Streams-PMMLScoringSample.ipynb

{
 "cells": [
  {
   "attachments": {
    "how-it-works.jpg": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "id": "8c9753f6-7c69-4c91-9349-d344731cefff"
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   "source": [
    "# IBM Streams PMML scoring sample application\n",
    "\n",
    "This sample demonstrates creating a Streams Python application to perform scoring with a PMML model and viewing the results.\n",
    "\n",
    "\n",
    "<ol>\n",
    "    <li><a href=\"#setup\">Setup</a>\n",
    "        <ul style=\"list-style-type:none;\">\n",
    "            <li>1.1. <a href=\"#setupStreams\">Connection to Streams Instance</a></li>\n",
    "            <li>1.2. <a href=\"#setupPackages\">Install additional packages</a></li>\n",
    "            <li>1.3. <a href=\"#setupDataModel\">Add data and model</a></li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li><a href=\"#create\">Create the application</a>\n",
    "        <ul style=\"list-style-type:none;\">\n",
    "            <li>2.1. <a href=\"#defineInput\">Define Data Input</a></li>\n",
    "            <li>2.2. <a href=\"#analyzeData\">Analyze Data (ML scoring)</a>\n",
    "                <ul style=\"list-style-type:none;\">\n",
    "                    <li>2.2.1. <a href=\"#prepareData\">Prepare Data for scoring</a></li>\n",
    "                    <li>2.2.2. <a href=\"#scoreData\">Score Data</a></li>\n",
    "                </ul>\n",
    "            </li>\n",
    "            <li>2.3 <a href=\"#defineOutput\">Define Data Output</a></li>\n",
    "        </ul>\n",
    "    </li>\n",
    "    <li><a href=\"#submit\">Submit the application</a></li>\n",
    "    <li><a href=\"#view\">Connect to the running application to view data</a></li>\n",
    "    <li><a href=\"#cancel\">Stop the application</a></li>\n",
    "</ol>\n",
    "\n",
    "<a name=\"overview\"></a>\n",
    "\n",
    "# Overview\n",
    "\n",
    "**About the sample**\n",
    "\n",
    "This sample application is about using heart disease patients' health data and a Machine Learning (ML) model to determine the best drug treatment based on each patient's health records. \n",
    "The patient data includes age, sex, blood-pressure, cholesterol, sodium and potassium concentration and a reference drug. \n",
    "\n",
    "__Creating the model is not part of this notebook.__\n",
    "This ML scenario is a sample scenario provided in IBM Cloud for creating a ML model inside Watson Studio with the SPSS Flow Modeler.\n",
    "There you can learn how to create a trained model within IBM Cloud.\n",
    "\n",
    "This application will show that ML scoring can be done within IBM Streams in real-time.\n",
    "\n",
    "\n",
    "**How it works**\n",
    "\n",
    "With the Streams Python API, we will define step by step a topology, which is a graph describing the flow of data in our streaming application through a number of processing functions. Running the Python code in the notebook will create this graph/flow and finally submit it to the Streams instance.\n",
    "\n",
    "![how-it-works.jpg](attachment:how-it-works.jpg)\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "\n",
    "### Documentation & Information links\n",
    "\n",
    "- [Streams Python development guide](https://ibmstreams.github.io/streamsx.documentation/docs/latest/python/)\n",
    "- [streamsx.topology Python API](https://streamsxtopology.readthedocs.io/)\n",
    "- [streamsx.pmml Python API](https://streamsxpmml.readthedocs.io/)\n",
    "- [PMML at Wikipedia](https://en.wikipedia.org/wiki/Predictive_Model_Markup_Language)\n",
    "- [PMML specification at Data Mining Group](http://dmg.org/pmml/v4-3/GeneralStructure.html)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0c029913-a031-4e5a-828b-48cba2744f9d"
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   "source": [
    "\n",
    "<a id=\"setup\"></a>\n",
    "\n",
    "# 1. Setup\n",
    "\n",
    "  \n",
    "  \n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>What do we need to set up for our sample application?</b>  \n",
    "<ul>    \n",
    "<li>A connection to a running <b>IBM Streams instance</b> <br> \n",
    "   This notebook describes using a Streams instance in IBM Cloud Pak for Data.</li>\n",
    "<li><b>streamsx.topology</b> - the Python package providing the IBM Streams standard/basic function set</li>\n",
    "<li><b>streamsx.pmml</b> - the Python package providing the IBM Streams PMML scoring functionality</li>\n",
    "<li><b>Data set</b> with patient health data we will let \"stream\" through the application</li>\n",
    "<li><b>Trained PMML model</b> that will predict the best drug treatment for the patients </li>\n",
    "</ul>\n",
    "</div>\n",
    "\n",
    "\n",
    "\n",
    "<a id=\"setupStreams\"></a>\n",
    "\n",
    "### 1.1 Add credentials for the IBM Streams service\n",
    "\n",
    "In order to submit a Streams application you need to provide the name of the Streams instance.\n",
    "\n",
    "1. From the navigation menu, click **Services > Instances**.\n",
    "2. Update the value of `streams_instance_name` in the cell below according to your Streams instance name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2fc306d9-cffa-4a2e-b6d6-8bc5d39d5a77"
   },
   "outputs": [],
   "source": [
    "from icpd_core import icpd_util\n",
    "streams_instance_name = \"sample-streams\" ## Change this to Streams instance\n",
    "\n",
    "try:\n",
    "    cfg=icpd_util.get_service_instance_details(name=streams_instance_name, instance_type=\"streams\")\n",
    "except TypeError:\n",
