week_5_homework/week_5_homework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 5 Homework"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mpipenv\u001b[0m, version 2024.3.0\n"
     ]
    }
   ],
   "source": [
    "!pipenv --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mPipfile.lock not found, creating\u001b[0m\u001b[1;33m...\u001b[0m\n",
      "Locking\u001b[0m \u001b[33m[packages]\u001b[0m dependencies...\u001b[0m\n",
      "Locking\u001b[0m \u001b[33m[dev-packages]\u001b[0m dependencies...\u001b[0m\n",
      "\u001b[1mUpdated Pipfile.lock (ed6d5d614626ae28e274e453164affb26694755170ccab3aa5866f093d51d3e4)!\u001b[0m\n",
      "To activate this project's virtualenv, run \u001b[33mpipenv shell\u001b[0m.\n",
      "Alternatively, run a command inside the virtualenv with \u001b[33mpipenv run\u001b[0m.\n",
      "\u001b[1;32mInstalling scikit-learn==1.5.2...\u001b[0m\n",
      "✔ Installation Succeeded\n",
      "To activate this project's virtualenv, run \u001b[33mpipenv shell\u001b[0m.\n",
      "Alternatively, run a command inside the virtualenv with \u001b[33mpipenv run\u001b[0m.\n",
      "\u001b[1mInstalling dependencies from Pipfile.lock \u001b[0m\u001b[1;39m(51d3e4)...\u001b[0m\n",
      "\u001b[32mAll dependencies are now up-to-date!\u001b[0m\n",
      "\u001b[1;32mUpgrading\u001b[0m scikit-\u001b[33mlearn\u001b[0m==\u001b[1;36m1.5\u001b[0m.\u001b[1;36m2\u001b[0m in \u001b[39m dependencies.\u001b[0m\n",
      "\u001b[?25lBuilding requirements...\n",
      "\u001b[2KResolving dependencies....\n",
      "\u001b[2K✔ Success! Locking packages...\n",
      "\u001b[2K\u001b[32m⠙\u001b[0m Locking packages...\n",
      "\u001b[1A\u001b[2K\u001b[?25lBuilding requirements...\n",
      "\u001b[2KResolving dependencies....\n",
      "\u001b[2K✔ Success! Locking packages...\n",
      "\u001b[2K\u001b[32m⠧\u001b[0m Locking packages...\n",
      "\u001b[1A\u001b[2KTo activate this project's virtualenv, run \u001b[33mpipenv shell\u001b[0m.\n",
      "Alternatively, run a command inside the virtualenv with \u001b[33mpipenv run\u001b[0m.\n",
      "\u001b[1mInstalling dependencies from Pipfile.lock \u001b[0m\u001b[1;39m(344639)...\u001b[0m\n",
      "\u001b[32mAll dependencies are now up-to-date!\u001b[0m\n",
      "\u001b[1mInstalling dependencies from Pipfile.lock \u001b[0m\u001b[1;39m(344639)...\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pipenv install scikit-learn==1.5.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first hash for scikit-learn from Pipfile.lock is: `03b6158efa3faaf1feea3faa884c840ebd61b6484167c711548fce208ea09445`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "with open(\"model1.bin\", \"rb\") as f_model, open(\"dv.bin\", \"rb\") as f_dv:\n",
    "    model = pickle.load(f_model)\n",
    "    dv = pickle.load(f_dv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.759)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = {\"job\": \"management\", \"duration\": 400, \"poutcome\": \"success\"}\n",
    "\n",
    "X = dv.transform([client])\n",
    "\n",
    "model.predict_proba(X)[0][1].round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code to serve the model as a web service is in [question4.py](question4.py).\n",
    "\n",
    "We use `pipenv shell` and then run `gunicorn --bind=0.0.0.0:9696 question4:app` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'probability': 0.335}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "url = \"http://localhost:9696/predict\"\n",
    "client = {\"job\": \"student\", \"duration\": 280, \"poutcome\": \"failure\"}\n",
    "requests.post(url, json=client).json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Size of image is found using `docker images` and is `130 MB`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dockerfile can be found [here](./Dockerfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build the image:\n",
    "\n",
    "```bash\n",
    "docker build -t question6 .\n",
    "```\n",
    "\n",
    "Then we run the container:\n",
    "\n",
    "```bash\n",
    "docker run "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'probability': 0.757}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "url = \"http://localhost:9696/predict\"\n",
    "client = {\"job\": \"management\", \"duration\": 400, \"poutcome\": \"success\"}\n",
    "requests.post(url, json=client).json()"
   ]
  }
 ],
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