my-pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Try and do the aml exercise independently\r\n",
        "# Create/USe a workspace\r\n",
        "\r\n",
        "from azureml.core import Workspace"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "gather": {
          "logged": 1627627324038
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the Workspace\r\n",
        "ws = Workspace.from_config()"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627325156
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Name: {ws.name}, Subscription Id: {ws.subscription_id}, Resorce Group: {ws.resource_group}, Location: {ws.location}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: quick-starts-ws-152995, Subscription Id: 81cefad3-d2c9-4f77-a466-99a7f541c7bb, Resorce Group: aml-quickstarts-152995, Location: southcentralus\n"
          ]
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627325651
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an experiment for the run\r\n",
        "from azureml.core import Experiment\r\n",
        "\r\n",
        "exp = Experiment(workspace=ws, name=\"my-automl-exp\")"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627326628
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "exp"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "Experiment(Name: my-automl-exp,\nWorkspace: quick-starts-ws-152995)",
            "text/html": "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>my-automl-exp</td><td>quick-starts-ws-152995</td><td><a href=\"https://ml.azure.com/experiments/id/51c4f5ad-0130-46f8-a2a8-532bbaf8b9d0?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-152995/workspaces/quick-starts-ws-152995&amp;tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\" target=\"_blank\" rel=\"noopener\">Link to Azure Machine Learning studio</a></td><td><a href=\"https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.Experiment?view=azure-ml-py\" target=\"_blank\" rel=\"noopener\">Link to Documentation</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627327134
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Next, we will create/attach a compute cluster\r\n",
        "# Access the cluster. if non-existent, then create one\r\n",
        "compute_cluster_name = \"my-cc\"\r\n",
        "\r\n",
        "from azureml.core.compute import ComputeTarget\r\n",
        "from azureml.exceptions import ComputeTargetException\r\n",
        "from azureml.core.compute import AmlCompute\r\n",
        "\r\n",
        "\r\n"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627327726
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "try:\r\n",
        "    compute_cluster = ComputeTarget(workspace=ws, name=compute_cluster_name)\r\n",
        "except ComputeTargetException:\r\n",
        "    # Cluster does not exist - create it\r\n",
        "    prov_cfg = AmlCompute.provisioning_configuration(vm_size=\"Standard_DS3_v2\", max_nodes=4, description=\"Pipeline cfg\")\r\n",
        "\r\n",
        "    compute_cluster = ComputeTarget.create(ws, compute_cluster_name, prov_cfg)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627328436
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Wait for compute cluster to be built\r\n",
        "compute_cluster.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating......\n",
            "SucceededProvisioning operation finished, operation \"Succeeded\"\n",
            "Succeeded\n",
            "AmlCompute wait for completion finished\n",
            "\n",
            "Minimum number of nodes requested have been provisioned\n"
          ]
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627358324
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We next to retrieve the Dataset\r\n",
        "# 'https://raw.githubusercontent.com/Azure/MachineLearningNotebooks/master/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv'\r\n",
        "# Once retrieved - this dataset will subsequently be used for the model\r\n",
        "\r\n",
        "# Loop through the WorkSpace datasets and see if our datset already exists\r\n",
        "# if not, create the dataset\r\n",
        "from azureml.core.dataset import Dataset\r\n",
        "uri = 'https://raw.githubusercontent.com/Azure/MachineLearningNotebooks/master/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv'\r\n",
        "\r\n",
        "ds = Dataset.Tabular.from_delimited_files(uri)\r\n"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627363413
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We next specify the Pipeline, starting with the configuration\r\n",
        "ds"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "{\n  \"source\": [\n    \"https://raw.githubusercontent.com/Azure/MachineLearningNotebooks/master/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv\"\n  ],\n  \"definition\": [\n    \"GetFiles\",\n    \"ParseDelimited\",\n    \"DropColumns\",\n    \"SetColumnTypes\"\n  ]\n}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627364057
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = ds.to_pandas_dataframe()"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627370807
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "          instant      season          yr        mnth     weekday  weathersit  \\\ncount  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \nmean   366.000000    2.496580    0.500684    6.519836    2.997264    1.395349   \nstd    211.165812    1.110807    0.500342    3.451913    2.004787    0.544894   \nmin      1.000000    1.000000    0.000000    1.000000    0.000000    1.000000   \n25%    183.500000    2.000000    0.000000    4.000000    1.000000    1.000000   \n50%    366.000000    3.000000    1.000000    7.000000    3.000000    1.000000   \n75%    548.500000    3.000000    1.000000   10.000000    5.000000    2.000000   \nmax    731.000000    4.000000    1.000000   12.000000    6.000000    3.000000   \n\n             temp       atemp         hum   windspeed       casual  \\\ncount  731.000000  731.000000  731.000000  731.000000   731.000000   \nmean     0.495385    0.474354    0.627894    0.190486   848.176471   \nstd      0.183051    0.162961    0.142429    0.077498   686.622488   \nmin      0.059130    0.079070    0.000000    0.022392     2.000000   \n25%      0.337083    0.337842    0.520000    0.134950   315.500000   \n50%      0.498333    0.486733    0.626667    0.180975   713.000000   \n75%      0.655417    0.608602    0.730209    0.233214  1096.000000   \nmax      0.861667    0.840896    0.972500    0.507463  3410.000000   \n\n        registered          cnt  \ncount   731.000000   731.000000  \nmean   3656.172367  4504.348837  \nstd    1560.256377  1937.211452  \nmin      20.000000    22.000000  \n25%    2497.000000  3152.000000  \n50%    3662.000000  4548.000000  \n75%    4776.500000  5956.000000  \nmax    6946.000000  8714.000000  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>instant</th>\n      <th>season</th>\n      <th>yr</th>\n      <th>mnth</th>\n      <th>weekday</th>\n      <th>weathersit</th>\n      <th>temp</th>\n      <th>atemp</th>\n      <th>hum</th>\n      <th>windspeed</th>\n      <th>casual</th>\n      <th>registered</th>\n      <th>cnt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>366.000000</td>\n      <td>2.496580</td>\n      <td>0.500684</td>\n      <td>6.519836</td>\n      <td>2.997264</td>\n      <td>1.395349</td>\n      <td>0.495385</td>\n      <td>0.474354</td>\n      <td>0.627894</td>\n      <td>0.190486</td>\n      <td>848.176471</td>\n      <td>3656.172367</td>\n      <td>4504.348837</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>211.165812</td>\n      <td>1.110807</td>\n      <td>0.500342</td>\n      <td>3.451913</td>\n      <td>2.004787</td>\n      <td>0.544894</td>\n      <td>0.183051</td>\n      <td>0.162961</td>\n      <td>0.142429</td>\n      <td>0.077498</td>\n      <td>686.622488</td>\n      <td>1560.256377</td>\n      <td>1937.211452</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.059130</td>\n      <td>0.079070</td>\n      <td>0.000000</td>\n      <td>0.022392</td>\n      <td>2.000000</td>\n      <td>20.000000</td>\n      <td>22.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>183.500000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.337083</td>\n      <td>0.337842</td>\n      <td>0.520000</td>\n      <td>0.134950</td>\n      <td>315.500000</td>\n      <td>2497.000000</td>\n      <td>3152.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>366.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>0.498333</td>\n      <td>0.486733</td>\n      <td>0.626667</td>\n      <td>0.180975</td>\n      <td>713.000000</td>\n      <td>3662.000000</td>\n      <td>4548.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>548.500000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>10.000000</td>\n      <td>5.000000</td>\n      <td>2.000000</td>\n      <td>0.655417</td>\n      <td>0.608602</td>\n      <td>0.730209</td>\n      <td>0.233214</td>\n      <td>1096.000000</td>\n      <td>4776.500000</td>\n      <td>5956.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>731.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>12.000000</td>\n      <td>6.000000</td>\n      <td>3.000000</td>\n      <td>0.861667</td>\n      <td>0.840896</td>\n      <td>0.972500</td>\n      <td>0.507463</td>\n      <td>3410.000000</td>\n      <td>6946.000000</td>\n      <td>8714.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627371342
