(os.path.join(mounted_input_path, 'train-images-idx3-ubyte'),
        os.path.join(mounted_input_path, 'train-labels-idx1-ubyte'),
        os.path.join(mounted_output_path, 'mnist_train.csv'), 60000)
 convert(os.path.join(mounted_input_path, 't10k-images-idx3-ubyte'),
        os.path.join(mounted_input_path, 't10k-labels-idx1-ubyte'),
        os.path.join(mounted_output_path, 'mnist_test.csv'), 10000)
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/t10k-images-idx3-ubyte
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/t10k-images-idx3-ubyte
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/t10k-labels-idx1-ubyte
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/t10k-labels-idx1-ubyte
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train-images-idx3-ubyte
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train-images-idx3-ubyte
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train-labels-idx1-ubyte
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train-labels-idx1-ubyte
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train.py
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/keras-mnist-fashion/train.py
@@ -1,120 +0,0 @@
 import keras
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Flatten
 from keras.layers import Conv2D, MaxPooling2D
 from keras.layers.normalization import BatchNormalization
 from keras.utils import to_categorical
 from keras.callbacks import Callback
 import numpy as np
 import pandas as pd
 import os
 import matplotlib.pyplot as plt
 from sklearn.model_selection import train_test_split
 from azureml.core import Run
 # dataset object from the run
 run = Run.get_context()
 dataset = run.input_datasets['prepared_fashion_ds']
 # split dataset into train and test set
 (train_dataset, test_dataset) = dataset.random_split(percentage=0.8, seed=111)
 # load dataset into pandas dataframe
 data_train = train_dataset.to_pandas_dataframe()
 data_test = test_dataset.to_pandas_dataframe()
 img_rows, img_cols = 28, 28
 input_shape = (img_rows, img_cols, 1)
 X = np.array(data_train.iloc[:, 1:])
 y = to_categorical(np.array(data_train.iloc[:, 0]))
 # here we split validation data to optimiza classifier during training
 X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=13)
 # test data
 X_test = np.array(data_test.iloc[:, 1:])
 y_test = to_categorical(np.array(data_test.iloc[:, 0]))
 X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1).astype('float32') / 255
 X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1).astype('float32') / 255
 X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1).astype('float32') / 255
 batch_size = 256
 num_classes = 10
 epochs = 10
 # construct neuron network
 model = Sequential()
 model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 kernel_initializer='he_normal',
                 input_shape=input_shape))
 model.add(MaxPooling2D((2, 2)))
 model.add(Dropout(0.25))
 model.add(Conv2D(64, (3, 3), activation='relu'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Dropout(0.25))
 model.add(Conv2D(128, (3, 3), activation='relu'))
 model.add(Dropout(0.4))
 model.add(Flatten())
 model.add(Dense(128, activation='relu'))
 model.add(Dropout(0.3))
 model.add(Dense(num_classes, activation='softmax'))
 model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adam(),
              metrics=['accuracy'])
 # start an Azure ML run
 run = Run.get_context()
 class LogRunMetrics(Callback):
    # callback at the end of every epoch
    def on_epoch_end(self, epoch, log):
        # log a value repeated which creates a list
        run.log('Loss', log['loss'])
        run.log('Accuracy', log['accuracy'])
 history = model.fit(X_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(X_val, y_val),
                    callbacks=[LogRunMetrics()])
 score = model.evaluate(X_test, y_test, verbose=0)
 # log a single value
 run.log("Final test loss", score[0])
 print('Test loss:', score[0])
 run.log('Final test accuracy', score[1])
 print('Test accuracy:', score[1])
 plt.figure(figsize=(6, 3))
 plt.title('Fashion MNIST with Keras ({} epochs)'.format(epochs), fontsize=14)
 plt.plot(history.history['accuracy'], 'b-', label='Accuracy', lw=4, alpha=0.5)
 plt.plot(history.history['loss'], 'r--', label='Loss', lw=4, alpha=0.5)
 plt.legend(fontsize=12)
 plt.grid(True)
 # log an image
 run.log_image('Loss v.s. Accuracy', plot=plt)
 # create a ./outputs/model folder in the compute target
 # files saved in the "./outputs" folder are automatically uploaded into run history
 os.makedirs('./outputs/model', exist_ok=True)
 # serialize NN architecture to JSON
 model_json = model.to_json()
 # save model JSON
 with open('./outputs/model/model.json', 'w') as f:
    f.write(model_json)
 # save model weights
 model.save_weights('./outputs/model/model.h5')
 print("model saved in ./outputs/model folder")
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb
@@ -1,488 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License [2017] Zalando SE, https://tech.zalando.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Build a simple ML pipeline for image classification\n",
        "\n",
        "## Introduction\n",
        "This tutorial shows how to train a simple deep neural network using the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset and Keras on Azure Machine Learning. Fashion-MNIST is a dataset of Zalando's article images\u00e2\u20ac\u201dconsisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.\n",
        "\n",
        "Learn how to:\n",
        "\n",
        "> * Set up your development environment\n",
        "> * Create the Fashion MNIST dataset\n",
        "> * Create a machine learning pipeline to train a simple deep learning neural network on a remote cluster\n",
        "> * Retrieve input datasets from the experiment and register the output model with datasets\n",
        "\n",
        "## Prerequisite:\n",
        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
        "    * install the latest version of AzureML SDK\n",
        "    * create a workspace and its configuration file (`config.json`)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set up your development environment\n",
        "\n",
        "All the setup for your development work can be accomplished in a Python notebook.  Setup includes:\n",
        "\n",
        "* Importing Python packages\n",
        "* Connecting to a workspace to enable communication between your local computer and remote resources\n",
        "* Creating an experiment to track all your runs\n",
        "* Creating a remote compute target to use for training\n",
        "\n",
        "### Import packages\n",
        "\n",
        "Import Python packages you need in this session. Also display the Azure Machine Learning SDK version."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "import azureml.core\n",
        "from azureml.core import Workspace, Dataset, Datastore, ComputeTarget, RunConfiguration, Experiment\n",
        "from azureml.core.runconfig import CondaDependencies\n",
        "from azureml.pipeline.steps import PythonScriptStep, EstimatorStep\n",
        "from azureml.pipeline.core import Pipeline, PipelineData\n",
        "from azureml.train.dnn import TensorFlow\n",
        "\n",
        "# check core SDK version number\n",
        "print(\"Azure ML SDK Version: \", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Connect to workspace\n",
        "\n",
        "Create a workspace object from the existing workspace. `Workspace.from_config()` reads the file **config.json** and loads the details into an object named `workspace`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# load workspace\n",
        "workspace = Workspace.from_config()\n",
        "print('Workspace name: ' + workspace.name, \n",
        "      'Azure region: ' + workspace.location, \n",
        "      'Subscription id: ' + workspace.subscription_id, \n",
        "      'Resource group: ' + workspace.resource_group, sep='\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create experiment and a directory\n",
        "\n",
        "Create an experiment to track the runs in your workspace and a directory to deliver the necessary code from your computer to the remote resource."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# create an ML experiment\n",
        "exp = Experiment(workspace=workspace, name='keras-mnist-fashion')\n",
        "\n",
        "# create a directory\n",
        "script_folder = './keras-mnist-fashion'\n",
        "os.makedirs(script_folder, exist_ok=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create or Attach existing compute resource\n",
        "By using Azure Machine Learning Compute, 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 Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
        "\n",
        "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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",
        "cluster_name = \"your-cluster-name\"\n",
        "\n",
        "try:\n",
        "    compute_target = ComputeTarget(workspace=workspace, name=cluster_name)\n",
        "    print('Found existing compute target')\n",
        "except ComputeTargetException:\n",
        "    print('Creating a new compute target...')\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_NC6', \n",
        "                                                           max_nodes=4)\n",
        "\n",
        "    # create the cluster\n",
        "    compute_target = ComputeTarget.create(workspace, cluster_name, compute_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 uses 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",
        "# use get_status() to get a detailed status for the current cluster. \n",
        "print(compute_target.get_status().serialize())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create the Fashion MNIST dataset\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. The data remains in its existing location, so no extra storage cost is incurred. \n",
        "\n",
        "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create a dataset from it. We will now upload the [Fashion MNIST](./keras-mnist-fashion) to the default datastore (blob) within your workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "datastore = workspace.get_default_datastore()\n",
        "datastore.upload_files(files = ['keras-mnist-fashion/t10k-images-idx3-ubyte', 'keras-mnist-fashion/t10k-labels-idx1-ubyte',\n",
        "                               'keras-mnist-fashion/train-images-idx3-ubyte','keras-mnist-fashion/train-labels-idx1-ubyte'],\n",
        "                       target_path = 'mnist-fashion',\n",
        "                       overwrite = True,\n",
        "                       show_progress = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Then we will create an unregistered FileDataset pointing to the path in the datastore. You can also create a dataset from multiple paths. [Learn More](https://aka.ms/azureml/howto/createdatasets) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fashion_ds = Dataset.File.from_files([(datastore, 'mnist-fashion')])\n",
        "\n",
        "# list the files referenced by fashion_ds\n",
        "fashion_ds.to_path()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Build 2-step ML pipeline\n",
        "\n",
        "The [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines) enables data scientists to create and manage multiple simple and complex workflows concurrently. A typical pipeline would have multiple tasks to prepare data, train, deploy and evaluate models. Individual steps in the pipeline can make use of diverse compute options (for example: CPU for data preparation and GPU for training) and languages. [Learn More](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines)\n",
        "\n",
        "\n",
        "### Step 1: data preparation\n",
        "\n",
        "In step one, we will load the image and labels from Fashion MNIST dataset into mnist_train.csv and mnist_test.csv\n",
        "\n",
        "Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. Both mnist_train.csv and mnist_test.csv contain 785 columns. The first column consists of the class labels, which represent the article of clothing. The rest of the columns contain the pixel-values of the associated image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# set up the compute environment to install required packages\n",
        "conda = CondaDependencies.create(\n",
        "        pip_packages=['azureml-sdk','azureml-dataprep[fuse,pandas]'],\n",
        "        pin_sdk_version=False)\n",
        "\n",
        "conda.set_pip_option('--pre')\n",
        "\n",
        "run_config = RunConfiguration()\n",
