Lab1 Experiments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1: Azure ML experiments\n",
    "<br>\n",
    "\n",
    "<img src='https://github.com/retkowsky/images/blob/master/AzureMLservicebanniere.png?raw=true'>\n",
    "\n",
    "\n",
    "Documentation : https://docs.microsoft.com/en-us/azure/machine-learning/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, you complete the end-to-end steps to get started with the Azure Machine Learning Python SDK running \n",
    "in Jupyter notebooks. \n",
    "\n",
    "In this tutorial, you:\n",
    "- Run some Python code\n",
    "- Log results and metrics into an Azure ML experiment using run.log\n",
    "- Save a ML model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> https://docs.microsoft.com/en-us/azure/machine-learning/concept-azure-machine-learning-architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/retkowsky/images/blob/master/AzureML%20Archi.jpg?raw=true\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Azure architectures: https://docs.microsoft.com/en-us/azure/architecture/browse/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "gather": {
     "logged": 1604047479622
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today is 2021-11-23 16:41:44.090348\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "today = datetime.datetime.now()\n",
    "print(\"Today is\", today)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy, matplotlib, pandas, sklearn\n",
    "import numpy as np\n",
    "import azureml.core\n",
    "import time\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are using Python 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31) \n",
      "[GCC 7.3.0]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(\"You are using Python\", sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are using Azure ML 1.33.0\n"
     ]
    }
   ],
   "source": [
    "print(\"You are using Azure ML\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Releases:\n",
      "- scipy = 1.4.1\n",
      "- numpy = 1.19.5\n",
      "- matplotlib = 3.3.4\n",
      "- pandas = 1.1.5\n",
      "- sklearn = 0.22.2.post1\n",
      "- Azure ML = 1.33.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Releases:\")\n",
    "print('- scipy = {}'.format(scipy.__version__))\n",
    "print('- numpy = {}'.format(np.__version__))\n",
    "print('- matplotlib = {}'.format(matplotlib.__version__))\n",
    "print('- pandas = {}'.format(pandas.__version__))\n",
    "print('- sklearn = {}'.format(sklearn.__version__))\n",
    "print(\"- Azure ML =\",azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Azure ML Release notes: https://docs.microsoft.com/en-us/azure/machine-learning/azure-machine-learning-release-notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's connect to the Azure ML workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azure ML Workspace name: azuremlworkshop\n",
      "Azure region: eastus\n",
      "Resource group: azuremlworkshop-rg\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print('Azure ML Workspace name: ' + ws.name, \n",
    "      'Azure region: ' + ws.location, \n",
    "      'Resource group: ' + ws.resource_group, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are using scikit-learn 0.22.2.post1\n"
     ]
    }
   ],
   "source": [
    "# scikit-learn version\n",
    "import sklearn\n",
    "print(\"You are using scikit-learn\", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> https://scikit-learn.org/stable/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data : DIABETES\n",
    "From Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499, we have\n",
    "\n",
    "\"Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442 diabetes patients, as well as the response of interest, a quantitative measure of disease progression one year after baseline.\"\n",
    "> https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_diabetes(return_X_y = True)\n",
    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test population in %\n",
    "testsizepct=0.30\n",
    "testsizepct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testsizepct, random_state=0)\n",
    "\n",
    "data = {\n",
    "    \"train\":{\"X\": X_train, \"y\": y_train},        \n",
    "    \"test\":{\"X\": X_test, \"y\": y_test}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset = 309 observations - 69.91 %\n",
      "Test dataset = 133 observations - 30.09 %\n"
