Medium-Ortho-ML-Actual-Code.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Ortho Dataset Multiclass Classification\n",
        "\n",
        "**Goal** - Create similar model outputs to Automated ML and Designer with coded examples\n",
        "\n",
        "Outline of Notebook\n",
        "- Import Libraries and Initial Setup\n",
        "- (Optional) Create datastore and upload dataset from UCI ML datasets directly on notebooks\n",
        "- Load dataset and perform initial exploratory data analysis\n",
        "- Prepare data for model\n",
        "- Train model and understand performance with test dataset\n",
        "- Next Steps\n",
        "\n",
        "## Notes and References\n",
        "- Link to Source Data: http://archive.ics.uci.edu/ml/datasets/vertebral+column\n",
        "- Link to additional inforamtion on GitHub: https://github.com/van-william/azure-ml-demo"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries and Initial Setup"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Standard Python ML Libraries\n",
        "import logging\n",
        "import json\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import arff\n",
        "import requests, zipfile\n",
        "from IPython.display import Image\n",
        "from IPython.display import display\n",
        "import sys\n",
        "import math\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import random\n",
        "random.seed(42)\n",
        "\n",
        "#Import Azure libraries\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.dataset import Dataset\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.core import Datastore\n",
        "\n",
        "# Import Sklearn\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score \n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, f1_score, fbeta_score"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1650139635355
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# connect to your workspace\n",
        "ws = Workspace.from_config()\n",
        "\n",
        "# connect to a new or existing experiment to capture all run inforamtion\n",
        "experiment_name = \"ortho-experiment-medium\"\n",
        "exp = Experiment(workspace=ws, name=experiment_name)"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "gather": {
          "logged": 1650139636612
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "curr_path = os.getcwd()\n",
        "print(curr_path)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "/mnt/batch/tasks/shared/LS_root/mounts/clusters/compute-2-vanbuskirk/code/Users/william.n.vanbuskirk/ortho-ml\n"
        }
      ],
      "execution_count": 3,
      "metadata": {
        "gather": {
          "logged": 1650139636747
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Custom Heatmap Plot\n",
        "# Source: https://towardsdatascience.com/better-heatmaps-and-correlation-matrix-plots-in-python-41445d0f2bec\n",
        "def corr_plot(df):\n",
        "    corr = df.corr()\n",
        "    # Generate a mask for the upper triangle\n",
        "    mask = np.zeros_like(corr, dtype=np.bool_)\n",
        "    mask[np.triu_indices_from(mask)] = True\n",
        "    # Set up the matplotlib figure\n",
        "    f, ax = plt.subplots(figsize=(10, 10))\n",
        "    # Generate a custom diverging colormap\n",
        "    cmap = sns.diverging_palette(220, 11, as_cmap=True)\n",
        "    # Draw the heatmap with the mask and correct aspect ratio\n",
        "    sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,\n",
        "    square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1650139637823
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create Datastore and Upload Dataset\n",
        "\n",
        "- We've already walked through how to extract the ortho data from UCI datasets, but the code below shows how to extract the data directly from the web\n",
        "- This provides the opportunity for additional automation and functionality with a wider range of datasets\n",
        "- The code block belows downloads the zip file of the files and then unzips it to the current directory.  Later the files are read in as a pandas dataframe\n",
        "- We'll later push register these files to a datastore and later a dataset.\n",
        "- If you want to skip ahead, you can also read in the existing ortho dataset from the dataset module"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extract Data from UCI Site in python"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# direct link to the zipped file of data from UCI's website\n",
        "f_zip = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00212/vertebral_column_data.zip'\n",
        "\n",
        "# Download the data\n",
        "r = requests.get(f_zip)\n",
        "\n",
        "# Write the zip file to the azure machine learning\n",
        "with open(curr_path+'/ortho-zip.zip', 'wb') as f:\n",
        "    f.write(r.content)\n",
        "\n",
        "# Open and unzip the file\n",
        "with zipfile.ZipFile(curr_path+'/ortho-zip.zip', 'r') as zip:\n",
        "    # printing all the contents of the zip file\n",
        "    print('directory: \\n')\n",
        "    zip.printdir()\n",
        "    # extracting all the files\n",
        "    zip.extractall()\n",
        "\n",
        "# Load the .arff file\n",
        "data_dict = arff.load(open(curr_path+'/column_3C_weka.arff'))\n",
        "\n",
        "# Create a pandas dataframe from the .arff file\n",
        "data_arff = data_dict[\"data\"]\n",
        "attributes = data_dict['attributes']\n",
        "features = [item[0] for item in attributes]\n",
        "ortho_3C_df = pd.DataFrame(data = data_arff, columns = features)\n",
        "ortho_3C_df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "directory: \n\nFile Name                                             Modified             Size\ncolumn_2C.dat                                  2011-08-09 16:13:06        11796\ncolumn_2C_weka.arff                            2011-08-09 16:02:30        25142\ncolumn_3C.dat                                  2011-08-09 16:12:10        11796\ncolumn_3C_weka.arff                            2011-08-09 16:00:40        26389\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "   pelvic_incidence  pelvic_tilt  lumbar_lordosis_angle  sacral_slope  \\\n0         63.027817    22.552586              39.609117     40.475232   \n1         39.056951    10.060991              25.015378     28.995960   \n2         68.832021    22.218482              50.092194     46.613539   \n3         69.297008    24.652878              44.311238     44.644130   \n4         49.712859     9.652075              28.317406     40.060784   \n\n   pelvic_radius  degree_spondylolisthesis   class  \n0      98.672917                 -0.254400  Hernia  \n1     114.405425                  4.564259  Hernia  \n2     105.985135                 -3.530317  Hernia  \n3     101.868495                 11.211523  Hernia  \n4     108.168725                  7.918501  Hernia  ",
            "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>pelvic_incidence</th>\n      <th>pelvic_tilt</th>\n      <th>lumbar_lordosis_angle</th>\n      <th>sacral_slope</th>\n      <th>pelvic_radius</th>\n      <th>degree_spondylolisthesis</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>63.027817</td>\n      <td>22.552586</td>\n      <td>39.609117</td>\n      <td>40.475232</td>\n      <td>98.672917</td>\n      <td>-0.254400</td>\n      <td>Hernia</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>39.056951</td>\n      <td>10.060991</td>\n      <td>25.015378</td>\n      <td>28.995960</td>\n      <td>114.405425</td>\n      <td>4.564259</td>\n      <td>Hernia</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>68.832021</td>\n      <td>22.218482</td>\n      <td>50.092194</td>\n      <td>46.613539</td>\n      <td>105.985135</td>\n      <td>-3.530317</td>\n      <td>Hernia</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>69.297008</td>\n      <td>24.652878</td>\n      <td>44.311238</td>\n      <td>44.644130</td>\n      <td>101.868495</td>\n      <td>11.211523</td>\n      <td>Hernia</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>49.712859</td>\n      <td>9.652075</td>\n      <td>28.317406</td>\n      <td>40.060784</td>\n      <td>108.168725</td>\n      <td>7.918501</td>\n      <td>Hernia</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "gather": {
