{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# German Credit Scoring Feed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook can be scheduled to provide a continuous flow of data to your German Credit Risk model. Paste your Watson Machine Learning credentials below, and verify that the value of the **DEPLOYMENT_NAME** variable matches the name of your deployed German Credit Risk model. You can also set the maximum and minimum number of records that will be scored per run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade watson-machine-learning-client --no-cache | tail -n 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "WML_CREDENTIALS = {\n",
    "    \"PASTE HERE\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEPLOYMENT_NAME = \"Spark German Risk Deployment - Final\"\n",
    "MIN_RECORDS = 30\n",
    "MAX_RECORDS = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json, random\n",
    "from watson_machine_learning_client import WatsonMachineLearningAPIClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wml_client = WatsonMachineLearningAPIClient(WML_CREDENTIALS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wml_deployments = wml_client.deployments.get_details()\n",
    "scoring_url = None\n",
    "for deployment in wml_deployments['resources']:\n",
    "    if DEPLOYMENT_NAME == deployment['entity']['name']:\n",
    "        scoring_url = deployment['entity']['scoring_url']\n",
    "        break\n",
    "    \n",
    "print(\"Scoring URL: {}\".format(scoring_url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    with open('german_credit_feed.json', 'r') as scoring_file:\n",
    "        scoring_data = json.load(scoring_file)\n",
    "    print('file found')\n",
    "    \n",
    "except:\n",
    "    !wget https://raw.githubusercontent.com/emartensibm/german-credit/master/german_credit_feed.json\n",
    "    with open('german_credit_feed.json', 'r') as scoring_file:\n",
    "        scoring_data = json.load(scoring_file)\n",
    "    print('file downloaded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fields = scoring_data['fields']\n",
    "values = []\n",
    "for _ in range(0, random.randint(MIN_RECORDS, MAX_RECORDS)):\n",
    "    values.append(random.choice(scoring_data['values']))\n",
    "payload_scoring = {\"fields\": fields, \"values\": values}\n",
    "\n",
    "scoring_response = wml_client.deployments.score(scoring_url, payload_scoring)\n",
    "print(scoring_response)"
   ]
  }
 ],
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