Breast Cancer Prediction.ipynb

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   "source": [
    "# Breast Cancer Prediction \n",
    "_**Predict Breast Cancer using SageMaker's Linear-Learner with features derived from images of Breast Mass**_\n",
    "\n",
    "---\n",
    "\n",
    "---\n",
    "\n",
    "## Contents\n",
    "\n",
    "1. [Background](#Background)\n",
    "1. [Setup](#Setup)\n",
    "1. [Data](#Data)\n",
    "1. [Train](#Train)\n",
    "1. [Host](#Host)\n",
    "1. [Predict](#Predict)\n",
    "1. [Extensions](#Extensions)\n",
    "\n",
    "---\n",
    "\n",
    "## Background\n",
    "This notebook illustrates how one can use SageMaker's algorithms for solving applications which require `linear models` for prediction. For this illustration, we have taken an example for breast cancer prediction using UCI'S breast cancer diagnostic data set. The purpose here is to use this data set to build a predictve model of whether a breast mass image indicates benign or malignant tumor. The data set will be used to illustrate:\n",
    "\n",
    "* Basic setup for using SageMaker.\n",
    "* Converting datasets to protobuf format used by the Amazon SageMaker algorithms and uploading to S3. \n",
    "* Training SageMaker's linear learner on the data set.\n",
    "* Hosting the trained model.\n",
    "* Scoring using the trained model.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "## Setup\n",
    "\n",
    "Let's start by specifying:\n",
    "\n",
    "* The SageMaker role arn used to give learning and hosting access to your data. The snippet below will use the same role used by your SageMaker notebook instance, if you're using other.  Otherwise, specify the full ARN of a role with the SageMakerFullAccess policy attached.\n",
    "* The S3 bucket that you want to use for training and storing model objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# %pip install --upgrade boto3 sagemaker -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "isConfigCell": true,
    "nbpresent": {
     "id": "6427e831-8f89-45c0-b150-0b134397d79a"
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import boto3\n",
    "import re\n",
    "import sagemaker\n",
    "\n",
    "role = sagemaker.get_execution_role()\n",
    "region = boto3.Session().region_name\n",
    "\n",
    "# S3 bucket for saving code and model artifacts.\n",
    "# Feel free to specify a different bucket and prefix\n",
    "bucket = sagemaker.Session().default_bucket()\n",
    "\n",
    "prefix = (\n",
    "    \"sagemaker/DEMO-breast-cancer-prediction\"  # place to upload training files within the bucket\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b2548d66-6f8f-426f-9cda-7a3cd1459abd"
    }
   },
   "source": [
    "Now we'll import the Python libraries we'll need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbpresent": {
     "id": "bb88eea9-27f3-4e47-9133-663911ea09a9"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import io\n",
    "import time\n",
    "import json\n",
    "import sagemaker.amazon.common as smac"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "142777ae-c072-448e-b941-72bc75735d01"
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   },
   "source": [
    "---\n",
    "## Data sources\n",
    "\n",
    "\n",
    "> Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
    "\n",
    "> Breast Cancer Wisconsin (Diagnostic) Data Set [https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)].\n",
    "\n",
    "> _Also see:_ Breast Cancer Wisconsin (Diagnostic) Data Set [https://www.kaggle.com/uciml/breast-cancer-wisconsin-data].\n",
    "\n",
    "Let's download the data and save it in the local folder with the name data.csv and take a look at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "nbpresent": {
     "id": "f8976dad-6897-4c7e-8c95-ae2f53070ef5"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(569, 32)\n"
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       "      <td>1203.0</td>\n",
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       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
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       "      <td>0.4504</td>\n",
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       "      <td>386.1</td>\n",
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       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
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       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
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       "\n",
       "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