          "logged": 1650139639832
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Register the pandas dataframe to the datastore and dataset\n",
        "- Instead of querying this data from the website each time, we can create a registered dataset on Azure ML for easy access\n",
        "- This also allows us to update new information to the same dataset name for version control"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "datastore = Datastore.get(ws, 'azure_ml_ortho_data')\n",
        "dataset = Dataset.Tabular.register_pandas_dataframe(ortho_3C_df, datastore, 'Ortho-3C-dataset-from-notebook', show_progress=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Validating arguments.\nArguments validated.\nSuccessfully obtained datastore reference and path.\nUploading file to managed-dataset/14a216c0-27f7-4c7a-95b6-012b6de22484/\nSuccessfully uploaded file to datastore.\nCreating and registering a new dataset.\nSuccessfully created and registered a new dataset.\n"
        }
      ],
      "execution_count": 6,
      "metadata": {
        "gather": {
          "logged": 1650139645563
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Read dataset from Azure machine learning datasets and initial data exploration\n",
        "- We did this in a bit of a redundant way to just show the ability to write data to datastores and datastores\n",
        "- We'll read in the dataset file and start to examine the data\n",
        "- Note, you can read the prepped dataset from my GitHub if preferred too\n",
        "https://github.com/van-william/azure-ml-demo"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = Dataset.get_by_name(ws, name='Ortho-3C-dataset-from-notebook')\n",
        "# Note, this differs from the dataset consume example code, as the workspace (ws) has already been defined\n",
        "\n",
        "# Convert to pandas dataframe and preview\n",
        "ortho_3C_df = dataset.to_pandas_dataframe()\n",
        "ortho_3C_df.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "   pelvic_incidence  pelvic_tilt  lumbar_lordosis_angle  sacral_slope  \\\n0         63.027817    22.552586              39.609117     40.475232   \n1         39.056951    10.060991              25.015378     28.995960   \n2         68.832021    22.218482              50.092194     46.613539   \n3         69.297008    24.652878              44.311238     44.644130   \n4         49.712859     9.652075              28.317406     40.060784   \n\n   pelvic_radius  degree_spondylolisthesis   class  \n0      98.672917                 -0.254400  Hernia  \n1     114.405425                  4.564259  Hernia  \n2     105.985135                 -3.530317  Hernia  \n3     101.868495                 11.211523  Hernia  \n4     108.168725                  7.918501  Hernia  ",
            "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>pelvic_incidence</th>\n      <th>pelvic_tilt</th>\n      <th>lumbar_lordosis_angle</th>\n      <th>sacral_slope</th>\n      <th>pelvic_radius</th>\n      <th>degree_spondylolisthesis</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>63.027817</td>\n      <td>22.552586</td>\n      <td>39.609117</td>\n      <td>40.475232</td>\n      <td>98.672917</td>\n      <td>-0.254400</td>\n      <td>Hernia</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>39.056951</td>\n      <td>10.060991</td>\n      <td>25.015378</td>\n      <td>28.995960</td>\n      <td>114.405425</td>\n      <td>4.564259</td>\n      <td>Hernia</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>68.832021</td>\n      <td>22.218482</td>\n      <td>50.092194</td>\n      <td>46.613539</td>\n      <td>105.985135</td>\n      <td>-3.530317</td>\n      <td>Hernia</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>69.297008</td>\n      <td>24.652878</td>\n      <td>44.311238</td>\n      <td>44.644130</td>\n      <td>101.868495</td>\n      <td>11.211523</td>\n      <td>Hernia</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>49.712859</td>\n      <td>9.652075</td>\n      <td>28.317406</td>\n      <td>40.060784</td>\n      <td>108.168725</td>\n      <td>7.918501</td>\n      <td>Hernia</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1650139646429
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initial Exploratory Data Analysis (EDA)\n",
        "- First, we'll use the pandas df.describe() to look at some overall descriptive statistics for each field\n",
        "- Later on, we'll review some correlation plots and principle component analysis"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ortho_3C_df.describe(include = 'all')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "        pelvic_incidence  pelvic_tilt  lumbar_lordosis_angle  sacral_slope  \\\ncount         310.000000   310.000000             310.000000    310.000000   \nunique               NaN          NaN                    NaN           NaN   \ntop                  NaN          NaN                    NaN           NaN   \nfreq                 NaN          NaN                    NaN           NaN   \nmean           60.496653    17.542822              51.930930     42.953831   \nstd            17.236520    10.008330              18.554064     13.423102   \nmin            26.147921    -6.554948              14.000000     13.366931   \n25%            46.430294    10.667069              37.000000     33.347122   \n50%            58.691038    16.357689              49.562398     42.404912   \n75%            72.877696    22.120395              63.000000     52.695888   \nmax           129.834041    49.431864             125.742385    121.429566   \n\n        pelvic_radius  degree_spondylolisthesis              class  \ncount      310.000000                310.000000                310  \nunique            NaN                       NaN                  3  \ntop               NaN                       NaN  Spondylolisthesis  \nfreq              NaN                       NaN                150  \nmean       117.920655                 26.296694                NaN  \nstd         13.317377                 37.559027                NaN  \nmin         70.082575                -11.058179                NaN  \n25%        110.709196                  1.603727                NaN  \n50%        118.268178                 11.767934                NaN  \n75%        125.467674                 41.287352                NaN  \nmax        163.071041                418.543082                NaN  ",