    "    cfg=icpd_util.get_service_instance_details(name=streams_instance_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6c26e9f1-477c-45d2-9d56-8409f65d00f0"
   },
   "source": [
    "<a id=\"setupPackages\"></a>\n",
    "\n",
    "### 1.2 Import  the `streamsx.pmml` package and verify the package version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "b8f47aba-a1f8-430d-b182-dd1498822c46"
   },
   "outputs": [],
   "source": [
    "# show the installed versions of both packages\n",
    "import streamsx.topology.context\n",
    "import streamsx.pmml as pmml\n",
    "print(\"INFO: streamsx package version: \" + streamsx.topology.context.__version__)\n",
    "print(\"INFO: streamsx.pmml package version: \" + pmml.__version__)"
   ]
  },
  {
   "attachments": {
    "add_data_set.jpg": {
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H7HP/AER//wAuvW//AJMo/wCIdj9jn/oj/wD5det//Jlfa9FAHxR/xDsfsc/9Ef8A/Lr1v/5Mo/4h2P2Of+iP/wDl163/APJlfa9FAHxR/wAQ7H7HP/RH/wDy69b/APkyj/iHY/Y5/wCiP/8Al163/wDJlfa9FAHxR/xDsfsc/wDRH/8Ay69b/wDkyrmnf8G/H7ImlQGOD4SeWhbcR/wlOtHn8bv2r7LooA+GP+HFX7K3/RLf/Ll1f/5Ko/4cVfsrf9Et/wDLl1f/AOSq+t6KAPkj/hxV+yt/0S3/AMuXV/8A5Ko/4cVfsrf9Et/8uXV//kqvreigD5I/4cVfsrf9Et/8uXV//kqj/hxV+yt/0S3/AMuXV/8A5Kr63ooA+SP+HFX7K3/RLf8Ay5dX/wDkqj/hxV+yt/0S3/y5dX/+Sq+t6KAPmz/hwX+yX/0Sf/y59Z/+S6P+HBf7Jf8A0Sf/AMufWf8A5Lr7DooA+PP+HBf7Jf8A0Sf/AMufWf8A5Lo/4cF/sl/9En/8ufWf/kuvsOigD48/4cF/sl/9En/8ufWf/kuj/hwX+yX/ANEn/wDLn1n/AOS6+w6KAPjz/hwX+yX/ANEn/wDLn1n/AOS6P+HBf7Jf/RJ//Ln1n/5Lr7DooA+PP+HBf7Jf/RJ//Ln1n/5LpH/4IEfskyoVb4TBlYYIPibWSCP/AALr7EooA+PP+HBf7Jf/AESf/wAufWf/AJLo/wCHBf7Jf/RJ/wDy59Z/+S6+w6KAPjz/AIcF/sl/9En/APLn1n/5Lo/4cF/sl/8ARJ//AC59Z/8AkuvsOigD48/4cF/sl/8ARJ//AC59Z/8AkuvOP2xf+CI/7MPws/ZF+KfifQfhl9g13w54Q1bVNOuf+Ei1aX7PcwWU0sT7HumRtrqpwwKnGCCOK/QqvIP+Cgxx+wR8b/8AsQNe/wDTdPWuH/ix9UZV/wCFL0Z9B0UUVkanN1DqP/IPn/65t/Kpqh1H/kHz/wDXNv5UATUUUUAFfnh+3f8A8Fzh8DvjHc/Dr4VeGLXxp4l0+6Gn3d3deZJa/aydv2aGKIiSZw5CnDD5gVGetfofX86Pwj+H3/CGf8FKdM8P/EKLTbeLT/GTRa/LrWrvokVtCszNNdfalngaORE3TRFZRudYwBJuCN34CjCblKetuh+peGPD2XZjVxWJzGPOqEOZQv8AFvrur2ttdK7Vz9Hv2FP+C6S/G/4yWnw7+Kfha28GeI9Suzp9reWrSR2ouwSv2eaKYmSJ2cbB8zfMQDjrX6IV/ON8QvhdNrv/AAUd1bw14EuoNYXUvHLnw/dabqq6ys1vLd+bbSi5WSTzSImRnZ3LqQ3mEOrY/o5p4+jCDjKHUfihw7luW1cNiMujyKtDmcL7baq7dr32vbTQKKKK88/LAooooAK8J/a+/a/1D4GeIvDXgnwT4Z/4Tf4neNfNfS9Ka5Fta2lvEMy3l1KfuQp+BYggEYr3avzR+KP7Y/wa+L3/AAUs1Z9S8YyR+E9d+E118PzcQwTJc22p3OttbSRxoULpL5TF9237mG6V0Yenzyva6R9TwnlH13EznOk6kKcXJpJtN/ZT5bPfWyabSaTW5658Pf8Agoj8QfAPiXQpfirp3ws1rwJ4k1ZNBTxb8O9bfUbLQ9QkP7u3vVcsy5JwXBAU/UV9pV+dMH/BGXWPgT+zbqnw58Ia83i7/hZHi3R5/EOp3irp66HpllK8/mQRBnLS5+UlXBbcuAADX6LU8R7PR0zfiyGU80KuVyTTck7Jxuly2k4ttxbblG2l1FSsrhRRRXMfHnH0UUUAFfn38I/+Chn7QXijwJ8G/H2uaZ8G08C/FbxrZ+ElhsbbUhq1v5t3NBI5V5jEvFtKVO5+qZU5IH6CV+S/7PEXw1tNf+DvxDtP2TP+Ea8O+MPGunaP4f8AE3/C0Lq8+zX/ANsZEk+yH528t7eVtsiqjeVgnDDPZhYxalzK/wB3n3a/XY/QuCcFhq+HxMsRR9o042f7vrGq+Vc9Sm7yspLlU3aD90+3PiB+0N8Tvhx/wUD+H3gG+tfAcvw3+JP9o/2ZNBHdtrkH2HTVnl85i4gXM7ALtVsx9drc19GV+Vf7W3i7wR8R/Gnxd+KPiH9lX/hMNH8EeKJvC2ueKf8AhZlxp/nz2kkNlEfsaAMuVa2+4jAb+WJDGv1UqcRTUYxdunl+jffyODirKoYTDYOpGHLJw5Z/BZzjaTfuVJu/LOKfMoN2Ure81EooorlPjDsKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvH/APgoT/yYL8cP+yf69/6brivYK8f/AOChP/Jgvxw/7J/r3/puuK1ofxY+qMq/8KXoz6EooorI1ObqHUf+QfP/ANc2/lU1Q6j/AMg+f/rm38qAJqKKKACvnn9rz/gl98I/21NXGreLNGurLxCEEbaxpE4tbyVVXaokJVkk2jAG9SQAAOOK+hqKqE5QfNF2Z25fmWKwNZYjB1HCa6xdn/w3kfPP7I3/AAS9+EX7FusnV/Cei3V54h2GNdY1acXV5ErDawjIVUjyCQdigkEjOOK+hqKKJzlN80ndhmGZYrHVniMZUc5vrJ3f/DBRRRUnEFFFFABXjyfsG/C1P2pG+MX/AAjFofG7wCPzzzAJv+foRfd+0bcL5nXA9cmvYaKqMpR2Z04bG4jD83sJuPMnF2bV090+6fYKKKKk5gooooA4+iiigAr5L+Hf/BH3wX8OL/wd9n+Ivxk1DR/Aut2/iDSdCvtfgl0mC6hmMysLcWwVcsz7im1j5j8gsTX1pRVwqzhpFnp5fnONwMZwwlRxU7XtbW10vwk/vZ8l/ET/AII++C/iPf8AjH7R8RfjJp+j+OtbuPEGraFY6/BFpM91NMJmY25tirYZU2l9zDy05JUGvrSiiidSU9JMMwznG46MIYuo5KF7Xtpeyf4RX3IKKKKg8w7CiiigAooooAKKKKACiiigAooooAKKKKACiiigArx//goT/wAmDfHD/sn+vf8ApuuK9grx/wD4KE/8mDfHD/sn+vf+m64rWh/Fj6oyr/wpejPoSiiisjU40314B/x5p/3+/wDsagu9QvJbd0+xoN6lc+f0z/wGtKU4FeGfHH/god8F/wBnvXpNN8WfETw/p+pW7bZ7KF3vbm3PpJFAruh9mANVGMpO0Vc6sJgcTi6nssLTlOXaKbf3I96/sy9/54R/9/P/AK1H9mXv/PCP/v5/9avMfgd/wUi+Bn7R+tw6Z4P+JfhzUdUuWCQWM7vY3Vyx/hjiuFjdz7KCa9uolGUXaSsGLwOJwk/ZYqnKEu0k0/uZz/8AZl7/AM8I/wDv5/8AWo/sy9/54R/9/P8A61dBRUnKc/8A2Ze/88I/+/n/ANaj+zL3/nhH/wB/P/rV0FFAHP8A9mXv/PCP/v5/9aj+zL3/AJ4R/wDfz/61dBRQBz/9mXv/ADwj/wC/n/1qP7Mvf+eEf/fz/wCtXQUUAc//AGZe/wDPCP8A7+f/AFqP7Mvf+eEf/fz/AOtXQUUAc/8A2Ze/88I/+/n/ANaj+zL3/nhH/wB/P/rV0FFAHP8A9mXv/PCP/v5/9aj+zL3/AJ4R/wDfz/61dBRQB8T/APDC/wC1D/0ddH/4bPS//jlH/DC/7UP/AEddH/4bPS//AI5X2xRW3t5dl9y/yPov9aMZ/JR/8J6H/wArPif/AIYX/ah/6Ouj/wDDZ6X/APHKP+GF/wBqH/o66P8A8Nnpf/xyvtiij28uy+5f5B/rRjP5KP8A4T0P/lZ8T/8ADC/7UP8A0ddH/wCGz0v/AOOUf8ML/tQ/9HXR/wDhs9L/APjlfbFFHt5dl9y/yD/WjGfyUf8Awnof/Kz4n/4YX/ah/wCjro//AA2el/8Axyj/AIYX/ah/6Ouj/wDDZ6X/APHK+2KKPby7L7l/kH+tGM/ko/8AhPQ/+VnP/wBmXv8Azwj/AO/n/wBaj+zL3/nhH/38/wDrV0FFYnzpz/8AZl7/AM8I/wDv5/8AWo/sy9/54R/9/P8A61dBRQBz/wDZl7/zwj/7+f8A1qP7Mvf+eEf/AH8/+tXQUUAc/wD2Ze/88I/+/n/1qP7Mvf8AnhH/AN/P/rV0FFAHP/2Ze/8APCP/AL+f/Wo/sy9/54R/9/P/AK1dBRQBz/8AZl7/AM8I/wDv5/8AWo/sy9/54R/9/P8A61dBRQBz/wDZl7/zwj/7+f8A1qP7Mvf+eEf/AH8/+tXQUUAc/wD2Ze/88I/+/n/1q8f/AOChOnXafsC/HEvEiqPh/rxJD5x/xLrj2r6Arxv/AIKK/wDKPv46/wDZPdf/APTbcVrQ/ix9UZV/4UvRnslFFFZGp+VH/Bcv/gptrPwe1EfB/wAAajJpmtXNss/iLU7dytxZxSKGjto2H3GdCHZhyFKgfeJr8hJJGlcsxLMxySTkk17b/wAFKdYvNc/b8+L018WMyeKb23XccnyopTHF+HlomPavYP8Agkr4Pg+Ott8QPhz4v8IaZqnwp1GxGq+IfE0ois7jwZLArCG9S9YcYBk/csSGG8gbfMDfT0YxoUFJL1P7L4dweE4a4bp4ilBP3Yym7pOTla7u92r+7Hrolqz4ySQxOGUlWU5BBwQa/Y//AIINf8FRdb+M2ot8GviHqcura3bWzXHhvVLl91xdwxLmS1kY8uyIpdWJLFQ4P3RXyB/wVs0C0+Ben/D/AOF3g3wjpml/CzTbI61oPiaMRXlz40mnVRNfNeKOQQIwYlIC/JkbREF8i/4Jj6zeaF/wUJ+Dk1iXEz+K7G3bacHypZRFL+Hlu+faitGNeg216BxDg8LxLw3UxFWnb3ZSpu