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Register the dataset that is created\r\n",
        "key = \"Bikesharing Dataset\"\r\n",
        "description = \"Bike Sharing Data\"\r\n",
        "reg_ds = ds.register(ws, name=key, description=description)"
      ],
      "outputs": [],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627372307
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg_ds"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "{\n  \"source\": [\n    \"https://raw.githubusercontent.com/Azure/MachineLearningNotebooks/master/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv\"\n  ],\n  \"definition\": [\n    \"GetFiles\",\n    \"ParseDelimited\",\n    \"DropColumns\",\n    \"SetColumnTypes\"\n  ],\n  \"registration\": {\n    \"id\": \"1f44d6c5-36e3-46e2-8c9f-c823af6b4b76\",\n    \"name\": \"Bikesharing Dataset\",\n    \"version\": 1,\n    \"description\": \"Bike Sharing Data\",\n    \"workspace\": \"Workspace.create(name='quick-starts-ws-152995', subscription_id='81cefad3-d2c9-4f77-a466-99a7f541c7bb', resource_group='aml-quickstarts-152995')\"\n  }\n}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627372790
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg_df = reg_ds.to_pandas_dataframe()\r\n",
        "reg_df.describe()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "          instant      season          yr        mnth     weekday  weathersit  \\\ncount  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \nmean   366.000000    2.496580    0.500684    6.519836    2.997264    1.395349   \nstd    211.165812    1.110807    0.500342    3.451913    2.004787    0.544894   \nmin      1.000000    1.000000    0.000000    1.000000    0.000000    1.000000   \n25%    183.500000    2.000000    0.000000    4.000000    1.000000    1.000000   \n50%    366.000000    3.000000    1.000000    7.000000    3.000000    1.000000   \n75%    548.500000    3.000000    1.000000   10.000000    5.000000    2.000000   \nmax    731.000000    4.000000    1.000000   12.000000    6.000000    3.000000   \n\n             temp       atemp         hum   windspeed       casual  \\\ncount  731.000000  731.000000  731.000000  731.000000   731.000000   \nmean     0.495385    0.474354    0.627894    0.190486   848.176471   \nstd      0.183051    0.162961    0.142429    0.077498   686.622488   \nmin      0.059130    0.079070    0.000000    0.022392     2.000000   \n25%      0.337083    0.337842    0.520000    0.134950   315.500000   \n50%      0.498333    0.486733    0.626667    0.180975   713.000000   \n75%      0.655417    0.608602    0.730209    0.233214  1096.000000   \nmax      0.861667    0.840896    0.972500    0.507463  3410.000000   \n\n        registered          cnt  \ncount   731.000000   731.000000  \nmean   3656.172367  4504.348837  \nstd    1560.256377  1937.211452  \nmin      20.000000    22.000000  \n25%    2497.000000  3152.000000  \n50%    3662.000000  4548.000000  \n75%    4776.500000  5956.000000  \nmax    6946.000000  8714.000000  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>instant</th>\n      <th>season</th>\n      <th>yr</th>\n      <th>mnth</th>\n      <th>weekday</th>\n      <th>weathersit</th>\n      <th>temp</th>\n      <th>atemp</th>\n      <th>hum</th>\n      <th>windspeed</th>\n      <th>casual</th>\n      <th>registered</th>\n      <th>cnt</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n      <td>731.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>366.000000</td>\n      <td>2.496580</td>\n      <td>0.500684</td>\n      <td>6.519836</td>\n      <td>2.997264</td>\n      <td>1.395349</td>\n      <td>0.495385</td>\n      <td>0.474354</td>\n      <td>0.627894</td>\n      <td>0.190486</td>\n      <td>848.176471</td>\n      <td>3656.172367</td>\n      <td>4504.348837</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>211.165812</td>\n      <td>1.110807</td>\n      <td>0.500342</td>\n      <td>3.451913</td>\n      <td>2.004787</td>\n      <td>0.544894</td>\n      <td>0.183051</td>\n      <td>0.162961</td>\n      <td>0.142429</td>\n      <td>0.077498</td>\n      <td>686.622488</td>\n      <td>1560.256377</td>\n      <td>1937.211452</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.059130</td>\n      <td>0.079070</td>\n      <td>0.000000</td>\n      <td>0.022392</td>\n      <td>2.000000</td>\n      <td>20.000000</td>\n      <td>22.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>183.500000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.337083</td>\n      <td>0.337842</td>\n      <td>0.520000</td>\n      <td>0.134950</td>\n      <td>315.500000</td>\n      <td>2497.000000</td>\n      <td>3152.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>366.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>7.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>0.498333</td>\n      <td>0.486733</td>\n      <td>0.626667</td>\n      <td>0.180975</td>\n      <td>713.000000</td>\n      <td>3662.000000</td>\n      <td>4548.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>548.500000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>10.000000</td>\n      <td>5.000000</td>\n      <td>2.000000</td>\n      <td>0.655417</td>\n      <td>0.608602</td>\n      <td>0.730209</td>\n      <td>0.233214</td>\n      <td>1096.000000</td>\n      <td>4776.500000</td>\n      <td>5956.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>731.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>12.000000</td>\n      <td>6.000000</td>\n      <td>3.000000</td>\n      <td>0.861667</td>\n      <td>0.840896</td>\n      <td>0.972500</td>\n      <td>0.507463</td>\n      <td>3410.000000</td>\n      <td>6946.000000</td>\n      <td>8714.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627373259
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "reg_df.columns"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "Index(['instant', 'date', 'season', 'yr', 'mnth', 'weekday', 'weathersit',\n       'temp', 'atemp', 'hum', 'windspeed', 'casual', 'registered', 'cnt'],\n      dtype='object')"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627373863
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if key in ws.datasets.keys(): \r\n",
        "        found = True\r\n",
        "        dataset = ws.datasets[key] \r\n"
      ],
      "outputs": [],
      "execution_count": 17,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627374363
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'{key}')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bikesharing Dataset\n"
          ]
        }
      ],
      "execution_count": 18,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627374680
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.pipeline.core import Pipeline\r\n"
      ],
      "outputs": [],
      "execution_count": 19,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627374985
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ml_pipeline = Pipeline(\r\n",
        "    description=description,\r\n",
        "    workspace=ws,\r\n",
        "    steps=[]\r\n",
        ")"
      ],
      "outputs": [],
