churn_server/churn_server.ipynb

{
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   "source": [
    "# **Churn Server**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the following section we create a new model serving function which wraps our class , and specify model and other resources.\n",
    "Deploying the serving function will provide us an http endpoint that can handle requests in real time.\n",
    "This function is part of the [customer-churn-prediction demo](https://github.com/mlrun/demos/tree/master/customer-churn-prediction).<br>\n",
    "To see how the model is trained or how the data-set is generated, check out `coxph_trainer` and `xgb_trainer` functions from the function marketplace repository."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Steps**\n",
    "1. [Setup function parameters](#Setup-function-parameters)\n",
    "2. [Importing the function](#Importing-the-function)\n",
    "3. [Testing the function locally](#Testing-the-function-locally)\n",
    "4. [Testing the function remotely](#Testing-the-function-remotely)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Following packages are required, make sure to install\n",
    "# !pip install xgboost==1.3.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Setup function parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up models path\n",
    "xgb_model_path = 'https://s3.wasabisys.com/iguazio/models/function-marketplace-models/churn_server/xgb_model.pkl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Importing the function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-14 06:10:16,104 [info] loaded project function-marketplace from MLRun DB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<mlrun.serving.states.TaskStep at 0x7f8f2306ca90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import mlrun\n",
    "mlrun.set_environment(project='function-marketplace')\n",
    "\n",
    "# Importing the function from the hub\n",
    "fn = mlrun.import_function(\"hub://churn_server:development\")\n",
    "fn.apply(mlrun.auto_mount())\n",
    "\n",
    "# Manually specifying needed packages \n",
    "fn.spec.build.commands = ['pip install lifelines==0.22.8', 'pip install xgboost==1.3.1']\n",
    "\n",
    "# Adding the model \n",
    "fn.add_model(key='xgb_model', model_path=xgb_model_path ,class_name='ChurnModel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Testing the function locally**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note that this function is a serving function, hence not needs to run, but deployed.<br>\n",
    "\n",
    "in order to test locally without deploying to server, mlrun provides mocking api that simulate the action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-14 06:10:19,145 [info] model xgb_model was loaded\n",
      "> 2021-10-14 06:10:19,145 [info] Initializing endpoint records\n",
      "> 2021-10-14 06:10:19,164 [info] Loaded ['xgb_model']\n"
     ]
    }
   ],
   "source": [
    "# When mocking, class has to be present\n",
    "from churn_server import *\n",
    "\n",
    "# Mocking function\n",
    "server = fn.to_mock_server()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "   gender  senior  partner  deps  tenure  PhoneService  MultipleLines  \\\n",
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       "\n",
       "   OnlineSecurity  OnlineBackup  DeviceProtection  ...  PaperlessBilling  \\\n",
       "0               1             0                 0  ...                 1   \n",
       "1               0             0                 0  ...                 1   \n",
       "2               0             0                 0  ...                 1   \n",
       "3               0             1                 1  ...                 0   \n",
       "4               0             1                 1  ...                 1   \n",
       "\n",
       "   MonthlyCharges  tenure_map  ISP_1  ISP_2  Contract_1  Contract_2  \\\n",
       "0          101.90         2.0      1      0           1           0   \n",
       "1           85.70         0.0      1      0           0           0   \n",
       "2           69.55         0.0      1      0           0           0   \n",
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       "4          104.60         3.0      1      0           0           1   \n",
       "\n",
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       "1          0          1          0  \n",
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#declaring test_set path\n",
    "test_set_path = \"https://s3.wasabisys.com/iguazio/data/function-marketplace-data/churn_server/test_set.csv\"\n",
    "\n",
    "# Getting the data\n",
    "x_test = pd.read_csv(test_set_path)\n",
    "y_test = x_test['labels']\n",
    "x_test.drop(['labels'],axis=1,inplace=True)\n",
    "x_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# KFServing protocol event\n",
    "event_data = {\"inputs\": x_test.values.tolist()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = server.test(path='/v2/models/xgb_model/predict',body=event_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "When mocking to server, returned dict has the following fields : id, model_name, outputs\n"
     ]
    }
   ],
   "source": [
    "print(f'When mocking to server, returned dict has the following fields : {\", \".join([x for x in response.keys()])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Testing the function remotely**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> 2021-10-14 06:10:20,163 [info] Starting remote function deploy\n",
      "2021-10-14 06:10:20  (info) Deploying function\n",
      "2021-10-14 06:10:20  (info) Building\n",
      "2021-10-14 06:10:20  (info) Staging files and preparing base images\n",
      "2021-10-14 06:10:20  (info) Building processor image\n",
      "2021-10-14 06:10:21  (info) Build complete\n",
      "2021-10-14 06:10:29  (info) Function deploy complete\n",
      "> 2021-10-14 06:10:30,408 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-function-marketplace-churn-server.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['default-tenant.app.dev39.lab.iguazeng.com:31984']}\n"
     ]
    }
   ],
   "source": [
    "address = fn.deploy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model's accuracy : 0.7913907284768212\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import requests\n",
    "\n",
    "# using requests to predict\n",
    "response = requests.put(address + \"/v2/models/xgb_model/predict\", json=json.dumps(event_data))\n",
    "\n",
    "# returned data is a string \n",
    "y_predict = json.loads(response.text)['outputs']\n",
    "accuracy = sum(1 for x,y in zip(y_predict,y_test) if x == y) / len(y_test)\n",
    "print(f\"model's accuracy : {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Back to the top](#Churn-Server)"
   ]
  }
 ],
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