            "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>pelvic_incidence</th>\n      <th>pelvic_tilt</th>\n      <th>lumbar_lordosis_angle</th>\n      <th>sacral_slope</th>\n      <th>pelvic_radius</th>\n      <th>degree_spondylolisthesis</th>\n      <th>class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>310.000000</td>\n      <td>310.000000</td>\n      <td>310.000000</td>\n      <td>310.000000</td>\n      <td>310.000000</td>\n      <td>310.000000</td>\n      <td>310</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>Spondylolisthesis</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>150</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>60.496653</td>\n      <td>17.542822</td>\n      <td>51.930930</td>\n      <td>42.953831</td>\n      <td>117.920655</td>\n      <td>26.296694</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>17.236520</td>\n      <td>10.008330</td>\n      <td>18.554064</td>\n      <td>13.423102</td>\n      <td>13.317377</td>\n      <td>37.559027</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>26.147921</td>\n      <td>-6.554948</td>\n      <td>14.000000</td>\n      <td>13.366931</td>\n      <td>70.082575</td>\n      <td>-11.058179</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>46.430294</td>\n      <td>10.667069</td>\n      <td>37.000000</td>\n      <td>33.347122</td>\n      <td>110.709196</td>\n      <td>1.603727</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>58.691038</td>\n      <td>16.357689</td>\n      <td>49.562398</td>\n      <td>42.404912</td>\n      <td>118.268178</td>\n      <td>11.767934</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>72.877696</td>\n      <td>22.120395</td>\n      <td>63.000000</td>\n      <td>52.695888</td>\n      <td>125.467674</td>\n      <td>41.287352</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>129.834041</td>\n      <td>49.431864</td>\n      <td>125.742385</td>\n      <td>121.429566</td>\n      <td>163.071041</td>\n      <td>418.543082</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1650139646597
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ortho_3C_df.std() / ortho_3C_df.mean()\n",
        "# This computes the coefficient of variation for each field.  \n",
        "# It normalizes standard deviation to the mean, so the spread of data can be compared across variables of different means"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "pelvic_incidence            0.284917\npelvic_tilt                 0.570509\nlumbar_lordosis_angle       0.357283\nsacral_slope                0.312501\npelvic_radius               0.112935\ndegree_spondylolisthesis    1.428279\ndtype: float64"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1650139646754
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_plot(ortho_3C_df)\n",
        "# This codes creates a correlation plot between fields\n",
        "# Initially, I am curious if any of the fields relate to each other\n",
        "# It seems sacral_slope has a relatively high correlation with pelvic_incidence"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x720 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "gather": {
          "logged": 1650139647110
        },
        "jupyter": {
          "outputs_hidden": false,
          "source_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(ortho_3C_df)\n",
        "# This produces a pairplot of all fields\n",
        "# A pair plot shows a scatter plot comparing all variables and a histogram along the diagonal\n",
        "# This is also used for a rough cut correlation visual"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "<seaborn.axisgrid.PairGrid at 0x7f5e4e97a6d0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1080x1080 with 42 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 11,
      "metadata": {
        "gather": {
          "logged": 1650139656853
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Try PCA for visualization\n",
        "- Beyond these descriptive analytics, we can also perform a Principle Component Analysis (PCA) on the data\n",
        "- This involves combining our 6 numeric fields into 2 principle components that compose the majority of the variance\n",
        "- Essentially, we are shifting 6 dimensions to 2 dimensions but trying to obtain as much information as we can\n",
        "- This helps to visualize where classes sit on this 2D plane"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "pca_df = ortho_3C_df[['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle',\n",
        "       'sacral_slope', 'pelvic_radius', 'degree_spondylolisthesis']]\n",
        "pca = PCA(n_components=2)\n",
        "scaler = StandardScaler()\n",
        "scaled_df = scaler.fit_transform(pca_df)\n",
        "pca_dimensions_scaled = pd.DataFrame(data = pca.fit_transform(scaled_df), columns = ['PC1', 'PC2'])\n",
        "clustered_scaled_df = pd.concat([ortho_3C_df, pca_dimensions_scaled], axis=1, join=\"inner\")"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "gather": {
          "logged": 1650139656988
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data=clustered_scaled_df, x=\"PC1\", y=\"PC2\", hue=\"class\")"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f5e6e157a90>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 13,
      "metadata": {
        "gather": {
          "logged": 1650139657242
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis Conclusions\n",
        "- From the PCA analysis, it seems that differentiating between class Hernia and class Normal will be the most difficult\n",
        "- Note: This was already confirmed in the Azure AutoML section as well as the Designer Pipeline section. Spondylolisthesis is easier to differentiate"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building Machine Learning Pipeline\n",
        "- It's important to create a replicable environment for running multiple runs or changing specific parameters in machine learning\n",
        "- Because of this, we'll do everything in the context of a Machine Learning Pipeline on Azure Machine Learning"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Folder for Pipeline"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "# Create a folder for the pipeline step files\n",
        "experiment_folder = 'ortho_pipeline_scripts'\n",
        "os.makedirs(experiment_folder, exist_ok=True)\n",
        "\n",
        "print(experiment_folder)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "ortho_pipeline_scripts\n"
        }
      ],
      "execution_count": 14,
      "metadata": {
        "gather": {
          "logged": 1650139657356
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Data Prep Script\n",
        "- Instead of running code in the notebook itself, we will create python files that the pipeline will map to\n",
        "- The command %%writefile writes the contents of a cell into a python file instead of executing the contents directly\n",
        "- This also allows us to overwrite the python file in the notebook with relative ease\n",
        "- I vary the method used by Microsoft's DP100 by specifically referencing datasets by name; I find this to be a more intuitive approach"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile $experiment_folder/prep_ortho.py\r\n",
        "# Import libraries\r\n",
        "import os\r\n",
        "import argparse\r\n",
        "import pandas as pd\r\n",
        "from azureml.core import Run\r\n",
        "from sklearn.preprocessing import StandardScaler\r\n",
        "\r\n",
        "#import core aml libraries\r\n",
        "import azureml.core\r\n",
        "from azureml.core.experiment import Experiment\r\n",
        "from azureml.core.workspace import Workspace\r\n",
        "from azureml.core.dataset import Dataset\r\n",
        "from azureml.core import Datastore\r\n",
        "\r\n",
        "\r\n",
        "# Get parameters\r\n",
        "parser = argparse.ArgumentParser()\r\n",
        "parser.add_argument(\"--input-name\", type=str, dest='dataset_name', help='raw dataset name')\r\n",
        "parser.add_argument('--prepped-name', type=str, dest='prepped_data_name', default='prepped_data', help='output dataset name')\r\n",
        "args = parser.parse_args()\r\n",
        "\r\n",
        "# Get the experiment run context\r\n",
        "run = Run.get_context()\r\n",
        "ws = run.experiment.workspace\r\n",
        "\r\n",
        "# load the data (passed as an input dataset)\r\n",
