{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Python Tour of Data Science: High Performance Computing\n",
    "\n",
    "[Michaël Defferrard](http://deff.ch), *PhD student*, [EPFL](http://epfl.ch) [LTS2](http://lts2.epfl.ch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise: is Python slow ?\n",
    "\n",
    "That is one of the most heard complain about Python. Because [CPython](https://en.wikipedia.org/wiki/CPython), the Python reference implementation, [interprets the language](https://en.wikipedia.org/wiki/Interpreted_language) (i.e. it compiles Python code to intermediate bytecode which is then interpreted by a virtual machine), it is inherentably slower than [compiled languages](https://en.wikipedia.org/wiki/Compiled_language), especially for computation heavy tasks such as number crunching.\n",
    "\n",
    "There are three ways around it that we'll explore in this exercise:\n",
    "1. Specialized libraries.\n",
    "1. Compile Python to machine code.\n",
    "1. Implement in a compiled language and call from Python.\n",
    "\n",
    "In this exercise we'll compare many possible implementations of a function. Our goal is to compare the execution time of our implementations and get a sense of the many ways to write efficient Python code. We test seven implementations:\n",
    "\n",
    "Along the exercise we'll use the function\n",
    "$$accuracy(\\hat{y}, y) = \\frac1n \\sum_{i=0}^{i=n-1} 1(\\hat{y}_i = y_i),$$\n",
    "where $1(x)$ is the [indicator function](https://en.wikipedia.org/wiki/Indicator_function) and $n$ is the number of samples. This function computes the accuracy, i.e. the percentage of correct predictions, of a classifier. A pure Python implementation is given below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def accuracy_python(y_pred, y_true):\n",
    "    \"\"\"Plain Python implementation.\"\"\"\n",
    "    num_correct = 0\n",
    "    for y_pred_i, y_true_i in zip(y_pred, y_true):\n",
    "        if y_pred_i == y_true_i:\n",
    "            num_correct += 1\n",
    "    return num_correct / len(y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we test and measure the execution time of the above implementation. The [%timeit](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=timeit#magic-timeit) function provided by IPython is a useful helper to measure the execution time of a line of Python code. As we'll see, the above implementation is very inefficient compared to what we can achieve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected accuracy: 0.1\n",
      "Empirical accuracy: 0.099648\n",
      "10 loops, best of 3: 156 ms per loop\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "c = 10  # Number of classes.\n",
    "n = int(1e6)  # Number of samples.\n",
    "\n",
    "y_true = np.random.randint(0, c, size=n)\n",
    "y_pred = np.random.randint(0, c, size=n)\n",
    "print('Expected accuracy: {}'.format(1/c))\n",
    "print('Empirical accuracy: {}'.format(accuracy_python(y_pred, y_true)))\n",
    "\n",
    "%timeit accuracy_python(y_pred, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Specialized libraries\n",
    "\n",
    "Specialized libraries, which provide efficient compiled implementations of the heavy computations, is an easy way to solve the performance problem. That is for example NumPy, which uses efficient BLAS and LAPACK implementations as a backend. SciPy and scikit-learn fall in the same category.\n",
    "\n",
    "Implement below the accuracy function using:\n",
    "1. Only functions provided by [NumPy](http://www.numpy.org/). The idea here is to *vectorize* the computation.\n",
    "2. The implementation of [scikit-learn](http://scikit-learn.org/), our machine learning library.\n",
    "\n",
    "Then test that it provides the correct result and measure it's execution time. How much faster are they compared to the pure Python implementation ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 2.23 ms per loop\n",
      "10 loops, best of 3: 52 ms per loop\n"
     ]
    }
   ],
   "source": [
    "def accuracy_numpy(y_pred, y_true):\n",
    "    \"\"\"Numpy implementation.\"\"\"\n",
    "    return np.sum(y_pred == y_true) / y_true.size\n",
    "\n",
    "def accuracy_sklearn(y_pred, y_true):\n",
    "    \"\"\"Scikit-learn implementation.\"\"\"\n",
    "    from sklearn import metrics\n",
    "    return metrics.accuracy_score(y_pred, y_true)\n",
    "\n",
    "assert np.allclose(accuracy_numpy(y_pred, y_true), accuracy_python(y_pred, y_true))\n",
    "assert np.allclose(accuracy_sklearn(y_pred, y_true), accuracy_python(y_pred, y_true))\n",
    "\n",
    "%timeit accuracy_numpy(y_pred, y_true)\n",
    "%timeit accuracy_sklearn(y_pred, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Compiled Python\n",
    "\n",
    "The second option of choice, when the algorirthm does not exist in our favorite libraries and we have to implement it, it to implement in Python and compile it to machine code.\n",
    "\n",
    "Below you'll compile Python with two frameworks.\n",