        "run_config.environment.python.conda_dependencies = conda"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Intermediate data (or output of a step) is represented by a `PipelineData` object. preprared_fashion_ds is produced as the output of step 1, and used as the input of step 2. PipelineData introduces a data dependency between steps, and creates an implicit execution order in the pipeline. You can register a `PipelineData` as a dataset and version the output data automatically. [Learn More](https://docs.microsoft.com/azure/machine-learning/service/how-to-version-track-datasets#version-a-pipeline-output-dataset) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# define output data\n",
        "prepared_fashion_ds = PipelineData('prepared_fashion_ds', datastore=datastore).as_dataset()\n",
        "\n",
        "# register output data as dataset\n",
        "prepared_fashion_ds = prepared_fashion_ds.register(name='prepared_fashion_ds', create_new_version=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "A **PythonScriptStep** is a basic, built-in step to run a Python Script on a compute target. It takes a script name and optionally other parameters like arguments for the script, compute target, inputs and outputs. If no compute target is specified, default compute target for the workspace is used. You can also use a [**RunConfiguration**](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfiguration?view=azure-ml-py) to specify requirements for the PythonScriptStep, such as conda dependencies and docker image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "prep_step = PythonScriptStep(name='prepare step',\n",
        "                             script_name=\"prepare.py\",\n",
        "                             # mount fashion_ds dataset to the compute_target\n",
        "                             inputs=[fashion_ds.as_named_input('fashion_ds').as_mount()],\n",
        "                             outputs=[prepared_fashion_ds],\n",
        "                             source_directory=script_folder,\n",
        "                             compute_target=compute_target,\n",
        "                             runconfig=run_config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Step 2: train CNN with Keras\n",
        "\n",
        "Next, we construct an `azureml.train.dnn.TensorFlow` estimator object. The TensorFlow estimator is providing a simple way of launching a TensorFlow training job on a compute target. It will automatically provide a docker image that has TensorFlow installed.\n",
        "\n",
        "[EstimatorStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep?view=azure-ml-py) adds a step to run Tensorflow Estimator in a Pipeline. It takes a dataset as the input."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# set up training step with Tensorflow estimator\n",
        "est = TensorFlow(entry_script='train.py',\n",
        "                 source_directory=script_folder,                 \n",
        "                 pip_packages = ['azureml-sdk','keras','numpy','scikit-learn', 'matplotlib'],\n",
        "                 compute_target=compute_target)\n",
        "\n",
        "est_step = EstimatorStep(name='train step',\n",
        "                         estimator=est,\n",
        "                         estimator_entry_script_arguments=[],\n",
        "                         # parse prepared_fashion_ds into TabularDataset and use it as the input\n",
        "                         inputs=[prepared_fashion_ds.parse_delimited_files()],\n",
        "                         compute_target=compute_target)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Build the pipeline\n",
        "Once we have the steps (or steps collection), we can build the [pipeline](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline.pipeline?view=azure-ml-py).\n",
        "\n",
        "A pipeline is created with a list of steps and a workspace. Submit a pipeline using [submit](https://docs.microsoft.com/python/api/azureml-core/azureml.core.experiment(class)?view=azure-ml-py#submit-config--tags-none----kwargs-). When submit is called, a [PipelineRun](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun?view=azure-ml-py) is created which in turn creates [StepRun](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun?view=azure-ml-py) objects for each step in the workflow."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# build pipeline & run experiment\n",
        "pipeline = Pipeline(workspace, steps=[prep_step, est_step])\n",
        "run = exp.submit(pipeline)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Monitor the PipelineRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "inputHidden": false,
        "outputHidden": false
      },
      "outputs": [],
      "source": [
        "run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.find_step_run('train step')[0].get_metrics()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Register the input dataset and the output model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Azure Machine Learning dataset makes it easy to trace how your data is used in ML. [Learn More](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-version-track-datasets#track-datasets-in-experiments)<br>\n",
        "For each Machine Learning experiment, you can easily trace the datasets used as the input through `Run` object."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get input datasets\n",
        "prep_step = run.find_step_run('prepare step')[0]\n",
        "inputs = prep_step.get_details()['inputDatasets']\n",
        "input_dataset = inputs[0]['dataset']\n",
        "\n",
        "# list the files referenced by input_dataset\n",
        "input_dataset.to_path()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Register the input Fashion MNIST dataset with the workspace so that you can reuse it in other experiments or share it with your colleagues who have access to your workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fashion_ds = input_dataset.register(workspace = workspace,\n",
        "                                    name = 'fashion_ds',\n",
        "                                    description = 'image and label files from fashion mnist',\n",
        "                                    create_new_version = True)\n",
        "fashion_ds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Register the output model with dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.find_step_run('train step')[0].register_model(model_name = 'keras-model', model_path = 'outputs/model/', \n",
        "                                                  datasets =[('train test data',fashion_ds)])"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "sihhu"
      }
    ],
    "category": "tutorial",
    "compute": [
      "Remote"
    ],
    "datasets": [
      "Fashion MNIST"
    ],
    "deployment": [
      "None"
    ],
    "exclude_from_index": false,
    "framework": [
      "Azure ML"
    ],
    "friendly_name": "Datasets with ML Pipeline",
    "index_order": 1,
    "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": "3.6.9"
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    },
    "star_tag": [
      "featured"
    ],
    "tags": [
      "Dataset",
      "Pipeline",
      "Estimator",
      "ScriptRun"
    ],
    "task": "Train"
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb
@@ -1,568 +0,0 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Tabular Time Series Related API Demo with NOAA Weather Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved. <br>\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this notebook, you will learn how to use the Tabular Time Series related API to filter the data by time windows for sample data uploaded to Azure blob storage. \n",
        "\n",
        "The detailed APIs to be demoed in this script are:\n",
        "- Create Tabular Dataset instance\n",
        "- Assign fine timestamp column and coarse timestamp column for Tabular Dataset to activate Time Series related APIs\n",
        "- Clear fine timestamp column and coarse timestamp column\n",
        "- Filter in data before a specific time\n",
        "- Filter in data after a specific time\n",
        "- Filter in data in a specific time range\n",
        "- Filter in data for recent time range\n",
        "\n",
        "Besides above APIs, you'll also see:\n",
        "- Create and load a Workspace\n",
        "- Load National Oceanic & Atmospheric (NOAA) weather data into Azure blob storage\n",
        "- Create and register NOAA weather data as a Tabular dataset\n",
        "- Re-load Tabular Dataset from your Workspace"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Import Dependencies\n",
        "\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, run the cells below to install the Azure Machine Learning Python SDK and create an Azure ML Workspace that's required for this demo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prepare Environment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Print out your version of the Azure ML Python SDK. Version 1.0.60 or above is required for TabularDataset with timeseries attribute. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "azureml.data.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import packages\n",
        "import os\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "from calendar import monthrange\n",
        "from datetime import datetime, timedelta\n",
        "\n",
        "from azureml.core import Dataset, Datastore, Workspace, Run\n",
        "from azureml.opendatasets import NoaaIsdWeather"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Set up Configuraton and Create Azure ML Workspace\n",
        "\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) first if you haven't already to establish your connection to the Azure ML Workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws     = Workspace.from_config()\n",
        "dstore = ws.get_default_datastore()\n",
        "\n",
        "dset_name = 'weather-data-florida'\n",
        "\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, dstore.name, sep = '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load Data to Blob Storage\n",
        "\n",
        "This demo uses public NOAA weather data. You can replace this data with your own. The first cell below creates a Pandas Dataframe object with the first 6 months of 2019 NOAA weather data. The last cell saves the data to a CSV file and uploads the CSV file to Azure blob storage to the location specified in the datapath variable. Currently, the Dataset class only reads uploaded files from blob storage. \n",
        "\n",
        "**NOTE:** to reduce the size of data, we will only keep specific rows with a given stationName."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_years = [2019]\n",
        "\n",
        "for year in target_years:\n",
        "    for month in range(1, 12+1):\n",
        "        path = 'data/{}/{:02d}/'.format(year, month)\n",
        "        \n",
        "        try:  \n",
        "            start = datetime(year, month, 1)\n",
        "            end   = datetime(year, month, monthrange(year, month)[1]) + timedelta(days=1)\n",
        "            isd   = NoaaIsdWeather(start, end).to_pandas_dataframe()\n",
        "            isd   = isd[isd['stationName'].str.contains('FLORIDA', regex=True, na=False)]\n",
        "            \n",
        "            os.makedirs(path, exist_ok=True)\n",
        "            isd.to_parquet(path + 'data.parquet')\n",
        "        except Exception as e:\n",
        "            print('Month {} in year {} likely has no data.\\n'.format(month, year))\n",
        "            print('Exception: {}'.format(e))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Upload data to blob storage so it can be used as a Dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dstore.upload('data', dset_name, overwrite=True, show_progress=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create & Register Tabular Dataset with time-series trait from Blob\n",
        "\n",
        "The API on Tabular datasets with time-series trait is specially designed to handle Tabular time-series data and time related operations more efficiently. By registering your time-series dataset, you are publishing your dataset to your workspace so that it is accessible to anyone with the same subscription id. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create Tabular Dataset instance from blob storage datapath.\n",
        "\n",
        "**TIP:** you can set virtual columns in the partition_format. I.e. if you partition the weather data by state and city, the path can be '/{STATE}/{CITY}/{coarse_time:yyy/MM}/data.parquet'. STATE and CITY would then appear as virtual columns in the dataset, allowing for efficient filtering by these grains. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "datastore_path = [(dstore, dset_name + '/*/*/data.parquet')]\n",
