Session #5 Homework.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e34ae53",
   "metadata": {},
   "source": [
    "## Homework\n",
    "\n",
    "In this homework, we will use Credit Card Data from [the previous homework](https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/course-zoomcamp/cohorts/2022/04-evaluation/homework.md).\n",
    "\n",
    "> Note: sometimes your answer doesn't match one of the options exactly. That's fine. \n",
    "Select the option that's closest to your solution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d4e64c",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "\n",
    "* Install Pipenv\n",
    "* What's the version of pipenv you installed?\n",
    "* Use `--version` to find out"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "d8b0e1cb",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "**pipenv, version 2022.10.9**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b9f288",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "\n",
    "* Use Pipenv to install Scikit-Learn version 1.0.2\n",
    "* What's the first hash for scikit-learn you get in Pipfile.lock?\n",
    "\n",
    "Note: you should create an empty folder for homework\n",
    "and do it there. \n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c627d29b",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "**7faf93643695bae1d84821ed7160b7102db12c695626f65e6a38806af2a44f5c**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ef8658",
   "metadata": {},
   "source": [
    "## Models\n",
    "\n",
    "We've prepared a dictionary vectorizer and a model.\n",
    "\n",
    "They were trained (roughly) using this code:\n",
    "\n",
    "```python\n",
    "features = ['reports', 'share', 'expenditure', 'owner']\n",
    "dicts = df[features].to_dict(orient='records')\n",
    "\n",
    "dv = DictVectorizer(sparse=False)\n",
    "X = dv.fit_transform(dicts)\n",
    "\n",
    "model = LogisticRegression(solver='liblinear').fit(X, y)\n",
    "```\n",
    "\n",
    "> **Note**: You don't need to train the model. This code is just for your reference.\n",
    "\n",
    "And then saved with Pickle. Download them:\n",
    "\n",
    "* [DictVectorizer](https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/course-zoomcamp/cohorts/2022/05-deployment/homework/dv.bin?raw=true)\n",
    "* [LogisticRegression](https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/course-zoomcamp/cohorts/2022/05-deployment/homework/model1.bin?raw=true)\n",
    "\n",
    "With `wget`:\n",
    "\n",
    "```bash\n",
    "PREFIX=https://raw.githubusercontent.com/alexeygrigorev/mlbookcamp-code/master/course-zoomcamp/cohorts/2022/05-deployment/homework\n",
    "wget $PREFIX/model1.bin\n",
    "wget $PREFIX/dv.bin\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967e0fc6",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "\n",
    "Let's use these models!\n",
    "\n",
    "* Write a script for loading these models with pickle\n",
    "* Score this client:\n",
    "\n",
    "```json\n",
    "{\"reports\": 0, \"share\": 0.001694, \"expenditure\": 0.12, \"owner\": \"yes\"}\n",
    "```\n",
    "\n",
    "What's the probability that this client will get a credit card? \n",
    "\n",
    "* **0.162**\n",
    "* 0.391\n",
    "* 0.601\n",
    "* 0.993\n",
    "\n",
    "If you're getting errors when unpickling the files, check their checksum:\n",
    "\n",
    "```bash\n",
    "$ md5sum model1.bin dv.bin\n",
    "3f57f3ebfdf57a9e1368dcd0f28a4a14  model1.bin\n",
    "6b7cded86a52af7e81859647fa3a5c2e  dv.bin\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6f8239a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pickle-mixin\n",
      "  Downloading pickle-mixin-1.0.2.tar.gz (5.1 kB)\n",
      "Building wheels for collected packages: pickle-mixin\n",
      "  Building wheel for pickle-mixin (setup.py): started\n",
      "  Building wheel for pickle-mixin (setup.py): finished with status 'done'\n",
      "  Created wheel for pickle-mixin: filename=pickle_mixin-1.0.2-py3-none-any.whl size=6008 sha256=41293ac2aa08d37dc667036b1d040229931499db971c299296c7865fc8635a9a\n",
      "  Stored in directory: c:\\users\\darkcorp\\appdata\\local\\pip\\cache\\wheels\\58\\c6\\8b\\061bd4edc8cea2b2235758c3e50473d08499236fbfdd21e6b3\n",
      "Successfully built pickle-mixin\n",
      "Installing collected packages: pickle-mixin\n",
      "Successfully installed pickle-mixin-1.0.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pickle-mixin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37e59acd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c49ad19",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_file = 'https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/course-zoomcamp/cohorts/2022/05-deployment/homework/model1.bin?raw=true'\n",
    "dict_vectorizer_file = 'https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/course-zoomcamp/cohorts/2022/05-deployment/homework/dv.bin?raw=true'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f74e6203",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(model_file, 'rb') as f_in_model:\n",
    "    model = pickle.load(f_in_model)\n",
    "f_in_model.close()\n",
    "\n",
    "with open(dict_vectorizer_file, 'rb') as f_in_dv:\n",
    "    dv = pickle.load(f_in_dv)\n",
    "f_in_dv.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d9942b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(DictVectorizer(sparse=False), LogisticRegression(solver='liblinear'))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a23261f",
   "metadata": {},
   "outputs": [],
   "source": [
    "customer = {\"reports\": 0, \"share\": 0.001694, \"expenditure\": 0.12, \"owner\": \"yes\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "60577a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12    , 0.      , 1.      , 0.      , 0.001694]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = dv.transform([customer])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac9ad704",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16213414434326598"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(X)[0, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0c357f7",
