From 9176aefae0842be1e279e5a704d6145d0d8700e3 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Jun 2014 17:57:31 -0400
Subject: [PATCH] multiprocessing tutorial

---
 README.md                             |   1 +
 tutorials/multiprocessing_intro.ipynb | 669 ++++++++++++++++++++++----
 2 files changed, 578 insertions(+), 92 deletions(-)

diff --git a/README.md b/README.md
index 462a41e..db04777 100755
--- a/README.md
+++ b/README.md
@@ -36,6 +36,7 @@
 
 - Using Cython with and without IPython magic [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/running_cython.ipynb)]
 
+- Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1)]
 
 
 <br>
diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index c131162..380d846 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -1,13 +1,57 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f70ae9a75bf1eda857e582939e9a34df3db49efdb72d1585834c31f3c5f6de10"
+  "signature": "sha256:82b19ba527d6db0f573ef2e3b9d7924274ed0bf6d50c7e4f3325a8572b1c54f8"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "\n",
+      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1)  \n",
+      "\n",
+      "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)  \n",
+      "\n",
+      "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import time\n",
+      "print('Last updated: %s' %time.strftime('%d/%m/%Y'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Last updated: 19/06/2014\n"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+      "<hr>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 1,
@@ -38,13 +82,28 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Depending on the application, two different approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-task\" of an process and those threads will have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
+      "Depending on the application, two different approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-task\" of a single process and those threads will usually have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
       "\n",
       "A safer approach (although less efficient due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n",
       "\n",
       "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores."
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](https://github.com/rasbt/python_reference/blob/master/Images/multiprocessing_scheme.png)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 2,
@@ -53,14 +112,66 @@
       "Sections"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Introduction to the multiprocessing module](#Introduction-to-the-multiprocessing-module)\n",
+      "\n",
+      "    - [The Process class](#The-Process-class)\n",
+      "    \n",
+      "    - [Defining an own Process class](#Defining-an-own-Process-class)\n",
+      "    \n",
+      "    - [The Pool class](#The-Pool-class)\n",
+      "    \n",
+      "- [Parzen kernel density estimation as benchmarking function](#Parzen-kernel-density-estimation-as-benchmarking-function)\n",
+      "    \n",
+      "    - [The Parzen-window method in a nutshell](#The-Parzen-window-method-in-a-nutshell)\n",
+      "    \n",
+      "    - [Sample data and timeit benchmarks](#Sample-data-and-timeit-benchmarks)\n",
+      "    \n",
+      "    - [Benchmarking functions](#Benchmarking-functions)\n",
+      "    \n",
+      "    - [Preparing the plotting of the results](#Preparing-the-plotting-of-the-results)\n",
+      "\n",
+      "- [Results](#Results)\n",
+      "\n",
+      "- [Conclusion](#Conclusion)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 1,
      "metadata": {},
      "source": [
       "Introduction to the `multiprocessing` module"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The [multiprocessing](https://docs.python.org/dev/library/multiprocessing.html) module in Python's Standard Libarary has a lot of powerful features. If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the [official documentation](https://docs.python.org/dev/library/multiprocessing.html) as an entry point.  \n",
+      "\n",
+      "In the following sections, I want to provide a brief overview of different approaches to show how the `multiprocessing` module can be used for prallel programming."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 3,
@@ -73,7 +184,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The most basic approach is to use the `Process` class from the multiprocessing module.  \n",
+      "The most basic approach is probably to use the `Process` class from the `multiprocessing` module.  \n",
       "Here, we will use a simple queue function to compute the cubes for the 6 numbers 1, 2, 3, 4, 5, and 6 in 6 parallel processes."