        "dataset        = Dataset.Tabular.from_parquet_files(path=datastore_path, partition_format = dset_name + '/{coarse_time:yyyy/MM}/data.parquet')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Assign fine timestamp column for Tabular Dataset to activate Time Series related APIs. The column to be assigned should be a Date type, otherwise the assigning will fail."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# for this demo, leave out coarse_time so fine_grain_timestamp is used\n",
        "tsd = dataset.with_timestamp_columns(fine_grain_timestamp='datetime') # , coarse_grain_timestamp='coarse_time')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Register the dataset for easy access from anywhere in Azure ML and to keep track of versions, lineage. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# register dataset to Workspace\n",
        "registered_ds = tsd.register(ws, dset_name, create_new_version=True, description='Data for Tabular Dataset with time-series trait demo.', tags={ 'type': 'TabularDataset' })"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Reload the Dataset from Workspace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get dataset by dataset name\n",
        "tsd = Dataset.get_by_name(ws, name=dset_name)\n",
        "tsd.to_pandas_dataframe().head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Filter Data by Time Windows\n",
        "\n",
        "Once your data has been loaded into the notebook, you can query by time using the time_before(), time_after(), time_between(), and time_recent() functions. You can also choose to drop or keep certain columns.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Before Time Input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# select data that occurs before a specified date\n",
        "tsd2 = tsd.time_before(datetime(2019, 6, 12))\n",
        "tsd2.to_pandas_dataframe().tail(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## After Time Input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# select data that occurs after a specified date\n",
        "tsd2 = tsd.time_after(datetime(2019, 5, 30))\n",
        "tsd2.to_pandas_dataframe().head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Before & After Time Inputs\n",
        "\n",
        "You can chain time functions together."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**NOTE:** You must set the coarse_grain_timestamp to None to filter on the fine_grain_timestamp. The below cell will fail unless the second line is uncommented "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# select data that occurs within a given time range\n",
        "#tsd  = tsd.with_timestamp_columns(fine_grain_timestamp='datetime', coarse_grain_timestamp=None)\n",
        "tsd2 = tsd.time_after(datetime(2019, 1, 2)).time_before(datetime(2019, 1, 10))\n",
        "tsd2.to_pandas_dataframe().head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Time Range Input"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# another way to select data that occurs within a given time range\n",
        "tsd2 = tsd.time_between(start_time=datetime(2019, 1, 31, 23, 59, 59), end_time=datetime(2019, 2, 7))\n",
        "tsd2.to_pandas_dataframe().head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Time Recent Input"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This function takes in a datetime.timedelta and returns a dataset containing the data from datetime.now()-timedelta() to datetime.now()."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "tsd2 = tsd.time_recent(timedelta(weeks=5, days=0))\n",
        "tsd2.to_pandas_dataframe().head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**NOTE:** This will return an empty dataframe there is no data within the last 2 days."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "tsd2 = tsd.time_recent(timedelta(days=2))\n",
        "tsd2.to_pandas_dataframe().tail(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Drop Columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<font color=red>The columns to be dropped should NOT include timstamp columns.</font><br>Below operation will lead to exception."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "try:\n",
        "    tsd2 = tsd.drop_columns(columns=['snowDepth', 'version', 'datetime'])\n",
        "except Exception as e:\n",
        "    print('Expected exception : {}'.format(str(e)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Drop will succeed if modify column list to exclude timestamp columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "tsd2 = tsd.drop_columns(columns=['snowDepth', 'version', 'upload_date'])\n",
        "tsd2.take(5).to_pandas_dataframe().sort_values(by='datetime')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Keep Columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<font color=red>The columns to be kept should ALWAYS include timstamp columns.</font><br>Below operation will lead to exception."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "try:\n",
        "    tsd2 = tsd.keep_columns(columns=['snowDepth'], validate=False)\n",
        "except Exception as e:\n",
        "    print('Expected exception : {}'.format(str(e)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Keep will succeed if modify column list to include timestamp columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "tsd2 = tsd.keep_columns(columns=['snowDepth', 'datetime', 'coarse_time'], validate=False)\n",
        "tsd2.to_pandas_dataframe().tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Resetting Timestamp Columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Rules for reseting are:\n",
        "- You cannot assign 'None' to fine_grain_timestamp while assign a valid column name to coarse_grain_timestamp because coarse_grain_timestamp is optional while fine_grain_timestamp is mandatory for Tabular time series data.\n",
        "- If you assign 'None' to fine_grain_timestamp, then both fine_grain_timestamp and coarse_grain_timestamp will all be cleared.\n",
        "- If you assign only 'None' to coarse_grain_timestamp, then only coarse_grain_timestamp will be cleared."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Illegal clearing, exception is expected.\n",
        "try:\n",
        "    tsd2 = tsd.with_timestamp_columns(fine_grain_timestamp=None, coarse_grain_timestamp='coarse_time')\n",
        "except Exception as e:\n",
        "    print('Cleaning not allowed because {}'.format(str(e)))\n",
        "\n",
        "# clear both\n",
        "tsd2 = tsd.with_timestamp_columns(fine_grain_timestamp=None, coarse_grain_timestamp=None)\n",
        "print('after clean both with None/None, timestamp columns are: {}'.format(tsd2.timestamp_columns))\n",
        "\n",
        "# clear coarse_grain_timestamp only and assign 'datetime' as fine timestamp column\n",
        "tsd2 = tsd2.with_timestamp_columns(fine_grain_timestamp='datetime', coarse_grain_timestamp=None)\n",
        "print('after clean coarse timestamp column, timestamp columns are: {}'.format(tsd2.timestamp_columns))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/work-with-data/datasets-tutorial/datasets-tutorial.png)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "ylxiong"
      }
    ],
    "category": "tutorial",
    "compute": [
      "Local"
    ],
    "datasets": [
      "NOAA"
    ],
    "deployment": [
      "None"
    ],
    "exclude_from_index": false,
    "framework": [
      "Azure ML"
    ],
    "friendly_name": "Filtering data using Tabular Timeseiries Dataset related API",
    "index_order": 1,
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    },
    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
    "star_tag": [
      "featured"
    ],
    "tags": [
      "Dataset",
      "Tabular Timeseries"
    ],
    "task": "Filtering"
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-dataset/iris.csv
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-dataset/iris.csv
@@ -1,151 +0,0 @@
 sepal_length,sepal_width,petal_length,petal_width,species
 5.1,3.5,1.4,0.2,Iris-setosa
 4.9,3,1.4,0.2,Iris-setosa
 4.7,3.2,1.3,0.2,Iris-setosa
 4.6,3.1,1.5,0.2,Iris-setosa
 5,3.6,1.4,0.2,Iris-setosa
 5.4,3.9,1.7,0.4,Iris-setosa
 4.6,3.4,1.4,0.3,Iris-setosa
 5,3.4,1.5,0.2,Iris-setosa
 4.4,2.9,1.4,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 5.4,3.7,1.5,0.2,Iris-setosa
 4.8,3.4,1.6,0.2,Iris-setosa
 4.8,3,1.4,0.1,Iris-setosa
 4.3,3,1.1,0.1,Iris-setosa
 5.8,4,1.2,0.2,Iris-setosa
 5.7,4.4,1.5,0.4,Iris-setosa
 5.4,3.9,1.3,0.4,Iris-setosa
 5.1,3.5,1.4,0.3,Iris-setosa
 5.7,3.8,1.7,0.3,Iris-setosa
 5.1,3.8,1.5,0.3,Iris-setosa
 5.4,3.4,1.7,0.2,Iris-setosa
 5.1,3.7,1.5,0.4,Iris-setosa
 4.6,3.6,1,0.2,Iris-setosa
 5.1,3.3,1.7,0.5,Iris-setosa
 4.8,3.4,1.9,0.2,Iris-setosa
 5,3,1.6,0.2,Iris-setosa
 5,3.4,1.6,0.4,Iris-setosa
 5.2,3.5,1.5,0.2,Iris-setosa
 5.2,3.4,1.4,0.2,Iris-setosa
 4.7,3.2,1.6,0.2,Iris-setosa
 4.8,3.1,1.6,0.2,Iris-setosa
 5.4,3.4,1.5,0.4,Iris-setosa
 5.2,4.1,1.5,0.1,Iris-setosa
 5.5,4.2,1.4,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 5,3.2,1.2,0.2,Iris-setosa
 5.5,3.5,1.3,0.2,Iris-setosa
 4.9,3.1,1.5,0.1,Iris-setosa
 4.4,3,1.3,0.2,Iris-setosa
 5.1,3.4,1.5,0.2,Iris-setosa
 5,3.5,1.3,0.3,Iris-setosa
 4.5,2.3,1.3,0.3,Iris-setosa
 4.4,3.2,1.3,0.2,Iris-setosa
 5,3.5,1.6,0.6,Iris-setosa
 5.1,3.8,1.9,0.4,Iris-setosa
 4.8,3,1.4,0.3,Iris-setosa
 5.1,3.8,1.6,0.2,Iris-setosa
 4.6,3.2,1.4,0.2,Iris-setosa
 5.3,3.7,1.5,0.2,Iris-setosa
 5,3.3,1.4,0.2,Iris-setosa
 7,3.2,4.7,1.4,Iris-versicolor
 6.4,3.2,4.5,1.5,Iris-versicolor
 6.9,3.1,4.9,1.5,Iris-versicolor
 5.5,2.3,4,1.3,Iris-versicolor
 6.5,2.8,4.6,1.5,Iris-versicolor
 5.7,2.8,4.5,1.3,Iris-versicolor
 6.3,3.3,4.7,1.6,Iris-versicolor
 4.9,2.4,3.3,1,Iris-versicolor
 6.6,2.9,4.6,1.3,Iris-versicolor
 5.2,2.7,3.9,1.4,Iris-versicolor
 5,2,3.5,1,Iris-versicolor
 5.9,3,4.2,1.5,Iris-versicolor
 6,2.2,4,1,Iris-versicolor
 6.1,2.9,4.7,1.4,Iris-versicolor
 5.6,2.9,3.6,1.3,Iris-versicolor
 6.7,3.1,4.4,1.4,Iris-versicolor
 5.6,3,4.5,1.5,Iris-versicolor
 5.8,2.7,4.1,1,Iris-versicolor
 6.2,2.2,4.5,1.5,Iris-versicolor
 5.6,2.5,3.9,1.1,Iris-versicolor
 5.9,3.2,4.8,1.8,Iris-versicolor
 6.1,2.8,4,1.3,Iris-versicolor
 6.3,2.5,4.9,1.5,Iris-versicolor
 6.1,2.8,4.7,1.2,Iris-versicolor
 6.4,2.9,4.3,1.3,Iris-versicolor
 6.6,3,4.4,1.4,Iris-versicolor
 6.8,2.8,4.8,1.4,Iris-versicolor
 6.7,3,5,1.7,Iris-versicolor
 6,2.9,4.5,1.5,Iris-versicolor
 5.7,2.6,3.5,1,Iris-versicolor
 5.5,2.4,3.8,1.1,Iris-versicolor
 5.5,2.4,3.7,1,Iris-versicolor
 5.8,2.7,3.9,1.2,Iris-versicolor
 6,2.7,5.1,1.6,Iris-versicolor
 5.4,3,4.5,1.5,Iris-versicolor
 6,3.4,4.5,1.6,Iris-versicolor
 6.7,3.1,4.7,1.5,Iris-versicolor
 6.3,2.3,4.4,1.3,Iris-versicolor
 5.6,3,4.1,1.3,Iris-versicolor
 5.5,2.5,4,1.3,Iris-versicolor
 5.5,2.6,4.4,1.2,Iris-versicolor
 6.1,3,4.6,1.4,Iris-versicolor
 5.8,2.6,4,1.2,Iris-versicolor
 5,2.3,3.3,1,Iris-versicolor
 5.6,2.7,4.2,1.3,Iris-versicolor
 5.7,3,4.2,1.2,Iris-versicolor
 5.7,2.9,4.2,1.3,Iris-versicolor
 6.2,2.9,4.3,1.3,Iris-versicolor
 5.1,2.5,3,1.1,Iris-versicolor
 5.7,2.8,4.1,1.3,Iris-versicolor
 6.3,3.3,6,2.5,Iris-virginica
 5.8,2.7,5.1,1.9,Iris-virginica
 7.1,3,5.9,2.1,Iris-virginica
 6.3,2.9,5.6,1.8,Iris-virginica
 6.5,3,5.8,2.2,Iris-virginica
 7.6,3,6.6,2.1,Iris-virginica
 4.9,2.5,4.5,1.7,Iris-virginica
 7.3,2.9,6.3,1.8,Iris-virginica
 6.7,2.5,5.8,1.8,Iris-virginica
 7.2,3.6,6.1,2.5,Iris-virginica
 6.5,3.2,5.1,2,Iris-virginica
 6.4,2.7,5.3,1.9,Iris-virginica
 6.8,3,5.5,2.1,Iris-virginica
 5.7,2.5,5,2,Iris-virginica
 5.8,2.8,5.1,2.4,Iris-virginica
 6.4,3.2,5.3,2.3,Iris-virginica
 6.5,3,5.5,1.8,Iris-virginica
 7.7,3.8,6.7,2.2,Iris-virginica
 7.7,2.6,6.9,2.3,Iris-virginica
 6,2.2,5,1.5,Iris-virginica
 6.9,3.2,5.7,2.3,Iris-virginica
 5.6,2.8,4.9,2,Iris-virginica
 7.7,2.8,6.7,2,Iris-virginica
 6.3,2.7,4.9,1.8,Iris-virginica
 6.7,3.3,5.7,2.1,Iris-virginica
 7.2,3.2,6,1.8,Iris-virginica
 6.2,2.8,4.8,1.8,Iris-virginica
 6.1,3,4.9,1.8,Iris-virginica
 6.4,2.8,5.6,2.1,Iris-virginica
 7.2,3,5.8,1.6,Iris-virginica
 7.4,2.8,6.1,1.9,Iris-virginica
 7.9,3.8,6.4,2,Iris-virginica
 6.4,2.8,5.6,2.2,Iris-virginica
 6.3,2.8,5.1,1.5,Iris-virginica
 6.1,2.6,5.6,1.4,Iris-virginica
 7.7,3,6.1,2.3,Iris-virginica
 6.3,3.4,5.6,2.4,Iris-virginica
 6.4,3.1,5.5,1.8,Iris-virginica
 6,3,4.8,1.8,Iris-virginica
 6.9,3.1,5.4,2.1,Iris-virginica
 6.7,3.1,5.6,2.4,Iris-virginica
 6.9,3.1,5.1,2.3,Iris-virginica
 5.8,2.7,5.1,1.9,Iris-virginica
 6.8,3.2,5.9,2.3,Iris-virginica
 6.7,3.3,5.7,2.5,Iris-virginica
 6.7,3,5.2,2.3,Iris-virginica
 6.3,2.5,5,1.9,Iris-virginica
 6.5,3,5.2,2,Iris-virginica
 6.2,3.4,5.4,2.3,Iris-virginica
 5.9,3,5.1,1.8,Iris-virginica
--- a/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb
+++ b/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb
@@ -1,656 +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/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Train with Azure Machine Learning datasets\n",