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       "1  ...         24.99          23.41           158.80      1956.0   \n",
       "2  ...         23.57          25.53           152.50      1709.0   \n",
       "3  ...         14.91          26.50            98.87       567.7   \n",
       "4  ...         22.54          16.67           152.20      1575.0   \n",
       "\n",
       "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
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       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   symmetry_worst  fractal_dimension_worst  \n",
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       "      <th>count</th>\n",
       "      <td>5.690000e+02</td>\n",
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       "      <td>569.000000</td>\n",
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       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
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       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>3.037183e+07</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
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       "      <th>25%</th>\n",
       "      <td>8.692180e+05</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
       "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
       "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
       "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
       "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "count     569.000000  ...    569.000000     569.000000       569.000000   \n",
       "mean        0.181162  ...     16.269190      25.677223       107.261213   \n",
       "std         0.027414  ...      4.833242       6.146258        33.602542   \n",
       "min         0.106000  ...      7.930000      12.020000        50.410000   \n",
       "25%         0.161900  ...     13.010000      21.080000        84.110000   \n",
       "50%         0.179200  ...     14.970000      25.410000        97.660000   \n",
       "75%         0.195700  ...     18.790000      29.720000       125.400000   \n",
       "max         0.304000  ...     36.040000      49.540000       251.200000   \n",
       "\n",
       "        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count   569.000000        569.000000         569.000000       569.000000   \n",
       "mean    880.583128          0.132369           0.254265         0.272188   \n",
       "std     569.356993          0.022832           0.157336         0.208624   \n",
       "min     185.200000          0.071170           0.027290         0.000000   \n",
       "25%     515.300000          0.116600           0.147200         0.114500   \n",
       "50%     686.500000          0.131300           0.211900         0.226700   \n",
       "75%    1084.000000          0.146000           0.339100         0.382900   \n",
       "max    4254.000000          0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s3 = boto3.client(\"s3\")\n",
    "\n",
    "filename = \"wdbc.csv\"\n",
    "s3.download_file(\"sagemaker-sample-files\", \"datasets/tabular/breast_cancer/wdbc.csv\", filename)\n",
    "data = pd.read_csv(filename, header=None)\n",
    "\n",
    "# specify columns extracted from wbdc.names\n",
    "data.columns = [\n",
    "    \"id\",\n",
    "    \"diagnosis\",\n",
    "    \"radius_mean\",\n",
    "    \"texture_mean\",\n",
    "    \"perimeter_mean\",\n",
    "    \"area_mean\",\n",
    "    \"smoothness_mean\",\n",
    "    \"compactness_mean\",\n",
    "    \"concavity_mean\",\n",
    "    \"concave points_mean\",\n",
    "    \"symmetry_mean\",\n",
    "    \"fractal_dimension_mean\",\n",
    "    \"radius_se\",\n",
    "    \"texture_se\",\n",
    "    \"perimeter_se\",\n",
    "    \"area_se\",\n",
    "    \"smoothness_se\",\n",
    "    \"compactness_se\",\n",
    "    \"concavity_se\",\n",
    "    \"concave points_se\",\n",
    "    \"symmetry_se\",\n",
    "    \"fractal_dimension_se\",\n",
    "    \"radius_worst\",\n",
    "    \"texture_worst\",\n",
    "    \"perimeter_worst\",\n",
    "    \"area_worst\",\n",
    "    \"smoothness_worst\",\n",
    "    \"compactness_worst\",\n",
    "    \"concavity_worst\",\n",
    "    \"concave points_worst\",\n",
    "    \"symmetry_worst\",\n",
    "    \"fractal_dimension_worst\",\n",
    "]\n",
    "\n",
    "# save the data\n",
    "data.to_csv(\"data.csv\", sep=\",\", index=False)\n",
    "\n",
    "# print the shape of the data file\n",
    "print(data.shape)\n",
    "\n",
    "# show the top few rows\n",
    "display(data.head())\n",
    "\n",
    "# describe the data object\n",
    "display(data.describe())\n",
    "\n",
    "# we will also summarize the categorical field diganosis\n",