6bjy3s7ra9vej01T1R/S/RRRXzB/GgUUUUAFFFFABRXwd4t/ad8W63/wAFFfjJ4C1P4m/GLwt4W8H2egy6HZ+BPAEHiHY91au9wbmQaPfvGCyqV8xkHL4yFO3D/bU/bW+IHwP0f9pqHwt8Stcn1j4Y2Xg7+zoL7wppscekNfTwxzSpcAt9qe4UyGRZbaJYiQIxgZqaclPka+1a3zaX6obVpOHa3/k3Lb/0pfj13/Q2ivm34rf8FO/BHwd8UeLLC70Pxnrek/Dj+zo/GviPS7S2k0zwtJelfKWcPOlxKVV0kf7LDNsVxnBBUaen/wDBQzw/4n+LuueF/Dfgn4j+LrHwtrNnoOteIdF062utO0u6uUR03x/aBePGqyKXlitnjQEsW2gkXFOXw6/0rP0d1Z7O6tuQpLl5n/Wl7eqW63XU9/ooopFBRRRQAUUUUAFFFFABRRXB/tRfGtP2b/2cfHPj6S2F6PB+h3erLbl9guGhiZ1QnsGYAfjUVaipwdSWyV/uLp03UmoR3bsd5RXzF+y54X+Mfiv4efDf4neJPjg1zb67paa/4l8N3PhfTv7FEF1CJ0gspYkhu7fyFcKJZ57jdsyy84q78K/+Cnvgf4q6v4Y8nw/420fw/wCPv7QHg/xFqVpax6d4qNluMi26pcPcRF40eSP7VDBvVDjnCnSovZylGe8d/L9NbO3ezsZwlzxU47Pbz/4br26n0hRXy78Av+Cr3gn4/wCsfDiK38IfEXw5pfxZ+3R+F9X1uysorPUp7NWaeDEV1JMjgI21njEb7SFcmovgz/wVq8DfGfUPATReEPiNoGhfEnWLvw9oeu6vZWUdhcajbGYPbER3UkwLeS21/KMZJxvDK4Uaalyvf/g2/PRd7rugukr/ANdX+jv2s77M+p6K+a/h1/wVK+H/AMR/FfhiCLR/GGmeFvHWqXujeF/F9/a2yaL4gu7UsHji23DXMe7y5SjTwRK/ltg5wDL4A/4KR6X8W9Jh1fwr8L/i7r3hfVodSl0LxHa6VZnS9dNkzq4Vzdh7USFH8p75LZJNuA2SAYnNRi5vZK/4X+Ttqk9X0K5XzcvW9vzX6O72Vn2Po+ivg34Of8FEPGnx0+H37LviTW59V+HV58T/ABJc6fe6fY+H7DUNO8UxpC7jbI9889jb/Kfnw0xkQ5jCYLey/D//AIKceBviL4p0CK10Lxna+EvF/iGfwp4e8Y3Nraromt6nEZFNvEFuGul3NDMqSS28cbNGwDcjOvs5czh1Tt8/d/PmSS3vpba8uSS5ulr/APpX/wAi3fa2t97fRtFfK/wr/wCCtvgX4qan4PMfg/4j6NoPjnxLceENL1/U7KyTT5NVhMoNsRHdPP8AP5R2yCExEsFLhg6r1/7O/wC33o37Tet6afDfgT4k/wDCJ65c6jaaV4un022bRr57KV4pN3lXElzbBmjfyzdQQh9uB8xCmI+9rH+tE/xTTXdNW3HP3HaXp+f/AMjL0s+x7zRRRQAUUUUAFFFFABXjf/BRT/lH38df+ye6/wD+m24r2SvG/wDgop/yj7+Ov/ZPdf8A/TbcVth/4sfVGVf+FL0Z7JRRRWJqfhj/AMF8v2JtT+Fn7QM3xY0uzlm8KeOHjF9JGuU07UFjVCr+glCbwT1bePQV8R6P8b/F3h74Tat4FsfEGp2vhDXryLUNQ0qKUrb3c8QwjuO+OMjoSiEglFI/pu8deC9J+InhW+0TXdNs9X0jUoTDdWl3EJYZ0PUMp4r89Pjl/wAG8Pw38Z+I5LvwZ4r17wTHdSZ+xSwLqlrBntGGZJAP96Rj717OFzCCgoVeh/Q/BHirl9PAU8uz1WdOyjPl5k1H4bpXalHSzSe19Gfk3rvxr8WeJ/hbongnUNf1K88KeHLma80zTJZd0FlLNjzGQdRnGcZwCzkAF2z91f8ABvl+w7qnxZ/aIh+Lmq2csPhPwG8gsZZFwmo6i0bIqp6iEPvJHRvLHqK+n/gV/wAG1fwx8Ca/BfeN/GHiLx1HbuHFjDbrpNpPjtJteSUr/uSIfev0N8EeBtG+GnhSx0Lw/pdjo2jaZEILWzs4VihgQdAqjj/Gniswg4OFHqHG/irl9TAVMuyJXdS6lPl5UlL4rJ2blLW7aW99WatFFFeKfzwFFFFABRRRQB5B8J/2Uv8AhV/7XPxY+Kf9vfbv+FoWuj239l/YfK/sz7BDJFnzvMPm+Z5mcbE24x82c15P+0P/AMEr4v2gNY/aAu5fHEmmf8Lxs/D8EaJpAl/sKTSWDo5JmH2hZGC5X90QMgMScj63oqeVK1umi/B/ogvq33tf/t21vu5V92vU+JvHv/BHi08XfFzxV4wj1L4U3WpeP47CbXpPEnwttvEMtpewRiKefS2uboi0SdVUmKZboK2W3HOB0PxW/wCCYl58XPjrYeK7zxV4I0y30vX7DWdPvNK+H1vZeKtLgs1RYtOt9XS4BFqdnzLLbyNtd0DBdoT64oq4ScHFx+y015Nbfd07LTZslxTTi+qs/R7/AH9fPXcKKKKRQUUUUAFFFFABRRRQAVhfE74c6T8YPhzrvhTXrb7ZoviSwn02+hzjzIZkKOAexwxwexrdoqZxU4uMtmVCTjJSjuj5u+EX7GnxI8AeH/Cfg3VvjQus/DPwfaS6ZHpUHhOK21XXbHyzDBbahfSTzJIscJCFre2t3cgMWBrkfhD/AMErJ/h+PhtouseP4df8EfBl9Sl8Gacnh/7JqEMl0skcbX939pdLryIpXC+VBb7mwzdNp+v6KqfvtuWt9/PfX1V3Z7pu61Jj7qSjolt+q9H1Wzsr7I+R/hN/wSx/4Vd4O/Zw0n/hOvt3/DPt9qV75v8AYvlf2/8AbFmXbjzz9n2eb1zJu29Bnir8Pv8Agk8ngf4RfBDwnL46W/j+DnjS78XvO2h7BrYuJbqQ2xT7QfJx9qxvy+dn3Rnj7CopyfNJylrdqT9U0/zS02C2ij2VvlaS/Kcvv8lb4p/Z7/4I2eH/ANnnxXoqWL/C3UvC/h/VLq/tWvfhnZy+KbiKUyFLefWWnLERGTKSRQRSDyowWKhlb0D9l39hfxj+zFoGgeCLD4rrcfCTwsmpQ2Hh+LwvDDqd5DdSSPFHeag80okEHmvtNvb27MQu8sAVP0tRStePK9U1b10tr39Xrq+7Hd83N1vf/hu3y8uyt8gfDD/glvqXgbwn8CNEv/iHYalp/wABPENzquleR4ae2l1K0lhkjW2nY3kg81WldjMihWG0eUuCxX4Xf8EqT8PB4F8LzeOoNR+FHwy8YTeNvDfh9fD/AJOpxXjPPJFFcX/2hkmgikuJWCrbRufkBchefr6iqUmpc3W9/n7uvquVWfTV9XeWk48r2tb5e9p/5PJejt2Pj3wL/wAEoT4K+FPwn8Mjx99pPwv+JEvxCFydD2f2nvmuJfsmz7QfLx5+PM3N93OznjT+Dn/BNa/8A/tS6J8UNY8W+D7jVNEfVZZp/DPgK38Nan4ne+YE/wBr3MU7peLGOVCwREyBXLEht31fRUw91px6f/IqP/pMUvl3HP3783X/ADlL85N/P0CiiigAooooAKKKKACvG/8Agonz/wAE+/jr/wBk91//ANNtxXsleOf8FE/+Ufnx0/7J7r//AKbbitsP/Fj6oyr/AMKXoz2OiiisTU5ef7p+lY93/wAfcX++P5itif7p+lY93/x9xf74/mKAO6/tG3/57w/99ij+0bf/AJ7w/wDfYrBooA3v7Rt/+e8P/fYqN9as42w13bA+hlX/ABrFooA2f7dsv+fy1/7+r/jR/btl/wA/lr/39X/GsaigDwPx3/wWZ/Zr+GnjjWfDmtfEkWes+H76fTb+3GgapKILiGRo5E3pbMjYdWG5SVOMgkc1lf8AD9D9lj/oqP8A5ber/wDyLX0jRW16XWL+9f5H0EMTkailPD1W+v76C/D2D/Nnzd/w/Q/ZY/6Kj/5ber//ACLR/wAP0P2WP+io/wDlt6v/APItfSNFHNR/lf3r/Ir6zkP/AEDVv/B8P/mc+bv+H6H7LH/RUf8Ay29X/wDkWj/h+h+yx/0VH/y29X/+Ra+kaKOaj/K/vX+QfWch/wCgat/4Ph/8znzd/wAP0P2WP+io/wDlt6v/APItH/D9D9lj/oqP/lt6v/8AItfSNFHNR/lf3r/IPrOQ/wDQNW/8Hw/+ZzZ/t2y/5/LX/v6v+NH9u2X/AD+Wv/f1f8axqKxPnTZ/t2y/5/LX/v6v+NH9u2X/AD+Wv/f1f8axqKANn+3bL/n8tf8Av6v+NH9u2X/P5a/9/V/xrGooA2f7dsv+fy1/7+r/AI0f27Zf8/lr/wB/V/xrGooA2f7dsv8An8tf+/q/40f27Zf8/lr/AN/V/wAaxqKANn+3bL/n8tf+/q/40f27Zf8AP5a/9/V/xrGooA2f7dsv+fy1/wC/q/40f27Zf8/lr/39X/GsaigDZ/t2y/5/LX/v6v8AjR/btl/z+Wv/AH9X/GsaigDZ/t2y/wCfy1/7+r/jR/btl/z+Wv8A39X/ABrGooA2f7dsv+fy1/7+r/jR/btl/wA/lr/39X/GsaigDZ/t2y/5/LX/AL+r/jR/btl/z+Wv/f1f8axqKANuPWbSU4W6tmPXAlB/rT/7Rt/+e8P/AH2KwaKAN7+0bf8A57w/99ivHf8AgohfQP8A8E//AI5gTREn4fa+AA45/wCJbcV6JXkX/BQJd/7BnxtHr4B10f8AlOnrbD/xY+q/MxxH8KXoz6BooorE2OXn+6fpWPd/8fcX++P5itif7p+lY93/AMfcX++P5igDcooooAKKKYZDvwBn156UAPopHJA4GfxoQkjkY/GgBaKbvbd93jPXNK5IHAz+NAC0UiEkcjH40m9t33eM9c0AOopHJA4GfxoQkjkY/GgBaKbvbd93jPXNK5IHAz+NAC0UiEkcjH40m9t33eM9c0AOopHJA4GfxoQkjkY/GgBaKbvbd93jPXNK5IHAz+NAC0UiEkcjH40m9t33eM9c0AOopHJA4GfxoQkjkY/GgBaKbvbd93jPXNK5IHAz+NAC0UiEkcjH40m9t33eM9c0AOopHJA4GfxoQkjkY/GgBaKbvbd93jPXNK5IHAz+NAC0UiEkcjH40m9t33eM9c0AOopHJA4GfxoQkjkY/GgBaKYJDvwRj0560+gAryT9v3/kxH41/wDYha7/AOm+evW68k/b8/5MR+Nf/Yha7/6b562w38aPqvzMMT/Bn6P8j3+iiisTc5ef7p+lY93/AMfcX++P5itif7p+lY93/wAfcX++P5igDcooooAKQEZPr3paaqYcn1oAdRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAhIyPXtS01ky4PpTqACvJf2+F3/ALCvxpHTPgPXBn0/4l89etV5N+3v/wAmLfGn/sRNc/8ATfPW2H/ix9V+Zhiv4M/R/ke+0UUVibnLz/dP0rHu/wDj7i/3x/MVsT/dP0rHu/8Aj7i/3x/MUAblFFFABTVJ8xvTjFOpA+WI9KAFooooAKKKKACiiigAor5VuP2i/jNf+Hvit400m5+G134b+GPiLU7H/hHJtCvIdR1WysNry7dR+3GKOd4y+0m0ZNwUEAEsPVdC/bT+HfiLxjbeH7bVtQfV5tLtdamiXRr14dOs7mBp4Z7q4WIwW8bIrfNLIq5UrnPFEXzRUl1Sfykrr71f7mD0k49m184uz+52+9HqtFeVeDP21fhx48s9TuLHWdRig0vSpddMl/oWoaet/p8QzJd2ZuIEF7AoKkyW3mqPMi5/eJum+D/7ZHw7+PHiK20vwzrV7c3d/YHVNPN3ot9p8Or2oKhp7OW5hjju413puaBnC71zjIppNuy/rf8Ayf3PsF0ld/1t/mvvXc9Por5z+MX7YGpeE/2rp/AVldx6DpGieErjxDqmpX/gXWNWhZwkxQrcwPFBFDEI97MS/mtmBWjlrX8Cftbxy/EfxZb+ItX8OQ+DfC3gbQvFba8LaXT0l+2m+86Z1lkby4dtrGyIfnXewZnOMJNOHP0/4Ml/7a/XoOSak49dPx5Urd/jjse60V5Rp37bXw41Dwhr+tSarrGmW/hhLeTULXVfDmp6ZqMa3L+XbFLK4t0uZhNIDHF5UbeY4KJuYEV1vwm+NHh/426NeXugTaiRp10bK9ttR0q70q9sptiSbJra6jinjJjkjcb0G5XVhkEGnZ3a7f1+q+8m6tc6qivnb9kn/goB4f8AjrpGhaXr1ydP8aa3f6nYQxQ6JfwaXdyWlxcL5MF5IhtpJxbwh3iWdnG2Q7QFIHe+A/2v/h98TPHSeHtG1m7ub25NytlcSaPe2+nas1uSJ1s72SFbW7ZMMWFvLIQEc9FYhdL/AD+Xcb03PTKK8d0L9vf4XeJ/AEniiw1vV7rQvtMNla3KeGtU/wCJtcSsyrBYp9n33swZHDR2wkdCjbwu04734V/FzQPjR4U/tnw9eTXNmlxLaTJc2c1ldWc8TbZIZ7edEmhkU9UkRWwQcYIJO/l/X6oHpozpaK8z+Hv7X/w++KXjBNE0TWby4ubnz/sNzNo97a6frHkEib7DeSwrbXu0Bifs0knyqzfdBIv/AAK/aV8I/tL+FpNb8FXmparo6ojxX82jXtja3YcEgwS3EUaz7SrK/lFvLYFX2txSckouXZX+QdbfI72ivl39iH/gpDo/x/8Ahj4NPjOeHRPGvi2/vtPt4bXRNQt9JuZoJ5wkMV3IrwGYwxKxj88tknCjIWvQdP8A29vhVqfh3xRrEfiW5XRfBjTxaxqU2i38NjazQyiF7cTvAI5LjzCqrBGzSvvTYrBlzrOlKEuVo9fFZBmOHrzw86MuaMuV2i2r8zirO2qlKLUX1toew0V5NB+3D8MpNOe4m16+08waxZ6Dc22o6HqFjd2N3eY+yi4t5oElgjlyNs0qLEf79S6z+2x8MNCtPHE0/imMr8OL630zxEsNjczyWFzcOscMQRI2aVmdwo8oP82QeQQJ5Jdv60/+SX3rujn/ALKxrdlRlfT7L6tJdOraS7tpdT1SivLNH/bT+G2v/ENPDFpr9zLqMurSaDHcf2RerpsuoxxtI9mt8YRatOFRsxiUtlSMZBFHw+/bU+GfxS8dWfh3Q/Ehu9Q1RrtNMlfTbuCx1lrR9lytldyRLb3ZjOdwgkc4Vj0UkChJ7Lz+QTyvGwTlOjJJK7916LXXbbR6+T7M9ToooqTgCiiigAooooAaxPmL6c5p1IXwwHrS0AFeTft7/wDJi3xo/wCxE1z/ANN89es15Z+3NZtqH7E3xhgX70/gjWo1+psJxWtB2qxb7oxxKbozS7P8j3eiiisjY5ef7p+lY93/AMfcX++P5itif7p+lY93/wAfcX++P5igDcooooAKQLhifWlpqqRIx9cYoAdRRRQAUUUUAFFFFAHyrcfs6fGaw8PfFbwXpNt8NrTw38T/ABFqd9/wkc2u3k2o6VZX+1JdunfYRFJOkYfaDdqm4qSSAVPVaZ+xU0N18X9Ia/is/C3xC8I6X4T01oJXkvbKK2srm0d5AVVc4mQrhjnac7a+gKKlwi6XsntZL5RVkvkr/eylNqp7Vb3b+bd397t9x8g/FX4DfEHVfhTc6x4+j8GaZb/CzwDrmnaZ/wAI9qFxeNrs1xp32d7iZJraEWiLHFkQo05LS/60CP8Aea37Lfwj8f8AxGufgl4u8X23hDRtD+HnhQppK6Nq11fXWtSXdlBCJLiOS2hS2VYlJ8tXuMu4+fCZb6oorXnbnOctXK33rn1/8n9Fbba2fIlGMY6KN/x5dP8AyX1d9+/jHxk/Z11v4h/EXxvq9ldaXFbeJPh1N4RtVnkkV47t5bhw7gIQIsSryCW4Py+vlviT9gDxX4t07xHolxqmiWuleJPhz4d8Nm/tr+6ju7HVNInmmUrHGkZe2kMozIs8Uq7CAmW3L9c0VC0jyLb/AIM3+c5P7i27u78vw5P/AJXH8e58dWv7CPi3V9C8YXWo+GfA39sazpVrpdta658RPFXi/wC0xxXQuXU3920UtlllQxSW8DPbyxiXMuRGvtX7I3wm8Y/Cbw3r8Xi3UmkTU9TF1pmknxNf+J/7DgEEUbRDUr9EurjfIkkuHQCPzdi5AyfW6KcXy3S7W/G7/FfLW1ru82v/AF5W/Lrv3bPmfwf+xh4l0P4UfBzQLq/0Rrj4f+N7/wASam8NxMFmtZxqoVYG8sMZR9uiyGCgbXwxwN3K/s7/APBPfxB8JPGngSz1W307VPD3w4d30/Vrr4ieJdSnu2S3kt7eSPRpnFhYyCOU7irzoBvVEQODH9hUVKikml11+dkl91tBy95Wfa3y1/zZ8n3v7BXiN/2TvhJ4Y+32k/iv4WXZvfJsvEup6FaarviuIJIhqFmFurf5LjcJFjblNrIVckelfsp/s43Xwi+G3imx1qxsNM1Pxhqc+oXy2PiXVfEEmHgit08y/wBSYzzyiOFBvCRKAFAjG0s3s1FOVpc6l9u9/na/5L7tOo+Z3jLrHb8f839/ofMPws/ZN8fWOm/Cfwl4nk8Hw+EvgpOtxpOqaVfXEuo+Imgsp7G1E9q9ukVmPKuC8mye43OgC7Qcj1T9kP4O6n8Af2WvA3gjWJ7G51Xwzo0GnXUtk7vbySIuCUZlVivplQfavSqKJNyU1L7er83rr+P5EpJWt00Xkux8O/Bv/gnl8TtE+Gnwb8A+J7nwJD4a+F3jV/Gc2qaVq15NfXzrNdzQ2ywPaxoF3XIDuZegICcfN0Lf8E6vE+o/sa+LfAM+t6FbeJLzxxdeNNHuI3mksWb7cLuCC4+RHAYLscqrbC25Q+0A/YNFautNy5ut7/O8Xf8A8lj92u7Pp6vFuPnU9ponz8+i+1zTk3rfd1JXW2yVkj5H+In7Bvi748aZ8add8TT+GdE8X/ETSdOsNDstM1G4vLHSJtOzNa3El09vFIzNckE7YBsRcDeSa4zQf+CVviyy+JPw51y61nwu8U96us/FCJHnx4gv4tRm1KB7dTF86rPO6EyFD5aoMHGK+66KcK8oSUo9Nv6+5+qXYVPizMIU5UYNKL6W29xQVr6+7GMbXu+ZKWr1Pk/R/wBjX4g2XxzXVNOl8NeA/Dt34jvdX1pvD3ijV5YdetJ4pENvJo8qiyiuG3IXuo3yXj8wRgttHPfsuf8ABN3Xvgdr3w9sdbsvD2v6X8N7+7udP1yXxtr8tw6uZRC8Oktixs5NsiiTa8qNtYBRv3L9o0UoVpQVo9kvu2+7y08riqcVY+dKVHmspKzte+0lve70nJWbcbWstFYooorI+bCiiigAooooAQrlgfSlprKTIp9M5p1ABXlf7dNw1p+xJ8YpU4ePwPrTqfQiwnNeqV5P+3ocfsNfGf8A7ETXP/TfPWtDWrFPujDFNqjNrs/yPfKKKKyNzl5/un6Vj3f/AB9xf74/mK1mffAD6qDWTd/8fcX++P5igDcooooAKar5dh6Yp1ICNx9e9AC0UUUAFFFFABRRRQAUUUUAV9VjuptLuUsZoLe9aJhbyzwmaKKTB2s6BkLqDglQ6kjjcOtfDH7GniDxv8N/hr4F8J+HZfhpbeL/AItanr+u3niB/ClxHFHHZXBWZ7qFL4SX11LJLGFczxbIwch/L+b7vrzbW/2R/AOveBNG8OvpF7Z2Hhy5lvNJn07WL3T9Q02WVnMrQ3sEyXMe/wA2QMFkAZWKkFeKWzuvn6f8PZ+drD6Wf9O1v67bnjFn+198S/HnjLwZ4J0NfA2k+Jr3X/EHhrX9Uv8ATLq9sFl0qOOQXNrbJcxSbZg4/dSTZjLkeZJ5eZdb/go14C1n4mTfBvQre58GyaTqfjeKDULHxD4cfWbO+cWV3JEXhF1CrRqY3OxtxLmJgy+WQ/sHhf8AZr8E+DJ/Ckum6ILebwULv+yJftU7yRPdjF1LIzOTPLKcs8kxd2dmcsWYseg8XfDzR/Hd9odzqtp9qm8Oaiuq6c3mun2e5WKSISYUgN8ksgw2V+bOMgEV9uEuzi352avp0vZtLZfiTraa7qaXlzJpX72ulfy7t3+QvD3jCLxX8efhdJHoXh3QRpvxh8X6e6aNYi0S9aHTdQQ3Mq5O+4kxud/4m5xWl8Af+Cgfjv4w+LfCmoxeF9R1Pw141uZoY9LtPh74gsZPD8JSR7e6m1u4X+z7pD5aJII44gpuAUeQRnzPf5P2TvAb3+m3SaPdW9zpHiW48XWk1tqt5BJHqdxvE8pZJQWSQSOGhbMRViCmOKo6X+xX8N9H8YLrUWh3ryQyzz22nz61fz6PYyThxM9vpzzNZwNIJJQxihUsJpQeJHDTG6jFPpGKfqoxUvLVp200Wq3snKzlJrzt/wCBTa+5NddXe91vwH7H/wC1D45+KfxD/sHx9/Y2j6zPpEmoyeHpPCGr+H7/AEqWOaOOSKK4u3ltdWhQyBWubVo1B8tthWYbMH4i/HLxz8B/2j/jr4q1HXNO1vwP4F8D6drkPhtNPuIpmJGolVjuGumiidnh/ey/ZzvTyxtXytz+z/B79kbwF8BteXUvDWl6jBdQWR02zF7rl/qUWlWhZWNtZxXM0iWkJKR5jt1jQiKMYwiga2t/s9+EvEfxIvvFd9ps1zquq6QNB1CN7+5+walZAykRXFn5n2afHnSgNJEzASMAQDinPWK5d7P8b2tv5K7u93r1qDV3z6q8fwcb9t7PbTbbpwXw2+MHj/wn8b/DXgn4iXHg7Wp/G+hXms6ddeHNMuNOTTJLNrYT20yz3M/nqwu0KTKYv9WwMQ3ArmfGv9rHxF8N/B/7RGoWNloss3wk0qG+0dZ4ZWW5d9OF0RcYkBZd5x8hQ7e+ea9D+D37L3gv4FanLe+HrHVftslounx3Gqa5f6xLZ2qncLa3a8mlNvBkKfKiKISiZX5Vxj/Gj9iD4a/tA69qeo+KNG1O4n12wTTNVSy8Qajplvq9vHv8tLqG1njiuNnmPtMqsVzwRgYqVmkl5/m7fcrLztd6iw75JJ1NbW+e1/S+v5bbeZfEX9pf4tv45+Lq+Fm+HNnoXwm0ix1hotV0y9ubvWvNsWu5bcSR3MaW/wBxgs2yXG4ZiO3LcZ4d/aeufC/jn4xeN9A0yGTU/Gk3gWz0e21FmSC1uNUtYoYXuNuG2RGcM6jBbYVBUsCPT9Q/4J/aF8Qvj/8AEfxN4wS6vtC8WxaXbWmn6f4i1KxjuYLa18qWG/t4JIobmIt91JvNUqzggb2B9E1/9kz4f+KNV8UXd9oHnnxnYW+m6vAb65W0uYrcAW7LbiQRRyxYXZNGiyrtXDjAxL2a6XX3Xd1f+8rL+7a6u986akoxUnqlZvzsrP5O7/vXs7K1vIvHv7Wvjf4Laj4q8G+J9V8CT+LtNtNH1LTNctPD2piyuYNQu5rQQrpME11d3F2j28hSGGf98HX5odrGue8F/t9+NvF3hm98P29roreNz4/s/BFjq+o+F9U0Kw2XFiuofbJtKu5FvIykIlQQtOBK6xssgWQY9vj/AGK/h2vg3UdEl0zWbxNVvLbULnUbzxJqd1rRntiGt5F1KS4a9jMRB8vZMNm59uN7ZqWP7B3ww0/w9rumjRdYmj8SXdrqOoXFz4l1S4vpby1x5F4t1JcNPHdJhR58brKQiAuQqgEN3z67eWnNF/LRSjda633ta5be7pv562av562lZ6aW7t8Z+xTD4ktv2kf2govFlxol7rcWs6Os11pFtLa2tyo0mDY6wyvI8RK4yhlkwc4c19J143b/ALAvwsi8O6lpkmh6tewaxrFjr9/NfeJNUvLu9vrIobaeS4luGmcx+WmAXKnYoIOBXslVpypdUkvuilf776drd7I/r8X+lv6V2UUUVIBRRRQAUUUUAFFFFADWfDqPXNOpCRuHr2paACvJ/wBvT/kxn4z/APYia5/6b569Yryf9vT/AJMZ+M//AGImuf8ApvnrbD/xY+qOfF/wJ+j/ACPfKKKKxOg5Qf8AHqv+4Kyrv/j7i/3x/MVqj/j1X/cFZV3/AMfcX++P5igDcooooAKaqYdj606mqSZG/DFADqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAGsmXU+lOprEiRfxzTqACvJ/wBvT/kxn4z/APYia5/6b569Yryf9vT/AJMZ+M//AGImuf8ApvnrbD/xY+qOfF/wJ+j/ACPfKKKKxOg5Qf8AHqv+4Kyrv/j7i/3x/MVqj/j1X/cFZV3/AMfcX++P5igDcooooAKQPliPSlpAuGJ9aAFooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAQvhgPWlpCuWB9KWgAryf9vT/AJMZ+M//AGImuf8Apvnr1ivJ/wBvT/kxn4z/APYia5/6b562w/8AFj6o58X/AAJ+j/I98ooorE6DlB/x6r/uCsq7/wCPuL/fH8xWqP8Aj1X/AHBWVd/8fcX++P5igDcooooAKZtYSZGMHrmn0UAI+ccY/GhM45x+FLRQA35938OM0r5xxj8aWigBEzjnH4Unz7v4cZp1FACPnHGPxoTOOcfhS0UAN+fd/DjNK+ccY/GlooARM45x+FJ8+7+HGadRQAj5xxj8aEzjnH4UtFADfn3fw4zSvnHGPxpaKAETOOcfhSfPu/hxmnUUAI+ccY/GhM45x+FLRQA35938OM0r5xxj8aWigBEzjnH4Unz7v4cZp1FACPnHGPxoTOOcfhS0UAN+fd/DjNK+ccY/GlooARM45x+FJ8+7+HGadRQAj5xxj8aEzjnH4UtFADNrGTJxgdMU+iigAryf9vT/AJMZ+M//AGImuf8Apvnr1ivJ/wBvT/kxn4z/APYia5/6b562w/8AFj6o58X/AAJ+j/I98ooorE6DlB/x6r/uCsq7/wCPuL/fH8xWqP8Aj1X/AHBWVd/8fcX++P5igDcorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGorZ/sS2/uH/vtv8aP7Etv7h/77b/GgDGryf8Ab0/5MZ+M/wD2Imuf+m+evcf7Etv7h/77b/GvJP2/dIt4v2EfjWwQ5XwFrpHzt/0D5/etsP8AxY+qOfF/wJ+j/I9mooorE6DlB/x6r/uCsq7/AOPuL/fH8xRRQB6BRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV5F/wAFAf8Akw342/8AYg67/wCm6eiitsP/ABY+qOfF/wACfo/yPXaKKKxOg//Z"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "id": "ec364b86-ffbe-4716-9d76-798cae5a7be3"
   },
   "source": [
    "<a id=\"setupDataModel\"></a>\n",
    "\n",
    "## 1.3 Add data and model to your project\n",
    "\n",
    "The PMML model and the data set of patient data need to be imported into the project. \n",
    "\n",
    "Importing models and data sets into your Cloud Pak project is fairly simple. Both are asset types which can be imported by drag'n'drop.\n",
    "\n",
    "1. Download (right click -> Save As) the CSV data-set from GitHub: [Drug_dataset.csv](https://raw.githubusercontent.com/IBMStreams/pypi.streamsx.pmml/develop/sample/drug/Drug_dataset.csv)\n",
    "2. Download (right click -> Save As) the PMML model xml file from GitHub: [Drug_pmml_model.xml](https://raw.githubusercontent.com/IBMStreams/pypi.streamsx.pmml/develop/sample/drug/Drug_pmml_model.xml)\n",
    "3. Add the downloaded files to the project with __Find and add data set__ direct from from the open notebook page.\n",
    "![add_data_set.jpg](attachment:add_data_set.jpg)\n",
    "![add_data_set2.jpg](attachment:add_data_set2.jpg)\n",
    "    - Upload the sample data set: Select the __Files__ tab and click __browse__ and browse to the downloaded  `Drug_dataset.csv` file. The file appears in the Files list and is added to your project.\n",
    "\n",
    "    - Next, upload the model: Select the __Files__ tab and click __browse__ and browse to the downloaded  `Drug_pmml_model.xml` file.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "f564913d-d297-4a0f-a34f-fa7ced571800"
   },
   "source": [
    "<a id=\"create\"></a>\n",
    "\n",
    "# 2. Create the application\n",
    "\n",
    "\n",
    "All Streams applications start with  a `Topology` object, so we start by creating one.<br>Additionally we add our data set to the topology as a dependency, which means that is bundled within the built application and so available at the environment where the application will run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "37a8085e-d5a6-49c1-aa4c-ca761e59881c"
   },
   "outputs": [],
   "source": [
    "from streamsx.topology.topology import Topology\n",
    "import os\n",
    "\n",
    "topo = Topology(name=\"PMMLScoring\", namespace=\"sample\")\n",
    "\n",
    "# add files to be contained in the archive which is deployed to the node running the application\n",
    "# in this sample we need the `dataset` with sample data to be present at the worker node\n",
    "topo.add_file_dependency('/project_data/data_asset/Drug_dataset.csv', 'etc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "680238c2-1bdb-40c3-a5ed-af0fee22db79"
   },
   "source": [
    "<a id=\"defineInput\"></a>\n",
    "\n",
    "## 2.1 Define Data Input \n",
    "\n",
    "Let's take a look at the sample data, a set of patient medical records:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0a933f43-b8c2-4747-a85d-c3283a336160"
   },
   "outputs": [],
   "source": [
    "sample_data = '/project_data/data_asset/Drug_dataset.csv'\n",
    "!head $sample_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2e8cae1c-839e-4a01-b44b-abb9e6dbd1db"
   },
   "source": [
    "We will use this CSV file as data source for this sample. To simulate an endless stream of data we will read the file repeatedly and submit each line in the file as a tuple. The class below reads the data set and produces a stream of Python `dict` objects which also include 2 additional fields, `NA_to_K` and the prediction result `Drug`. The produced `Stream` is a stream of Python `dict`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "52910804-2727-4174-8eab-0ae698d08c4c"