        "Datasets are categorized into TabularDataset and FileDataset based on how users consume them in training. \n",
        "* A TabularDataset represents data in a tabular format by parsing the provided file or list of files. TabularDataset can be created from csv, tsv, parquet files, SQL query results etc. For the complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). It provides you with the ability to materialize the data into a pandas DataFrame.\n",
        "* A FileDataset references single or multiple files in your datastores or public urls. This provides you with the ability to download or mount the files to your compute. The files can be of any format, which enables a wider range of machine learning scenarios including deep learning.\n",
        "\n",
        "In this tutorial, you will learn how to train with Azure Machine Learning datasets:\n",
        "\n",
        "&#x2611; Use datasets directly in your training script\n",
        "\n",
        "&#x2611; Use datasets to mount files to a remote compute"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prerequisites\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 established your connection to the AzureML Workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Check core SDK version number\n",
        "import azureml.core\n",
        "\n",
        "print('SDK version:', azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Initialize Workspace\n",
        "\n",
        "Initialize a workspace object from persisted configuration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create Experiment\n",
        "\n",
        "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "experiment_name = 'train-with-datasets'\n",
        "\n",
        "from azureml.core import Experiment\n",
        "exp = Experiment(workspace=ws, name=experiment_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create or Attach existing compute resource\n",
        "By using Azure Machine Learning Compute, 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 Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
        "\n",
        "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "import os\n",
        "\n",
        "# choose a name for your cluster\n",
        "compute_name = os.environ.get('AML_COMPUTE_CLUSTER_NAME', 'cpu-cluster')\n",
        "compute_min_nodes = os.environ.get('AML_COMPUTE_CLUSTER_MIN_NODES', 0)\n",
        "compute_max_nodes = os.environ.get('AML_COMPUTE_CLUSTER_MAX_NODES', 4)\n",
        "\n",
        "# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6\n",
        "vm_size = os.environ.get('AML_COMPUTE_CLUSTER_SKU', 'STANDARD_D2_V2')\n",
        "\n",
        "\n",
        "if compute_name in ws.compute_targets:\n",
        "    compute_target = ws.compute_targets[compute_name]\n",
        "    if compute_target and type(compute_target) is AmlCompute:\n",
        "        print('found compute target. just use it. ' + compute_name)\n",
        "else:\n",
        "    print('creating a new compute target...')\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size=vm_size,\n",
        "                                                                min_nodes=compute_min_nodes, \n",
        "                                                                max_nodes=compute_max_nodes)\n",
        "\n",
        "    # create the cluster\n",
        "    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)\n",
        "    \n",
        "    # can poll for a minimum number of nodes and for a specific timeout. \n",
        "    # if no min node count is provided it will use the scale settings for the cluster\n",
        "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
        "    \n",
        "     # For a more detailed view of current AmlCompute status, use get_status()\n",
        "    print(compute_target.get_status().serialize())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You now have the necessary packages and compute resources to train a model in the cloud.\n",
        "## Use datasets directly in training\n",
        "\n",
        "### Create a TabularDataset\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. The data remains in its existing location, so no extra storage cost is incurred. \n",
        "\n",
        "Every workspace comes with a default [datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and create dataset from it. We will now upload the [Iris data](./train-dataset/Iris.csv) to the default datastore (blob) within your workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "datastore = ws.get_default_datastore()\n",
        "datastore.upload_files(files = ['./train-dataset/iris.csv'],\n",
        "                       target_path = 'train-dataset/tabular/',\n",
        "                       overwrite = True,\n",
        "                       show_progress = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Then we will create an unregistered TabularDataset pointing to the path in the datastore. You can also create a dataset from multiple paths. [learn more](https://aka.ms/azureml/howto/createdatasets) \n",
        "\n",
        "[TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a Pandas or Spark DataFrame. You can create a TabularDataset object from .csv, .tsv, and parquet files, and from SQL query results. For a complete list, see [TabularDatasetFactory](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py) class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "dataset-remarks-tabular-sample"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core import Dataset\n",
        "dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'train-dataset/tabular/iris.csv')])\n",
        "\n",
        "# preview the first 3 rows of the dataset\n",
        "dataset.take(3).to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a training script\n",
        "\n",
        "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train_titanic.py` in the script_folder. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "script_folder = os.path.join(os.getcwd(), 'train-dataset')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $script_folder/train_iris.py\n",
        "\n",
        "import os\n",
        "\n",
        "from azureml.core import Dataset, Run\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.externals import joblib\n",
        "\n",
        "run = Run.get_context()\n",
        "# get input dataset by name\n",
        "dataset = run.input_datasets['iris']\n",
        "\n",
        "df = dataset.to_pandas_dataframe()\n",
        "\n",
        "x_col = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
        "y_col = ['species']\n",
        "x_df = df.loc[:, x_col]\n",
        "y_df = df.loc[:, y_col]\n",
        "\n",
        "#dividing X,y into train and test data\n",
        "x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=223)\n",
        "\n",
        "data = {'train': {'X': x_train, 'y': y_train},\n",
        "\n",
        "        'test': {'X': x_test, 'y': y_test}}\n",
        "\n",
        "clf = DecisionTreeClassifier().fit(data['train']['X'], data['train']['y'])\n",
        "model_file_name = 'decision_tree.pkl'\n",
        "\n",
        "print('Accuracy of Decision Tree classifier on training set: {:.2f}'.format(clf.score(x_train, y_train)))\n",
        "print('Accuracy of Decision Tree classifier on test set: {:.2f}'.format(clf.score(x_test, y_test)))\n",
        "\n",
        "os.makedirs('./outputs', exist_ok=True)\n",
        "with open(model_file_name, 'wb') as file:\n",
        "    joblib.dump(value=clf, filename='outputs/' + model_file_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Configure and use datasets as the input to Estimator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can ask the system to build a conda environment based on your dependency specification. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Environment\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "conda_env = Environment('conda-env')\n",
        "conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-sdk',\n",
        "                                                                             'azureml-dataprep[pandas,fuse]',\n",
        "                                                                             'scikit-learn'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "An estimator object is used to submit the run. Azure Machine Learning has pre-configured estimators for common machine learning frameworks, as well as generic Estimator. Create a generic estimator for by specifying\n",
        "\n",
        "* The name of the estimator object, `est`\n",
        "* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
        "* The training script name, train_titanic.py\n",
        "* The input dataset for training\n",
        "* The compute target. In this case you will use the AmlCompute you created\n",
        "* The environment definition for the experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.estimator import Estimator\n",
        "\n",
        "est = Estimator(source_directory=script_folder, \n",
        "                entry_script='train_iris.py', \n",
        "                # pass dataset object as an input with name 'titanic'\n",
        "                inputs=[dataset.as_named_input('iris')],\n",
        "                compute_target=compute_target,\n",
        "                environment_definition= conda_env) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit job to run\n",
        "Submit the estimator to the Azure ML experiment to kick off the execution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run = exp.submit(est)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "\n",
        "# monitor the run\n",
        "RunDetails(run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Use datasets to mount files to a remote compute\n",
        "\n",
        "You can use the `Dataset` object to mount or download files referred by it. When you mount a file system, you attach that file system to a directory (mount point) and make it available to the system. Because mounting load files at the time of processing, it is usually faster than download.<br> \n",
        "Note: mounting is only available for Linux-based compute (DSVM/VM, AMLCompute, HDInsights)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Upload data files into datastore\n",
        "We will first load diabetes data from `scikit-learn` to the train-dataset folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_diabetes\n",
        "import numpy as np\n",
        "\n",
        "training_data = load_diabetes()\n",
        "np.save(file='train-dataset/features.npy', arr=training_data['data'])\n",
        "np.save(file='train-dataset/labels.npy', arr=training_data['target'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now let's upload the 2 files into the default datastore under a path named `diabetes`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "datastore.upload_files(['train-dataset/features.npy', 'train-dataset/labels.npy'], target_path='diabetes', overwrite=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a FileDataset\n",
        "\n",
        "[FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.file_dataset.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public URLs. Using this method, you can download or mount the files to your compute as a FileDataset object. The files can be in any format, which enables a wider range of machine learning scenarios, including deep learning."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Dataset\n",
        "\n",
        "dataset = Dataset.File.from_files(path = [(datastore, 'diabetes/')])\n",
        "\n",
        "# see a list of files referenced by dataset\n",
        "dataset.to_path()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a training script\n",
        "\n",
        "To submit the job to the cluster, first create a training script. Run the following code to create the training script called `train_diabetes.py` in the script_folder. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile $script_folder/train_diabetes.py\n",
        "\n",
        "import os\n",
        "import glob\n",
        "\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import train_test_split\n",
        "from azureml.core.run import Run\n",
        "from sklearn.externals import joblib\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "os.makedirs('./outputs', exist_ok=True)\n",
        "\n",
        "run = Run.get_context()\n",
        "base_path = run.input_datasets['diabetes']\n",
        "\n",
        "X = np.load(glob.glob(os.path.join(base_path, '**/features.npy'), recursive=True)[0])\n",
        "y = np.load(glob.glob(os.path.join(base_path, '**/labels.npy'), recursive=True)[0])\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=0)\n",
