CRIM_23_Distance_Metrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance Metrics\n",
    "\n",
    "### What Can You Do with this Notebook?\n",
    "\n",
    "Here we explore various standard methods for measuring the 'edit distance' between related strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import textdistance as td\n",
    "from ipywidgets import interact\n",
    "import intervals\n",
    "from intervals import * \n",
    "from intervals import main_objs\n",
    "import intervals.visualizations as viz\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALGORITHMS = [\n",
    "    \"Levenshtein\",\n",
    "    \"Hamming\",\n",
    "]\n",
    "\n",
    "CALCULATIONS = [\"Distance\", \"Similarity\"]\n",
    "\n",
    "\n",
    "algorithm_to_apply = {\n",
    "        (\"Levenshtein\", \"Distance\", True): td.levenshtein.normalized_distance,\n",
    "        (\"Levenshtein\", \"Similarity\", True): td.levenshtein.normalized_similarity,\n",
    "        (\"Levenshtein\", \"Distance\", False): td.levenshtein.distance,\n",
    "        (\"Levenshtein\", \"Similarity\", False): td.levenshtein.similarity,\n",
    "        \n",
    "        (\"Hamming\", \"Distance\", True): td.hamming.normalized_distance,\n",
    "        (\"Hamming\", \"Similarity\", True): td.hamming.normalized_similarity,\n",
    "        (\"Hamming\", \"Distance\", False): td.hamming.distance,\n",
    "        (\"Hamming\", \"Similarity\", False): td.hamming.similarity,\n",
    "        \n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distance metrics\n",
    "\n",
    "[**Levenshtein distance**: ](https://en.wikipedia.org/wiki/Levenshtein_distance) *'The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.'*\n",
    "\n",
    "For Levenshtein distance, the following *edits* are available. This example is drawn from [here](https://github.com/life4/textdistance/blob/master/textdistance/algorithms/edit_based.py):\n",
    "\n",
    "        * deletion:     ABC -> BC, AC, AB\n",
    "        * insertion:    ABC -> ABCD, EABC, AEBC..\n",
    "        * substitution: ABC -> ABE, ADC, FBC..\n",
    "\n",
    "[**Hamming distance**](https://en.wikipedia.org/wiki/Hamming_distance) '*The Hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different.*'\n",
    "\n",
    "**Normalised distance:** *Many distance metrics, like Levenshtein and Hamming distance, represent a count of some kind. While these values are easy to interpret, they are less easy to compare across metrics. Checking the \"normalised\" box below will normalise the metrics to a value between zero and one, for easier comparison.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise\n",
    "\n",
    "Try entering different pairs of words below to see how their similarity could be calculated (\"pesto\" and \"pasta\", for instance). See what happens to the Hamming distance if you add a single letter to the start of otherwise similar words:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f7626be55304e8e8691f83057c30c16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Text(value='', description='string1'), Text(value='', description='string2'), Dropdown(d…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def test_metric(string1=\"\", \n",
    "                string2=\"\",\n",
    "                algorithm=ALGORITHMS,\n",
    "                calculation=CALCULATIONS,\n",
    "                normalised=False\n",
    "               ):\n",
    "\n",
    "    print(algorithm_to_apply[(algorithm, calculation, normalised)](string1, string2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are *many* different text distance metrics that could be applied to music. For a more complete guide see [here](https://itnext.io/string-similarity-the-basic-know-your-algorithms-guide-3de3d7346227)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Musical example of distance metrics"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at calculating the Levenshtein distance between these two melodies. First we might try encoding the melodies as strings of letters to represent the notes. This are analogous to the words we tried above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "melody_1 = \"dbdded\"\n",
    "melody_2 = \"dbded\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td.levenshtein.distance(melody_1, melody_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, this gives us a Levenshtein distance of 1 because we can move from the first string of text to the second with a single deletion. Encoding melodies as letters, however, is somewhat inflexible, and won't recognise the same intervallic pattern with different starting pitches. Instead, we might encode the melodies as a series of chromatic intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "melody_intervals_1 = [-3, 3, 0, 2, -2]\n",