   "metadata": {},
   "source": [
    "## Question 4\n",
    "\n",
    "Now let's serve this model as a web service\n",
    "\n",
    "* Install Flask and gunicorn (or waitress, if you're on Windows)\n",
    "* Write Flask code for serving the model\n",
    "* Now score this client using `requests`:\n",
    "\n",
    "```python\n",
    "url = \"YOUR_URL\"\n",
    "client = {\"reports\": 0, \"share\": 0.245, \"expenditure\": 3.438, \"owner\": \"yes\"}\n",
    "requests.post(url, json=client).json()\n",
    "```\n",
    "\n",
    "What's the probability that this client will get a credit card?\n",
    "\n",
    "* 0.274\n",
    "* 0.484\n",
    "* 0.698\n",
    "* **0.928**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "72567943",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PONG'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "\n",
    "url = 'http://127.0.0.1:9696/ping'\n",
    "response = requests.get(url).text\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0ddcee33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9282218018527452"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'http://localhost:9696/predict'\n",
    "client = {\"reports\": 0, \"share\": 0.245, \"expenditure\": 3.438, \"owner\": \"yes\"}\n",
    "response = requests.post(url, json=client).json()\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d991c5a",
   "metadata": {},
   "source": [
    "## Docker\n",
    "\n",
    "Install [Docker](https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/course-zoomcamp/05-deployment/06-docker.md). We will use it for the next two questions.\n",
    "\n",
    "For these questions, we prepared a base image: `svizor/zoomcamp-model:3.9.12-slim`. \n",
    "You'll need to use it (see Question 5 for an example).\n",
    "\n",
    "This image is based on `python:3.9.12-slim` and has a logistic regression model \n",
    "(a different one) as well a dictionary vectorizer inside. \n",
    "\n",
    "This is how the Dockerfile for this image looks like:\n",
    "\n",
    "```docker \n",
    "FROM python:3.9.12-slim\n",
    "WORKDIR /app\n",
    "COPY [\"model2.bin\", \"dv.bin\", \"./\"]\n",
    "```\n",
    "\n",
    "We already built it and then pushed it to [`svizor/zoomcamp-model:3.9.12-slim`](https://hub.docker.com/r/svizor/zoomcamp-model).\n",
    "\n",
    "> **Note**: You don't need to build this docker image, it's just for your reference."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28dba7d0",
   "metadata": {},
   "source": [
    "## Question 5\n",
    "\n",
    "Download the base image `svizor/zoomcamp-model:3.9.12-slim`. You can easily make it by using [docker pull](https://docs.docker.com/engine/reference/commandline/pull/) command.\n",
    "\n",
    "So what's the size of this base image?\n",
    "\n",
    "* 15 Mb\n",
    "* **125 Mb**\n",
    "* 275 Mb\n",
    "* 415 Mb\n",
    "\n",
    "You can get this information when running `docker images` - it'll be in the \"SIZE\" column."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "fab3f9a6",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "REPOSITORY              TAG           IMAGE ID       CREATED      SIZE\n",
    "svizor/zoomcamp-model   3.9.12-slim   571a6fdc554b   8 days ago   125MB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fa29496",
   "metadata": {},
   "source": [
    "## Dockerfile\n",
    "\n",
    "Now create your own Dockerfile based on the image we prepared.\n",
    "\n",
    "It should start like that:\n",
    "\n",
    "```docker\n",
    "FROM svizor/zoomcamp-model:3.9.12-slim\n",
    "# add your stuff here\n",
    "```\n",
    "\n",
    "Now complete it:\n",
    "\n",
    "* Install all the dependencies form the Pipenv file\n",
    "* Copy your Flask script\n",
    "* Run it with Gunicorn \n",
    "\n",
    "After that, you can build your docker image."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef81463",
   "metadata": {},
   "source": [
    "## Question 6\n",
    "\n",
    "Let's run your docker container!\n",
    "\n",
    "After running it, score this client once again:\n",
    "\n",
    "```python\n",
    "url = \"YOUR_URL\"\n",
    "client = {\"reports\": 0, \"share\": 0.245, \"expenditure\": 3.438, \"owner\": \"yes\"}\n",
    "requests.post(url, json=client).json()\n",
    "```\n",
    "\n",
    "What's the probability that this client will get a credit card now?\n",
    "\n",
    "* 0.289\n",
    "* 0.502\n",
    "* 0.769\n",
    "* 0.972"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6281c301",
   "metadata": {},
   "source": [
    "I had to go to WLS 2 from WLS 1 while making the Docker work right.\n",
    "After that I can't access my /home directory via windows explorer as it is inside ext4.vhdx now.\n",
    "How can I push my Dockerfile into Ubuntu?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7fa57f7",
   "metadata": {},
   "source": [
    "## Submit the results\n",
    "\n",
    "* Submit your results here: https://forms.gle/jU2we8f9WeLgX3qa6\n",
    "* You can submit your solution multiple times. In this case, only the last submission will be used \n",
    "* If your answer doesn't match options exactly, select the closest one"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29fbe3b8",
   "metadata": {},
   "source": [
    "## Deadline\n",
    "\n",
    "The deadline for submitting is **10 October 2022 (Monday), 23:00 CEST (Berlin time)**. \n",
    "\n",
    "After that, the form will be closed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8caf9d81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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