        "data = {'train': {'X': X_train, 'y': y_train},\n",
        "        'test': {'X': X_test, 'y': y_test}}\n",
        "\n",
        "# list of numbers from 0.0 to 1.0 with a 0.05 interval\n",
        "alphas = np.arange(0.0, 1.0, 0.05)\n",
        "\n",
        "for alpha in alphas:\n",
        "    # use Ridge algorithm to create a regression model\n",
        "    reg = Ridge(alpha=alpha)\n",
        "    reg.fit(data['train']['X'], data['train']['y'])\n",
        "\n",
        "    preds = reg.predict(data['test']['X'])\n",
        "    mse = mean_squared_error(preds, data['test']['y'])\n",
        "    run.log('alpha', alpha)\n",
        "    run.log('mse', mse)\n",
        "\n",
        "    model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)\n",
        "    with open(model_file_name, 'wb') as file:\n",
        "        joblib.dump(value=reg, filename='outputs/' + model_file_name)\n",
        "\n",
        "    print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Configure & Run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import ScriptRunConfig\n",
        "\n",
        "src = ScriptRunConfig(source_directory=script_folder, \n",
        "                      script='train_diabetes.py', \n",
        "                      # to mount the dataset on the remote compute and pass the mounted path as an argument to the training script\n",
        "                      arguments =[dataset.as_named_input('diabetes').as_mount()])\n",
        "\n",
        "src.run_config.framework = 'python'\n",
        "src.run_config.environment = conda_env\n",
        "src.run_config.target = compute_target.name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run = exp.submit(config=src)\n",
        "\n",
        "# monitor the run\n",
        "RunDetails(run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Display run results\n",
        "You now have a model trained on a remote cluster. Retrieve all the metrics logged during the run, including the accuracy of the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(run.get_metrics())\n",
        "metrics = run.get_metrics()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register datasets\n",
        "Use the register() method to register datasets to your workspace so they can be shared with others, reused across various experiments, and referred to by name in your training script."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dataset = dataset.register(workspace = ws,\n",
        "                           name = 'diabetes dataset',\n",
        "                           description='training dataset',\n",
        "                           create_new_version=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Register models with datasets\n",
        "The last step in the training script wrote the model files in a directory named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special directory in that all content in this directory is automatically uploaded to your workspace. This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace.\n",
        "\n",
        "You can register models with datasets for reproducibility and auditing purpose."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# find the index where MSE is the smallest\n",
        "indices = list(range(0, len(metrics['mse'])))\n",
        "min_mse_index = min(indices, key=lambda x: metrics['mse'][x])\n",
        "\n",
        "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
        "    metrics['mse'][min_mse_index], \n",
        "    metrics['alpha'][min_mse_index]\n",
        "))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# find the best model\n",
        "best_alpha = metrics['alpha'][min_mse_index]\n",
        "model_file_name = 'ridge_{0:.2f}.pkl'.format(best_alpha)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# register the best model with the input dataset\n",
        "model = run.register_model(model_name='sklearn_diabetes', model_path=os.path.join('outputs', model_file_name),\n",
        "                           datasets =[('training data',dataset)])"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "sihhu"
      }
    ],
    "category": "tutorial",
    "compute": [
      "Remote"
    ],
    "datasets": [
      "Iris",
      "Diabetes"
    ],
    "deployment": [
      "None"
    ],
    "exclude_from_index": false,
    "framework": [
      "Azure ML"
    ],
    "friendly_name": "Train with Datasets (Tabular and File)",
    "index_order": 1,
    "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": "3.6.9"
    },
    "star_tag": [
      "featured"
    ],
    "tags": [
      "Dataset",
      "Estimator",
      "ScriptRun"
    ],
    "task": "Train"
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/index.md
+++ b/index.md
@@ -12,7 +12,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [Using Azure ML environments](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/using-environments/using-environments.ipynb) | Creating and registering environments | None | Local | None | None | None |
 | [Estimators in AML with hyperparameter tuning](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/how-to-use-estimator.ipynb) | Use the Estimator pattern in Azure Machine Learning SDK | None | AML Compute | None | None | None |
 ## Tutorials
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
@@ -25,10 +24,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | :star:[Data drift on aks](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/monitor-models/data-drift/drift-on-aks.ipynb) | Filtering | NOAA | Remote | AKS | Azure ML | Dataset, Timeseries, Drift |
 | [Train and deploy a model using Python SDK](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) | Training and deploying a model from a notebook | Diabetes | Local | Azure Container Instance | None | None |
 | :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | Remote | None | Azure ML | Dataset, Timeseries, Drift |
 | :star:[Filtering data using Tabular Timeseiries Dataset related API](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/tabular-timeseries-dataset-filtering.ipynb) | Filtering | NOAA | Local | None | Azure ML | Dataset, Tabular Timeseries |
 | :star:[Introduction to labeled datasets](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/labeled-datasets/labeled-datasets.ipynb) | Train |  | Remote | None | Azure ML | Dataset, label, Estimator |
 | :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun |
 | :star:[Train with Datasets (Tabular and File)](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb) | Train | Iris, Diabetes | Remote | None | Azure ML | Dataset, Estimator, ScriptRun |
 | [Forecasting away from training data](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb) | Forecasting | None | Remote | None | Azure ML AutoML | Forecasting, Confidence Intervals |
 | [Automated ML run with basic edition features.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb) | Classification | Bankmarketing | AML | ACI | None | featurization, explainability, remote_run, AutomatedML |
 | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) | Classification | Creditcard | AML Compute | None | None | remote_run, AutomatedML |
@@ -50,7 +45,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | :star:[Azure Machine Learning Pipelines with Data Dependency](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-data-dependency-steps.ipynb) | Demonstrates how to construct a Pipeline with data dependency between steps | Custom | AML Compute | None | Azure ML | None |
 | [How to use run a notebook as a step in AML Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-notebook-runner-step.ipynb) | Demonstrates the use of NotebookRunnerStep | Custom | AML Compute | None | Azure ML | None |
 ## Training
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
@@ -78,7 +72,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [Use MLflow with AML for a remote training run](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train-remote.ipynb) | Use MLflow tracking APIs together with AML for storing your metrics and artifacts | Diabetes | AML Compute | None | None | None |
 ## Deployment
@@ -94,12 +87,10 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [Register Spark model and deploy as webservice](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/spark/model-register-and-deploy-spark.ipynb) |  | Iris | None | Azure Container Instance | PySpark |  |
 ## Other Notebooks
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
 | [DNN Text Featurization](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb) | Text featurization using DNNs for classification | None | AML Compute | None | None | None |
 | [Automated ML Grouping with Pipeline.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-grouping/auto-ml-forecasting-grouping.ipynb) | Use AzureML Pipeline to trigger multiple Automated ML runs. | Orange Juice Sales | AML Compute | Azure Container Instance | Scikit-learn, Pytorch | AutomatedML |
 | [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) |  |  |  |  |  |  |
 | [lightgbm-example](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/gbdt/lightgbm/lightgbm-example.ipynb) |  |  |  |  |  |  |
 | [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) |  |  |  |  |  |  |
@@ -129,15 +120,13 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 | [train-explain-model-on-amlcompute-and-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb) |  |  |  |  |  |  |
 | [training_notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/notebook_runner/training_notebook.ipynb) |  |  |  |  |  |  |
 | [nyc-taxi-data-regression-model-building](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb) |  |  |  |  |  |  |
 | [pipeline-batch-scoring](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.ipynb) |  |  |  |  |  |  |
 | [authentication-in-azureml](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/manage-azureml-service/authentication-in-azureml/authentication-in-azureml.ipynb) |  |  |  |  |  |  |
 | [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | None | None | None | None | None |
 | [distributed-cntk-with-custom-docker](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/distributed-cntk-with-custom-docker/distributed-cntk-with-custom-docker.ipynb) |  |  |  |  |  |  |
 | [notebook_example](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training-with-deep-learning/how-to-use-estimator/notebook_example.ipynb) |  |  |  |  |  |  |
 | [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master//setup-environment/configuration.ipynb) |  |  |  |  |  |  |
-| [img-classification-part1-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/img-classification-part1-training.ipynb) |  |  |  |  |  |  |
+| [tutorial-1st-experiment-sdk-train](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb) |  |  |  |  |  |  |
-| [img-classification-part2-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/img-classification-part2-deploy.ipynb) |  |  |  |  |  |  |
+| [img-classification-part1-training](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb) |  |  |  |  |  |  |
-| [regression-automated-ml](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/regression-automated-ml.ipynb) |  |  |  |  |  |  |
+| [img-classification-part2-deploy](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb) |  |  |  |  |  |  |
-| [tutorial-1st-experiment-sdk-train](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/tutorial-1st-experiment-sdk-train.ipynb) |  |  |  |  |  |  |
+| [tutorial-pipeline-batch-scoring-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb) |  |  |  |  |  |  |
-| [tutorial-pipeline-batch-scoring-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/tutorial-pipeline-batch-scoring-classification.ipynb) |  |  |  |  |  |  |
+| [regression-automated-ml](https://github.com/Azure/MachineLearningNotebooks/blob/master//tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb) |  |  |  |  |  |  |
--- a/setup-environment/configuration.ipynb
+++ b/setup-environment/configuration.ipynb
@@ -102,7 +102,7 @@
      "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 1.1.0rc0 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -1,27 +1,35 @@
-## Azure Machine Learning service Tutorial
+# Azure Machine Learning Tutorials
-Complete these tutorials to learn how to train and deploy models using Azure Machine Learning services and Python SDK. These Notebooks accompany the
+Azure Machine Learning, a cloud-based environment you can use to train, deploy, automate, manage, and track ML models.
 two sets of tutorial articles for:
- * [Image classification using MNIST dataset](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-train-models-with-aml)
+Azure Machine Learning can be used for any kind of machine learning, from classical ML to supervised, unsupervised, and deep learning.