        "print(\"Loading Data...\")\r\n",
        "# NOTE: I prefer to get the dataset by name instead of using dataset.as_named_input\r\n",
        "df = Dataset.get_by_name(ws, name=args.dataset_name).to_pandas_dataframe()\r\n",
        "\r\n",
        "# Normalize the numeric columns\r\n",
        "scaler = StandardScaler()\r\n",
        "num_cols = ['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle',\r\n",
        "            'sacral_slope', 'pelvic_radius', 'degree_spondylolisthesis']\r\n",
        "df[num_cols] = scaler.fit_transform(df[num_cols])\r\n",
        "\r\n",
        "# Save the prepped data\r\n",
        "datastore = Datastore.get(ws, 'azure_ml_ortho_data')\r\n",
        "dataset = Dataset.Tabular.register_pandas_dataframe(df, datastore, args.prepped_data_name, show_progress=True)\r\n",
        "\r\n",
        "# End the run\r\n",
        "run.complete()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Overwriting ortho_pipeline_scripts/prep_ortho.py\n"
        }
      ],
      "execution_count": 15,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1649642961956
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Script for Using Datasets\r\n",
        "- Instead of running code in the notebook itself, we will create python files that the pipeline will map to\r\n",
        "- The command %%writefile writes the contents of a cell into a python file instead of executing the contents directly\r\n",
        "- This also allows us to overwrite the python file in the notebook with relative ease\r\n",
        "- I vary the method used by Microsoft's DP100 by specifically referencing datasets by name; I find this to be a more intuitive approach"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile $experiment_folder/train_ortho.py\r\n",
        "# Import libraries\r\n",
        "import argparse, joblib, os\r\n",
        "from azureml.core import Run\r\n",
        "from azureml.core import Model\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.ensemble import GradientBoostingClassifier\r\n",
        "from sklearn.model_selection import GridSearchCV\r\n",
        "from sklearn.svm import SVC\r\n",
        "from sklearn.metrics import roc_auc_score, roc_curve\r\n",
        "\r\n",
        "#import core aml libraries\r\n",
        "import azureml.core\r\n",
        "from azureml.core.experiment import Experiment\r\n",
        "from azureml.core.workspace import Workspace\r\n",
        "from azureml.core.dataset import Dataset\r\n",
        "from azureml.core import Datastore\r\n",
        "\r\n",
        "# Get the experiment run context\r\n",
        "run = Run.get_context()\r\n",
        "ws = run.experiment.workspace\r\n",
        "\r\n",
        "\r\n",
        "# Get script arguments\r\n",
        "parser = argparse.ArgumentParser()\r\n",
        "\r\n",
        "# Input dataset\r\n",
        "parser.add_argument(\"--prepped-name\", type=str, dest='prepped_data_name', help='training dataset name')\r\n",
        "\r\n",
        "# Add arguments to args collection\r\n",
        "args = parser.parse_args()\r\n",
        "\r\n",
        "# Get the experiment run context\r\n",
        "run = Run.get_context()\r\n",
        "\r\n",
        "# load the prepared data file in the training folder\r\n",
        "print(\"Loading Data...\")\r\n",
        "# NOTE: I prefer to get the dataset by name instead of using dataset.as_named_input\r\n",
        "df = Dataset.get_by_name(ws, name=args.prepped_data_name).to_pandas_dataframe()\r\n",
        "\r\n",
        "# Separate features and labels\r\n",
        "num_cols = ['pelvic_incidence', 'pelvic_tilt', 'lumbar_lordosis_angle','sacral_slope', 'pelvic_radius', 'degree_spondylolisthesis']\r\n",
        "X = df[num_cols].values\r\n",
        "y = df['class'].values\r\n",
        "\r\n",
        "# Split data into training set and test set\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\r\n",
        "\r\n",
        "# Train a Gradient Boosting classification model with the specified hyperparameters\r\n",
        "print('Training a classification model')\r\n",
        "boosted_tree = GradientBoostingClassifier()\r\n",
        "\r\n",
        "# Build dictionary of parameters for gridsearchCV hyperparameter tuning\r\n",
        "# Any model can be used; this is used as an example only\r\n",
        "parameters = {'learning_rate':[0.001, 0.01, 0.1],\r\n",
        "'n_estimators':[300, 400, 500], 'min_samples_split': [2,4,8]}\r\n",
        "clf = GridSearchCV(boosted_tree, parameters)\r\n",
        "clf.fit(X_train, y_train)\r\n",
        "\r\n",
        "# calculate accuracy\r\n",
        "y_hat = clf.predict(X_test)\r\n",
        "acc = np.average(y_hat == y_test)\r\n",
        "print('Accuracy:', acc)\r\n",
        "run.log('Accuracy', float(acc))\r\n",
        "\r\n",
        "# calculate AUC\r\n",
        "auc = roc_auc_score(y_hat, clf.predict_proba(X_test), multi_class='ovr')\r\n",
        "print('AUC: ' + str(auc))\r\n",
        "run.log('AUC', float(auc))\r\n",
        "\r\n",
        "# Save the trained model in the outputs folder\r\n",
        "print(\"Saving model...\")\r\n",
        "os.makedirs('outputs', exist_ok=True)\r\n",
        "model_file = os.path.join('outputs', 'ortho_model.pkl')\r\n",
        "joblib.dump(value=clf, filename=model_file)\r\n",
        "\r\n",
        "# Register the model\r\n",
        "print('Registering model...')\r\n",
        "Model.register(workspace=run.experiment.workspace,\r\n",
        "               model_path = model_file,\r\n",
        "               model_name = 'ortho_model',\r\n",
        "               tags={'Training context':'Pipeline'},\r\n",
        "               properties={'AUC': np.float(auc), 'Accuracy': np.float(acc)})\r\n",
        "\r\n",
        "run.complete()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Overwriting ortho_pipeline_scripts/train_ortho.py\n"
        }
      ],
      "execution_count": 16,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1649634775261
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create Compute\r\n",
        "- Here, a compute cluster is created for running the azure ML pipeline"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "cluster_name = \"training\"\n",
        "\n",
        "try:\n",
        "    # Check for existing compute target\n",
        "    training_cluster = ComputeTarget(workspace=ws, name=cluster_name)\n",
        "    print('Found existing cluster, use it.')\n",
        "except ComputeTargetException:\n",
        "    # If it doesn't already exist, create it\n",
        "    try:\n",
        "        compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D1', max_nodes=2)\n",
        "        training_cluster = ComputeTarget.create(ws, cluster_name, compute_config)\n",
        "        training_cluster.wait_for_completion(show_output=True)\n",
        "    except Exception as ex:\n",
        "        print(ex)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Found existing cluster, use it.\n"
        }
      ],
      "execution_count": 17,
      "metadata": {
        "gather": {
          "logged": 1650139665602
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build Environment\r\n",
        "- Here, an environment is built for the pipeline\r\n",
        "- Custom or curated pipelines can be created\r\n",
        "- I selected a standard SKlearn environment"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Workspace, Environment\n",
        "from azureml.core.runconfig import RunConfiguration\n",
        "\n",
        "#use curated environment\n",
        "env = Environment.get(workspace=ws, name=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu\")\n",
        "\n",
        "# Create a new runconfig object for the pipeline\n",
        "pipeline_run_config = RunConfiguration()\n",