    "display(data.diagnosis.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Key observations:\n",
    "* Data has 569 observations and 32 columns.\n",
    "* First field is 'id'.\n",
    "* Second field, 'diagnosis', is an indicator of the actual diagnosis ('M' = Malignant; 'B' = Benign).\n",
    "* There are 30 other numeric features available for prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Features and Labels\n",
    "#### Split the data into 80% training, 10% validation and 10% testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_split = np.random.rand(len(data))\n",
    "train_list = rand_split < 0.8\n",
    "val_list = (rand_split >= 0.8) & (rand_split < 0.9)\n",
    "test_list = rand_split >= 0.9\n",
    "\n",
    "data_train = data[train_list]\n",
    "data_val = data[val_list]\n",
    "data_test = data[test_list]\n",
    "\n",
    "train_y = ((data_train.iloc[:, 1] == \"M\") + 0).to_numpy()\n",
    "train_X = data_train.iloc[:, 2:].to_numpy()\n",
    "\n",
    "val_y = ((data_val.iloc[:, 1] == \"M\") + 0).to_numpy()\n",
    "val_X = data_val.iloc[:, 2:].to_numpy()\n",
    "\n",
    "test_y = ((data_test.iloc[:, 1] == \"M\") + 0).to_numpy()\n",
    "test_X = data_test.iloc[:, 2:].to_numpy();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ff9d10f9-b611-423b-80da-6dcdafd1c8b9"
    }
   },
   "source": [
    "Now, we'll convert the datasets to the recordIO-wrapped protobuf format used by the Amazon SageMaker algorithms, and then upload this data to S3.  We'll start with training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "nbpresent": {
     "id": "cd8e3431-79d9-40b6-91d1-d67cd61894e7"
    }
   },
   "outputs": [],
   "source": [
    "train_file = \"linear_train.data\"\n",
    "\n",
    "f = io.BytesIO()\n",
    "smac.write_numpy_to_dense_tensor(f, train_X.astype(\"float32\"), train_y.astype(\"float32\"))\n",
    "f.seek(0)\n",
    "\n",
    "boto3.Session().resource(\"s3\").Bucket(bucket).Object(\n",
    "    os.path.join(prefix, \"train\", train_file)\n",
    ").upload_fileobj(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "71cbcebd-a2a5-419e-8e50-b2bc0909f564"
    }
   },
   "source": [
    "Next we'll convert and upload the validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "nbpresent": {
     "id": "bd113b8e-adc1-4091-a26f-a426149fe604"
    }
   },
   "outputs": [],
   "source": [
    "validation_file = \"linear_validation.data\"\n",
    "\n",
    "f = io.BytesIO()\n",
    "smac.write_numpy_to_dense_tensor(f, val_X.astype(\"float32\"), val_y.astype(\"float32\"))\n",
    "f.seek(0)\n",
    "\n",
    "boto3.Session().resource(\"s3\").Bucket(bucket).Object(\n",
    "    os.path.join(prefix, \"validation\", validation_file)\n",
    ").upload_fileobj(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f3b125ad-a2d5-464c-8cfa-bd203034eee4"
    }
   },
   "source": [
    "---\n",
    "## Train\n",
    "\n",
    "Now we can begin to specify our linear model.  Amazon SageMaker's Linear Learner actually fits many models in parallel, each with slightly different hyperparameters, and then returns the one with the best fit.  This functionality is automatically enabled.  We can influence this using parameters like:\n",
    "\n",
    "- `num_models` to increase to total number of models run.  The specified parameters will always be one of those models, but the algorithm also chooses models with nearby parameter values in order to find a solution nearby that may be more optimal.  In this case, we're going to use the max of 32.\n",
    "- `loss` which controls how we penalize mistakes in our model estimates.  For this case, let's use absolute loss as we haven't spent much time cleaning the data, and absolute loss will be less sensitive to outliers.\n",
    "- `wd` or `l1` which control regularization.  Regularization can prevent model overfitting by preventing our estimates from becoming too finely tuned to the training data, which can actually hurt generalizability.  In this case, we'll leave these parameters as their default \"auto\" though."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specify container images used for training and hosting SageMaker's linear-learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import image_uris\n",
    "\n",
    "container = image_uris.retrieve(region=boto3.Session().region_name, framework=\"linear-learner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbpresent": {
     "id": "397fb60a-c48b-453f-88ea-4d832b70c919"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job name is: DEMO-linear-2023-02-20-18-10-59\n"
     ]
    }
   ],
   "source": [
    "linear_job = \"DEMO-linear-\" + time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.gmtime())\n",