      ]
     },
@@ -117,7 +228,15 @@
        ]
       }
      ],
-     "prompt_number": 33
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
@@ -127,6 +246,20 @@
       "Defining an own `Process` class"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can also define our own `Process` classes based on the `multiprocessing.Process` parent-class. Note that we will define a `run` method, which will be automatically executed when we start a process-instance via `.start()`."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -142,7 +275,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 24
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -178,7 +311,15 @@
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
@@ -192,7 +333,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Another more convenient approach for simple parallel processing tasks is the `Pool` class.  \n",
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another and more convenient approach for simple parallel processing tasks is provided by the `Pool` class.  \n",
       "\n",
       "There are four methods that are particularly interesing:\n",
       "\n",
@@ -224,7 +372,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -245,7 +393,7 @@
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -266,13 +414,13 @@
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished and return the results. The `async` variants, in contrast, will submit the processes to run in the background without locking the main program, which can be quite useful for certain application.  \n",
+      "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished and return the results. The `async` variants, in contrast, will submit the processes to be run in the background without locking the main program, which can be quite useful for certain applications.  \n",
       "However, if we use this asynchronous approach, we need to use the `get` method in order to obtain the `return` values."
      ]
     },
@@ -296,16 +444,41 @@
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 1,
      "metadata": {},
      "source": [
       "Parzen kernel density estimation as benchmarking function"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the following approach, I want to do a simple comparison of a serial vs. multiprocessing approach where I will use a slightly more complex function than the `cube` example, which he have been using above.  \n",
+      "\n",
+      "Here, I define a function for performing a Kernel density estimation for probability density functions using the Parzen-window technique.  \n",
+      "I don't want to go into much detail about the theory of this technique, since we are mostly interested to see how `multiprocessing` can be used for performance improvements, but you are welcome to read my more detailed article about the [Parzen-window method here](http://sebastianraschka.com/Articles/2014_parzen_density_est.html). "
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -313,6 +486,17 @@
       "import numpy as np\n",
       "\n",
       "def parzen_estimation(x_samples, point_x, h):\n",
+      "    \"\"\"\n",
+      "    Implementation of a hypercube kernel for Parzen-window estimation.\n",
+      "\n",
+      "    Keyword arguments:\n",
+      "        x_sample:training sample, 'd x 1'-dimensional numpy array\n",
+      "        x: point x for density estimation, 'd x 1'-dimensional numpy array\n",
+      "        h: window width\n",
+      "        \n",
+      "    Returns the predicted pdf as float.\n",
+      "\n",
+      "    \"\"\"\n",
       "    k_n = 0\n",
       "    for row in x_samples:\n",
       "        x_i = (point_x - row[:,np.newaxis]) / (h)\n",
@@ -326,7 +510,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -340,6 +524,30 @@
       "<hr>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "The Parzen-window method in a nutshell"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "So what this function does in a nutshell: It counts points in a defined region (the so-called window), and divides the number of those points inside by the number of total points to estimate the probability of a single point being in a certain region.\n",
+      "\n",
+      "Below is a simple example where our window is represented by a hypercube centered at the origin, and we want to get an estimate of the probability for a point being in the center of the plot based on the hypercube."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -349,7 +557,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -396,13 +604,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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ihlQ2q334Wi4WYr246T1q7agPB3rxFzv6xpk5T94JqKDooK7biMVirnG/EEhK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\nU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1D1/LxUKsFze9R60d9eFAL/5iR984M+fJOwEVFB3UdRuxWMw17hcCSQnmeR6B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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x105285828>"
+        "<matplotlib.figure.Figure at 0x109e81f60>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -424,7 +632,15 @@
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
@@ -434,6 +650,20 @@
       "Sample data and `timeit` benchmarks"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the section below, we will create a random dataset from a bivariate Gaussian distribution with a mean vector centered at the origin and a identity matrix as covariance matrix. "
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -445,86 +675,51 @@
       "# Generate random 2D-patterns\n",
       "mu_vec = np.array([0,0])\n",
       "cov_mat = np.array([[1,0],[0,1]])\n",
-      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 1000)"
+      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import multiprocessing\n",
-      "\n",
-      "def serial(samples, x, widths):\n",