        "\n",
        "# Use the compute you created above. \n",
        "pipeline_run_config.target = training_cluster\n",
        "\n",
        "# Assign the environment to the run configuration\n",
        "pipeline_run_config.environment = env\n",
        "\n",
        "print (\"Run configuration created.\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Run configuration created.\n"
        }
      ],
      "execution_count": 18,
      "metadata": {
        "gather": {
          "logged": 1650139666558
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Build Pipeline Configuration\r\n",
        "- Here, the pipeline steps are connected with the appropriate environment and compute target"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.pipeline.steps import PythonScriptStep\r\n",
        "\r\n",
        "# Get the training dataset\r\n",
        "ortho_ds_name = 'Ortho-3C-dataset-from-notebook'\r\n",
        "prepped_name = 'Ortho-3C-prepped'\r\n",
        "\r\n",
        "# Step 1, Run the data prep script\r\n",
        "prep_step = PythonScriptStep(name = \"Prepare Data\",\r\n",
        "                                source_directory = experiment_folder,\r\n",
        "                                script_name = \"prep_ortho.py\",\r\n",
        "                                arguments = ['--input-name', ortho_ds_name,\r\n",
        "                                             '--prepped-name', prepped_name],\r\n",
        "                                compute_target = training_cluster,\r\n",
        "                                runconfig = pipeline_run_config,\r\n",
        "                                allow_reuse = True)\r\n",
        "\r\n",
        "# Step 2, run the training script\r\n",
        "train_step = PythonScriptStep(name = \"Train and Register Model\",\r\n",
        "                                source_directory = experiment_folder,\r\n",
        "                                script_name = \"train_ortho.py\",\r\n",
        "                                arguments = ['--prepped-name', prepped_name],\r\n",
        "                                compute_target = training_cluster,\r\n",
        "                                runconfig = pipeline_run_config,\r\n",
        "                                allow_reuse = True)\r\n",
        "\r\n",
        "print(\"Pipeline steps defined\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Pipeline steps defined\n"
        }
      ],
      "execution_count": 19,
      "metadata": {
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "gather": {
          "logged": 1650139668004
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Run Pipeline"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Experiment\n",
        "from azureml.widgets import RunDetails\n",
        "from azureml.pipeline.core import Pipeline, StepSequence\n",
        "from azureml.pipeline.steps import PythonScriptStep\n",
        "\n",
        "# Construct the pipeline\n",
        "step_sequence = StepSequence(steps=[prep_step, train_step])\n",
        "pipeline = Pipeline(workspace=ws, steps=step_sequence)\n",
        "print(\"Pipeline is built.\")\n",
        "\n",
        "# Create an experiment and run the pipeline\n",
        "pipeline_run = exp.submit(pipeline, regenerate_outputs=True)\n",
        "print(\"Pipeline submitted for execution.\")\n",
        "RunDetails(pipeline_run).show()\n",
        "pipeline_run.wait_for_completion(show_output=True)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Pipeline is built.\nCreated step Prepare Data [af4cc1f8][95e85b18-7e35-483b-ac2e-42d7139d6837], (This step will run and generate new outputs)\nCreated step Train and Register Model [05531c20][20f14b77-d638-4fca-ab7c-0c65ac6a54de], (This step will run and generate new outputs)\nSubmitted PipelineRun 2c121866-755c-4d62-bf19-84345548c6db\nLink to Azure Machine Learning Portal: https://ml.azure.com/runs/2c121866-755c-4d62-bf19-84345548c6db?wsid=/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourcegroups/ml-demo/workspaces/ortho-ml-demo&tid=513294a0-3e20-41b2-a970-6d30bf1546fa\nPipeline submitted for execution.\n"
        },
        {
          "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": "604afb1129874db4b854f3f722b7a172"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/runs/2c121866-755c-4d62-bf19-84345548c6db?wsid=/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourcegroups/ml-demo/workspaces/ortho-ml-demo&tid=513294a0-3e20-41b2-a970-6d30bf1546fa\", \"run_id\": \"2c121866-755c-4d62-bf19-84345548c6db\", \"run_properties\": {\"run_id\": \"2c121866-755c-4d62-bf19-84345548c6db\", \"created_utc\": \"2022-04-16T20:07:54.871211Z\", \"properties\": {\"azureml.runsource\": \"azureml.PipelineRun\", \"runSource\": \"SDK\", \"runType\": \"SDK\", \"azureml.parameters\": \"{}\", \"azureml.continue_on_step_failure\": \"False\", \"azureml.pipelineComponent\": \"pipelinerun\"}, \"tags\": {}, \"end_time_utc\": \"2022-04-16T20:12:03.432225Z\", \"status\": \"Completed\", \"log_files\": {\"logs/azureml/executionlogs.txt\": 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\"0:00:45\", \"run_number\": 1650139677, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2022-04-16T20:07:57.682548Z\", \"is_reused\": \"\"}, {\"run_id\": \"8f8420e0-7712-4ad7-8de1-6d3d6404ef69\", \"name\": \"Train and Register Model\", \"status\": \"Finished\", \"start_time\": \"2022-04-16T20:08:55.707906Z\", \"created_time\": \"2022-04-16T20:08:46.154565Z\", \"end_time\": \"2022-04-16T20:12:01.135017Z\", \"duration\": \"0:03:14\", \"run_number\": 1650139726, \"metric\": null, \"run_type\": \"azureml.StepRun\", \"training_percent\": null, \"created_time_dt\": \"2022-04-16T20:08:46.154565Z\", \"is_reused\": \"\"}], \"children_metrics\": {\"categories\": null, \"series\": null, \"metricName\": null}, \"run_metrics\": [], \"run_logs\": \"[2022-04-16 20:07:57Z] Submitting 1 runs, first five are: af4cc1f8:724ee4b3-de57-4ebc-af51-fd80c15305b4\\n[2022-04-16 20:08:45Z] Completing processing run id 724ee4b3-de57-4ebc-af51-fd80c15305b4.\\n[2022-04-16 20:08:46Z] Submitting 1 runs, first five are: 05531c20:8f8420e0-7712-4ad7-8de1-6d3d6404ef69\\n[2022-04-16 20:12:02Z] Completing processing run id 8f8420e0-7712-4ad7-8de1-6d3d6404ef69.\\n\\nRun is completed.\", \"graph\": {\"datasource_nodes\": {}, \"module_nodes\": {\"af4cc1f8\": {\"node_id\": \"af4cc1f8\", \"name\": \"Prepare Data\", \"status\": \"Finished\", \"_is_reused\": false, \"run_id\": \"724ee4b3-de57-4ebc-af51-fd80c15305b4\"}, \"05531c20\": {\"node_id\": \"05531c20\", \"name\": \"Train and Register Model\", \"status\": \"Finished\", \"_is_reused\": false, \"run_id\": \"8f8420e0-7712-4ad7-8de1-6d3d6404ef69\"}}, \"edges\": [{\"source_node_id\": \"af4cc1f8\", \"source_node_name\": \"Prepare Data\", \"source_name\": \"_run_after_output\", \"target_name\": \"_run_after_input_0\", \"dst_node_id\": \"05531c20\", \"dst_node_name\": \"Train and Register Model\"}], \"child_runs\": [{\"run_id\": 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\"send_telemetry\": false, \"log_level\": \"INFO\", \"sdk_version\": \"1.36.0\"}, \"loading\": false}"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "PipelineRunId: 2c121866-755c-4d62-bf19-84345548c6db\nLink to Azure Machine Learning Portal: https://ml.azure.com/runs/2c121866-755c-4d62-bf19-84345548c6db?wsid=/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourcegroups/ml-demo/workspaces/ortho-ml-demo&tid=513294a0-3e20-41b2-a970-6d30bf1546fa\nPipelineRun Status: NotStarted\nPipelineRun Status: Running\n\n\nStepRunId: 724ee4b3-de57-4ebc-af51-fd80c15305b4\nLink to Azure Machine Learning Portal: https://ml.azure.com/runs/724ee4b3-de57-4ebc-af51-fd80c15305b4?wsid=/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourcegroups/ml-demo/workspaces/ortho-ml-demo&tid=513294a0-3e20-41b2-a970-6d30bf1546fa\nStepRun( Prepare