    "\n",
    "print(\"Job name is:\", linear_job)\n",
    "\n",
    "linear_training_params = {\n",
    "    \"RoleArn\": role,\n",
    "    \"TrainingJobName\": linear_job,\n",
    "    \"AlgorithmSpecification\": {\"TrainingImage\": container, \"TrainingInputMode\": \"File\"},\n",
    "    \"ResourceConfig\": {\"InstanceCount\": 1, \"InstanceType\": \"ml.c4.2xlarge\", \"VolumeSizeInGB\": 10},\n",
    "    \"InputDataConfig\": [\n",
    "        {\n",
    "            \"ChannelName\": \"train\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": \"s3://{}/{}/train/\".format(bucket, prefix),\n",
    "                    \"S3DataDistributionType\": \"ShardedByS3Key\",\n",
    "                }\n",
    "            },\n",
    "            \"CompressionType\": \"None\",\n",
    "            \"RecordWrapperType\": \"None\",\n",
    "        },\n",
    "        {\n",
    "            \"ChannelName\": \"validation\",\n",
    "            \"DataSource\": {\n",
    "                \"S3DataSource\": {\n",
    "                    \"S3DataType\": \"S3Prefix\",\n",
    "                    \"S3Uri\": \"s3://{}/{}/validation/\".format(bucket, prefix),\n",
    "                    \"S3DataDistributionType\": \"FullyReplicated\",\n",
    "                }\n",
    "            },\n",
    "            \"CompressionType\": \"None\",\n",
    "            \"RecordWrapperType\": \"None\",\n",
    "        },\n",
    "    ],\n",
    "    \"OutputDataConfig\": {\"S3OutputPath\": \"s3://{}/{}/\".format(bucket, prefix)},\n",
    "    \"HyperParameters\": {\n",
    "        \"feature_dim\": \"30\",\n",
    "        \"mini_batch_size\": \"100\",\n",
    "        \"predictor_type\": \"regressor\",\n",
    "        \"epochs\": \"10\",\n",
    "        \"num_models\": \"32\",\n",
    "        \"loss\": \"absolute_loss\",\n",
    "    },\n",
    "    \"StoppingCondition\": {\"MaxRuntimeInSeconds\": 60 * 60},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's kick off our training job in SageMaker's distributed, managed training, using the parameters we just created.  Because training is managed, we don't have to wait for our job to finish to continue, but for this case, let's use boto3's 'training_job_completed_or_stopped' waiter so we can ensure that the job has been started."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "InProgress\n",
      "CPU times: user 233 ms, sys: 5.83 ms, total: 239 ms\n",
      "Wall time: 4min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "region = boto3.Session().region_name\n",
    "sm = boto3.client(\"sagemaker\")\n",
    "\n",
    "sm.create_training_job(**linear_training_params)\n",
    "\n",
    "status = sm.describe_training_job(TrainingJobName=linear_job)[\"TrainingJobStatus\"]\n",
    "print(status)\n",
    "sm.get_waiter(\"training_job_completed_or_stopped\").wait(TrainingJobName=linear_job)\n",
    "if status == \"Failed\":\n",
    "    message = sm.describe_training_job(TrainingJobName=linear_job)[\"FailureReason\"]\n",
    "    print(\"Training failed with the following error: {}\".format(message))\n",
    "    raise Exception(\"Training job failed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "2adcc348-9ab5-4a8a-8139-d0ecd740208a"
    }
   },
   "source": [
    "---\n",
    "## Host\n",
    "\n",
    "Now that we've trained the linear algorithm on our data, let's setup a model which can later be hosted.  We will:\n",
    "1. Point to the scoring container\n",
    "1. Point to the model.tar.gz that came from training\n",
    "1. Create the hosting model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbpresent": {
     "id": "c88fb868-01d2-4991-8953-28814c022bdc"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:sagemaker:us-east-1:576042411576:model/demo-linear-2023-02-20-18-10-59\n"
     ]
    }
   ],
   "source": [
    "linear_hosting_container = {\n",
    "    \"Image\": container,\n",
    "    \"ModelDataUrl\": sm.describe_training_job(TrainingJobName=linear_job)[\"ModelArtifacts\"][\n",
    "        \"S3ModelArtifacts\"\n",
    "    ],\n",
    "}\n",
    "\n",
    "create_model_response = sm.create_model(\n",
    "    ModelName=linear_job, ExecutionRoleArn=role, PrimaryContainer=linear_hosting_container\n",
    ")\n",
    "\n",
    "print(create_model_response[\"ModelArn\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we've setup a model, we can configure what our hosting endpoints should be.  Here we specify:\n",
    "1. EC2 instance type to use for hosting\n",
    "1. Initial number of instances\n",
    "1. Our hosting model name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEMO-linear-endpoint-config-2023-02-20-18-16-25\n",