-      "    return [parzen_estimation(samples, x, w) for w in widths]\n",
-      "\n",
-      "def multiprocess(processes, samples, x, widths):\n",
-      "    pool = multiprocessing.Pool(processes=processes)\n",
-      "    return [pool.apply(parzen_estimation, args=(samples, x, w)) for w in widths]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "widths = np.arange(0.1, 1.3, 0.1)\n",
-      "point_x = np.array([[0],[0]])\n",
-      "results = []\n",
-      "\n",
-      "results.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import serial, x_2Dgauss, point_x, widths').timeit(number=1))\n",
-      "\n",
-      "results.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
-      "\n",
-      "results.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
-      "\n",
-      "results.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
-      "\n",
-      "results.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "OSError",
-       "evalue": "[Errno 24] Too many open files",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-28-4be468cf2eb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m results.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n\u001b[0;32m---> 11\u001b[0;31m             'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m results.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/timeit.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/timeit.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
-        "\u001b[0;32m<ipython-input-17-1745161bc4ac>\u001b[0m in \u001b[0;36mmultiprocess\u001b[0;34m(processes, samples, x, widths)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmultiprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mpool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparzen_estimation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwidths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/context.py\u001b[0m in \u001b[0;36mPool\u001b[0;34m(self, processes, initializer, initargs, maxtasksperchild)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         return Pool(processes, initializer, initargs, maxtasksperchild,\n\u001b[0;32m--> 118\u001b[0;31m                     context=self.get_context())\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mRawValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypecode_or_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/pool.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, processes, initializer, initargs, maxtasksperchild, context)\u001b[0m\n\u001b[1;32m    148\u001b[0m                  maxtasksperchild=None, context=None):\n\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mget_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_queues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_taskqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/pool.py\u001b[0m in \u001b[0;36m_setup_queues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setup_queues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_outqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_quick_put\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/context.py\u001b[0m in \u001b[0;36mSimpleQueue\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;34m'''Returns a queue object'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mqueues\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSimpleQueue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     def Pool(self, processes=None, initializer=None, initargs=(),\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/queues.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ctx)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduplex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    336\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rlock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/connection.py\u001b[0m in \u001b[0;36mPipe\u001b[0;34m(duplex)\u001b[0m\n\u001b[1;32m    518\u001b[0m             \u001b[0mc2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 520\u001b[0;31m             \u001b[0mfd1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfd2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    521\u001b[0m             \u001b[0mc1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwritable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m             \u001b[0mc2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mOSError\u001b[0m: [Errno 24] Too many open files"
-       ]
-      }
-     ],
-     "prompt_number": 28
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      " If you create 100 processes on a machine with four cores, the process scheduler may put 25 processes on each core, but since each core can still only work on one process at a time, they won't get work done any faster than if you only had one on each. (In fact, it will be much slower, because there is a lot of overhead associated with switching between processes.)"
+      "The expected probability of a point at the center of the distribution is ~ 0.15915 as we can see below.  \n",
+      "And our goal is here to use the Parzen-window approach to predict this density based on the sample data set that we have created above.  \n",
+      "\n",
+      "\n",
+      "In order to make a \"good\" prediction via the Parzen-window technique, it is - among other things - crucial to select an appropriate window with. Here, we will use multiple processes to predict the density at the center of the bivariate Gaussian distribution using different window widths."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from scipy.stats import multivariate_normal\n",
+      "var = multivariate_normal(mean=[0,0], cov=[[1,0],[0,1]])\n",
+      "print('actual probability density:', var.pdf([0,0]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "actual probability density: 0.159154943092\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