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "from streamsx.ec import get_application_directory\n",
    "\n",
    "# a class which can be used in topology as a source\n",
    "class CSVFileReader:\n",
    "    def __init__(self, file_name):\n",
    "        self.file_name = file_name\n",
    "    def __call__(self):\n",
    "        # Convert each row in the file to a dict\n",
    "        drug_file =  os.path.join(get_application_directory(), \"etc\", self.file_name)\n",
    "        col_names = [\"age\", \"sex\", \"bloodPressure\", \"cholesterol\", \n",
    "                     \"bloodSodiumConcentration\", \"bloodPotassionConcentration\", \"referenceDrug\"]\n",
    "       \n",
    "        # run this indefinitely so that there will always be data for the view\n",
    "        while True:\n",
    "            with open(drug_file) as handle:\n",
    "                reader = csv.DictReader(handle, delimiter=',',\n",
    "                                                fieldnames=col_names)\n",
    "                #Use this to skip the header line if your file has one\n",
    "                next(reader)\n",
    "                #yield the lines in the file one at a time\n",
    "                for row in reader:\n",
    "                    yield dict(age= int(row[\"age\"]),\n",
    "                                     sex = row[\"sex\"], \n",
    "                                    bloodPressure= row[\"bloodPressure\"],\n",
    "                                    cholesterol=row[\"cholesterol\"] ,\n",
    "                                    bloodSodiumConcentration=float(row[\"bloodSodiumConcentration\"]),\n",
    "                                    bloodPotassionConcentration=float(row[\"bloodPotassionConcentration\"]),\n",
    "                                    referenceDrug= row[ \"referenceDrug\"],\n",
    "                                    #add placeholder attributes that will be computed later\n",
    "                                    NA_to_K=0, predictedDrug=\"\")\n",
    "\n",
    "# let the csv file reader the source/edge node in our topology, producing the 'records' stream                    \n",
    "records = topo.source(CSVFileReader(\"Drug_dataset.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1a1a0459-6ed1-4a98-a7d5-1fb209ff378f"
   },
   "source": [
    "<a id=\"analyzeData\"></a>\n",
    "## 2.2 Analyze and score data\n",
    "\n",
    "\n",
    "Now that we have created a stream of patient records, we need to define the analytics we want to perform on our data. We want to use the PMML model to predict the best drug treatment based on the input stream data (patients' medical data).  \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<p>These are the core steps you need for using PMML model scoring in your application:</p>\n",
    "<ul>\n",
    "    <li><b>Data preparation</b><br>Make sure your input stream meets your model's expected input features\n",
    "    </li>\n",
    "    <li><b>Score using <code>streamsx.pmml.score()</code>, specifying: </b><br>\n",
    "        - the model file to be used<br>\n",
    "        - the mapping from input stream fields to model input features<br>\n",
    "        - the mapping from output features to output stream fields\n",
    "    </li>\n",
    "</ul>\n",
    "</div>\n",
    "\n",
    "<a id=\"prepareData\"></a>\n",
    "### 2.2.1 Prepare data\n",
    "Before scoring there is the need to make sure that your data is in the format that the model expects. \n",
    "\n",
    "First, our input data contains sodium and potassium concentration as separate attributes: `bloodSodiumConcentration` and `bloodPotassionConcentration`, but the model expects the quotient of both: NA/K.\n",
    "\n",
    "We define the `NA_to_K` class to compute the Na/K value to each tuple from the input stream. This class will be invoked using the `map` function, which will be done later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "42be6766-1ac7-4859-9f51-6ddbfea470de"
   },
   "outputs": [],
   "source": [
    "# class providing a callable that calculates the NA / K ratio expected by the model\n",
    "class NA_to_K():\n",
    "    def __call__(self, frame):\n",
    "        frame[\"Na_to_K\"] = frame[\"bloodSodiumConcentration\"] / frame[\"bloodPotassionConcentration\"]\n",
    "        return frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d74172b4-f753-4e48-bf3a-7328e86747d7"
   },
   "source": [
    "Second, our input is a stream of Python `dict` objects, but  the `pmml.score()` function requires an input stream with a  [structured schema](https://streamsxtopology.readthedocs.io/en/stable/streamsx.topology.schema.html#streamsx.topology.schema.StreamSchema).\n",
    "In the cell below we first define the structured schema, and then:\n",
    "- Add the Na/K feature to the input `Stream`\n",
    "- Convert it to a `StructuredSchema`\n",
    "\n",
    "Both steps are done by applying a `map()` function to the input `Stream`, `records`. The two `map()` calls can be chained and produce the `Stream` called `preprocess`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "92d30b3e-dca9-4357-a586-5ab79c64c5ca"
   },
   "outputs": [],
   "source": [
    "from streamsx.topology.schema import StreamSchema\n",
    "# create streams schema for data from file\n",
    "rawData = StreamSchema(\n",
    "    \"tuple<int32 age, rstring sex, rstring bloodPressure, rstring cholesterol, float64 bloodSodiumConcentration, float64 bloodPotassionConcentration,rstring referenceDrug>\"\n",
    ")\n",
    "# extend the schema with preprocessed fields and scoring result fields\n",