 * [Regression using NYC Taxi dataset](https://docs.microsoft.com/en-us/azure/machine-learning/service/tutorial-data-prep)
-If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
+This folder contains a collection of Jupyter Notebooks with the code used in accompanying step-by-step tutorials.
-### Create first ML experiment
+## Set up your environment.
-* [Part 1](https://docs.microsoft.com/azure/machine-learning/service/tutorial-quickstart-setup): Set up workspace & dev environment
+If you are using an Azure Machine Learning Notebook VM, everything is already set up for you. Otherwise, see the [get started creating your first ML experiment with the Python SDK tutorial](https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-sdk-setup).
 * [Part 2](tutorial-quickstart-train-model.ipynb): Learn the foundational design patterns in Azure Machine Learning service, and train a simple scikit-learn model based on the diabetes data set
-### Image classification
+## Introductory Samples
- * [Part 1](img-classification-part1-training.ipynb): Train an image classification model with Azure Machine Learning.
+The following tutorials are intended to provide an introductory overview of Azure Machine Learning.
 * [Part 2](img-classification-part2-deploy.ipynb): Deploy an image classification model from first tutorial in Azure Container Instance (ACI).
- ### Regression
+| Tutorial | Description | Notebook | Task | Framework | 
- * [Part 1](regression-part1-data-prep.ipynb): Prepare the data using Azure Machine Learning Data Prep SDK.
+| --- | --- | --- | --- | --- |
- * [Part 2](regression-part2-automated-ml.ipynb): Train a model using Automated Machine Learning.
+| [Train your first ML Model](https://docs.microsoft.com/azure/machine-learning/tutorial-1st-experiment-sdk-train) | Learn the foundational design patterns in Azure Machine Learning and train a scikit-learn model based on a diabetes data set. | [tutorial-quickstart-train-model.ipynb](create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb) | Regression | Scikit-Learn
 | [Train an image classification model](https://docs.microsoft.com/azure/machine-learning/tutorial-train-models-with-aml) | Train a scikit-learn image classification model. | [img-classification-part1-training.ipynb](image-classification-mnist-data/img-classification-part1-training.ipynb) | Image Classification | Scikit-Learn
 | [Deploy an image classification model](https://docs.microsoft.com/azure/machine-learning/tutorial-deploy-models-with-aml) | Deploy a scikit-learn image classification model to Azure Container Instances. | [img-classification-part2-deploy.ipynb](image-classification-mnist-data/img-classification-part2-deploy.ipynb) | Image Classification | Scikit-Learn
 | [Use automated machine learning to predict taxi fares](https://docs.microsoft.com/azure/machine-learning/tutorial-auto-train-models) | Train a regression model to predict taxi fares using Automated Machine Learning. | [regression-part2-automated-ml.ipynb](regression-automl-nyc-taxi-data/regression-automated-ml.ipynb) | Regression | Automated ML 
- Also find quickstarts and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
+## Advanced Samples
 The following tutorials are intended to provide examples of more advanced feature in Azure Machine Learning.
 | Tutorial | Description | Notebook | Task | Framework | 
 | --- | --- | --- | --- | --- |
 | [Build an Azure Machine Learning pipeline for batch scoring](https://docs.microsoft.com/azure/machine-learning/tutorial-pipeline-batch-scoring-classification) | Create an Azure Machine Learning pipeline to run batch scoring image classification jobs | [tutorial-pipeline-batch-scoring-classification.ipynb](machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb) | Image Classification | TensorFlow
 Complete these tutorials to learn how to train and deploy models using Azure Machine Learning services and Python SDK. These Notebooks accompany the tutorial articles for:
 For additional documentation and resources, see the [official documentation site for Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/).
 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/README.png)
--- a/tutorials/create-first-ml-experiment/imgs/experiment_main.png
+++ b/tutorials/create-first-ml-experiment/imgs/experiment_main.png
--- a/tutorials/create-first-ml-experiment/imgs/model_download.png
+++ b/tutorials/create-first-ml-experiment/imgs/model_download.png
--- a/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb
+++ b/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb
@@ -317,7 +317,9 @@
        "\n",
        "If you used a cloud notebook server, stop the VM when you are not using it to reduce cost.\n",
        "\n",
-        "1. In your workspace, select **Notebook VMs**.\n",
+        "1. In your workspace, select **Compute**.\n",
        "\n",
        "1. Select the **Notebook VMs** tab in the compute page.\n",
        "\n",
        "1. From the list, select the VM.\n",
        "\n",
--- a/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.yml
+++ b/tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.yml
--- a/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb
+++ b/tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb
--- a/tutorials/image-classification-mnist-data/img-classification-part1-training.yml
+++ b/tutorials/image-classification-mnist-data/img-classification-part1-training.yml
--- a/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb
+++ b/tutorials/image-classification-mnist-data/img-classification-part2-deploy.ipynb
--- a/tutorials/image-classification-mnist-data/img-classification-part2-deploy.yml
+++ b/tutorials/image-classification-mnist-data/img-classification-part2-deploy.yml
--- a/tutorials/image-classification-mnist-data/sklearn_mnist_model.pkl
+++ b/tutorials/image-classification-mnist-data/sklearn_mnist_model.pkl
--- a/tutorials/image-classification-mnist-data/utils.py
+++ b/tutorials/image-classification-mnist-data/utils.py
--- a/tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py
+++ b/tutorials/machine-learning-pipelines-advanced/scripts/batch_scoring.py
@@ -0,0 +1,83 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import os
 import argparse
 import datetime
 import time
 import tensorflow as tf
 from math import ceil
 import numpy as np
 import shutil
 from tensorflow.contrib.slim.python.slim.nets import inception_v3
 from azureml.core import Run
 from azureml.core.model import Model
 from azureml.core.dataset import Dataset
 slim = tf.contrib.slim
 image_size = 299
 num_channel = 3
 def get_class_label_dict():
    label = []
    proto_as_ascii_lines = tf.gfile.GFile("labels.txt").readlines()
    for l in proto_as_ascii_lines:
        label.append(l.rstrip())
    return label
 def init():
    global g_tf_sess, probabilities, label_dict, input_images
    parser = argparse.ArgumentParser(description="Start a tensorflow model serving")
    parser.add_argument('--model_name', dest="model_name", required=True)
    parser.add_argument('--labels_name', dest="labels_name", required=True)
    args, _ = parser.parse_known_args()
    workspace = Run.get_context(allow_offline=False).experiment.workspace
    label_ds = Dataset.get_by_name(workspace=workspace, name=args.labels_name)
    label_ds.download(target_path='.', overwrite=True)
    label_dict = get_class_label_dict()
    classes_num = len(label_dict)
    with slim.arg_scope(inception_v3.inception_v3_arg_scope()):
        input_images = tf.placeholder(tf.float32, [1, image_size, image_size, num_channel])
        logits, _ = inception_v3.inception_v3(input_images,
                                              num_classes=classes_num,
                                              is_training=False)
        probabilities = tf.argmax(logits, 1)
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    g_tf_sess = tf.Session(config=config)
    g_tf_sess.run(tf.global_variables_initializer())
    g_tf_sess.run(tf.local_variables_initializer())
    model_path = Model.get_model_path(args.model_name)
    saver = tf.train.Saver()
    saver.restore(g_tf_sess, model_path)
 def file_to_tensor(file_path):
    image_string = tf.read_file(file_path)
    image = tf.image.decode_image(image_string, channels=3)
    image.set_shape([None, None, None])
    image = tf.image.resize_images(image, [image_size, image_size])
    image = tf.divide(tf.subtract(image, [0]), [255])
    image.set_shape([image_size, image_size, num_channel])
    return image
 def run(mini_batch):
    result_list = []
    for file_path in mini_batch:
        test_image = file_to_tensor(file_path)
        out = g_tf_sess.run(test_image)
        result = g_tf_sess.run(probabilities, feed_dict={input_images: [out]})
        result_list.append(os.path.basename(file_path) + ": " + label_dict[result[0]])
    return result_list
--- a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb
+++ b/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb
@@ -15,19 +15,16 @@
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/pipeline-batch-scoring/pipeline-batch-scoring.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Note**: Azure Machine Learning recently released ParallelRunStep for public preview, this will allow for parallelization of your workload across many compute nodes without the difficulty of orchestrating worker pools and queues. See the [batch inference notebooks](../contrib/batch_inferencing/) for examples on how to get started."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Use Azure Machine Learning Pipelines for batch prediction\n",
        "\n",
        "## Note\n",
        "This notebook uses public preview functionality (ParallelRunStep). Please install azureml-contrib-pipeline-steps package before running this notebook.\n",
        "\n",
        "\n",
        "In this tutorial, you use Azure Machine Learning service pipelines to run a batch scoring image classification job. The example job uses the pre-trained [Inception-V3](https://arxiv.org/abs/1512.00567) CNN (convolutional neural network) Tensorflow model to classify unlabeled images. Machine learning pipelines optimize your workflow with speed, portability, and reuse so you can focus on your expertise, machine learning, rather than on infrastructure and automation. After building and publishing a pipeline, you can configure a REST endpoint to enable triggering the pipeline from any HTTP library on any platform.\n",
        "\n",
        "\n",
@@ -37,6 +34,7 @@
        "> * Create data objects to fetch and output data\n",
        "> * Download, prepare, and register the model to your workspace\n",
        "> * Provision compute targets and create a scoring script\n",
        "> * Use ParallelRunStep to do batch scoring\n",
        "> * Build, run, and publish a pipeline\n",
        "> * Enable a REST endpoint for the pipeline\n",
        "\n",
@@ -111,14 +109,14 @@
      "source": [
        "## Create data objects\n",
        "\n",
-        "When building pipelines, `DataReference` objects are used for reading data from workspace datastores, and `PipelineData` objects are used for transferring intermediate data between pipeline steps.\n",
+        "When building pipelines, `Dataset` objects are used for reading data from workspace datastores, and `PipelineData` objects are used for transferring intermediate data between pipeline steps.\n",
        "\n",
        "This batch scoring example only uses one pipeline step, but in use-cases with multiple steps, the typical flow will include:\n",
        "\n",
-        "1. Using `DataReference` objects as **inputs** to fetch raw data, performing some transformations, then **outputting** a `PipelineData` object.\n",
+        "1. Using `Dataset` objects as **inputs** to fetch raw data, performing some transformations, then **outputting** a `PipelineData` object.\n",
        "1. Use the previous step's `PipelineData` **output object** as an *input object*, repeated for subsequent steps.\n",
        "\n",
-        "For this scenario you create `DataReference` objects corresponding to the datastore directories for both the input images and the classification labels (y-test values). You also create a `PipelineData` object for the batch scoring output data."