    "1. [Numba](http://numba.pydata.org) is a [just-in-time (JIT)](https://en.wikipedia.org/wiki/Just-in-time_compilation) compiler for Python, using the [LLVM](http://llvm.org) compiler infrastructure.\n",
    "1. [Cython](http://cython.org), which requires type information, [transpiles](https://en.wikipedia.org/wiki/Source-to-source_compiler) Python to C then compiles the generated C code.\n",
    "\n",
    "While these two approaches offer maximal compatibility with the CPython and NumPy ecosystems, another approach is to use another Python implementation such as [PyPy](http://pypy.org), which features a just-in-time compiler and supports multiple back-ends (C, CLI, JVM). Alternatives are [Jython](http://www.jython.org), which runs Python on the Java platform, and [IronPython](http://ironpython.net) / [PythonNet](http://pythonnet.github.io) for the .NET platform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from numba import jit\n",
    "# Decorator, same as accuracy_numba = jit(accuracy_python)\n",
    "\n",
    "@jit\n",
    "def accuracy_numba(y_pred, y_true):\n",
    "    \"\"\"Plain Python implementation, compiled by LLVM through Numba.\"\"\"\n",
    "    num_correct = 0\n",
    "    for y_pred_i, y_true_i in zip(y_pred, y_true):\n",
    "        if y_pred_i == y_true_i:\n",
    "            num_correct += 1\n",
    "    return num_correct / len(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.5/site-packages/Cython/Distutils/old_build_ext.py:30: UserWarning: Cython.Distutils.old_build_ext does not properly handle dependencies and is deprecated.\n",
      "  \"Cython.Distutils.old_build_ext does not properly handle dependencies \"\n"
     ]
    }
   ],
   "source": [
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "cimport numpy as np\n",
    "cimport cython\n",
    "\n",
    "@cython.boundscheck(False)  # Turn off bounds-checking for entire function.\n",
    "@cython.wraparound(False)   # Turn off negative index wrapping for entire function.\n",
    "def accuracy_cython(np.ndarray[long, ndim=1] y_pred, np.ndarray[long, ndim=1] y_true):\n",
    "    \"\"\"Python implementation with type information, transpiled to C by Cython.\"\"\"\n",
    "    cdef int num_total = len(y_true)\n",
    "    cdef int num_correct = 0\n",
    "    cdef int n = y_pred.size\n",
    "    for i in range(n):\n",
    "        if y_pred[i] == y_true[i]:\n",
    "            num_correct += 1\n",
    "    return num_correct / num_total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate below the performance of those two implementations, while testing their correctness. How do they compare with plain Python and specialized libraries ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.17 ms per loop\n",
      "1000 loops, best of 3: 1.07 ms per loop\n"
     ]
    }
   ],
   "source": [
    "assert np.allclose(accuracy_numba(y_pred, y_true), accuracy_python(y_pred, y_true))\n",
    "assert np.allclose(accuracy_cython(y_pred, y_true), accuracy_python(y_pred, y_true))\n",
    "\n",
    "%timeit accuracy_numba(y_pred, y_true)\n",
    "%timeit accuracy_cython(y_pred, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Using C from Python\n",
    "\n",
    "Here we'll explore our third option to make Python faster: implement in another language ! Below you'll\n",
    "1. implement the accuracy function in C,\n",
    "1. compile it, e.g. with the GNU compiler collection ([GCC](https://gcc.gnu.org/)),\n",
    "1. call it from Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting function.c\n"
     ]
    }
   ],
   "source": [
    "%%file function.c\n",
    "\n",
    "double accuracy(long* y_pred, long* y_true, int n) {\n",
    "    int num_correct = 0;\n",
    "\n",
    "    for(int i = 0; i < n; i++) {\n",
    "        if(y_pred[i] == y_true[i])\n",
    "            num_correct++;\n",
    "    }\n",
    "\n",
    "    return (double) num_correct / n;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below cell describe a shell script, which will be executed by IPython as if you typed the commands in your terminal. Those commands are compiling the above C program into a dynamic library with GCC. You can use any other compiler, text editor or IDE to produce the C library. Windows users, you may want to use Microsoft toolchain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "libfunction.so: ELF 64-bit LSB shared object, x86-64, version 1 (SYSV), dynamically linked, BuildID[sha1]=23d5e34e0dcd19e3a614dde9fdb3295247b59527, not stripped\n"
     ]
    }
   ],
   "source": [
    "%%script sh\n",
    "FILE=function\n",
    "gcc -c -O3 -Wall -std=c11 -pedantic -fPIC -o $FILE.o $FILE.c\n",
    "gcc -o lib$FILE.so -shared $FILE.o\n",
    "file lib$FILE.so"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below cell finally create a wrapper around our C library so that we can easily use it from Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import ctypes\n",
    "\n",
    "libfunction = np.ctypeslib.load_library('libfunction', './')\n",
    "libfunction.accuracy.restype = ctypes.c_double\n",