Data ) Status: Running\n\nStepRun(Prepare Data) Execution Summary\n========================================\nStepRun( Prepare Data ) Status: Finished\n{'runId': '724ee4b3-de57-4ebc-af51-fd80c15305b4', 'target': 'training', 'status': 'Completed', 'startTimeUtc': '2022-04-16T20:08:14.252826Z', 'endTimeUtc': '2022-04-16T20:08:43.561472Z', 'services': {}, 'properties': {'ContentSnapshotId': '12c9e192-d00c-4e77-bea4-a357f98b2ca8', 'StepType': 'PythonScriptStep', 'ComputeTargetType': 'AmlCompute', 'azureml.moduleid': '95e85b18-7e35-483b-ac2e-42d7139d6837', 'azureml.moduleName': 'Prepare Data', 'azureml.runsource': 'azureml.StepRun', 'azureml.nodeid': 'af4cc1f8', 'azureml.pipelinerunid': '2c121866-755c-4d62-bf19-84345548c6db', 'azureml.pipeline': '2c121866-755c-4d62-bf19-84345548c6db', 'azureml.pipelineComponent': 'masterescloud', '_azureml.ComputeTargetType': 'amlctrain', 'ProcessInfoFile': 'azureml-logs/process_info.json', 'ProcessStatusFile': 'azureml-logs/process_status.json'}, 'inputDatasets': [{'dataset': {'id': '67224ece-5fc2-4a9b-983c-fa8cfb8dbc6d'}, 'consumptionDetails': {'type': 'Reference'}}], 'outputDatasets': [{'identifier': {'savedId': 'c674b715-7549-435e-9716-0944c441813c', 'registeredId': '77dd1e82-58e3-4168-bd5d-e5b77a794b73', 'registeredVersion': '6'}, 'outputType': 'Reference', 'dataset': {\n  \"source\": [\n    \"('azure_ml_ortho_data', 'managed-dataset/da03ed16-0164-4b07-9004-e201c8a0407c/')\"\n  ],\n  \"definition\": [\n    \"GetDatastoreFiles\",\n    \"ReadParquetFile\",\n    \"DropColumns\"\n  ],\n  \"registration\": {\n    \"id\": \"c674b715-7549-435e-9716-0944c441813c\",\n    \"name\": \"Ortho-3C-prepped\",\n    \"version\": 6,\n    \"workspace\": \"Workspace.create(name='ortho-ml-demo', subscription_id='a728c376-964f-4402-984a-92e11703958a', resource_group='ml-demo')\"\n  }\n}}], 'runDefinition': {'script': 'prep_ortho.py', 'command': '', 'useAbsolutePath': False, 'arguments': ['--input-name', 'Ortho-3C-dataset-from-notebook', '--prepped-name', 'Ortho-3C-prepped'], 'sourceDirectoryDataStore': None, 'framework': 'Python', 'communicator': 'None', 'target': 'training', 'dataReferences': {}, 'data': {}, 'outputData': {}, 'datacaches': [], 'jobName': None, 'maxRunDurationSeconds': None, 'nodeCount': 1, 'instanceTypes': [], 'priority': None, 'credentialPassthrough': False, 'identity': None, 'environment': {'name': 'AzureML-sklearn-1.0-ubuntu20.04-py38-cpu', 'version': '14', 'assetId': 'azureml://registries/CuratedRegistry/environments/AzureML-sklearn-1.0-ubuntu20.04-py38-cpu/versions/14', 'python': {'interpreterPath': 'python', 'userManagedDependencies': True, 'condaDependencies': None, 'baseCondaEnvironment': None}, 'environmentVariables': {'EXAMPLE_ENV_VAR': 'EXAMPLE_VALUE'}, 'docker': {'baseImage': None, 'platform': {'os': 'Linux', 'architecture': 'amd64'}, 'baseDockerfile': \"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:20220329.v1\\n\\nENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/sklearn-1.0\\n# Create conda environment\\nRUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \\\\\\n    python=3.8 pip=21.3.1 -c anaconda -c conda-forge\\n\\n# Prepend path to AzureML conda environment\\nENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH\\n\\n# Install pip dependencies\\nRUN pip install 'matplotlib~=3.5.0' \\\\\\n                'psutil~=5.8.0' \\\\\\n                'tqdm~=4.62.0' \\\\\\n                'pandas~=1.3.0' \\\\\\n                'scipy~=1.7.0' \\\\\\n                'numpy~=1.21.0' \\\\\\n                'ipykernel~=6.0' \\\\\\n                'azureml-core==1.40.0' \\\\\\n                'azureml-defaults==1.40.0' \\\\\\n                'azureml-mlflow==1.40.0' \\\\\\n                'azureml-telemetry==1.40.0' \\\\\\n                'scikit-learn~=1.0.0'\\n\\n# This is needed for mpi to locate libpython\\nENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH\\n\", 'baseImageRegistry': {'address': None, 'username': None, 'password': None}, 'enabled': False, 'arguments': []}, 'spark': {'repositories': [], 'packages': [], 'precachePackages': True}, 'inferencingStackVersion': None}, 'history': {'outputCollection': True, 'directoriesToWatch': ['logs'], 'enableMLflowTracking': True, 'snapshotProject': True}, 'spark': {'configuration': {'spark.app.name': 'Azure ML Experiment', 'spark.yarn.maxAppAttempts': '1'}}, 'parallelTask': {'maxRetriesPerWorker': 0, 'workerCountPerNode': 1, 'terminalExitCodes': None, 'configuration': {}}, 'amlCompute': {'name': None, 'vmSize': None, 'retainCluster': False, 'clusterMaxNodeCount': 1}, 'aiSuperComputer': {'instanceType': 'D2', 'imageVersion': 'pytorch-1.7.0', 'location': None, 'aiSuperComputerStorageData': None, 'interactive': False, 'scalePolicy': None, 'virtualClusterArmId': None, 'tensorboardLogDirectory': None, 'sshPublicKey': None, 'sshPublicKeys': None, 'enableAzmlInt': True, 'priority': 'Medium', 'slaTier': 'Standard', 'userAlias': None}, 'kubernetesCompute': {'instanceType': None}, 'tensorflow': {'workerCount': 1, 'parameterServerCount': 1}, 'mpi': {'processCountPerNode': 1}, 'pyTorch': {'communicationBackend': 'nccl', 'processCount': None}, 'hdi': {'yarnDeployMode': 'Cluster'}, 'containerInstance': {'region': None, 'cpuCores': 2.0, 'memoryGb': 3.5}, 'exposedPorts': None, 'docker': {'useDocker': False, 'sharedVolumes': True, 'shmSize': '2g', 'arguments': []}, 'cmk8sCompute': {'configuration': {}}, 'commandReturnCodeConfig': {'returnCode': 'Zero', 'successfulReturnCodes': []}, 'environmentVariables': {}, 'applicationEndpoints': {}, 'parameters': []}, 'logFiles': {'logs/azureml/dataprep/backgroundProcess.log': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.724ee4b3-de57-4ebc-af51-fd80c15305b4/logs/azureml/dataprep/backgroundProcess.log?sv=2019-07-07&sr=b&sig=T8RHYeT7VAGI0AfQa0CIz28Wjk7PYRwt%2F3zQpoKHdLc%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A58%3A39Z&se=2022-04-17T04%3A08%3A39Z&sp=r', 'logs/azureml/dataprep/backgroundProcess_Telemetry.log': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.724ee4b3-de57-4ebc-af51-fd80c15305b4/logs/azureml/dataprep/backgroundProcess_Telemetry.log?sv=2019-07-07&sr=b&sig=SS5Q9N04xs%2BwljkSeZEfzbCw8T%2BnvS0Ujj52Syu5t64%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A58%3A39Z&se=2022-04-17T04%3A08%3A39Z&sp=r', 'logs/azureml/dataprep/rslex.log': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.724ee4b3-de57-4ebc-af51-fd80c15305b4/logs/azureml/dataprep/rslex.log?sv=2019-07-07&sr=b&sig=HuqkeZQ%2BecOMuFbPQQukMzRPKo4KgnC7jrFUpNfXwlI%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A58%3A39Z&se=2022-04-17T04%3A08%3A39Z&sp=r', 'logs/azureml/dataprep/rslex.log.2022-04-16-20': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.724ee4b3-de57-4ebc-af51-fd80c15305b4/logs/azureml/dataprep/rslex.log.2022-04-16-20?sv=2019-07-07&sr=b&sig=3DQxxD3I39JjwdtljFh4N4G1nUarZK6ElpVcpDZJh4E%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A58%3A39Z&se=2022-04-17T04%3A08%3A39Z&sp=r', 'logs/azureml/executionlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.724ee4b3-de57-4ebc-af51-fd80c15305b4/logs/azureml/executionlogs.txt?sv=2019-07-07&sr=b&sig=YXX0Lr9R0oKb%2F4mHby3thiEfJ%2FaKHCCvinauDsfLkvY%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A58%3A39Z&se=2022-04-17T04%3A08%3A39Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.724ee4b3-de57-4ebc-af51-fd80c15305b4/logs/azureml/stderrlogs.txt?sv=2019-07-07&sr=b&sig=7YeX1CUmJkI6A0QH06WZgmNkhrK1idNCngXgN0jnAng%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A58%3A39Z&se=2022-04-17T04%3A08%3A39Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.724ee4b3-de57-4ebc-af51-fd80c15305b4/logs/azureml/stdoutlogs.txt?sv=2019-07-07&sr=b&sig=NRqB1qNKn5CSBhvj0vrPQatws3o%2FHliNi0ZYFjXoUSk%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A58%3A39Z&se=2022-04-17T04%3A08%3A39Z&sp=r'}, 