      "execution_count": 20,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627375565
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We will specify the AutoMLStep in this instance\r\n",
        "from azureml.pipeline.steps.automl_step import AutoMLStep\r\n",
        "from azureml.pipeline.core import PipelineData\r\n",
        "from azureml.pipeline.core import TrainingOutput\r\n",
        "from azureml.train.automl.automlconfig import AutoMLConfig\r\n"
      ],
      "outputs": [],
      "execution_count": 21,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627376126
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "project_folder = 'pipeline-bike-folder'\r\n",
        "\r\n",
        "automl_settings={\r\n",
        "    'experiment_timeout_minutes':20,\r\n",
        "    'max_concurrent_iterations':5,\r\n",
        "    'primary_metric':'normalized_root_mean_squared_error',\r\n",
        "    'n_cross_validations':5,\r\n",
        "}\r\n",
        "\r\n",
        "automl_config = AutoMLConfig(\r\n",
        "    compute_target = compute_cluster,\r\n",
        "    task='forecasting',\r\n",
        "    training_data=reg_ds,\r\n",
        "    time_column_name='date',\r\n",
        "    label_column_name='cnt',\r\n",
        "    path=project_folder,\r\n",
        "    enable_early_stopping=True,\r\n",
        "    debug_log='automl_errors.log',\r\n",
        "    **automl_settings,\r\n",
        ")\r\n"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627376568
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\r\n",
        "ds = ws.get_default_datastore()\r\n",
        "\r\n",
        "# Define metrics_data, model_data to be used below in\r\n",
        "# AutoMLStep\r\n",
        "metrics_data = PipelineData(\r\n",
        "    name='metrics_data',\r\n",
        "    datastore=ds,\r\n",
        "    pipeline_output_name='metrics_output',\r\n",
        "    training_output=TrainingOutput(type='Metrics')\r\n",
        ")\r\n",
        "\r\n",
        "model_data = PipelineData(\r\n",
        "    name='model_data',\r\n",
        "    datastore=ds,\r\n",
        "    pipeline_output_name='best_model_output',\r\n",
        "    training_output=TrainingOutput(type='Model')\r\n",
        "\r\n",
        ")\r\n",
        "\r\n",
        "automlstep = AutoMLStep(\r\n",
        "    name=\"auto_ml_pipeline\",\r\n",
        "    automl_config=automl_config,\r\n",
        "    outputs=[metrics_data, model_data],\r\n",
        "    allow_reuse=True\r\n",
        ")"
      ],
      "outputs": [],
      "execution_count": 26,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627431906
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# The pipeline next needs to be submitted\r\n",
        "ml_pipeline = Pipeline(\r\n",
        "    description='Bike Sharing Pipeline',\r\n",
        "    workspace=ws,\r\n",
        "    steps=[automlstep]\r\n",
        ")\r\n",
        "pipeline_run = exp.submit(ml_pipeline)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Created step auto_ml_pipeline [d13ef921][fcf3c024-50ec-49bf-8261-772c768e2ad3], (This step will run and generate new outputs)\n",
            "Submitted PipelineRun 5072be7c-ff2f-434c-95e9-1eb067d33374\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/runs/5072be7c-ff2f-434c-95e9-1eb067d33374?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-152995/workspaces/quick-starts-ws-152995&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n"
          ]
        }
      ],
      "execution_count": 27,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627438110
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.widgets import RunDetails\r\n",
        "\r\n",
        "RunDetails(pipeline_run).show()\r\n"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "_PipelineWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', …",
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4e502e358e144d18bef5cb33d1ba5ad5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/runs/5072be7c-ff2f-434c-95e9-1eb067d33374?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-152995/workspaces/quick-starts-ws-152995&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\", \"run_id\": \"5072be7c-ff2f-434c-95e9-1eb067d33374\", \"run_properties\": {\"run_id\": \"5072be7c-ff2f-434c-95e9-1eb067d33374\", \"created_utc\": \"2021-07-30T06:43:56.280968Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"SDK\", \"runType\": \"SDK\", \"azureml.parameters\": \"{}\"}, \"tags\": {\"azureml.pipelineComponent\": \"pipelinerun\"}, \"end_time_utc\": \"2021-07-30T07:15:05.304418Z\", \"status\": \"Completed\", \"log_files\": {\"logs/azureml/executionlogs.txt\": \"https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.5072be7c-ff2f-434c-95e9-1eb067d33374/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=AbZB%2BhUee9%2F0EjEF2n2%2BOtMtRsyecHSniMFgZZ3YOzo%3D&st=2021-07-30T07%3A34%3A11Z&se=2021-07-30T15%3A44%3A11Z&sp=r\", \"logs/azureml/stderrlogs.txt\": \"https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.5072be7c-ff2f-434c-95e9-1eb067d33374/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=Nz6Wj%2B4etNaMKLIw26ROlUcrgIF6cyBBrNLqlB%2FogXg%3D&st=2021-07-30T07%3A34%3A11Z&se=2021-07-30T15%3A44%3A11Z&sp=r\", \"logs/azureml/stdoutlogs.txt\": \"https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.5072be7c-ff2f-434c-95e9-1eb067d33374/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=9R3syEcCvNtN9k9kfLbtieW%2B4ZctrSz0PHWpWGOXJXc%3D&st=2021-07-30T07%3A34%3A12Z&se=2021-07-30T15%3A44%3A12Z&sp=r\"}, \"log_groups\": [[\"logs/azureml/executionlogs.txt\", \"logs/azureml/stderrlogs.txt\", \"logs/azureml/stdoutlogs.txt\"]], \"run_duration\": \"0:31:09\", \"run_number\": \"1\", \"run_queued_details\": {\"status\": \"Finished\", \"details\": null}}, \"child_runs\": [{\"run_id\": \"aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a\", \"name\": \"auto_ml_pipeline\", \"status\": \"Finished\", \"start_time\": \"2021-07-30T06:44:17.470445Z\", \"created_time\": \"2021-07-30T06:44:00.819432Z\", \"end_time\": \"2021-07-30T07:14:36.057053Z\", \"duration\": \"0:30:35\", \"run_number\": 2, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-07-30T06:44:00.819432Z\", \"is_reused\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2021-07-30 06:44:00Z] Submitting 1 runs, first five are: d13ef921:aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a\\n[2021-07-30 07:15:04Z] Completing processing run id aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a.\\n\\nRun is completed.\", \"graph\": {\"datasource_nodes\": {\"bd31ad7e\": {\"node_id\": \"bd31ad7e\", \"name\": \"Bikesharing Dataset\"}}, \"module_nodes\": {\"d13ef921\": {\"node_id\": \"d13ef921\", \"name\": \"auto_ml_pipeline\", \"status\": \"Finished\", \"_is_reused\": false, \"run_id\": \"aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a\"}}, \"edges\": [{\"source_node_id\": \"bd31ad7e\", \"source_node_name\": \"Bikesharing Dataset\", \"source_name\": \"data\", \"target_name\": \"training_data\", \"dst_node_id\": \"d13ef921\", \"dst_node_name\": \"auto_ml_pipeline\"}], \"child_runs\": [{\"run_id\": \"aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a\", \"name\": \"auto_ml_pipeline\", \"status\": \"Finished\", \"start_time\": \"2021-07-30T06:44:17.470445Z\", \"created_time\": \"2021-07-30T06:44:00.819432Z\", \"end_time\": \"2021-07-30T07:14:36.057053Z\", \"duration\": \"0:30:35\", \"run_number\": 2, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2021-07-30T06:44:00.819432Z\", \"is_reused\": \"\"}]}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.31.0\"}, \"loading\": false}"
          },
          "metadata": {}
        }
      ],
      "execution_count": 28,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627627442870
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_run.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PipelineRunId: 5072be7c-ff2f-434c-95e9-1eb067d33374\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/runs/5072be7c-ff2f-434c-95e9-1eb067d33374?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-152995/workspaces/quick-starts-ws-152995&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n",
            "PipelineRun Status: Running\n",
            "\n",
            "\n",