      "Endpoint Config Arn: arn:aws:sagemaker:us-east-1:576042411576:endpoint-config/demo-linear-endpoint-config-2023-02-20-18-16-25\n"
     ]
    }
   ],
   "source": [
    "linear_endpoint_config = \"DEMO-linear-endpoint-config-\" + time.strftime(\n",
    "    \"%Y-%m-%d-%H-%M-%S\", time.gmtime()\n",
    ")\n",
    "print(linear_endpoint_config)\n",
    "create_endpoint_config_response = sm.create_endpoint_config(\n",
    "    EndpointConfigName=linear_endpoint_config,\n",
    "    ProductionVariants=[\n",
    "        {\n",
    "            \"InstanceType\": \"ml.m4.xlarge\",\n",
    "            \"InitialInstanceCount\": 1,\n",
    "            \"ModelName\": linear_job,\n",
    "            \"VariantName\": \"AllTraffic\",\n",
    "        }\n",
    "    ],\n",
    ")\n",
    "\n",
    "print(\"Endpoint Config Arn: \" + create_endpoint_config_response[\"EndpointConfigArn\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've specified how our endpoint should be configured, we can create them.  This can be done in the background, but for now let's run a loop that updates us on the status of the endpoints so that we know when they are ready for use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEMO-linear-endpoint-202302201816\n",
      "arn:aws:sagemaker:us-east-1:576042411576:endpoint/demo-linear-endpoint-202302201816\n",
      "Status: Creating\n",
      "Arn: arn:aws:sagemaker:us-east-1:576042411576:endpoint/demo-linear-endpoint-202302201816\n",
      "Status: InService\n",
      "CPU times: user 139 ms, sys: 10.2 ms, total: 149 ms\n",
      "Wall time: 5min 2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "linear_endpoint = \"DEMO-linear-endpoint-\" + time.strftime(\"%Y%m%d%H%M\", time.gmtime())\n",
    "print(linear_endpoint)\n",
    "create_endpoint_response = sm.create_endpoint(\n",
    "    EndpointName=linear_endpoint, EndpointConfigName=linear_endpoint_config\n",
    ")\n",
    "print(create_endpoint_response[\"EndpointArn\"])\n",
    "\n",
    "resp = sm.describe_endpoint(EndpointName=linear_endpoint)\n",
    "status = resp[\"EndpointStatus\"]\n",
    "print(\"Status: \" + status)\n",
    "\n",
    "sm.get_waiter(\"endpoint_in_service\").wait(EndpointName=linear_endpoint)\n",
    "\n",
    "resp = sm.describe_endpoint(EndpointName=linear_endpoint)\n",
    "status = resp[\"EndpointStatus\"]\n",
    "print(\"Arn: \" + resp[\"EndpointArn\"])\n",
    "print(\"Status: \" + status)\n",
    "\n",
    "if status != \"InService\":\n",
    "    raise Exception(\"Endpoint creation did not succeed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict\n",
    "### Predict on Test Data\n",
    "\n",
    "Now that we have our hosted endpoint, we can generate statistical predictions from it.  Let's predict on our test dataset to understand how accurate our model is.\n",
    "\n",
    "There are many metrics to measure classification accuracy.  Common examples include include:\n",
    "- Precision\n",
    "- Recall\n",
    "- F1 measure\n",
    "- Area under the ROC curve - AUC\n",
    "- Total Classification Accuracy \n",
    "- Mean Absolute Error\n",
    "\n",
    "For our example, we'll keep things simple and use total classification accuracy as our metric of choice. We will also evaluate  Mean Absolute  Error (MAE) as the linear-learner has been optimized using this metric, not necessarily because it is a relevant metric from an application point of view. We'll compare the performance of the linear-learner against a naive benchmark prediction which uses majority class observed in the training data set for prediction on the test data.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to convert an array to a csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def np2csv(arr):\n",
    "    csv = io.BytesIO()\n",
    "    np.savetxt(csv, arr, delimiter=\",\", fmt=\"%g\")\n",
    "    return csv.getvalue().decode().rstrip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll invoke the endpoint to get predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "runtime = boto3.client(\"runtime.sagemaker\")\n",
    "\n",
    "payload = np2csv(test_X)\n",
    "response = runtime.invoke_endpoint(\n",
    "    EndpointName=linear_endpoint, ContentType=\"text/csv\", Body=payload\n",
    ")\n",
    "result = json.loads(response[\"Body\"].read().decode())\n",
    "test_pred = np.array([r[\"score\"] for r in result[\"predictions\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare linear learner based mean absolute prediction errors from a baseline prediction which uses majority class to predict every instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE Baseline : 0.554\n",