    "melody_intervals_2 = [-3, 3, 2, -2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td.levenshtein.distance(melody_intervals_1, melody_intervals_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, this gives us the same result as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading remote score...\n",
      "Successfully imported https://crimproject.org/mei/CRIM_Model_0008.mei\n",
      "{'title': 'Ave Maria', 'composer': 'Josquin Des Prés'}\n"
     ]
    }
   ],
   "source": [
    "# Select a prefix:\n",
    "\n",
    "# prefix = 'Music_Files/'\n",
    "prefix = 'https://crimproject.org/mei/'    \n",
    "\n",
    "# Add your filename here\n",
    "\n",
    "mei_file = 'CRIM_Model_0008.mei'\n",
    "\n",
    "url = prefix + mei_file\n",
    "\n",
    "piece = importScore(url)\n",
    "\n",
    "print(piece.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "...                         ...           ...  \n",
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piece.distance(n=4) > 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256.0</th>\n",
       "      <td>B4</td>\n",
       "      <td>G4</td>\n",
       "      <td>D4</td>\n",
       "      <td>G3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272.0</th>\n",
       "      <td>C5</td>\n",
       "      <td>G4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288.0</th>\n",
       "      <td>C5</td>\n",
       "      <td>G4</td>\n",
       "      <td>C4</td>\n",
       "      <td>C3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>544 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       [Superius] Altus Tenor Bassus\n",
       "0.0            G4  Rest  Rest   Rest\n",
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       "12.0           C5   NaN   NaN    NaN\n",
       "16.0           D5    G3   NaN    NaN\n",
       "20.0           E5    C4   NaN    NaN\n",
       "...           ...   ...   ...    ...\n",
       "1248.0         C5    E4    C4     A3\n",
       "1252.0        NaN    F4   NaN    NaN\n",
       "1256.0         B4    G4    D4     G3\n",
       "1272.0         C5    G4    C4     C3\n",
       "1288.0         C5    G4    C4     C3\n",
       "\n",
       "[544 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piece.notes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [Superius] Altus Tenor Bassus\n",
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       "1252.0                1             \n",
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       "1288.0          0     0     0      0\n",
       "\n",
       "[540 rows x 4 columns]"
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "piece.melodic(kind='z').fillna('')"
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  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>64.0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>68.0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>78.0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>92.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>94.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2_-2, 3_Held, 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>96.0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1_-2, 3_2, 3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>100.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3_2, 3_-2, 4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>104.0</th>\n",
       "      <td>6_Held, 5_Held, 8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3_-2, 4_4, 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>106.0</th>\n",
       "      <td>5_Held, 8_-2, 9</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>108.0</th>\n",
       "      <td>8_-2, 9_-2, 10</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>110.0</th>\n",
       "      <td>9_-2, 10_Held, 9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>112.0</th>\n",
       "      <td>10_Held, 9_Held, 8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>114.0</th>\n",
       "      <td>9_Held, 8_-2, 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>115.0</th>\n",
       "      <td>8_-2, 10_2, 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116.0</th>\n",