     ]
    }
   ],
   "source": [
    "nobstrain=len(data['train']['X'])\n",
    "nobstest=len(data['test']['X'])\n",
    "nobstotal=nobstrain+nobstest\n",
    "pcttrain=round(nobstrain/nobstotal*100,2)\n",
    "pcttest=round(nobstest/nobstotal*100,2)\n",
    "\n",
    "print (\"Training dataset =\", nobstrain, 'observations -', pcttrain, '%')\n",
    "print (\"Test dataset =\", nobstest, 'observations -', pcttest,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Let's build a model to predict Diabetes level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use a **Ridge** regression.<br> \n",
    "k is a parameter of this kind of regression model. We will play with multiple values of k during our experiment.\n",
    "<img src='https://github.com/retkowsky/images/blob/master/ridge.png?raw=true'>\n",
    "<br>\n",
    "\n",
    "> https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creation of an Azure ML experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import experiment\n",
    "experiment = Experiment(workspace=ws, name=\"Lab1-Experiments\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def regridge(k):\n",
    "    \n",
    "    start_time = time.time()\n",
    "    \n",
    "    # 1. Starting\n",
    "    run = experiment.start_logging(snapshot_directory=None)\n",
    "    \n",
    "    print('k parameter of the Ridge regression =', k)\n",
    "    print()    \n",
    "       \n",
    "    # 2. Logging\n",
    "    run.log('k', k) \n",
    "    run.log('Test Size', testsizepct)\n",
    "    run.log('Nobs Training', nobstrain)\n",
    "    run.log('Nobs Test', nobstest)\n",
    "    \n",
    "    print('Test Size =', testsizepct)\n",
    "    print('Numbers of obs for the Training dataset =', nobstrain)\n",
    "    print('Number of obs for the Testing dataset =', nobstest)    \n",
    "    print()\n",
    "    \n",
    "    # 3. ML model\n",
    "    regression_model = Ridge(alpha=k)\n",
    "    regression_model.fit(data['train']['X'], data['train']['y'])\n",
    "    preds = regression_model.predict(data['test']['X'])\n",
    "\n",
    "    # 4. Logging ML metrics\n",
    "    from sklearn.metrics import r2_score\n",
    "    import math\n",
    "    \n",
    "    mse = round(mean_squared_error(data['test']['y'], preds), 4)\n",
    "    rmse = round(math.sqrt(mse), 4)\n",
    "    R2 = round(r2_score(data['test']['y'], preds), 4)\n",
    "    \n",
    "    print(\"Model quality indicators:\")\n",
    "    print('- RMSE =', rmse)\n",
    "    print('- MSE =', mse)\n",
    "    print('- R2 =', R2)\n",
    "    \n",
    "    run.log(\"rmse\", rmse)\n",
    "    run.log('mse', mse)\n",
    "    run.log('R2', R2)\n",
    "\n",
    "    # 5. Saving the model\n",
    "    \n",
    "    model_name = \"RegRidgeModele-k-\" + str(k) + \".pkl\"\n",
    "    filename = \"outputs/\" + model_name\n",
    "    \n",
    "    joblib.dump(value=regression_model, filename=filename)\n",
    "    run.upload_file(name=model_name, path_or_stream=filename)\n",
    "    \n",
    "    AMLV=azureml.core.VERSION\n",
    "    \n",
    "    # 6. Tags\n",
    "    run.tag(\"Langage\", \"Python\")\n",
    "    run.tag(\"Version_Python\", \"3.6.9\")\n",
    "    run.tag(\"Version_AzureML\", AMLV)\n",
    "    run.tag(\"Team\", \"DataScience France\")\n",
    "    run.tag(\"Country\", \"France\") \n",
    "    run.tag(\"Author\", \"Serge\") \n",
    "    \n",
    "    # 7. Graph generation\n",
    "    %matplotlib inline\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    fig = plt.figure(1)\n",
    "    idx = np.argsort(data['test']['y'])\n",
    "    plt.plot(data['test']['y'][idx],preds[idx])\n",
    "    \n",
    "    fig.savefig(\"RegRidgeGraphique-k-\"+str(k)+\".png\")\n",
    "    plt.title('Ridge', fontsize=10)\n",
    "    \n",
    "    run.log_image(name='My Graph', plot=plt) # Logging the graph into the experiment\n",
    "    \n",
    "    print('\\nTime elapsed = {}'.format(str(time.strftime('%H:%M:%S', time.gmtime(time.time() - start_time)))))\n",
    "\n",
    "    # 6. End\n",
    "    run.complete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's call the Python function with different k values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k parameter of the Ridge regression = 0\n",
      "\n",
      "Test Size = 0.3\n",
      "Numbers of obs for the Training dataset = 309\n",
      "Number of obs for the Testing dataset = 133\n",
      "\n",