      "Test MAE Linear: 0.274\n"
     ]
    }
   ],
   "source": [
    "test_mae_linear = np.mean(np.abs(test_y - test_pred))\n",
    "test_mae_baseline = np.mean(\n",
    "    np.abs(test_y - np.median(train_y))\n",
    ")  ## training median as baseline predictor\n",
    "\n",
    "print(\"Test MAE Baseline :\", round(test_mae_baseline, 3))\n",
    "print(\"Test MAE Linear:\", round(test_mae_linear, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare predictive accuracy using a classification threshold of 0.5 for the predicted and compare against the majority class prediction from training data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction Accuracy: 83.9 %\n",
      "Baseline Accuracy: 44.6 %\n"
     ]
    }
   ],
   "source": [
    "test_pred_class = (test_pred > 0.5) + 0\n",
    "test_pred_baseline = np.repeat(np.median(train_y), len(test_y))\n",
    "\n",
    "prediction_accuracy = np.mean((test_y == test_pred_class)) * 100\n",
    "baseline_accuracy = np.mean((test_y == test_pred_baseline)) * 100\n",
    "\n",
    "print(\"Prediction Accuracy:\", round(prediction_accuracy, 1), \"%\")\n",
    "print(\"Baseline Accuracy:\", round(baseline_accuracy, 1), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ClientError",
     "evalue": "An error occurred (ValidationException) when calling the DeleteEndpoint operation: Could not find endpoint \"arn:aws:sagemaker:us-east-1:576042411576:endpoint/demo-linear-endpoint-202302201816\".",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mClientError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_10129/3649626519.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete_endpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mEndpointName\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinear_endpoint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.10/site-packages/botocore/client.py\u001b[0m in \u001b[0;36m_api_call\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    528\u001b[0m                 )\n\u001b[1;32m    529\u001b[0m             \u001b[0;31m# The \"self\" in this scope is referring to the BaseClient.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_api_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    532\u001b[0m         \u001b[0m_api_call\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpy_operation_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.10/site-packages/botocore/client.py\u001b[0m in \u001b[0;36m_make_api_call\u001b[0;34m(self, operation_name, api_params)\u001b[0m\n\u001b[1;32m    958\u001b[0m             \u001b[0merror_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsed_response\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Error\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Code\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    959\u001b[0m             \u001b[0merror_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 960\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0merror_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparsed_response\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moperation_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    961\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    962\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mparsed_response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mClientError\u001b[0m: An error occurred (ValidationException) when calling the DeleteEndpoint operation: Could not find endpoint \"arn:aws:sagemaker:us-east-1:576042411576:endpoint/demo-linear-endpoint-202302201816\"."
     ]
    }
   ],
   "source": [
    "sm.delete_endpoint(EndpointName=linear_endpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Extensions\n",
    "\n",
    "- Our linear model does a good job of predicting breast cancer and has an overall accuracy of close to 92%. We can re-run the model with different values of the hyper-parameters, loss functions etc and see if we get improved prediction. Re-running the model with further tweaks to these hyperparameters may provide more accurate out-of-sample predictions.\n",
    "- We also did not do much feature engineering. We can create additional features by considering cross-product/intreaction of multiple features, squaring or raising higher powers of the features to induce non-linear effects, etc. If we expand the features using non-linear terms and interactions, we can then tweak the regulaization parameter to optimize the expanded model and hence generate improved forecasts.\n",
    "- As a further extension, we can use many of non-linear models available through SageMaker such as XGBoost, MXNet etc.\n"
   ]
  }
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