'submittedBy': 'William VanBuskirk'}\n\n\n\n\nStepRunId: 8f8420e0-7712-4ad7-8de1-6d3d6404ef69\nLink to Azure Machine Learning Portal: https://ml.azure.com/runs/8f8420e0-7712-4ad7-8de1-6d3d6404ef69?wsid=/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourcegroups/ml-demo/workspaces/ortho-ml-demo&tid=513294a0-3e20-41b2-a970-6d30bf1546fa\nStepRun( Train and Register Model ) Status: Running\n\nStepRun(Train and Register Model) Execution Summary\n====================================================\nStepRun( Train and Register Model ) Status: Finished\n{'runId': '8f8420e0-7712-4ad7-8de1-6d3d6404ef69', 'target': 'training', 'status': 'Completed', 'startTimeUtc': '2022-04-16T20:08:55.707906Z', 'endTimeUtc': '2022-04-16T20:12:01.135017Z', 'services': {}, 'properties': {'ContentSnapshotId': '12c9e192-d00c-4e77-bea4-a357f98b2ca8', 'StepType': 'PythonScriptStep', 'ComputeTargetType': 'AmlCompute', 'azureml.moduleid': '20f14b77-d638-4fca-ab7c-0c65ac6a54de', 'azureml.moduleName': 'Train and Register Model', 'azureml.runsource': 'azureml.StepRun', 'azureml.nodeid': '05531c20', 'azureml.pipelinerunid': '2c121866-755c-4d62-bf19-84345548c6db', 'azureml.pipeline': '2c121866-755c-4d62-bf19-84345548c6db', 'azureml.pipelineComponent': 'masterescloud', '_azureml.ComputeTargetType': 'amlctrain', 'ProcessInfoFile': 'azureml-logs/process_info.json', 'ProcessStatusFile': 'azureml-logs/process_status.json'}, 'inputDatasets': [{'dataset': {'id': 'c674b715-7549-435e-9716-0944c441813c'}, 'consumptionDetails': {'type': 'Reference'}}], 'outputDatasets': [], 'runDefinition': {'script': 'train_ortho.py', 'command': '', 'useAbsolutePath': False, 'arguments': ['--prepped-name', 'Ortho-3C-prepped'], 'sourceDirectoryDataStore': None, 'framework': 'Python', 'communicator': 'None', 'target': 'training', 'dataReferences': {}, 'data': {}, 'outputData': {}, 'datacaches': [], 'jobName': None, 'maxRunDurationSeconds': None, 'nodeCount': 1, 'instanceTypes': [], 'priority': None, 'credentialPassthrough': False, 'identity': None, 'environment': {'name': 'AzureML-sklearn-1.0-ubuntu20.04-py38-cpu', 'version': '14', 'assetId': 'azureml://registries/CuratedRegistry/environments/AzureML-sklearn-1.0-ubuntu20.04-py38-cpu/versions/14', 'python': {'interpreterPath': 'python', 'userManagedDependencies': True, 'condaDependencies': None, 'baseCondaEnvironment': None}, 'environmentVariables': {'EXAMPLE_ENV_VAR': 'EXAMPLE_VALUE'}, 'docker': {'baseImage': None, 'platform': {'os': 'Linux', 'architecture': 'amd64'}, 'baseDockerfile': \"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:20220329.v1\\n\\nENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/sklearn-1.0\\n# Create conda environment\\nRUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \\\\\\n    python=3.8 pip=21.3.1 -c anaconda -c conda-forge\\n\\n# Prepend path to AzureML conda environment\\nENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH\\n\\n# Install pip dependencies\\nRUN pip install 'matplotlib~=3.5.0' \\\\\\n                'psutil~=5.8.0' \\\\\\n                'tqdm~=4.62.0' \\\\\\n                'pandas~=1.3.0' \\\\\\n                'scipy~=1.7.0' \\\\\\n                'numpy~=1.21.0' \\\\\\n                'ipykernel~=6.0' \\\\\\n                'azureml-core==1.40.0' \\\\\\n                'azureml-defaults==1.40.0' \\\\\\n                'azureml-mlflow==1.40.0' \\\\\\n                'azureml-telemetry==1.40.0' \\\\\\n                'scikit-learn~=1.0.0'\\n\\n# This is needed for mpi to locate libpython\\nENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH\\n\", 'baseImageRegistry': {'address': None, 'username': None, 'password': None}, 'enabled': False, 'arguments': []}, 'spark': {'repositories': [], 'packages': [], 'precachePackages': True}, 'inferencingStackVersion': None}, 'history': {'outputCollection': True, 'directoriesToWatch': ['logs'], 'enableMLflowTracking': True, 'snapshotProject': True}, 'spark': {'configuration': {'spark.app.name': 'Azure ML Experiment', 'spark.yarn.maxAppAttempts': '1'}}, 'parallelTask': {'maxRetriesPerWorker': 0, 'workerCountPerNode': 1, 'terminalExitCodes': None, 'configuration': {}}, 'amlCompute': {'name': None, 'vmSize': None, 'retainCluster': False, 'clusterMaxNodeCount': 1}, 'aiSuperComputer': {'instanceType': 'D2', 'imageVersion': 'pytorch-1.7.0', 'location': None, 'aiSuperComputerStorageData': None, 'interactive': False, 'scalePolicy': None, 'virtualClusterArmId': None, 'tensorboardLogDirectory': None, 'sshPublicKey': None, 'sshPublicKeys': None, 'enableAzmlInt': True, 'priority': 'Medium', 'slaTier': 'Standard', 'userAlias': None}, 'kubernetesCompute': {'instanceType': None}, 'tensorflow': {'workerCount': 1, 'parameterServerCount': 1}, 'mpi': {'processCountPerNode': 1}, 'pyTorch': {'communicationBackend': 'nccl', 'processCount': None}, 'hdi': {'yarnDeployMode': 'Cluster'}, 'containerInstance': {'region': None, 'cpuCores': 2.0, 'memoryGb': 3.5}, 'exposedPorts': None, 'docker': {'useDocker': False, 'sharedVolumes': True, 'shmSize': '2g', 'arguments': []}, 'cmk8sCompute': {'configuration': {}}, 'commandReturnCodeConfig': {'returnCode': 'Zero', 'successfulReturnCodes': []}, 'environmentVariables': {}, 'applicationEndpoints': {}, 'parameters': []}, 'logFiles': {'logs/azureml/dataprep/backgroundProcess.log': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.8f8420e0-7712-4ad7-8de1-6d3d6404ef69/logs/azureml/dataprep/backgroundProcess.log?sv=2019-07-07&sr=b&sig=mtfPw4H4qy2g1wjTaTw%2BlrYPoTSDfKyKlp3eD5YwtIc%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A59%3A27Z&se=2022-04-17T04%3A09%3A27Z&sp=r', 'logs/azureml/dataprep/backgroundProcess_Telemetry.log': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.8f8420e0-7712-4ad7-8de1-6d3d6404ef69/logs/azureml/dataprep/backgroundProcess_Telemetry.log?sv=2019-07-07&sr=b&sig=k7E%2Flp3yCrYcgv7xyzAHLrCzmTpkpiE4S%2FBYUk1mxEE%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A59%3A27Z&se=2022-04-17T04%3A09%3A27Z&sp=r', 'logs/azureml/dataprep/rslex.log': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.8f8420e0-7712-4ad7-8de1-6d3d6404ef69/logs/azureml/dataprep/rslex.log?sv=2019-07-07&sr=b&sig=cZotSx%2FZM8s%2BGmtYuDg%2BmPpfX0Q2Tw%2B4pgk6X4Pxae8%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A59%3A27Z&se=2022-04-17T04%3A09%3A27Z&sp=r', 'logs/azureml/dataprep/rslex.log.2022-04-16-20': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.8f8420e0-7712-4ad7-8de1-6d3d6404ef69/logs/azureml/dataprep/rslex.log.2022-04-16-20?sv=2019-07-07&sr=b&sig=X%2FO7xrXHfP4otCj7ofK8GuBxii54%2F31uh64ZOhIs9nM%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A59%3A27Z&se=2022-04-17T04%3A09%3A27Z&sp=r', 'logs/azureml/executionlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.8f8420e0-7712-4ad7-8de1-6d3d6404ef69/logs/azureml/executionlogs.txt?sv=2019-07-07&sr=b&sig=TNAlMGMVE3vfSRi2eGQJtkFZ3OZy%2BnlyWbWE3uHMlJY%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A59%3A27Z&se=2022-04-17T04%3A09%3A27Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.8f8420e0-7712-4ad7-8de1-6d3d6404ef69/logs/azureml/stderrlogs.txt?sv=2019-07-07&sr=b&sig=35G%2FD02krE9Qq9lHEUeUfYf5BfxgxJTCe%2F6iSewt6q8%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A59%3A27Z&se=2022-04-17T04%3A09%3A27Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.8f8420e0-7712-4ad7-8de1-6d3d6404ef69/logs/azureml/stdoutlogs.txt?sv=2019-07-07&sr=b&sig=oav7AFlZmazGsTCYNHuvxD4GVvnZQ2Sb0gzpawgQHoE%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A59%3A27Z&se=2022-04-17T04%3A09%3A27Z&sp=r'}, 'submittedBy': 'William VanBuskirk'}\n\n\n\nPipelineRun Execution Summary\n==============================\nPipelineRun Status: Finished\n{'runId': '2c121866-755c-4d62-bf19-84345548c6db', 