      "Model quality indicators:\n",
      "- RMSE = 55.652\n",
      "- MSE = 3097.1461\n",
      "- R2 = 0.3929\n",
      "\n",
      "Time elapsed = 00:00:09\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regridge(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k parameter of the Ridge regression = 0.1\n",
      "\n",
      "Test Size = 0.3\n",
      "Numbers of obs for the Training dataset = 309\n",
      "Number of obs for the Testing dataset = 133\n",
      "\n",
      "Model quality indicators:\n",
      "- RMSE = 55.2271\n",
      "- MSE = 3050.0324\n",
      "- R2 = 0.4021\n",
      "\n",
      "Time elapsed = 00:00:07\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regridge(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k parameter of the Ridge regression = 0.2\n",
      "\n",
      "Test Size = 0.3\n",
      "Numbers of obs for the Training dataset = 309\n",
      "Number of obs for the Testing dataset = 133\n",
      "\n",
      "Model quality indicators:\n",
      "- RMSE = 55.0152\n",
      "- MSE = 3026.6677\n",
      "- R2 = 0.4067\n",
      "\n",
      "Time elapsed = 00:00:07\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regridge(0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k parameter of the Ridge regression = 0.3\n",
      "\n",
      "Test Size = 0.3\n",
      "Numbers of obs for the Training dataset = 309\n",
      "Number of obs for the Testing dataset = 133\n",
      "\n",
      "Model quality indicators:\n",
      "- RMSE = 55.0521\n",
      "- MSE = 3030.7343\n",
      "- R2 = 0.4059\n",
      "\n",
      "Time elapsed = 00:00:07\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regridge(0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxrwxrwx 1 root root 632 Nov 23 16:42 \u001b[0m\u001b[01;32moutputs/RegRidgeModele-k-0.1.pkl\u001b[0m*\r\n",
      "-rwxrwxrwx 1 root root 632 Nov 23 16:42 \u001b[01;32moutputs/RegRidgeModele-k-0.2.pkl\u001b[0m*\r\n",
      "-rwxrwxrwx 1 root root 632 Nov 23 16:42 \u001b[01;32moutputs/RegRidgeModele-k-0.3.pkl\u001b[0m*\r\n",
      "-rwxrwxrwx 1 root root 625 Nov 23 16:42 \u001b[01;32moutputs/RegRidgeModele-k-0.pkl\u001b[0m*\r\n"
     ]
    }
   ],
   "source": [
    "# Viewing the pkl files saved in your compute instance\n",
    "%ls outputs/RegRidgeModele*.pkl -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxrwxrwx 1 root root 23450 Nov 23 16:42 \u001b[0m\u001b[01;32mRegRidgeGraphique-k-0.1.png\u001b[0m*\r\n",
      "-rwxrwxrwx 1 root root 24992 Nov 23 16:42 \u001b[01;32mRegRidgeGraphique-k-0.2.png\u001b[0m*\r\n",
      "-rwxrwxrwx 1 root root 25407 Nov 23 16:42 \u001b[01;32mRegRidgeGraphique-k-0.3.png\u001b[0m*\r\n",
      "-rwxrwxrwx 1 root root 23785 Nov 23 16:42 \u001b[01;32mRegRidgeGraphique-k-0.png\u001b[0m*\r\n"
     ]
    }
   ],
   "source": [
    "# List of the graphs outputs saved in your compute instance\n",
    "%ls RegRidgeGraphique*.png -l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's save the best model into the model repository. All models are versioned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best model is for k=0.2 (the RMSE metric has the lowest value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymodel='RegRidgeModele-k-0.2.pkl'\n",
    "k=0.2\n",
    "\n",
    "RMSE = 55.0152\n",
    "MSE = 3026.6677\n",
    "R2 = 0.4067"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.33.0'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Azure ML version\n",
    "AMLS=azureml.core.VERSION\n",
    "AMLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registering model DiabetesRidgeModel\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.model import Model\n",
    "\n",
    "model = Model.register(model_path=\"outputs/\"+mymodel, \n",
    "                       model_name=\"DiabetesRidgeModel\", \n",
    "                       model_framework=Model.Framework.SCIKITLEARN, \n",
    "                       model_framework_version='0.22.2',            \n",
    "                       tags={'dataset': 'Diabetes', \n",
    "                             'algorithm': 'Ridge Regression', \n",
    "                             'k parameter':k, \n",
    "                             'MSE' : MSE, \n",
    "                             'R2' : R2,\n",
    "                             'RMSE' : RMSE,\n",
    "                             'Framework' : 'Azure ML',\n",
    "                             'Version Azure ML' : AMLS },\n",