            "StepRunId: aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a\n",
            "Link to Azure Machine Learning Portal: https://ml.azure.com/runs/aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a?wsid=/subscriptions/81cefad3-d2c9-4f77-a466-99a7f541c7bb/resourcegroups/aml-quickstarts-152995/workspaces/quick-starts-ws-152995&tid=660b3398-b80e-49d2-bc5b-ac1dc93b5254\n",
            "StepRun( auto_ml_pipeline ) Status: Running\n",
            "\n",
            "StepRun(auto_ml_pipeline) Execution Summary\n",
            "============================================\n",
            "StepRun( auto_ml_pipeline ) Status: Finished\n",
            "{'runId': 'aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a', 'target': 'my-cc', 'status': 'Completed', 'startTimeUtc': '2021-07-30T06:44:17.470445Z', 'endTimeUtc': '2021-07-30T07:14:36.057053Z', 'properties': {'ContentSnapshotId': '00000000-0000-0000-0000-000000000000', 'StepType': 'AutoMLStep', 'azureml.moduleid': 'fcf3c024-50ec-49bf-8261-772c768e2ad3', 'azureml.runsource': 'azureml.StepRun', 'azureml.nodeid': 'd13ef921', 'azureml.pipelinerunid': '5072be7c-ff2f-434c-95e9-1eb067d33374', 'num_iterations': '1000', 'training_type': 'TrainFull', 'acquisition_function': 'EI', 'metrics': 'accuracy', 'primary_metric': 'normalized_root_mean_squared_error', 'train_split': '0', 'MaxTimeSeconds': None, 'acquisition_parameter': '0', 'num_cross_validation': '5', 'target': 'my-cc', 'RawAMLSettingsString': None, 'AMLSettingsJsonString': '{\"path\": null, \"name\": \"placeholder\", \"subscription_id\": \"81cefad3-d2c9-4f77-a466-99a7f541c7bb\", \"resource_group\": \"aml-quickstarts-152995\", \"workspace_name\": \"quick-starts-ws-152995\", \"region\": \"southcentralus\", \"compute_target\": \"my-cc\", \"spark_service\": null, \"azure_service\": null, \"many_models\": false, \"pipeline_fetch_max_batch_size\": 1, \"enable_batch_run\": false, \"enable_run_restructure\": false, \"start_auxiliary_runs_before_parent_complete\": false, \"enable_code_generation\": false, \"iterations\": 1000, \"primary_metric\": \"normalized_root_mean_squared_error\", \"task_type\": \"regression\", \"data_script\": null, \"test_size\": 0.0, \"validation_size\": 0.0, \"n_cross_validations\": 5, \"y_min\": null, \"y_max\": null, \"num_classes\": null, \"featurization\": \"auto\", \"_ignore_package_version_incompatibilities\": false, \"is_timeseries\": true, \"max_cores_per_iteration\": 1, \"max_concurrent_iterations\": 5, \"iteration_timeout_minutes\": null, \"mem_in_mb\": null, \"enforce_time_on_windows\": false, \"experiment_timeout_minutes\": 20, \"experiment_exit_score\": null, \"whitelist_models\": null, \"blacklist_algos\": null, \"supported_models\": [\"RandomForest\", \"Average\", \"TensorFlowDNN\", \"ExtremeRandomTrees\", \"KNN\", \"Prophet\", \"ElasticNet\", \"XGBoostRegressor\", \"SGD\", \"SeasonalAverage\", \"AutoArima\", \"LassoLars\", \"ExponentialSmoothing\", \"Naive\", \"Arimax\", \"LightGBM\", \"SeasonalNaive\", \"TensorFlowLinearRegressor\", \"TCNForecaster\", \"GradientBoosting\", \"DecisionTree\"], \"private_models\": [], \"auto_blacklist\": true, \"blacklist_samples_reached\": false, \"exclude_nan_labels\": true, \"verbosity\": 20, \"_debug_log\": \"automl_errors.log\", \"show_warnings\": false, \"model_explainability\": true, \"service_url\": null, \"sdk_url\": null, \"sdk_packages\": null, \"enable_onnx_compatible_models\": false, \"enable_split_onnx_featurizer_estimator_models\": false, \"vm_type\": \"STANDARD_DS3_V2\", \"telemetry_verbosity\": 20, \"send_telemetry\": true, \"enable_dnn\": false, \"scenario\": \"SDK-1.13.0\", \"environment_label\": null, \"save_mlflow\": false, \"force_text_dnn\": false, \"enable_feature_sweeping\": false, \"time_column_name\": \"date\", \"grain_column_names\": null, \"drop_column_names\": [], \"max_horizon\": 1, \"dropna\": false, \"overwrite_columns\": true, \"transform_dictionary\": {\"min\": \"_automl_target_col\", \"max\": \"_automl_target_col\", \"mean\": \"_automl_target_col\"}, \"window_size\": null, \"country_or_region\": null, \"lags\": null, \"feature_lags\": null, \"seasonality\": \"auto\", \"use_stl\": null, \"short_series_handling\": true, \"freq\": null, \"short_series_handling_configuration\": \"auto\", \"target_aggregation_function\": null, \"enable_early_stopping\": true, \"early_stopping_n_iters\": 10, \"metrics\": null, \"enable_metric_confidence\": false, \"enable_ensembling\": true, \"enable_stack_ensembling\": false, \"ensemble_iterations\": 15, \"enable_tf\": false, \"enable_subsampling\": false, \"subsample_seed\": null, \"enable_nimbusml\": false, \"enable_streaming\": false, \"force_streaming\": false, \"track_child_runs\": true, \"allowed_private_models\": [], \"label_column_name\": \"cnt\", \"weight_column_name\": null, \"cv_split_column_names\": null, \"enable_local_managed\": false, \"_local_managed_run_id\": null, \"cost_mode\": 1, \"lag_length\": 0, \"metric_operation\": \"minimize\", \"preprocess\": true}', 'DataPrepJsonString': '{\\\\\"training_data\\\\\": {\\\\\"datasetId\\\\\": \\\\\"1f44d6c5-36e3-46e2-8c9f-c823af6b4b76\\\\\"}, \\\\\"datasets\\\\\": 0}', 'EnableSubsampling': 'False', 'runTemplate': 'AutoML', 'Orchestrator': 'automl', 'ClientType': 'Others', '_aml_system_scenario_identification': 'Remote.Parent', 'root_attribution': 'azureml.StepRun', 'snapshotId': '00000000-0000-0000-0000-000000000000', 'SetupRunId': 'aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_setup', 'SetupRunContainerId': 'dcid.aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_setup', 'forecasting_target_lags': '[0]', 'forecasting_target_rolling_window_size': '0', 'forecasting_max_horizon': '1', 'forecasting_freq': 'D', 'ProblemInfoJsonString': '{\"dataset_num_categorical\": 0, \"is_sparse\": false, \"subsampling\": false, \"has_extra_col\": true, \"dataset_classes\": 696, \"dataset_features\": 34, \"dataset_samples\": 731, \"single_frequency_class_detected\": false, \"series_column_count\": 1, \"series_count\": 1, \"series_len_min\": 731, \"series_len_max\": 731, \"series_len_avg\": 731.0, \"series_len_perc_25\": 731.0, \"series_len_perc_50\": 731.0, \"series_len_perc_75\": 731.0}', 'ModelExplainRunId': 'aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_ModelExplain'}, 'inputDatasets': [], 'outputDatasets': [], 'logFiles': {'logs/azureml/executionlogs.txt': 'https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=kZdENpnvBNETNZFOlofhC%2BbN%2BuQazb562V%2FDn1dT0cw%3D&st=2021-07-30T06%3A41%3A47Z&se=2021-07-30T14%3A51%3A47Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=H7dll53UJqxWBQ%2BQnQqzxdeTNfJLhdTjmgYZRZ9g%2BEI%3D&st=2021-07-30T06%3A41%3A47Z&se=2021-07-30T14%3A51%3A47Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=FxQ%2BWlAISf4%2B5dscSEDekaDDH%2F%2B2HTQzmWIXB5ZaCvM%3D&st=2021-07-30T06%3A41%3A47Z&se=2021-07-30T14%3A51%3A47Z&sp=r'}, 'submittedBy': 'ODL_User 152995'}\n",
            "\n",
            "\n",
            "\n",
            "PipelineRun Execution Summary\n",
            "==============================\n",
            "PipelineRun Status: Finished\n",
            "{'runId': '5072be7c-ff2f-434c-95e9-1eb067d33374', 'status': 'Completed', 'startTimeUtc': '2021-07-30T06:43:59.027405Z', 'endTimeUtc': '2021-07-30T07:15:05.304418Z', 'properties': {'azureml.runsource': 'azureml.PipelineRun', 'runSource': 'SDK', 'runType': 'SDK', 'azureml.parameters': '{}'}, 'inputDatasets': [], 'outputDatasets': [], 'logFiles': {'logs/azureml/executionlogs.txt': 'https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.5072be7c-ff2f-434c-95e9-1eb067d33374/logs/azureml/executionlogs.txt?sv=2019-02-02&sr=b&sig=QxjIcpX4PM0axcBVm1%2BQGaUw96HKsRIwXGu6uEWPEAA%3D&st=2021-07-30T06%3A34%3A02Z&se=2021-07-30T14%3A44%3A02Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.5072be7c-ff2f-434c-95e9-1eb067d33374/logs/azureml/stderrlogs.txt?sv=2019-02-02&sr=b&sig=jmMU%2B6d%2BDknxCJrHReGCwzB5MbaNGaWdNzHXgGG9VPA%3D&st=2021-07-30T06%3A34%3A02Z&se=2021-07-30T14%3A44%3A02Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://mlstrg152995.blob.core.windows.net/azureml/ExperimentRun/dcid.5072be7c-ff2f-434c-95e9-1eb067d33374/logs/azureml/stdoutlogs.txt?sv=2019-02-02&sr=b&sig=Z1pLbhadpoNQuMyngnrr%2BxmkD4fmVQkfyMYaqmBZhc8%3D&st=2021-07-30T06%3A34%3A02Z&se=2021-07-30T14%3A44%3A02Z&sp=r'}, 'submittedBy': 'ODL_User 152995'}\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": "'Finished'"
