language_distances.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measuring Similarities of Languages\n",
    "\n",
    "I always felt that the situation in with Spanish and Catalan in Catlunya is very similat to the situation in with High German and Swiss German in Switzerland, until I got into an argument with a linguist friend, who claimed that Swiss German is only a dialect. There seem to be two criteria distinguishing a dialect from a real language: Not everything can be expressed in the dialect or everything is understood by everyone speaking the language. Having gone through lectures on Effective Quantum Field Theories in Bernese Swiss German and having experienced problems with my High German in Germany, I guess Swiss German is a language too.\n",
    "\n",
    "Anyway, to resolve the dispute I turned to data and played around with measuring differences using compression algorithms on biblical texts. Enjoy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import zlib\n",
    "import itertools\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from data.luke23 import texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zipstance(text0, text1):\n",
    "    \"\"\"Similarity of texts based on compressability\"\"\"\n",
    "\n",
    "    def zipsize(text):\n",
    "        return sys.getsizeof(zlib.compress(bytes(text, 'UTF-8'), 9))\n",
    "\n",
    "    return 2 * zipsize(text0 + text1) / (zipsize(text0) + zipsize(text1)) - 1\n",
    "\n",
    "\n",
    "def dist_matrix(texts, metric):\n",
    "    dm = pd.DataFrame()\n",
    "    for combo in itertools.combinations_with_replacement(texts.keys(), 2):\n",
    "        dist = metric(texts[combo[0]], texts[combo[1]])\n",
    "        dm.loc[combo[0], combo[1]] = dist\n",
    "        dm.loc[combo[1], combo[0]] = dist\n",
    "    return dm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dm = dist_matrix(texts, zipstance)\n",
    "hm = sns.clustermap(\n",
    "    dm, standard_scale=None, method='single', cmap='viridis', annot=True)\n",
    "plt.setp(hm.ax_heatmap.xaxis.get_majorticklabels(), rotation='vertical')\n",
    "plt.setp(hm.ax_heatmap.yaxis.get_majorticklabels(), rotation='horizontal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a feeling for the quality of `zipstance` I use property-based testing. However, more recent versions of `hypothesis` are much better at finding failing edge cases 😃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import unittest\n",
    "import random\n",
    "import string\n",
    "\n",
    "from hypothesis import given, settings, HealthCheck\n",
    "from hypothesis.strategies import text, floats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Falsifying example: test_coincidence(self=<__main__.TestZipstance testMethod=test_coincidence>, a='0000000000', b='000000000/')\n",
      "\n",
      "You can reproduce this example by temporarily adding @reproduce_failure('3.66.0', b'AXicY2RgYMSN8MkxAiEAAtQAFg==') as a decorator on your test case\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "./home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py:1084: HypothesisDeprecationWarning: Your tests are hitting the settings timeout (60.00s). This functionality will go away in a future release and you should not rely on it. Instead, try setting max_examples to be some value lower than 0 (the number of examples your test successfully ran here). Or, if you would prefer your tests to run to completion, regardless of how long they take, you can set the timeout value to hypothesis.unlimited.\n",
      "  state.run()\n",
      "E"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can add @seed(23533036049746109963779717980988758793) to this test to reproduce this failure.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Falsifying example: test_triangle_inequality(self=<__main__.TestZipstance testMethod=test_triangle_inequality>, a='0000000000', b='000000000/', c='00/00/0000')\n",
      "\n",
      "You can reproduce this example by temporarily adding @reproduce_failure('3.66.0', b'AXicY2RgYMSN8MkxgiGUhcEA6wYABlIAIg==') as a decorator on your test case\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================\n",
      "ERROR: test_symmetry (__main__.TestZipstance)\n",
      "----------------------------------------------------------------------\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-5-7d6beddd76e9>\", line 16, in test_symmetry\n",
      "    @given(text(min_size=1000), text(min_size=1000))\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 1084, in wrapped_test\n",
      "    state.run()\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 806, in run\n",
      "    runner.valid_examples, run_time\n",
      "hypothesis.errors.Timeout: Ran out of time before finding a satisfying example for test_symmetry. Only found 0 examples in 60.04s.\n",
      "\n",
      "======================================================================\n",
      "FAIL: test_coincidence (__main__.TestZipstance)\n",