    "                       description=\"Ridge regression model to predict diabetes\",\n",
    "                       workspace=ws) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can retrieve some information about our saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DiabetesRidgeModel:2'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name : DiabetesRidgeModel\n",
      "Description : Ridge regression model to predict diabetes\n",
      "ID = DiabetesRidgeModel:2\n",
      "Version = 2\n"
     ]
    }
   ],
   "source": [
    "print('Name :', model.name)\n",
    "print('Description :', model.description)\n",
    "print('ID =', model.id)\n",
    "print('Version =', model.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'userObjectId': 'ff4137a8-2d37-4ee7-8675-9876eba469ce',\n",
       " 'userPuId': '1003200026778A56',\n",
       " 'userIss': 'https://sts.windows.net/72f988bf-86f1-41af-91ab-2d7cd011db47/',\n",
       " 'userTenantId': '72f988bf-86f1-41af-91ab-2d7cd011db47',\n",
       " 'userName': 'Serge Retkowsky'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.created_by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dataset': 'Diabetes',\n",
       " 'algorithm': 'Ridge Regression',\n",
       " 'k parameter': '0.2',\n",
       " 'MSE': '3026.6677',\n",
       " 'R2': '0.4067',\n",
       " 'RMSE': '55.0152',\n",
       " 'Framework': 'Azure ML',\n",
       " 'Version Azure ML': '1.33.0'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.tags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see the model into the **Models** section in the Azure ML Studio interface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"width:100%\"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>Lab1-Experiments</td><td>azuremlworkshop</td><td><a href=\"https://ml.azure.com/experiments/id/3a4b8f52-09af-42cc-8f5a-4704ad1ac45d?wsid=/subscriptions/70b8f39e-8863-49f7-b6ba-34a80799550c/resourcegroups/azuremlworkshop-rg/workspaces/azuremlworkshop&amp;tid=72f988bf-86f1-41af-91ab-2d7cd011db47\" 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>"
      ],
      "text/plain": [
       "Experiment(Name: Lab1-Experiments,\n",
       "Workspace: azuremlworkshop)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's download the model from the repository and apply it to the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.model import Model\n",
    "mymodel = Model(ws, 'DiabetesRidgeModel')\n",
    "#mymodel = Model(ws, 'DiabetesRidgeModel', version=5) To use a specific release of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model(workspace=Workspace.create(name='azuremlworkshop', subscription_id='70b8f39e-8863-49f7-b6ba-34a80799550c', resource_group='azuremlworkshop-rg'), name=DiabetesRidgeModel, id=DiabetesRidgeModel:2, version=2, tags={'dataset': 'Diabetes', 'algorithm': 'Ridge Regression', 'k parameter': '0.2', 'MSE': '3026.6677', 'R2': '0.4067', 'RMSE': '55.0152', 'Framework': 'Azure ML', 'Version Azure ML': '1.33.0'}, properties={})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymodel.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'DiabetesRidgeModel:2'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymodel.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dataset': 'Diabetes',\n",
       " 'algorithm': 'Ridge Regression',\n",
       " 'k parameter': '0.2',\n",
       " 'MSE': '3026.6677',\n",
       " 'R2': '0.4067',\n",
       " 'RMSE': '55.0152',\n",
       " 'Framework': 'Azure ML',\n",
       " 'Version Azure ML': '1.33.0'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mymodel.tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'RegRidgeModele-k-0.2.pkl'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download the pkl file into your local instance\n",
    "mymodel.download(exist_ok=\"True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxrwxrwx 1 root root 632 Nov 23 16:43 RegRidgeModele-k-0.2.pkl\r\n"
     ]
    }
   ],
   "source": [
    "# We can see the downloaded pkl file\n",
    "!ls RegRidgeModele-k-0.2.pkl -l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's apply the model to the X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "localmodel=joblib.load(\"RegRidgeModele-k-0.2.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([225.38288663, 234.75672822, 164.00312819, 123.46281612,\n",
       "       173.32012984, 240.20150018, 114.66510657, 186.84673729,\n",