    "libfunction.accuracy.argtypes = [\n",
    "    np.ctypeslib.ndpointer(dtype=np.int),\n",
    "    np.ctypeslib.ndpointer(dtype=np.int),\n",
    "    ctypes.c_int\n",
    "]\n",
    "\n",
    "def accuracy_c(y_pred, y_true):\n",
    "    n = y_pred.size\n",
    "    return libfunction.accuracy(y_pred, y_true, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate below the performance of your C implementation, and test its correctness. How does it compare with the others ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.1 ms per loop\n"
     ]
    }
   ],
   "source": [
    "assert np.allclose(accuracy_c(y_pred, y_true), accuracy_python(y_pred, y_true))\n",
    "%timeit accuracy_c(y_pred, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Using Fortran from Python\n",
    "\n",
    "Same idea as before, with Fortran ! [Fortran](https://en.wikipedia.org/wiki/Fortran) is an imperative programming language developed in the 50s, especially suited to numeric computation and scientific computing. While you probably won't write new code in Fortran, you may have to interface with legacy code (especially in large and old corporations). Here we'll resort to the [f2py](https://docs.scipy.org/doc/numpy-dev/f2py/) utility provided by the Numpy project for the (almost automatic) generation of a wrapper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting function.f\n"
     ]
    }
   ],
   "source": [
    "%%file function.f\n",
    "\n",
    "! The content of this cell is written to the \"function.f\" file in the current directory.\n",
    "\n",
    "      SUBROUTINE DACCURACY(YPRED, YTRUE, ACC, N)\n",
    "\n",
    "CF2PY INTENT(OUT) :: ACC\n",
    "CF2PY INTENT(HIDE) :: N\n",
    "      INTEGER*4 YPRED(N)\n",
    "      INTEGER*4 YTRUE(N)\n",
    "      DOUBLE PRECISION ACC\n",
    "      INTEGER N, NCORRECT\n",
    "\n",
    "      NCORRECT = 0\n",
    "\n",
    "      DO 10 J = 1, N\n",
    "         IF (YPRED(J) == YTRUE(J)) THEN\n",
    "            NCORRECT = NCORRECT + 1\n",
    "         END IF\n",
    " 10   CONTINUE\n",
    "\n",
    "      ACC = REAL(NCORRECT) / N\n",
    "      END"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below command compile the Fortran code and generate a Python wrapper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "!f2py -c -m function function.f >> /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import function\n",
    "def accuracy_fortran(y_pred, y_true):\n",
    "    return function.daccuracy(y_pred, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate below the performance of your Fortran implementation, and test its correctness. How does it compare with the others ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 2.12 ms per loop\n"
     ]
    }
   ],
   "source": [
    "assert np.allclose(accuracy_fortran(y_pred, y_true), accuracy_python(y_pred, y_true))\n",
    "%timeit accuracy_fortran(y_pred, y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Analysis\n",
    "\n",
    "Plot a graph with `n` as the x-axis and the execution time of the various methods on the y-axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "# Suppress outputs.\n",
    "\n",
    "N = 8  # Number of samples: from 1 to 10^N.\n",
    "\n",
    "methods = ['accuracy_python', 'accuracy_sklearn', 'accuracy_fortran', 'accuracy_numpy',\n",
    "           'accuracy_numba', 'accuracy_cython', 'accuracy_c']\n",
    "\n",
    "times = np.empty((len(methods), N+1))\n",
    "for n in range(0, N+1):\n",
    "    y_true = np.random.randint(2, size=10**n)\n",
    "    y_pred = np.random.randint(2, size=len(y_true))\n",
    "    for i, method in enumerate(methods):\n",
    "        res = %timeit -o eval(method)(y_pred, y_true)\n",
    "        times[i, n] = res.best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+9+QZbV/85os8tQvDqFGjWLhwIZGRkbRs2RJvb28sy2LgwIF5Hp7/a3JCulvJ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\niIiIiIiIlMH/Bwdd0TCUg5COAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83e45119b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(15, 5))\n",
    "for i, method in enumerate(methods):\n",
    "    plt.semilogy(range(N+1), times[i,:], '.-', label=method)\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylabel('execution time [s]')\n",
    "plt.xlabel('log_10 number of samples')\n",
    "plt.xticks(range(N+1))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To reflect on what we've done, answer the following questions:\n",
    "1. Which implementation is the fastest ?\n",
    "1. Which solution was the easiest to work with ?\n",
    "1. Which solution would you use in which situation ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out the compiled versions by Numba and Cython are almost as fast as C ! Although much easier to write. In this case, they are even faster than Fortran, NumPy and scikit-learn. This gives us the best of both worlds: an interpreted language for rapid development, which can then be compiled for efficient execution in production."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}