          },
          "metadata": {}
        }
      ],
      "execution_count": 29,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629307556
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "assert(pipeline_run.get_status() == \"Finished\")"
      ],
      "outputs": [],
      "execution_count": 31,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629428755
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retievemetrics\r\n",
        "metrics_output = pipeline_run.get_pipeline_output('metrics_output')"
      ],
      "outputs": [],
      "execution_count": 33,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629490681
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_downloaded = metrics_output.download('.', show_progress=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading azureml/aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a/metrics_data\n",
            "Downloaded azureml/aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a/metrics_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 34,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629493613
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json"
      ],
      "outputs": [],
      "execution_count": 35,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629498152
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(metrics_output._path_on_datastore) as f:\r\n",
        "    metrics_output_result=f.read()"
      ],
      "outputs": [],
      "execution_count": 36,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629596421
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "deserialized_metrics_output = json.loads(metrics_output_result)"
      ],
      "outputs": [],
      "execution_count": 37,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629671145
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "outputs": [],
      "execution_count": 39,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629704866
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(deserialized_metrics_output)"
      ],
      "outputs": [],
      "execution_count": 40,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629706003
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_2  \\\nnormalized_root_mean_squared_error                      [0.13336401288541186]   \nmean_absolute_error                                                  [1159.2]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                          [0.13336401288541186]   \nnormalized_median_absolute_error                        [0.13336401288541186]   \nroot_mean_squared_log_error                              [0.6985293680914578]   \nroot_mean_squared_error                                              [1159.2]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                                [1159.2]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.11765088085262347]   \nmean_absolute_percentage_error                              [60.231138457407]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_0  \\\nnormalized_root_mean_squared_error                      [0.07953417447378228]   \nmean_absolute_error                                       [691.3110445261157]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                          [0.07953417447378228]   \nnormalized_median_absolute_error                        [0.07953417447378228]   \nroot_mean_squared_log_error                             [0.31186941381654715]   \nroot_mean_squared_error                                   [691.3110445261157]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                     [691.3110445261157]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error                   [0.0525270846475049]   \nmean_absolute_percentage_error                            [40.80166342571418]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_4  \\\nnormalized_root_mean_squared_error                      [0.26447434221752675]   \nmean_absolute_error                                      [2298.8109825547426]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                          [0.26447434221752675]   \nnormalized_median_absolute_error                        [0.26447434221752675]   \nroot_mean_squared_log_error                              [0.7546133762185778]   \nroot_mean_squared_error                                  [2298.8109825547426]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                    [2298.8109825547426]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.12709691599346445]   \nmean_absolute_percentage_error                           [122.54409211402658]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_6  \\\nnormalized_root_mean_squared_error                      [0.08499332147579457]   \nmean_absolute_error                                       [738.7619502676064]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                          [0.08499332147579457]   \nnormalized_median_absolute_error                        [0.08499332147579457]   \nroot_mean_squared_log_error                              [0.3586573816185693]   \nroot_mean_squared_error                                   [738.7619502676064]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                     [738.7619502676064]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error                 [0.060407419929974185]   \nmean_absolute_percentage_error                           [33.405147376672936]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_1  \\\nnormalized_root_mean_squared_error                   [2.5737099526532125e-05]   \nmean_absolute_error                                      [0.2237068690846172]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                       [2.5737099526532125e-05]   \nnormalized_median_absolute_error                     [2.5737099526532125e-05]   \nroot_mean_squared_log_error                          [0.00011028662632259767]   \nroot_mean_squared_error                                  [0.2237068690846172]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                    [0.2237068690846172]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error               [1.8575194295079226e-05]   \nmean_absolute_percentage_error                         [0.011032673091018507]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_5  \\\nnormalized_root_mean_squared_error                      [0.07603379133521793]   \nmean_absolute_error                                       [660.8857142857144]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                          [0.07603379133521793]   \nnormalized_median_absolute_error                        [0.07603379133521793]   \nroot_mean_squared_log_error                              [0.3214089607663372]   \nroot_mean_squared_error                                   [660.8857142857144]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                     [660.8857142857144]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.05413379748284958]   \nmean_absolute_percentage_error                           [26.212179210428694]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_3  \\\nnormalized_root_mean_squared_error                       [0.1097100782328578]   \nmean_absolute_error                                                   [953.6]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                           [0.1097100782328578]   \nnormalized_median_absolute_error                         [0.1097100782328578]   \nroot_mean_squared_log_error                             [0.43670407984373905]   \nroot_mean_squared_error                                               [953.6]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                                 [953.6]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.07355255485668191]   \nmean_absolute_percentage_error                            [41.90871839259893]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_7  \\\nnormalized_root_mean_squared_error                    [5.231791887787208e-17]   \nmean_absolute_error                                   [4.547473508864641e-13]   \nr2_score                                                                [0.2]   \nnormalized_mean_absolute_error                        [5.231791887787208e-17]   \nnormalized_median_absolute_error                      [5.231791887787208e-17]   \nroot_mean_squared_log_error                                             [0.0]   \nroot_mean_squared_error                               [4.547473508864641e-13]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                 [4.547473508864641e-13]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error                                  [0.0]   \nmean_absolute_percentage_error                       [2.3181512639855854e-14]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_9  \\\nnormalized_root_mean_squared_error                     [0.024588103771260687]   \nmean_absolute_error                                      [213.71979797979793]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                         [0.024588103771260687]   \nnormalized_median_absolute_error                       [0.024588103771260687]   \nroot_mean_squared_log_error                             [0.09770103875253593]   \nroot_mean_squared_error                                  [213.71979797979793]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                    [213.71979797979793]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.01645544739351198]   \nmean_absolute_percentage_error                           [10.371120133270166]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_10  \\\nnormalized_root_mean_squared_error                      [0.021215429913336713]   \nmean_absolute_error                                        [184.4045168067227]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                          [0.021215429913336713]   \nnormalized_median_absolute_error                        [0.021215429913336713]   \nroot_mean_squared_log_error                              [0.08922462510476734]   \nroot_mean_squared_error                                    [184.4045168067227]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                      [184.4045168067227]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.015027794416149116]   \nmean_absolute_percentage_error                             [9.153887811458096]   \n\n                                        ...  \\\nnormalized_root_mean_squared_error      ...   \nmean_absolute_error                     ...   \nr2_score                                ...   \nnormalized_mean_absolute_error          ...   \nnormalized_median_absolute_error        ...   \nroot_mean_squared_log_error             ...   \nroot_mean_squared_error                 ...   \nexplained_variance                      ...   \nmedian_absolute_error                   ...   \nspearman_correlation                    ...   \nnormalized_root_mean_squared_log_error  ...   \nmean_absolute_percentage_error          ...   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_26  \\\nnormalized_root_mean_squared_error                      [0.021215429913336713]   \nmean_absolute_error                                        [184.4045168067227]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                          [0.021215429913336713]   \nnormalized_median_absolute_error                        [0.021215429913336713]   \nroot_mean_squared_log_error                              [0.08922462510476734]   \nroot_mean_squared_error                                    [184.4045168067227]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                      [184.4045168067227]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.015027794416149116]   \nmean_absolute_percentage_error                             [9.153887811458096]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_30  \\\nnormalized_root_mean_squared_error                    [0.00011343738235796933]   \nmean_absolute_error                                       [0.9859977274554694]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                        [0.00011343738235796933]   \nnormalized_median_absolute_error                      [0.00011343738235796933]   \nroot_mean_squared_log_error                            [0.0005254697934496732]   \nroot_mean_squared_error                                   [0.9859977274554694]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                     [0.9859977274554694]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                 [8.850305639934937e-05]   \nmean_absolute_percentage_error                           [0.05259537801513261]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_8  \\\nnormalized_root_mean_squared_error                    [4.247070633954834e-07]   \nmean_absolute_error                                    [0.003691553795033542]   \nr2_score                                                                [0.0]   \nnormalized_mean_absolute_error                        [4.247070633954834e-07]   \nnormalized_median_absolute_error                      [4.247070633954834e-07]   \nroot_mean_squared_log_error                           [1.969182268091174e-06]   \nroot_mean_squared_error                                [0.003691553795033542]   \nexplained_variance                                                      [1.0]   \nmedian_absolute_error                                  [0.003691553795033542]   \nspearman_correlation                                                   [-1.0]   \nnormalized_root_mean_squared_log_error               [3.3166254560389575e-07]   \nmean_absolute_percentage_error                        [0.0001970320837406983]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_16  \\\nnormalized_root_mean_squared_error                      [0.026411520869068027]   \nmean_absolute_error                                       [229.56893939393936]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                          [0.026411520869068027]   \nnormalized_median_absolute_error                        [0.026411520869068027]   \nroot_mean_squared_log_error                              [0.11646089406595621]   \nroot_mean_squared_error                                   [229.56893939393936]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                     [229.56893939393936]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.019615104815392477]   \nmean_absolute_percentage_error                            [12.105193318058427]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_20  \\\nnormalized_root_mean_squared_error                      [0.026783673105838096]   \nmean_absolute_error                                       [232.80368663594473]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                          [0.026783673105838096]   \nnormalized_median_absolute_error                        [0.026783673105838096]   \nroot_mean_squared_log_error                              [0.10123651218671252]   \nroot_mean_squared_error                                   [232.80368663594473]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                     [232.80368663594473]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                  [0.017050914932548174]   \nmean_absolute_percentage_error                            [10.177552128158805]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_22  \\\nnormalized_root_mean_squared_error                      [0.024188663249259876]   \nmean_absolute_error                                       [210.24786096256685]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                          [0.024188663249259876]   \nnormalized_median_absolute_error                        [0.024188663249259876]   \nroot_mean_squared_log_error                              [0.10317703711885215]   \nroot_mean_squared_error                                   [210.24786096256685]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                     [210.24786096256685]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                   [0.01737775082236407]   \nmean_absolute_percentage_error                            [10.764507193205713]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_32  \\\nnormalized_root_mean_squared_error                    [0.00014301275789730918]   \nmean_absolute_error                                       [1.2430668916434116]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                        [0.00014301275789730918]   \nnormalized_median_absolute_error                      [0.00014301275789730918]   \nroot_mean_squared_log_error                            [0.0004809562967002634]   \nroot_mean_squared_error                                   [1.2430668916434116]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                     [1.2430668916434116]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                 [8.100580239454313e-05]   \nmean_absolute_percentage_error                           [0.04813563179806398]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_33  \\\nnormalized_root_mean_squared_error                     [7.433784190706861e-05]   \nmean_absolute_error                                       [0.6461445218562403]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                         [7.433784190706861e-05]   \nnormalized_median_absolute_error                       [7.433784190706861e-05]   \nroot_mean_squared_log_error                           [0.00030160603604265643]   \nroot_mean_squared_error                                   [0.6461445218562403]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                     [0.6461445218562403]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                 [5.079845949474914e-05]   \nmean_absolute_percentage_error                          [0.030170511457020537]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_34  \\\nnormalized_root_mean_squared_error                    [0.00010647384107473642]   \nmean_absolute_error                                        [0.925470626621609]   \nr2_score                                                                 [0.0]   \nnormalized_mean_absolute_error                        [0.00010647384107473642]   \nnormalized_median_absolute_error                      [0.00010647384107473642]   \nroot_mean_squared_log_error                           [0.00038413995973787254]   \nroot_mean_squared_error                                    [0.925470626621609]   \nexplained_variance                                                       [1.0]   \nmedian_absolute_error                                      [0.925470626621609]   \nspearman_correlation                                                    [-1.0]   \nnormalized_root_mean_squared_log_error                 [6.469936225778665e-05]   \nmean_absolute_percentage_error                          [0.038432998017023746]   \n\n                                       aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_38  \nnormalized_root_mean_squared_error                    [1.5409804953297425e-07]  \nmean_absolute_error                                    [0.0013394202465406124]  \nr2_score                                                                 [0.0]  \nnormalized_mean_absolute_error                        [1.5409804953297425e-07]  \nnormalized_median_absolute_error                      [1.5409804953297425e-07]  \nroot_mean_squared_log_error                            [7.149066021838734e-07]  \nroot_mean_squared_error                                [0.0013394202465406124]  \nexplained_variance                                                       [1.0]  \nmedian_absolute_error                                  [0.0013394202465406124]  \nspearman_correlation                                                    [-1.0]  \nnormalized_root_mean_squared_log_error                [1.2040924163875155e-07]  \nmean_absolute_percentage_error                         [7.153195308087802e-05]  \n\n[12 rows x 36 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_2</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_0</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_4</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_6</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_1</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_5</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_3</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_7</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_9</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_10</th>\n      <th>...</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_26</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_30</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_8</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_16</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_20</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_22</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_32</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_33</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_34</th>\n      <th>aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a_38</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>normalized_root_mean_squared_error</th>\n      <td>[0.13336401288541186]</td>\n      <td>[0.07953417447378228]</td>\n      <td>[0.26447434221752675]</td>\n      <td>[0.08499332147579457]</td>\n      <td>[2.5737099526532125e-05]</td>\n      <td>[0.07603379133521793]</td>\n      <td>[0.1097100782328578]</td>\n      <td>[5.231791887787208e-17]</td>\n      <td>[0.024588103771260687]</td>\n      <td>[0.021215429913336713]</td>\n      <td>...</td>\n      <td>[0.021215429913336713]</td>\n      <td>[0.00011343738235796933]</td>\n      <td>[4.247070633954834e-07]</td>\n      <td>[0.026411520869068027]</td>\n      <td>[0.026783673105838096]</td>\n      <td>[0.024188663249259876]</td>\n      <td>[0.00014301275789730918]</td>\n      <td>[7.433784190706861e-05]</td>\n      <td>[0.00010647384107473642]</td>\n      <td>[1.5409804953297425e-07]</td>\n    </tr>\n    <tr>\n      <th>mean_absolute_error</th>\n      <td>[1159.2]</td>\n      <td>[691.3110445261157]</td>\n      <td>[2298.8109825547426]</td>\n      <td>[738.7619502676064]</td>\n      <td>[0.2237068690846172]</td>\n      <td>[660.8857142857144]</td>\n      <td>[953.6]</td>\n      <td>[4.547473508864641e-13]</td>\n      <td>[213.71979797979793]</td>\n      <td>[184.4045168067227]</td>\n      <td>...</td>\n      <td>[184.4045168067227]</td>\n      <td>[0.9859977274554694]</td>\n      <td>[0.003691553795033542]</td>\n      <td>[229.56893939393936]</td>\n      <td>[232.80368663594473]</td>\n      <td>[210.24786096256685]</td>\n      <td>[1.2430668916434116]</td>\n      <td>[0.6461445218562403]</td>\n      <td>[0.925470626621609]</td>\n      <td>[0.0013394202465406124]</td>\n    </tr>\n    <tr>\n      <th>r2_score</th>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.2]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>...</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n      <td>[0.0]</td>\n    </tr>\n    <tr>\n      <th>normalized_mean_absolute_error</th>\n      <td>[0.13336401288541186]</td>\n      <td>[0.07953417447378228]</td>\n      <td>[0.26447434221752675]</td>\n      <td>[0.08499332147579457]</td>\n      <td>[2.5737099526532125e-05]</td>\n      <td>[0.07603379133521793]</td>\n      <td>[0.1097100782328578]</td>\n      <td>[5.231791887787208e-17]</td>\n      <td>[0.024588103771260687]</td>\n      <td>[0.021215429913336713]</td>\n      <td>...</td>\n      <td>[0.021215429913336713]</td>\n      <td>[0.00011343738235796933]</td>\n      <td>[4.247070633954834e-07]</td>\n      <td>[0.026411520869068027]</td>\n      <td>[0.026783673105838096]</td>\n      <td>[0.024188663249259876]</td>\n      <td>[0.00014301275789730918]</td>\n      <td>[7.433784190706861e-05]</td>\n      <td>[0.00010647384107473642]</td>\n      <td>[1.5409804953297425e-07]</td>\n    </tr>\n    <tr>\n      <th>normalized_median_absolute_error</th>\n      <td>[0.13336401288541186]</td>\n      <td>[0.07953417447378228]</td>\n      <td>[0.26447434221752675]</td>\n      <td>[0.08499332147579457]</td>\n      <td>[2.5737099526532125e-05]</td>\n      <td>[0.07603379133521793]</td>\n      <td>[0.1097100782328578]</td>\n      <td>[5.231791887787208e-17]</td>\n      <td>[0.024588103771260687]</td>\n      <td>[0.021215429913336713]</td>\n      <td>...