+        "For this scenario you create `Dataset` objects corresponding to the datastore directories for both the input images and the classification labels (y-test values). You also create a `PipelineData` object for the batch scoring output data."
      ]
    },
    {
@@ -127,21 +125,11 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.data.data_reference import DataReference\n",
+        "from azureml.core.dataset import Dataset\n",
        "from azureml.pipeline.core import PipelineData\n",
        "\n",
-        "input_images = DataReference(datastore=batchscore_blob, \n",
+        "input_images = Dataset.File.from_files((batchscore_blob, \"batchscoring/images/\"))\n",
-        "                             data_reference_name=\"input_images\",\n",
+        "label_ds = Dataset.File.from_files((batchscore_blob, \"batchscoring/labels/*.txt\"))\n",
        "                             path_on_datastore=\"batchscoring/images\",\n",
        "                             mode=\"download\"\n",
        "                            )\n",
        "\n",
        "label_dir = DataReference(datastore=batchscore_blob, \n",
        "                          data_reference_name=\"input_labels\",\n",
        "                          path_on_datastore=\"batchscoring/labels\",\n",
        "                          mode=\"download\"                          \n",
        "                         )\n",
        "\n",
        "output_dir = PipelineData(name=\"scores\", \n",
        "                          datastore=def_data_store, \n",
        "                          output_path_on_compute=\"batchscoring/results\")"
@@ -150,6 +138,25 @@
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Next, we need to register the datasets with the workspace."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "input_images = input_images.register(workspace = ws, name = \"input_images\")\n",
        "label_ds = label_ds.register(workspace = ws, name = \"label_ds\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "## Download and register the model"
      ]
@@ -192,13 +199,17 @@
      "metadata": {},
      "outputs": [],
      "source": [
        "import shutil\n",
        "from azureml.core.model import Model\n",
-        " \n",
+        "\n",
        "# register downloaded model \n",
        "model = Model.register(model_path=\"models/inception_v3.ckpt\",\n",
        "                       model_name=\"inception\",\n",
        "                       tags={\"pretrained\": \"inception\"},\n",
        "                       description=\"Imagenet trained tensorflow inception\",\n",
-        "                       workspace=ws)"
+        "                       workspace=ws)\n",
        "# remove the downloaded dir after registration if you wish\n",
        "shutil.rmtree(\"models\")"
      ]
    },
    {
@@ -244,142 +255,16 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "To do the scoring, you create a batch scoring script `batch_scoring.py`, and write it to the current directory. The script takes input images, applies the classification model, and outputs the predictions to a results file.\n",
+        "To do the scoring, you create a batch scoring script `batch_scoring.py`, and write it to the current directory. The script takes a minibatch of input images, applies the classification model, and outputs the predictions to a results file.\n",
        "\n",
-        "The script `batch_scoring.py` takes the following parameters, which get passed from the `PythonScriptStep` that you create later:\n",
+        "The script `batch_scoring.py` takes the following parameters, which get passed from the `ParallelRunStep` that you create later:\n",
        "\n",
        "- `--model_name`: the name of the model being used\n",
-        "- `--label_dir` : the directory holding the `labels.txt` file \n",
+        "- `--labels_name` : the name of the `Dataset` holding the `labels.txt` file \n",
        "- `--dataset_path`: the directory containing the input images\n",
        "- `--output_dir` : the script will run the model on the data and output a `results-label.txt` to this directory\n",
        "- `--batch_size` : the batch size used in running the model\n",
        "\n",
        "The pipelines infrastructure uses the `ArgumentParser` class to pass parameters into pipeline steps. For example, in the code below the first argument `--model_name` is given the property identifier `model_name`. In the `main()` function, this property is accessed using `Model.get_model_path(args.model_name)`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile batch_scoring.py\n",
        "\n",
        "import os\n",
        "import argparse\n",
        "import datetime\n",
        "import time\n",
        "import tensorflow as tf\n",
        "from math import ceil\n",
        "import numpy as np\n",
        "import shutil\n",
        "from tensorflow.contrib.slim.python.slim.nets import inception_v3\n",
        "from azureml.core.model import Model\n",
        "\n",
        "slim = tf.contrib.slim\n",
        "\n",
        "parser = argparse.ArgumentParser(description=\"Start a tensorflow model serving\")\n",
        "parser.add_argument('--model_name', dest=\"model_name\", required=True)\n",
        "parser.add_argument('--label_dir', dest=\"label_dir\", required=True)\n",
        "parser.add_argument('--dataset_path', dest=\"dataset_path\", required=True)\n",
        "parser.add_argument('--output_dir', dest=\"output_dir\", required=True)\n",
        "parser.add_argument('--batch_size', dest=\"batch_size\", type=int, required=True)\n",
        "\n",
        "args = parser.parse_args()\n",
        "\n",
        "image_size = 299\n",
        "num_channel = 3\n",
        "\n",
        "# create output directory if it does not exist\n",
        "os.makedirs(args.output_dir, exist_ok=True)\n",
        "\n",
        "\n",
        "def get_class_label_dict(label_file):\n",
        "    label = []\n",
        "    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()\n",
        "    for l in proto_as_ascii_lines:\n",
        "        label.append(l.rstrip())\n",
        "    return label\n",
        "\n",
        "\n",
        "class DataIterator:\n",
        "    def __init__(self, data_dir):\n",
        "        self.file_paths = []\n",
        "        image_list = os.listdir(data_dir)\n",
        "        self.file_paths = [data_dir + '/' + file_name.rstrip() for file_name in image_list]\n",
        "\n",
        "        self.labels = [1 for file_name in self.file_paths]\n",
        "\n",
        "    @property\n",
        "    def size(self):\n",
        "        return len(self.labels)\n",
        "\n",
        "    def input_pipeline(self, batch_size):\n",
        "        images_tensor = tf.convert_to_tensor(self.file_paths, dtype=tf.string)\n",
        "        labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)\n",
        "        input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], shuffle=False)\n",
        "        labels = input_queue[1]\n",
        "        images_content = tf.read_file(input_queue[0])\n",
        "\n",
        "        image_reader = tf.image.decode_jpeg(images_content, channels=num_channel, name=\"jpeg_reader\")\n",
        "        float_caster = tf.cast(image_reader, tf.float32)\n",
        "        new_size = tf.constant([image_size, image_size], dtype=tf.int32)\n",
        "        images = tf.image.resize_images(float_caster, new_size)\n",
        "        images = tf.divide(tf.subtract(images, [0]), [255])\n",
        "\n",
        "        image_batch, label_batch = tf.train.batch([images, labels], batch_size=batch_size, capacity=5 * batch_size)\n",
        "        return image_batch\n",
        "\n",
        "\n",
        "def main(_):\n",
        "    label_file_name = os.path.join(args.label_dir, \"labels.txt\")\n",
        "    label_dict = get_class_label_dict(label_file_name)\n",
        "    classes_num = len(label_dict)\n",
        "    test_feeder = DataIterator(data_dir=args.dataset_path)\n",
        "    total_size = len(test_feeder.labels)\n",
        "    count = 0\n",
        "    \n",
        "    # get model from model registry\n",
        "    model_path = Model.get_model_path(args.model_name)\n",
        "    \n",
        "    with tf.Session() as sess:\n",
        "        test_images = test_feeder.input_pipeline(batch_size=args.batch_size)\n",
        "        with slim.arg_scope(inception_v3.inception_v3_arg_scope()):\n",
        "            input_images = tf.placeholder(tf.float32, [args.batch_size, image_size, image_size, num_channel])\n",
        "            logits, _ = inception_v3.inception_v3(input_images,\n",
        "                                                  num_classes=classes_num,\n",
        "                                                  is_training=False)\n",
        "            probabilities = tf.argmax(logits, 1)\n",
        "\n",
        "        sess.run(tf.global_variables_initializer())\n",
        "        sess.run(tf.local_variables_initializer())\n",
        "        coord = tf.train.Coordinator()\n",
        "        threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
        "        saver = tf.train.Saver()\n",
        "        saver.restore(sess, model_path)\n",
        "        out_filename = os.path.join(args.output_dir, \"result-labels.txt\")\n",
        "        with open(out_filename, \"w\") as result_file:\n",
        "            i = 0\n",
        "            while count < total_size and not coord.should_stop():\n",
        "                test_images_batch = sess.run(test_images)\n",
        "                file_names_batch = test_feeder.file_paths[i * args.batch_size:\n",
        "                                                          min(test_feeder.size, (i + 1) * args.batch_size)]\n",
        "                results = sess.run(probabilities, feed_dict={input_images: test_images_batch})\n",
        "                new_add = min(args.batch_size, total_size - count)\n",
        "                count += new_add\n",
        "                i += 1\n",
        "                for j in range(new_add):\n",
        "                    result_file.write(os.path.basename(file_names_batch[j]) + \": \" + label_dict[results[j]] + \"\\n\")\n",
        "                result_file.flush()\n",
        "            coord.request_stop()\n",
        "            coord.join(threads)\n",
        "\n",
        "        shutil.copy(out_filename, \"./outputs/\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    tf.app.run()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
@@ -407,26 +292,23 @@
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Environment\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
        "from azureml.core.runconfig import CondaDependencies, RunConfiguration\n",
        "\n",
        "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.13.1\", \"azureml-defaults\"])\n",
        "\n",
-        "amlcompute_run_config = RunConfiguration(conda_dependencies=cd)\n",
+        "env = Environment(name=\"parallelenv\")\n",
-        "amlcompute_run_config.environment.docker.enabled = True\n",
+        "env.python.conda_dependencies=cd\n",
-        "amlcompute_run_config.environment.docker.base_image = DEFAULT_GPU_IMAGE\n",
+        "env.docker.base_image = DEFAULT_GPU_IMAGE"
        "amlcompute_run_config.environment.spark.precache_packages = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "### Parameterize the pipeline\n",
+        "### Create the configuration to wrap the inference script\n",
-        "\n",
+        "Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use PythonScriptStep to create the pipeline step."