    "scoredData = rawData.extend(StreamSchema(\"tuple<float64 NA_to_K, rstring predictedDrug>\"))    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# adding two map nodes to our topology:\n",
    "#    - calculate Na to K \n",
    "#    - convert to structured schema\n",
    "# generating the stream 'preprocess'\n",
    "preprocess = records.map(NA_to_K()).map(lambda row: (row[\"age\"], row[\"sex\"],\n",
    "                                                    row[\"bloodPressure\"],\n",
    "                                                   row[\"cholesterol\"],\n",
    "                                                   row[\"bloodSodiumConcentration\"],\n",
    "                                                   row[\"bloodPotassionConcentration\"],\n",
    "                                                   row[ \"referenceDrug\"],\n",
    "                                                   row[\"Na_to_K\"],row[\"predictedDrug\"]),\n",
    "                                        schema=scoredData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "efe9bee4-2c8c-41a2-b5bf-8c70b71d3259"
   },
   "source": [
    "<a id=\"scoreData\"></a>\n",
    "### 2.2.2 Score data\n",
    "To invoke the `pmml.score` function to the `preprocess` stream, we need to:\n",
    "1. Specify the model to be used with the `model_path` parameter. To reference models in your Cloud Pak project use following schema:  \n",
    "`/project_data/data_asset/<yourModelFile>`  \n",
    "so for our sample:\n",
    "`/project_data/data_asset/Drug_pmml_sample.xml`\n",
    "\n",
    "2. Use the `model_input_attribute_mapping` parameter to define the mapping of the input stream fields to the model's input features. <br/>For example, comparing the model input features(fields) to the stream fields we produce, the input stream has the following fields:<br/>\n",
    "`age, sex, bloodPressure, cholesterol, bloodSodiumConcentration, bloodPotassionConcentration, Na_to_K, referenceDrug,predictedDrug`. \n",
    "<br/> However, the model expects these features:\n",
    "<br/> `Name,Age,BP, Cholesterol,Na_to_K`. This would be probably the normal case when using a model in an application, so we specify the mapping from fields in our stream to the input features the model expects.\n",
    "\n",
    "3. Define the mapping of the model result to fields in the output stream with the `model_output_attribute_mapping` parameter. <br/>Depending on the used model algorithm ([model meta data picture](#modelMetaDataPicture)) the model output may generate a number of values, e.g. the `PredictedValue` itself but also the `Confidence` of this prediction. Both are sub-values (PMML defines them as `result features`) of the target, i.e. the PMML definition of the field being predicted. In this case our target is called `Drug` and we are only interested in the prediction, so we reference `Drug.PredictedValue` in our output mapping.\n",
    "\n",
    "\n",
    "__Detailed documentation:__\n",
    "+ [PMML streamsx package documentation](https://streamsxpmml.readthedocs.io/en/latest/)  for usage details of the streamsx.pmml.score() function\n",
    "+ [IBM Streams PMML toolkit -  model types](https://www.ibm.com/support/knowledgecenter/SSCRJU_4.3.0/com.ibm.streams.tk20190528.doc/toolkits/dita/tk$com.ibm.streams.pmml/op$com.ibm.streams.pmml$PMMLScoring$1.html) for  supported model algorithms \n",
    "+ [IBM Streams PMML toolkit -  output features](https://www.ibm.com/support/knowledgecenter/SSCRJU_4.3.0/com.ibm.streams.tk20190528.doc/toolkits/dita/tk$com.ibm.streams.pmml/op$com.ibm.streams.pmml$PMMLScoring$2.html) for supported output features and their data types\n",
    "+ [PMML Specification - output features](http://dmg.org/pmml/v4-3/Output.html) for PMML specified output features for the different scoring algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c3cd497f-dd0e-4222-a743-081cc5c02e8c"
   },
   "outputs": [],
   "source": [
    "score = pmml.score(preprocess,\n",
    "    schema=scoredData,\n",
    "    model_path='/project_data/data_asset/Drug_pmml_model.xml', \n",
    "    model_input_attribute_mapping='Age=age,BP=bloodPressure,Cholesterol=cholesterol,Na_to_K=NA_to_K,Sex=sex', #\n",
    "    model_output_attribute_mapping='predictedDrug=Drug.PredictedValue'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7e2449ba-1cec-4a4f-bc29-af7194b7f396"
   },
   "source": [
    "<a id=\"defineOutput\"></a>\n",
    "## 2.3 Define Output\n",
    "\n",
    "The `score` stream is our final result.  We use `score.publish()` function to make this stream available to other applications. \n",
    "\n",
    "If you want to send the stream to another database or system, you would use a sink function (similar to the source function) and invoke it using e.g. `score.for_each`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5ad0149b-401b-478b-ae25-43e68eb2f3b4"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# publish results as JSON\n",
    "score.publish(topic=\"ScoredRecords\",schema=json,name=\"PublishScores\")\n",
    "\n",
    "# Other options include:\n",
    "# invoke another sink function:\n",
    "# score.for_each(func=send_to_db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "34c590b8-5229-478e-8932-fc8c9ba3b752"
   },
   "source": [
    "<b>Create a `View` to preview the tuples on the result `Stream` </b>\n",
    "\n",
    "\n",
    "A `View` is a connection to a `Stream` that becomes activated when the application is running. It provides an interface to get snapshots of the data flowing on this stream. We examine the data from within the notebook in section 4, below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1ea9885b-f48e-47c8-b685-52e5a5acc39d"
   },