       "      <td>10_2, 10_2, 9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17_2, 16_2, 15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16_2, 15_Held, 15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15_Held, 15_Held, 15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10_Held, 8_1, 8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8_1, 8_Held, 7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Bassus_Tenor Bassus_Altus     Bassus_[Superius]  \\\n",
       "16.0                  NaN          NaN                   NaN   \n",
       "20.0                  NaN          NaN                   NaN   \n",
       "32.0                  NaN          NaN                   NaN   \n",
       "36.0                  NaN          NaN                   NaN   \n",
       "48.0     12_4, 10_Held, 8          NaN                   NaN   \n",
       "52.0      10_Held, 8_1, 8          NaN                   NaN   \n",
       "56.0                  NaN          NaN     15_1, 15_Held, 14   \n",
       "60.0                  NaN          NaN     15_Held, 14_2, 12   \n",
       "62.0                  NaN          NaN        14_2, 12_2, 10   \n",
       "64.0                  NaN          NaN       12_2, 10_-3, 13   \n",
       "68.0                  NaN          NaN    10_-3, 13_Held, 12   \n",
       "72.0                  NaN          NaN  13_Held, 12_Held, 15   \n",
       "74.0                  NaN          NaN                   NaN   \n",
       "76.0                  NaN          NaN                   NaN   \n",
       "78.0                  NaN          NaN                   NaN   \n",
       "80.0                  NaN          NaN                   NaN   \n",
       "82.0                  NaN          NaN                   NaN   \n",
       "83.0                  NaN          NaN                   NaN   \n",
       "84.0                  NaN          NaN                   NaN   \n",
       "88.0                  NaN          NaN                   NaN   \n",
       "91.0                  NaN          NaN                   NaN   \n",
       "92.0                  NaN          NaN                   NaN   \n",
       "94.0                  NaN          NaN                   NaN   \n",
       "96.0                  NaN          NaN                   NaN   \n",
       "98.0                  NaN          NaN                   NaN   \n",
       "99.0                  NaN          NaN                   NaN   \n",
       "100.0                 NaN          NaN                   NaN   \n",
       "104.0   6_Held, 5_Held, 8          NaN                   NaN   \n",
       "106.0     5_Held, 8_-2, 9          NaN                   NaN   \n",
       "108.0      8_-2, 9_-2, 10          NaN                   NaN   \n",
       "110.0    9_-2, 10_Held, 9          NaN                   NaN   \n",
       "112.0  10_Held, 9_Held, 8          NaN                   NaN   \n",
       "114.0    9_Held, 8_-2, 10          NaN                   NaN   \n",
       "115.0      8_-2, 10_2, 10          NaN                   NaN   \n",
       "116.0       10_2, 10_2, 9          NaN                   NaN   \n",
       "124.0                 NaN          NaN        17_2, 16_2, 15   \n",
       "126.0                 NaN          NaN     16_2, 15_Held, 15   \n",
       "128.0                 NaN          NaN  15_Held, 15_Held, 15   \n",
       "140.0                 NaN          NaN                   NaN   \n",
       "144.0                 NaN          NaN                   NaN   \n",
       "\n",
       "               Tenor_Altus Tenor_[Superius]    Altus_[Superius]  \n",
       "16.0                   NaN              NaN    12_4, 10_Held, 8  \n",
       "20.0                   NaN              NaN     10_Held, 8_1, 8  \n",
       "32.0        5_4, 3_Held, 1              NaN                 NaN  \n",
       "36.0        3_Held, 1_1, 1              NaN                 NaN  \n",
       "48.0                   NaN              NaN                 NaN  \n",
       "52.0                   NaN              NaN                 NaN  \n",
       "56.0                   NaN              NaN                 NaN  \n",
       "60.0                   NaN              NaN                 NaN  \n",
       "62.0                   NaN              NaN                 NaN  \n",
       "64.0                   NaN              NaN                 NaN  \n",
       "68.0                   NaN              NaN                 NaN  \n",
       "72.0                   NaN              NaN   6_Held, 5_Held, 8  \n",
       "74.0                   NaN              NaN     5_Held, 8_-2, 9  \n",