        "Define a custom parameter for the pipeline to control the batch size. After the pipeline has been published and exposed via a REST endpoint, any configured parameters are also exposed and can be specified in the JSON payload when rerunning the pipeline with an HTTP request.\n",
        "\n",
        "Create a `PipelineParameter` object to enable this behavior, and define a name and default value."
      ]
    },
    {
@@ -435,8 +317,19 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.pipeline.core.graph import PipelineParameter\n",
+        "from azureml.contrib.pipeline.steps import ParallelRunConfig\n",
-        "batch_size_param = PipelineParameter(name=\"param_batch_size\", default_value=20)"
+        "\n",
        "parallel_run_config = ParallelRunConfig(\n",
        "    environment=env,\n",
        "    entry_script=\"batch_scoring.py\",\n",
        "    source_directory=\"scripts\",\n",
        "    output_action=\"append_row\",\n",
        "    mini_batch_size=\"20\",\n",
        "    error_threshold=1,\n",
        "    compute_target=compute_target,\n",
        "    process_count_per_node=2,\n",
        "    node_count=1\n",
        ")"
      ]
    },
    {
@@ -452,7 +345,7 @@
        "* input and output data, and any custom parameters\n",
        "* reference to a script or SDK-logic to run during the step\n",
        "\n",
-        "There are multiple classes that inherit from the parent class [`PipelineStep`](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py) to assist with building a step using certain frameworks and stacks. In this example, you use the [`PythonScriptStep`](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep?view=azure-ml-py) class to define your step logic using a custom python script. Note that if an argument to your script is either an input to the step or output of the step, it must be defined **both** in the `arguments` array, **as well as** in either the `input` or `output` parameter, respectively. \n",
+        "There are multiple classes that inherit from the parent class [`PipelineStep`](https://docs.microsoft.com/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep?view=azure-ml-py) to assist with building a step using certain frameworks and stacks. In this example, you use the [`ParallelRunStep`](https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunstep?view=azure-ml-py) class to define your step logic using a scoring script. \n",
        "\n",
        "An object reference in the `outputs` array becomes available as an **input** for a subsequent pipeline step, for scenarios where there is more than one step."
      ]
@@ -463,20 +356,20 @@
      "metadata": {},
      "outputs": [],
      "source": [
-        "from azureml.pipeline.steps import PythonScriptStep\n",
+        "from azureml.contrib.pipeline.steps import ParallelRunStep\n",
        "from datetime import datetime\n",
        "\n",
-        "batch_score_step = PythonScriptStep(\n",
+        "parallel_step_name = \"batchscoring-\" + datetime.now().strftime(\"%Y%m%d%H%M\")\n",
-        "    name=\"batch_scoring\",\n",
+        "\n",
-        "    script_name=\"batch_scoring.py\",\n",
+        "batch_score_step = ParallelRunStep(\n",
-        "    arguments=[\"--dataset_path\", input_images, \n",
+        "    name=parallel_step_name,\n",
-        "               \"--model_name\", \"inception\",\n",
+        "    inputs=[input_images.as_named_input(\"input_images\")],\n",
-        "               \"--label_dir\", label_dir, \n",
+        "    output=output_dir,\n",
-        "               \"--output_dir\", output_dir, \n",
+        "    models=[model],\n",
-        "               \"--batch_size\", batch_size_param],\n",
+        "    arguments=[\"--model_name\", \"inception\",\n",
-        "    compute_target=compute_target,\n",
+        "               \"--labels_name\", \"label_ds\"],\n",
-        "    inputs=[input_images, label_dir],\n",
+        "    parallel_run_config=parallel_run_config,\n",
-        "    outputs=[output_dir],\n",
+        "    allow_reuse=False\n",
        "    runconfig=amlcompute_run_config\n",
        ")"
      ]
    },
@@ -510,7 +403,7 @@
        "from azureml.pipeline.core import Pipeline\n",
        "\n",
        "pipeline = Pipeline(workspace=ws, steps=[batch_score_step])\n",
-        "pipeline_run = Experiment(ws, 'batch_scoring').submit(pipeline, pipeline_parameters={\"param_batch_size\": 20})\n",
+        "pipeline_run = Experiment(ws, \"batch_scoring\").submit(pipeline)\n",
        "pipeline_run.wait_for_completion(show_output=True)"
      ]
    },
@@ -534,14 +427,20 @@
      "metadata": {},
      "outputs": [],
      "source": [
        "batch_run = next(pipeline_run.get_children())\n",
        "batch_output = batch_run.get_output_data(\"scores\")\n",
        "batch_output.download(local_path=\"inception_results\")\n",
        "\n",
        "import pandas as pd\n",
        "for root, dirs, files in os.walk(\"inception_results\"):\n",
        "    for file in files:\n",
        "        if file.endswith(\"parallel_run_step.txt\"):\n",
        "            result_file = os.path.join(root,file)\n",
        "\n",
-        "step_run = list(pipeline_run.get_children())[0]\n",
+        "df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
        "step_run.download_file(\"./outputs/result-labels.txt\")\n",
        "\n",
        "df = pd.read_csv(\"result-labels.txt\", delimiter=\":\", header=None)\n",
        "df.columns = [\"Filename\", \"Prediction\"]\n",
-        "df.head(10)"
+        "print(\"Prediction has \", df.shape[0], \" rows\")\n",
        "df.head(10) "
      ]
    },
    {
@@ -599,7 +498,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "Get the REST url from the `endpoint` property of the published pipeline object. You can also find the REST url in your workspace in the portal. Build an HTTP POST request to the endpoint, specifying your authentication header. Additionally, add a JSON payload object with the experiment name and the batch size parameter. As a reminder, the `param_batch_size` is passed through to your `batch_scoring.py` script because you defined it as a `PipelineParameter` object in the step configuration.\n",
+        "Get the REST url from the `endpoint` property of the published pipeline object. You can also find the REST url in your workspace in the portal. Build an HTTP POST request to the endpoint, specifying your authentication header. Additionally, add a JSON payload object with the experiment name and the batch size parameter. As a reminder, the `process_count_per_node` is passed through to `ParallelRunStep` because you defined it is defined as a `PipelineParameter` object in the step configuration.\n",
        "\n",
        "Make the request to trigger the run. Access the `Id` key from the response dict to get the value of the run id."
      ]
@@ -616,8 +515,25 @@
        "response = requests.post(rest_endpoint, \n",
        "                         headers=auth_header, \n",
        "                         json={\"ExperimentName\": \"batch_scoring\",\n",
-        "                               \"ParameterAssignments\": {\"param_batch_size\": 50}})\n",
+        "                               \"ParameterAssignments\": {\"process_count_per_node\": 6}})"
-        "run_id = response.json()[\"Id\"]"
+      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "try:\n",
        "    response.raise_for_status()\n",
        "except Exception:    \n",
        "    raise Exception(\"Received bad response from the endpoint: {}\\n\"\n",
        "                    \"Response Code: {}\\n\"\n",
        "                    \"Headers: {}\\n\"\n",
        "                    \"Content: {}\".format(rest_endpoint, response.status_code, response.headers, response.content))\n",
        "\n",
        "run_id = response.json().get('Id')\n",
        "print('Submitted pipeline run: ', run_id)"
      ]
    },
    {
@@ -652,7 +568,8 @@
        "\n",
        "If you used a cloud notebook server, stop the VM when you are not using it to reduce cost.\n",
        "\n",
-        "1. In your workspace, select **Notebook VMs**.\n",
+        "1. In your workspace, select **Compute**.\n",
        "1. Select the **Notebook VMs** tab in the compute page.\n",
        "1. From the list, select the VM.\n",
        "1. Select **Stop**.\n",
        "1. When you're ready to use the server again, select **Start**.\n",
@@ -683,19 +600,16 @@
        "\n",
        "See the [how-to](https://docs.microsoft.com/azure/machine-learning/service/how-to-create-your-first-pipeline?view=azure-devops) for additional detail on building pipelines with the machine learning SDK."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
-        "name": "sanpil"
+        "name": [
          "sanpil",
          "trmccorm",
          "pansav"
        ]
      }
    ],
    "kernelspec": {
--- a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.yml
+++ b/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.yml
@@ -3,7 +3,7 @@ dependencies:
 - pip:
  - azureml-sdk
  - azureml-pipeline-core
-  - azureml-pipeline-steps
+  - azureml-contrib-pipeline-steps
  - pandas
  - requests
  - azureml-widgets
--- a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb
+++ b/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.ipynb
@@ -564,7 +564,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-        "1. In your workspace, select **Notebook VMs**.\n",
+        "1. In your workspace, select **Compute**.\n",
        "1. Select the **Notebook VMs** tab in the compute page.\n",
        "1. From the list, select the VM.\n",
        "1. Select **Stop**.\n",
        "1. When you're ready to use the server again, select **Start**."
--- a/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.yml
+++ b/tutorials/regression-automl-nyc-taxi-data/regression-automated-ml.yml
Author	SHA1	Message	Date
vizhur	9662505517	update samples from Release-37 as a part of SDK release	2020-02-08 04:49:27 +00:00
Harneet Virk	8e103c02ff	Merge pull request #779 from Azure/release_update/Release-36 update samples from Release-36 as a part of SDK release	2020-02-07 21:40:57 -07:00
vizhur	ecb5157add	update samples from Release-36 as a part of SDK release	2020-02-08 04:35:14 +00:00
Shané Winner	d7d23d5e7c	Update index.md	2020-02-05 22:41:22 -08:00
Harneet Virk	83a21ba53a	update samples from Release-35 as a part of SDK release (#765 ) Co-authored-by: vizhur <vizhur@live.com>	2020-02-05 20:03:41 -05:00
Harneet Virk	3c9cb89c1a	update samples from Release-18 as a part of 1.1.0rc0 SDK experimental release (#760 ) Co-authored-by: vizhur <vizhur@live.com>	2020-02-04 22:19:52 -05:00
Sheri Gilley	cca7c2e26f	add cell metadata	2020-02-04 11:31:07 -06:00
Harneet Virk	e895d7c2bf	update samples - test (#758 ) Co-authored-by: vizhur <vizhur@live.com>	2020-01-31 15:19:58 -05:00
Shané Winner	3588eb9665	Update index.md	2020-01-23 15:46:43 -08:00
Harneet Virk	a09e726f31	update samples - test (#748 ) Co-authored-by: vizhur <vizhur@live.com>	2020-01-23 16:50:29 -05:00