   "outputs": [],
   "source": [
    "score_view = score.view(name=\"ScoredRecords\", description=\"Sample of scored records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6ed56f1a-339c-4e8d-918a-cb67335de0da"
   },
   "source": [
    "<a name=\"submit\"></a>\n",
    "\n",
    "# 3. Submit the application\n",
    "A running Streams application is called a *job*. This next cell submits the application for execution and prints the resulting job id.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8bddaf5e-1b5c-4ff0-b877-22619f52348c"
   },
   "outputs": [],
   "source": [
    "from streamsx.topology import context\n",
    "\n",
    "# Disable SSL certificate verification if necessary\n",
    "cfg[context.ConfigParams.SSL_VERIFY] = False\n",
    "\n",
    "# build and submit\n",
    "submission_result = context.submit('DISTRIBUTED', \n",
    "                                   topo, \n",
    "                                   cfg)\n",
    "\n",
    "print(submission_result)\n",
    "# The submission_result object contains information about the running application, or job\n",
    "if submission_result.job:\n",
    "    streams_job = submission_result.job\n",
    "    print (\"JobId: \", streams_job.id , \"\\nJob name: \", streams_job.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5eed46ee-3b5f-4735-a26e-65886e07e87f"
   },
   "source": [
    "<a name=\"view\"></a>\n",
    "\n",
    "# 4. Use the `View` to access data from the job\n",
    "Now that the job is started, use the `View` object you created in step 2.3 to start retrieving data from a `Stream`.   \n",
    "   \n",
    "Compare the attributes 'referenceDrug' and 'predictedDrug' in the output below. The predicted drug is the result of the scoring. Both values should be equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "baff689c-9c23-43cf-9b15-9cef26d78d87"
   },
   "outputs": [],
   "source": [
    "# Connect to the view and display the data\n",
    "queue = score_view.start_data_fetch()\n",
    "try:\n",
    "    for val in range(10):\n",
    "        print(queue.get(timeout=60))    \n",
    "finally:\n",
    "    score_view.stop_data_fetch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c6ea01de-69d1-4fa3-9207-7e894ab01214"
   },
   "source": [
    "<b> Display the results in real time</b>  \n",
    "\n",
    "Calling `View.display()` from the notebook displays the results of the view in a table that is updated in real-time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "385151c5-b30d-4116-a074-34dd19717e86",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Display the results for 30 seconds\n",
    "score_view.display(duration=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e09cdcb3-4141-4f3c-8d3b-12b9d2e69da7"
   },
   "source": [
    "# <a name=\"status\"> </a> 4.1. See job status\n",
    "\n",
    "The tools available to monitor the running application depend on the version of Streams and your development environment.\n",
    "\n",
    "- **If you are using a Cloud Pak for Data 3.5 project:**  When you submit the `Topology`, you create a new <i>job run</i>. The job represents the application and the job run represents a single instance of the running application. \n",
    "\n",
    "    1. Open your project and click on the **Jobs** tab. This will show a list of the project's jobs.\n",
    "    1. Under the **Job name** column, find your job based on the `Job Name` [printed when you submitted the job](#submit). This will list all of the job runs for that job.\n",
    "    1. Click the **Run name** to open the job run. The run name will be the same as the `Job Name` printed above.\n",
    "    1. This will open the Job Details page.\n",
    "    \n",
    "        1. To open the Job Graph, click the **Streams job graph** link.\n",
    "        \n",
    "        1. To download logs, click the **Logs** tab and click **Create snapshot**, then download the snapshot.    \n",
    "    \n",
    "\n",
    "- **For all other development environments and versions of Streams**, [see this page for more information](http://ibmstreams.github.io/streamsx.documentation/docs/spl/quick-start/qs-4).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3aef4680-4a41-482b-b333-c7d2df32893b"
   },
   "source": [
    "<a name=\"cancel\"></a>\n",
    "\n",
    "# 5. Cancel the job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c1bfa5f5-f3c9-4c9a-8245-aa4074610768"
   },
   "source": [
    "The Streams job is running in the Streams service. You can cancel it within the notebook or delete it from **Projects** > **Jobs**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "060a6234-14c7-45e3-b095-2eb27512be32"
   },
   "outputs": [],
   "source": [
    "# cancel the job directly using the Job object\n",
    "streams_job.cancel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8dbcfab8-1b6e-478d-a94d-3de89d4effe5"
   },
   "source": [
    "# Summary\n",
    "\n",
    "We started with a `Stream` called `records`, which contained the data we wanted to analyze. Next, we used functions in the `Stream` object to perform simple preprocessing before we scored the data with a ML model and produced the `score` stream.\n",
    "\n",
    "After submitting the application to the Streams service, we connected to the `score_view` view to see the results within the notebook.\n",
    "\n",
    "\n",
    "### Next steps\n",
    "Learn more about the [Python API from the documentation](https://streamsxtopology.readthedocs.io/en/stable/index.html/). You can also visit the [Streams community for more resources](https://ibm.biz/streams-articles).\n"
   ]
  }
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