       "76.0                   NaN              NaN      8_-2, 9_-2, 10  \n",
       "78.0                   NaN              NaN    9_-2, 10_Held, 9  \n",
       "80.0                   NaN              NaN  10_Held, 9_Held, 8  \n",
       "82.0                   NaN              NaN    9_Held, 8_-2, 10  \n",
       "83.0                   NaN              NaN      8_-2, 10_2, 10  \n",
       "84.0                   NaN              NaN     10_2, 10_-2, 11  \n",
       "88.0   -3_Held, -4_Held, 1              NaN      10_-2, 11_4, 8  \n",
       "91.0      -4_Held, 1_-2, 2              NaN                 NaN  \n",
       "92.0         1_-2, 2_-2, 3              NaN                 NaN  \n",
       "94.0       2_-2, 3_Held, 2              NaN                 NaN  \n",
       "96.0     3_Held, 2_Held, 1              NaN                 NaN  \n",
       "98.0       2_Held, 1_-2, 3              NaN                 NaN  \n",
       "99.0          1_-2, 3_2, 3              NaN                 NaN  \n",
       "100.0         3_2, 3_-2, 4              NaN                 NaN  \n",
       "104.0         3_-2, 4_4, 1              NaN                 NaN  \n",
       "106.0                  NaN              NaN                 NaN  \n",
       "108.0                  NaN              NaN                 NaN  \n",
       "110.0                  NaN              NaN                 NaN  \n",
       "112.0                  NaN              NaN                 NaN  \n",
       "114.0                  NaN              NaN                 NaN  \n",
       "115.0                  NaN              NaN                 NaN  \n",
       "116.0                  NaN              NaN                 NaN  \n",
       "124.0                  NaN              NaN                 NaN  \n",
       "126.0                  NaN              NaN                 NaN  \n",
       "128.0                  NaN              NaN                 NaN  \n",
       "140.0                  NaN              NaN     10_Held, 8_1, 8  \n",
       "144.0                  NaN              NaN      8_1, 8_Held, 7  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piece.ngrams().head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = piece.distance(n=4)\n",
    "dist_stack = dist.stack().to_frame()\n",
    "\n",
    "\n",
    "# # # filter distances to threshold.  <2 is good\n",
    "edit_distance_threshold = 1\n",
    "distance_factor = edit_distance_threshold + 1\n",
    "filtered_dist_stack = dist_stack[dist_stack[0] < distance_factor]\n",
    "# filtered_dist_stack \n",
    "filtered_dist = filtered_dist_stack.reset_index()\n",
    "filtered_dist.rename(columns =  {'level_0':\"source\", 'level_1':'match'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>match</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(4, -1, 1, 2)</td>\n",
       "      <td>(4, -1, 1, 2)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(4, -1, 1, 2)</td>\n",
       "      <td>(3, -1, 1, 2)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(4, -1, 1, 2)</td>\n",
       "      <td>(4, -1, 1, 1)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(-1, 1, 2, -1)</td>\n",
       "      <td>(-1, 1, 2, -1)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(-1, 1, 2, -1)</td>\n",
       "      <td>(-2, 1, 2, -1)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(-1, 1, 2, -1)</td>\n",
       "      <td>(-1, 1, 1, -1)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(-1, 1, 2, -1)</td>\n",
       "      <td>(-1, 1, 2, 0)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(1, 2, -1, -1)</td>\n",
       "      <td>(1, 2, -1, -1)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(1, 2, -1, -1)</td>\n",
       "      <td>(1, 1, -1, -1)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(1, 2, -1, -1)</td>\n",
       "      <td>(1, 2, -1, 0)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           source           match  0\n",
       "0   (4, -1, 1, 2)   (4, -1, 1, 2)  0\n",
       "1   (4, -1, 1, 2)   (3, -1, 1, 2)  1\n",
       "2   (4, -1, 1, 2)   (4, -1, 1, 1)  1\n",
       "3  (-1, 1, 2, -1)  (-1, 1, 2, -1)  0\n",
       "4  (-1, 1, 2, -1)  (-2, 1, 2, -1)  1\n",
       "5  (-1, 1, 2, -1)  (-1, 1, 1, -1)  1\n",
       "6  (-1, 1, 2, -1)   (-1, 1, 2, 0)  1\n",
       "7  (1, 2, -1, -1)  (1, 2, -1, -1)  0\n",
       "8  (1, 2, -1, -1)  (1, 1, -1, -1)  1\n",
       "9  (1, 2, -1, -1)   (1, 2, -1, 0)  1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_dist.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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