      ]
     },
     {
@@ -532,7 +727,280 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Plotting the results"
+      "Benchmarking functions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Below, we will set up a benchmarking functions for our serial and multiprocessig approach that we can pass to our `timeit` benchmark function."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def serial(samples, x, widths):\n",
+      "    return [parzen_estimation(samples, x, w) for w in widths]\n",
+      "\n",
+      "def multiprocess(processes, samples, x, widths):\n",
+      "    pool = mp.Pool(processes=processes)\n",
+      "    return [pool.apply(parzen_estimation, args=(samples, x, w)) for w in widths]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Just to get an idea what the results would look like (i.e., the predicted densities for different window widths):"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "widths = np.arange(0.1, 1.3, 0.1)\n",
+      "point_x = np.array([[0],[0]])\n",
+      "results = []\n",
+      "\n",
+      "results = multiprocess(4, x_2Dgauss, point_x, widths)\n",
+      "for w,r in zip(widths, results):\n",
+      "    print('h = %s, p(x) = %s' %(w, r))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "h = 0.1, p(x) = 0.016\n",
+        "h = 0.2, p(x) = 0.0305\n",
+        "h = 0.3, p(x) = 0.045\n",
+        "h = 0.4, p(x) = 0.06175\n",
+        "h = 0.5, p(x) = 0.078\n",
+        "h = 0.6, p(x) = 0.0911666666667\n",
+        "h = 0.7, p(x) = 0.106\n",
+        "h = 0.8, p(x) = 0.117375\n",
+        "h = 0.9, p(x) = 0.132666666667\n",
+        "h = 1.0, p(x) = 0.1445\n",
+        "h = 1.1, p(x) = 0.157090909091\n",
+        "h = 1.2, p(x) = 0.1685\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Based on the results, we can say that the best window-width would be h=1.1, since the estimated result is close to the actual result ~0.15915."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "mu_vec = np.array([0,0])\n",
+      "cov_mat = np.array([[1,0],[0,1]])\n",
+      "n = 100000\n",
+      "\n",
+      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, n)\n",
+      "\n",
+      "benchmarks = []\n",
+      "\n",
+      "benchmarks.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import serial, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+      "\n",
+      "benchmarks.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+      "\n",
+      "benchmarks.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+      "\n",
+      "benchmarks.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+      "\n",
+      "benchmarks.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Preparing the plotting of the results"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import platform\n",
+      "\n",
+      "def print_sysinfo():\n",
+      "    \n",
+      "    print('\\nPython version  :', platform.python_version())\n",
+      "    print('compiler        :', platform.python_compiler())\n",
+      "    \n",
+      "    print('\\nsystem     :', platform.system())\n",
+      "    print('release    :', platform.release())\n",
+      "    print('machine    :', platform.machine())\n",
+      "    print('processor  :', platform.processor())\n",
+      "    print('CPU count  :', mp.cpu_count())\n",
+      "    print('interpreter:', platform.architecture()[0])\n",
+      "    print('\\n\\n')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "import numpy as np\n",
+      "\n",
+      "def plot_results():\n",
+      "    bar_labels = ['serial', '2', '3', '4', '6']\n",
+      "\n",
+      "    fig = plt.figure(figsize=(10,8))\n",
+      "\n",
+      "    # plot bars\n",
+      "    y_pos = np.arange(len(benchmarks))\n",
+      "    y_pos = [x for x in y_pos]\n",
+      "    plt.yticks(y_pos, bar_labels, fontsize=16)\n",
+      "    plt.barh(y_pos, benchmarks,\n",
+      "             align='center', alpha=0.4, color='g')\n",
+      "\n",
+      "    # annotation and labels\n",
+      "    plt.xlabel('time in seconds for n=%s' %n, fontsize=14)\n",
+      "    plt.ylabel('number of processes', fontsize=14)\n",
+      "    t = plt.title('Serial vs. Multiprocessing via Parzen-window estimation', fontsize=18)\n",
+      "    plt.ylim([-1,len(benchmarks)+0.5])\n",
+      "    plt.grid()\n",
+      "\n",
+      "    plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Results"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot_results()\n",
+      "print_sysinfo()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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uRmpqqt566y1ZluXRGKWmprqPG+iPPShvvvmmmjRpottvv119+vRxHz4mpzf2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\nznewV8UAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10b49e438>"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Python version  : 3.4.1\n",
+        "compiler        : GCC 4.2.1 (Apple Inc. build 5577)\n",
+        "\n",
+        "system     : Darwin\n",
+        "release    : 13.2.0\n",
+        "machine    : x86_64\n",
+        "processor  : i386\n",
+        "CPU count  : 4\n",
+        "interpreter: 64bit\n",
+        "\n",
+        "\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
      ]
     },
     {
@@ -542,6 +1010,23 @@
      "source": [
       "Conclusion"
      ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel.\n",
+      "However, on my particular machine it didn't make any differences if we submitted 2, 3, 4, or 6 processes in parallel.  \n",
+      "\n",
+      "In practice,  it doesn't make much sense to submit more processes than the CPU supports (e.g., here 6 processes for a 4-core CPU). For larger benchmarks, this would to a slower performace, because of an additional overhead for communicating between the additional processes."
+     ]
     }
    ],
    "metadata": {}