      "----------------------------------------------------------------------\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-5-7d6beddd76e9>\", line 8, in test_coincidence\n",
      "    def test_coincidence(self, a, b):\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 1084, in wrapped_test\n",
      "    state.run()\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 831, in run\n",
      "    falsifying_example.__expected_traceback,\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 604, in execute\n",
      "    result = self.test_runner(data, run)\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/executors.py\", line 58, in default_new_style_executor\n",
      "    return function(data)\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 595, in run\n",
      "    return test(*args, **kwargs)\n",
      "  File \"<ipython-input-5-7d6beddd76e9>\", line 8, in test_coincidence\n",
      "    def test_coincidence(self, a, b):\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 542, in test\n",
      "    result = self.test(*args, **kwargs)\n",
      "  File \"<ipython-input-5-7d6beddd76e9>\", line 13, in test_coincidence\n",
      "    self.assertTrue(bb <= ab)\n",
      "AssertionError: False is not true\n",
      "\n",
      "======================================================================\n",
      "FAIL: test_triangle_inequality (__main__.TestZipstance)\n",
      "----------------------------------------------------------------------\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-5-7d6beddd76e9>\", line 25, in test_triangle_inequality\n",
      "    def test_triangle_inequality(self, a, b, c):\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 1084, in wrapped_test\n",
      "    state.run()\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 831, in run\n",
      "    falsifying_example.__expected_traceback,\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 604, in execute\n",
      "    result = self.test_runner(data, run)\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/executors.py\", line 58, in default_new_style_executor\n",
      "    return function(data)\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 595, in run\n",
      "    return test(*args, **kwargs)\n",
      "  File \"<ipython-input-5-7d6beddd76e9>\", line 25, in test_triangle_inequality\n",
      "    def test_triangle_inequality(self, a, b, c):\n",
      "  File \"/home/germann/Software/anaconda/lib/python3.6/site-packages/hypothesis/core.py\", line 542, in test\n",
      "    result = self.test(*args, **kwargs)\n",
      "  File \"<ipython-input-5-7d6beddd76e9>\", line 26, in test_triangle_inequality\n",
      "    self.assertTrue(zipstance(a, c) <= zipstance(a, b) + zipstance(b, c))\n",
      "AssertionError: False is not true\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 5 tests in 65.873s\n",
      "\n",
      "FAILED (failures=2, errors=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.main.TestProgram at 0x7f89d0aac588>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class TestZipstance(unittest.TestCase):\n",
    "\n",
    "    @given(text(), text())\n",
    "    def test_positivity(self, a, b):\n",
    "        self.assertTrue(zipstance(a, b) >= 0)\n",
    "\n",
    "    @given(text(min_size=10), text(min_size=10))\n",
    "    def test_coincidence(self, a, b):\n",
    "        aa = zipstance(a, a)\n",
    "        bb = zipstance(b, b)\n",
    "        ab = zipstance(a, b)\n",
    "        self.assertTrue(aa <= ab)\n",
    "        self.assertTrue(bb <= ab)\n",
    "\n",
    "    @settings(suppress_health_check=HealthCheck.all())\n",
    "    @given(text(min_size=1000), text(min_size=1000))\n",
    "    def test_symmetry(self, a, b):\n",
    "        eps = 0.02\n",
    "        ab = zipstance(a, b)\n",
    "        ba = zipstance(b, a)\n",
    "        self.assertTrue(ab <= ba + eps)\n",
    "        self.assertTrue(ab >= ba - eps)\n",
    "\n",
    "    @given(text(min_size=10), text(min_size=10), text(min_size=10))\n",
    "    def test_triangle_inequality(self, a, b, c):\n",
    "        self.assertTrue(zipstance(a, c) <= zipstance(a, b) + zipstance(b, c))\n",
    "\n",
    "    @given(floats(0, 1))\n",
    "    def test_precision(self, ratio):\n",
    "        \"\"\"zipstance(a, x*a + (1 - x)*b) ~ 1 - x, if zipstance(a, b) ~ 1\"\"\"\n",
    "        eps = 0.02\n",
    "        n = 10000\n",
    "        a = ''.join([\n",
    "            random.choice(string.ascii_letters + '. ') for _ in range(n)])\n",
    "        b = ''.join([\n",
    "            random.choice(string.ascii_letters + '. ') for _ in range(n)])\n",
    "        dist = zipstance(a, a[0:int(ratio * n)] + b[int(ratio * n):])\n",
    "        self.assertTrue(dist >= 1 - ratio - eps)\n",
    "        self.assertTrue(dist <= 1 - ratio + eps)\n",
    "\n",
    "\n",
    "unittest.main(argv=['first-arg-is-ignored'], exit=False)"
   ]
  }
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