       "       147.81327349, 215.15222333, 157.67496598, 181.29031163,\n",
       "       114.71860965,  96.35335408, 235.73733821, 102.20957815,\n",
       "       147.67209551,  75.49004969, 101.20511519, 210.58415103,\n",
       "       185.33186517, 152.02131114, 160.30359928, 149.58478296,\n",
       "       197.65520638, 165.07380997, 115.98983757,  91.39581934,\n",
       "       177.25882651, 164.34061936, 174.33452649,  92.46591384,\n",
       "       140.53306243, 146.69112715, 149.45576607, 185.73324938,\n",
       "       163.12555341, 183.86975076, 133.42400217, 196.74441606,\n",
       "        93.43580034, 169.18689622, 147.92192731, 172.85033475,\n",
       "       179.93858885,  83.07219651, 136.61675205, 140.33734763,\n",
       "       132.6680819 , 220.56622016, 157.48518116,  90.73063296,\n",
       "       154.13365588, 159.89740219, 219.54872002, 177.72397388,\n",
       "       187.711351  , 115.43236395, 141.66560526, 172.65842486,\n",
       "       198.06726423, 160.8900295 , 160.64145986, 124.01127688,\n",
       "       244.34470657, 149.60634672,  90.42159674, 224.63904842,\n",
       "       199.85477162,  59.20789166,  88.52638222, 140.11495514,\n",
       "       112.67526953, 138.96342469, 136.62220219, 186.61801293,\n",
       "       118.95093032, 200.85197325, 217.84292082, 183.99047216,\n",
       "       150.39882822, 203.64538473,  70.37984832, 205.45148204,\n",
       "        88.04143737, 101.9174092 , 140.95167005, 189.06917737,\n",
       "       135.20453291, 152.56028415,  98.53228015, 128.66021699,\n",
       "        89.82689066, 154.2560757 , 127.11669304, 110.0321479 ,\n",
       "       223.00746531, 211.10276793, 136.59658398, 164.75253829,\n",
       "       184.38063805, 105.70493325, 208.35109466, 100.37493879,\n",
       "       208.98481298, 129.62065864, 219.10971746, 254.25872603,\n",
       "       121.00493611, 121.2977913 , 188.84821054, 120.33297775,\n",
       "       181.13423349, 106.6486232 , 102.46295664, 209.54413186,\n",
       "       225.45398709, 119.71357979, 263.4182524 , 127.43079763,\n",
       "       134.62888892, 119.33823733,  81.10941054, 227.77198116,\n",
       "       110.9800821 , 130.63053718, 199.82326789, 179.80540794,\n",
       "       183.12242861, 172.57443238, 191.90778094, 120.24955576,\n",
       "       169.72991723])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying the model to the test datasets\n",
    "localmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "modelpredictions=pd.DataFrame(localmodel.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>225.382887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>234.756728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>164.003128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123.462816</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>173.320130</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>240.201500</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>114.665107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>186.846737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>147.813273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>215.152223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0\n",
       "0  225.382887\n",
       "1  234.756728\n",
       "2  164.003128\n",
       "3  123.462816\n",
       "4  173.320130\n",
       "5  240.201500\n",
       "6  114.665107\n",
       "7  186.846737\n",
       "8  147.813273\n",
       "9  215.152223"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelpredictions.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can export the results into a csv file..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelpredictions.to_csv(r'ModelPredictions.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rwxrwxrwx 1 root root 2480 Nov 23 16:43 ModelPredictions.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls ModelPredictions.csv -l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> You can open now the **Lab2 notebook**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/retkowsky/images/blob/master/Powered-by-MS-Azure-logo-v2.png?raw=true\" height=\"300\" width=\"300\">"
   ]
  }
 ],
 "metadata": {
  "kernel_info": {
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  "kernelspec": {
   "display_name": "Python 3.6 - AzureML",
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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