Notebook from lightning talk demoing the fire library to create a command line tool for a linear programming problem.

Fire Lightning talk.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A mathematical/lego problem:\n",
    "\n",
    "Maximize $z=1600x_1+1000x_2$ such that:\n",
    "\n",
    "$$2x_1+x_2\\leq 6$$\n",
    "$$2x_1+2x_2\\leq 8$$\n",
    "$$x_1, x_2\\geq 0$$\n",
    "$$x_1, x_2\\in\\mathbb{Z}$$\n",
    "\n",
    "## Plotting the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YfkmOh3NHQLlB58HG8Ev4JPAPssuhi8srmbv6AB9vPkJbfx/+MiWaEWHShEtc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\nATJEcwLzktmQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f161679fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x1s = [0, 3]\n",
    "large_piece_constraint = [6 - 2 * x1 for x1 in x1s]\n",
    "small_piece_constraint = [4 - x1 for x1 in x1s]\n",
    "plt.plot(x1s, large_piece_constraint, label=\"$2x_1+x_2=6$\")\n",
    "plt.plot(x1s, small_piece_constraint, label=\"$2x_1+2x_2=8$\")\n",
    "plt.xlim(x1s)\n",
    "plt.ylim([0, 6])\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pulp  # Library for linear programming\n",
    "\n",
    "prob = pulp.LpProblem(\"Lego\", pulp.LpMaximize)\n",
    "\n",
    "x1 = pulp.LpVariable(\"Tables\", lowBound=0, cat=\"Integer\")\n",
    "x2 = pulp.LpVariable(\"Chairs\", lowBound=0, cat=\"Integer\")\n",
    "\n",
    "prob += 1600 * x1 + 1000 * x2\n",
    "\n",
    "prob += 2 * x1 + x2 <= 6\n",
    "prob += 2 * x1 + 2 * x2 <= 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0, 2.0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.value(), x2.value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def chairs_and_tables(cost_of_table=1600, cost_of_chair=1000,\n",
    "                      number_of_small_pieces=8, number_of_large_pieces=6):\n",
    "    prob = pulp.LpProblem(\"Lego\", pulp.LpMaximize)\n",
    "    x1 = pulp.LpVariable(\"Tables\", lowBound=0, cat=\"Integer\")\n",
    "    x2 = pulp.LpVariable(\"Chairs\", lowBound=0, cat=\"Integer\")\n",
    "    \n",
    "    prob += cost_of_table * x1 + cost_of_chair * x2\n",
    "    \n",
    "    prob += 2 * x1 + x2 <= number_of_large_pieces\n",
    "    prob += 2 * x1 + 2 * x2 <= number_of_small_pieces\n",
    "    \n",
    "    prob.solve()\n",
    "    return x1.value(), x2.value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0, 2.0)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chairs_and_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.0, 10.0)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chairs_and_tables(number_of_large_pieces=20, number_of_small_pieces=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's make a command line tool!\n",
    "\n",
    "- `from sys import argv`\n",
    "- `import argparse`\n",
    "- `import docopt`\n",
    "- `import fire`  \n",
    "\n",
    "\n",
    "Here's `main.py`:\n",
    "\n",
    "```python\n",
    "import fire\n",
    "import pulp\n",
    "\n",
    "def chairs_and_tables(cost_of_table=1600, cost_of_chair=1000,\n",
    "                      number_of_small_pieces=8, number_of_large_pieces=6):\n",
    "    prob = pulp.LpProblem(\"Lego\", pulp.LpMaximize)\n",
    "    x1 = pulp.LpVariable(\"Tables\", lowBound=0, cat=\"Integer\")\n",
    "    x2 = pulp.LpVariable(\"Chairs\", lowBound=0, cat=\"Integer\")\n",
    "    prob += cost_of_table * x1 + cost_of_chair * x2\n",
    "    prob += 2 * x1 + x2 <= number_of_large_pieces\n",
    "    prob += 2 * x1 + 2 * x2 <= number_of_small_pieces\n",
    "    prob.solve()\n",
    "    return x1.value(), x2.value()\n",
    "\n",
    "if __name__==\"__main__\":\n",
    "    fire.Fire(chairs_and_tables)\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `fire` we can then do:\n",
    "\n",
    "```bash\n",
    "$ python main.py --number_of_large_pieces 20 --number_of_small_pieces 30\n",
    "[5.0, 10.0]\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:fire-lightning]",
   "language": "python",
   "name": "conda-env-fire-lightning-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}

oh ，i don't kwon at all.