'status': 'Completed', 'startTimeUtc': '2022-04-16T20:07:56.535964Z', 'endTimeUtc': '2022-04-16T20:12:03.432225Z', 'services': {}, 'properties': {'azureml.runsource': 'azureml.PipelineRun', 'runSource': 'SDK', 'runType': 'SDK', 'azureml.parameters': '{}', 'azureml.continue_on_step_failure': 'False', 'azureml.pipelineComponent': 'pipelinerun'}, 'inputDatasets': [], 'outputDatasets': [], 'logFiles': {'logs/azureml/executionlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.2c121866-755c-4d62-bf19-84345548c6db/logs/azureml/executionlogs.txt?sv=2019-07-07&sr=b&sig=Z0r%2B35r8KGedNG96AuXOYoZUK41plJnkmWuDnwpIVos%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A57%3A56Z&se=2022-04-17T04%3A07%3A56Z&sp=r', 'logs/azureml/stderrlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.2c121866-755c-4d62-bf19-84345548c6db/logs/azureml/stderrlogs.txt?sv=2019-07-07&sr=b&sig=%2BWemil%2FRdW3dIkNFqbAD9QFGbQzyOXoE80JevSGcmI0%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A57%3A56Z&se=2022-04-17T04%3A07%3A56Z&sp=r', 'logs/azureml/stdoutlogs.txt': 'https://orthomldemo5651887075.blob.core.windows.net/azureml/ExperimentRun/dcid.2c121866-755c-4d62-bf19-84345548c6db/logs/azureml/stdoutlogs.txt?sv=2019-07-07&sr=b&sig=Lcw%2FL97Bo9xZFO7l86kWDttqfpMapf7sDiOLwGg2vNI%3D&skoid=92c5b1e7-bddd-42e9-a322-8cb492a1b387&sktid=513294a0-3e20-41b2-a970-6d30bf1546fa&skt=2022-04-16T18%3A42%3A53Z&ske=2022-04-18T02%3A52%3A53Z&sks=b&skv=2019-07-07&st=2022-04-16T19%3A57%3A56Z&se=2022-04-17T04%3A07%3A56Z&sp=r'}, 'submittedBy': 'William VanBuskirk'}\n\n"
        },
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "'Finished'"
          },
          "metadata": {}
        }
      ],
      "execution_count": 20,
      "metadata": {
        "gather": {
          "logged": 1650139924559
        },
        "jupyter": {
          "outputs_hidden": true
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for run in pipeline_run.get_children():\n",
        "    print(run.name, ':')\n",
        "    metrics = run.get_metrics()\n",
        "    for metric_name in metrics:\n",
        "        print('\\t',metric_name, \":\", metrics[metric_name])"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Train and Register Model :\n\t Accuracy : 0.7956989247311828\n\t AUC : 1.0\nPrepare Data :\n"
        }
      ],
      "execution_count": 21,
      "metadata": {
        "gather": {
          "logged": 1650139926559
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from azureml.core import Model\n",
        "\n",
        "for model in Model.list(ws):\n",
        "    print(model.name, 'version:', model.version)\n",
        "    for tag_name in model.tags:\n",
        "        tag = model.tags[tag_name]\n",
        "        print ('\\t',tag_name, ':', tag)\n",
        "    for prop_name in model.properties:\n",
        "        prop = model.properties[prop_name]\n",
        "        print ('\\t',prop_name, ':', prop)\n",
        "    print('\\n')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "ortho_model version: 4\n\t Training context : Pipeline\n\t AUC : 1.0\n\t Accuracy : 0.7956989247311828\n\n\northo_model version: 3\n\t Training context : Pipeline\n\t AUC : 0.9994877049180327\n\t Accuracy : 0.8172043010752689\n\n\northo_model version: 2\n\t Training context : Pipeline\n\t AUC : 0.9996584699453552\n\t Accuracy : 0.8172043010752689\n\n\northo_model version: 1\n\t Training context : Pipeline\n\t AUC : 0.9996584699453552\n\t Accuracy : 0.8172043010752689\n\n\ndiabetes_model version: 3\n\t Training context : Parameterized script\n\t AUC : 0.8484377332205582\n\t Accuracy : 0.774\n\n\ndiabetes_model version: 2\n\t Training context : Script\n\t AUC : 0.8483377282451863\n\t Accuracy : 0.774\n\n\ndiabetes_model version: 1\n\t Training context : Hyperdrive\n\t AUC : 0.9885804604667666\n\t Accuracy : 0.9457777777777778\n\n\ndiabetes_mitigated_20 version: 1\n\n\ndiabetes_mitigated_19 version: 1\n\n\ndiabetes_mitigated_18 version: 1\n\n\ndiabetes_mitigated_17 version: 1\n\n\ndiabetes_mitigated_16 version: 1\n\n\ndiabetes_mitigated_15 version: 1\n\n\ndiabetes_mitigated_14 version: 1\n\n\ndiabetes_mitigated_13 version: 1\n\n\ndiabetes_mitigated_12 version: 1\n\n\ndiabetes_mitigated_11 version: 1\n\n\ndiabetes_mitigated_10 version: 1\n\n\ndiabetes_mitigated_9 version: 1\n\n\ndiabetes_mitigated_8 version: 1\n\n\ndiabetes_mitigated_7 version: 1\n\n\ndiabetes_mitigated_6 version: 1\n\n\ndiabetes_mitigated_5 version: 1\n\n\ndiabetes_mitigated_4 version: 1\n\n\ndiabetes_mitigated_3 version: 1\n\n\ndiabetes_mitigated_2 version: 1\n\n\ndiabetes_mitigated_1 version: 1\n\n\ndiabetes_unmitigated version: 1\n\n\ndiabetes_classifier version: 1\n\n\namlstudio-predict-auto-price version: 1\n\t CreatedByAMLStudio : true\n\n\nAutoML84bd5e9fc19 version: 3\n\n\nAutoML84bd5e9fc19 version: 2\n\n\nAutoML84bd5e9fc19 version: 1\n\n\northo-classification-3C version: 1\n\n\n"
        }
      ],
      "execution_count": 22,
      "metadata": {
        "gather": {
          "logged": 1650139928868
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Publish the pipeline from the run\n",
        "published_pipeline = pipeline_run.publish_pipeline(\n",
        "    name=\"ortho-training-pipeline\", description=\"Trains ortho model\", version=\"1.0\")\n",
        "\n",
        "published_pipeline"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "Pipeline(Name: ortho-training-pipeline,\nId: 446ddbc4-abe4-48a2-8582-29f934952e8c,\nStatus: Active,\nEndpoint: https://eastus.api.azureml.ms/pipelines/v1.0/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourceGroups/ml-demo/providers/Microsoft.MachineLearningServices/workspaces/ortho-ml-demo/PipelineRuns/PipelineSubmit/446ddbc4-abe4-48a2-8582-29f934952e8c)",
            "text/html": "<table style=\"width:100%\"><tr><th>Name</th><th>Id</th><th>Status</th><th>Endpoint</th></tr><tr><td>ortho-training-pipeline</td><td><a href=\"https://ml.azure.com/pipelines/446ddbc4-abe4-48a2-8582-29f934952e8c?wsid=/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourcegroups/ml-demo/workspaces/ortho-ml-demo\" target=\"_blank\" rel=\"noopener\">446ddbc4-abe4-48a2-8582-29f934952e8c</a></td><td>Active</td><td><a href=\"https://eastus.api.azureml.ms/pipelines/v1.0/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourceGroups/ml-demo/providers/Microsoft.MachineLearningServices/workspaces/ortho-ml-demo/PipelineRuns/PipelineSubmit/446ddbc4-abe4-48a2-8582-29f934952e8c\" target=\"_blank\" rel=\"noopener\">REST Endpoint</a></td></tr></table>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 23,
      "metadata": {
        "gather": {
          "logged": 1650139929885
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rest_endpoint = published_pipeline.endpoint\n",
        "print(rest_endpoint)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "https://eastus.api.azureml.ms/pipelines/v1.0/subscriptions/a728c376-964f-4402-984a-92e11703958a/resourceGroups/ml-demo/providers/Microsoft.MachineLearningServices/workspaces/ortho-ml-demo/PipelineRuns/PipelineSubmit/446ddbc4-abe4-48a2-8582-29f934952e8c\n"
        }
      ],
      "execution_count": 24,
      "metadata": {
        "gather": {
          "logged": 1650139930112
        }
      }
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "cac4749ce6e64bfd07fafd5bf9c175e86cc05b1d81ce0d05824a22ecc489c963"
    },
    "kernel_info": {
      "name": "python38-azureml"
    },
    "kernelspec": {
      "name": "python38-azureml",
      "language": "python",
      "display_name": "Python 3.8 - AzureML"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "microsoft": {
      "host": {
        "AzureML": {
          "notebookHasBeenCompleted": true
        }
      }
    },
    "nteract": {
      "version": "nteract-front-end@1.0.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}