</td>\n      <td>[0.021215429913336713]</td>\n      <td>[0.00011343738235796933]</td>\n      <td>[4.247070633954834e-07]</td>\n      <td>[0.026411520869068027]</td>\n      <td>[0.026783673105838096]</td>\n      <td>[0.024188663249259876]</td>\n      <td>[0.00014301275789730918]</td>\n      <td>[7.433784190706861e-05]</td>\n      <td>[0.00010647384107473642]</td>\n      <td>[1.5409804953297425e-07]</td>\n    </tr>\n    <tr>\n      <th>root_mean_squared_log_error</th>\n      <td>[0.6985293680914578]</td>\n      <td>[0.31186941381654715]</td>\n      <td>[0.7546133762185778]</td>\n      <td>[0.3586573816185693]</td>\n      <td>[0.00011028662632259767]</td>\n      <td>[0.3214089607663372]</td>\n      <td>[0.43670407984373905]</td>\n      <td>[0.0]</td>\n      <td>[0.09770103875253593]</td>\n      <td>[0.08922462510476734]</td>\n      <td>...</td>\n      <td>[0.08922462510476734]</td>\n      <td>[0.0005254697934496732]</td>\n      <td>[1.969182268091174e-06]</td>\n      <td>[0.11646089406595621]</td>\n      <td>[0.10123651218671252]</td>\n      <td>[0.10317703711885215]</td>\n      <td>[0.0004809562967002634]</td>\n      <td>[0.00030160603604265643]</td>\n      <td>[0.00038413995973787254]</td>\n      <td>[7.149066021838734e-07]</td>\n    </tr>\n    <tr>\n      <th>root_mean_squared_error</th>\n      <td>[1159.2]</td>\n      <td>[691.3110445261157]</td>\n      <td>[2298.8109825547426]</td>\n      <td>[738.7619502676064]</td>\n      <td>[0.2237068690846172]</td>\n      <td>[660.8857142857144]</td>\n      <td>[953.6]</td>\n      <td>[4.547473508864641e-13]</td>\n      <td>[213.71979797979793]</td>\n      <td>[184.4045168067227]</td>\n      <td>...</td>\n      <td>[184.4045168067227]</td>\n      <td>[0.9859977274554694]</td>\n      <td>[0.003691553795033542]</td>\n      <td>[229.56893939393936]</td>\n      <td>[232.80368663594473]</td>\n      <td>[210.24786096256685]</td>\n      <td>[1.2430668916434116]</td>\n      <td>[0.6461445218562403]</td>\n      <td>[0.925470626621609]</td>\n      <td>[0.0013394202465406124]</td>\n    </tr>\n    <tr>\n      <th>explained_variance</th>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>...</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n      <td>[1.0]</td>\n    </tr>\n    <tr>\n      <th>median_absolute_error</th>\n      <td>[1159.2]</td>\n      <td>[691.3110445261157]</td>\n      <td>[2298.8109825547426]</td>\n      <td>[738.7619502676064]</td>\n      <td>[0.2237068690846172]</td>\n      <td>[660.8857142857144]</td>\n      <td>[953.6]</td>\n      <td>[4.547473508864641e-13]</td>\n      <td>[213.71979797979793]</td>\n      <td>[184.4045168067227]</td>\n      <td>...</td>\n      <td>[184.4045168067227]</td>\n      <td>[0.9859977274554694]</td>\n      <td>[0.003691553795033542]</td>\n      <td>[229.56893939393936]</td>\n      <td>[232.80368663594473]</td>\n      <td>[210.24786096256685]</td>\n      <td>[1.2430668916434116]</td>\n      <td>[0.6461445218562403]</td>\n      <td>[0.925470626621609]</td>\n      <td>[0.0013394202465406124]</td>\n    </tr>\n    <tr>\n      <th>spearman_correlation</th>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>...</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n      <td>[-1.0]</td>\n    </tr>\n    <tr>\n      <th>normalized_root_mean_squared_log_error</th>\n      <td>[0.11765088085262347]</td>\n      <td>[0.0525270846475049]</td>\n      <td>[0.12709691599346445]</td>\n      <td>[0.060407419929974185]</td>\n      <td>[1.8575194295079226e-05]</td>\n      <td>[0.05413379748284958]</td>\n      <td>[0.07355255485668191]</td>\n      <td>[0.0]</td>\n      <td>[0.01645544739351198]</td>\n      <td>[0.015027794416149116]</td>\n      <td>...</td>\n      <td>[0.015027794416149116]</td>\n      <td>[8.850305639934937e-05]</td>\n      <td>[3.3166254560389575e-07]</td>\n      <td>[0.019615104815392477]</td>\n      <td>[0.017050914932548174]</td>\n      <td>[0.01737775082236407]</td>\n      <td>[8.100580239454313e-05]</td>\n      <td>[5.079845949474914e-05]</td>\n      <td>[6.469936225778665e-05]</td>\n      <td>[1.2040924163875155e-07]</td>\n    </tr>\n    <tr>\n      <th>mean_absolute_percentage_error</th>\n      <td>[60.231138457407]</td>\n      <td>[40.80166342571418]</td>\n      <td>[122.54409211402658]</td>\n      <td>[33.405147376672936]</td>\n      <td>[0.011032673091018507]</td>\n      <td>[26.212179210428694]</td>\n      <td>[41.90871839259893]</td>\n      <td>[2.3181512639855854e-14]</td>\n      <td>[10.371120133270166]</td>\n      <td>[9.153887811458096]</td>\n      <td>...</td>\n      <td>[9.153887811458096]</td>\n      <td>[0.05259537801513261]</td>\n      <td>[0.0001970320837406983]</td>\n      <td>[12.105193318058427]</td>\n      <td>[10.177552128158805]</td>\n      <td>[10.764507193205713]</td>\n      <td>[0.04813563179806398]</td>\n      <td>[0.030170511457020537]</td>\n      <td>[0.038432998017023746]</td>\n      <td>[7.153195308087802e-05]</td>\n    </tr>\n  </tbody>\n</table>\n<p>12 rows × 36 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 41,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629712631
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_output = pipeline_run.get_pipeline_output('best_model_output')"
      ],
      "outputs": [],
      "execution_count": 42,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627629914264
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_download = model_output.download('.', show_progress=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading azureml/aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a/model_data\n",
            "Downloaded azureml/aadd9200-a29d-4b07-ad33-bfaf3a9a5d8a/model_data, 1 files out of an estimated total of 1\n"
          ]
        }
      ],
      "execution_count": 43,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": true
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627630332950
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "outputs": [],
      "execution_count": 44,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627630341148
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open(model_output._path_on_datastore, \"rb\") as f:\r\n",
        "    best_model = pickle.load(f)"
      ],
      "outputs": [],
      "execution_count": 45,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627630762519
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_model"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/plain": "ForecastingPipelineWrapper(pipeline=Pipeline(memory=None,\n                                             steps=[('timeseriestransformer',\n                                                     TimeSeriesTransformer(country_or_region=None, drop_column_names=[], featurization_config=FeaturizationConfig(\n    blocked_transformers=None,\n    column_purposes=None,\n    transformer_params=None,\n    dataset_language=None,\n    drop_columns=None\n), force_time_index_features=None, freq='D', gr...\n    timeseries_param_dict={'time_column_name': 'date', 'grain_column_names': None, 'drop_column_names': [], 'overwrite_columns': True, 'dropna': False, 'transform_dictionary': {'min': '_automl_target_col', 'max': '_automl_target_col', 'mean': '_automl_target_col'}, 'max_horizon': 1, 'origin_time_colname': 'origin', 'country_or_region': None, 'n_cross_validations': 5, 'short_series_handling': True, 'max_cores_per_iteration': 1, 'feature_lags': None, 'target_aggregation_function': None, 'seasonality': 7, 'use_stl': None, 'freq': <Day>, 'short_series_handling_configuration': 'auto', 'target_lags': [0], 'target_rolling_window_size': 0, 'arimax_raw_columns': ['instant', 'date', 'weekday', 'temp', 'atemp', 'hum', 'casual', 'weathersit', 'registered', 'windspeed', 'mnth', 'season', 'yr']}\n))],\n                                             verbose=False),\n                           stddev=[4.637531231849412e-13])"
          },
          "metadata": {}
        }
      ],
      "execution_count": 46,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1627630764349
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "outputs": [],
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3-azureml",
      "language": "python",
      "display_name": "Python 3.6 - AzureML"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.9",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernel_info": {
      "name": "python3-azureml"
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}