ExemPy-Basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6264770",
   "metadata": {},
   "source": [
    "# An introduction to ExemPy (formerly GCMPy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea1237b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "from ExemPy import *\n",
    "from ExemPy.utils import *\n",
    "from ExemPy.viz import *\n",
    "from ExemPy.GCM import *\n",
    "%aimport ExemPy, ExemPy.utils, ExemPy.viz, ExemPy.GCM\n",
    "%autoreload 1\n",
    "import math\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from scipy.optimize import minimize\n",
    "import seaborn as sns\n",
    "sns.set(style='ticks', context='paper')\n",
    "colors=[\"#e3c934\",\"#68c4bf\",\"#c51000\",\"#287271\"]\n",
    "sns.set_palette(colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0453593",
   "metadata": {},
   "source": [
    "## Set up data\n",
    "- Read in Peterson and Barney 1952\n",
    "- Convert Hz to Bark\n",
    "- Read in confusion matrix from PB52\n",
    "- Preview pb52"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d283ada",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>gender</th>\n",
       "      <th>speaker</th>\n",
       "      <th>vowel</th>\n",
       "      <th>repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>z0</th>\n",
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       "      <th>z3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>STRUT</td>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>930</td>\n",
       "      <td>1540</td>\n",
       "      <td>3120</td>\n",
       "      <td>3.131278</td>\n",
       "      <td>8.097439</td>\n",
       "      <td>11.266400</td>\n",
       "      <td>15.935984</td>\n",
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       "      <th>1087</th>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>55</td>\n",
       "      <td>TRAP</td>\n",
       "      <td>2</td>\n",
       "      <td>224</td>\n",
       "      <td>896</td>\n",
       "      <td>2040</td>\n",
       "      <td>3000</td>\n",
       "      <td>2.219744</td>\n",
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       "      <td>15.685726</td>\n",
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       "      <th>279</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>14</td>\n",
       "      <td>NURSE</td>\n",
       "      <td>2</td>\n",
       "      <td>111</td>\n",
       "      <td>420</td>\n",
       "      <td>1300</td>\n",
       "      <td>1570</td>\n",
       "      <td>0.906944</td>\n",
       "      <td>4.201176</td>\n",
       "      <td>10.161104</td>\n",
       "      <td>11.393994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>8</td>\n",
       "      <td>KIT</td>\n",
       "      <td>2</td>\n",
       "      <td>103</td>\n",
       "      <td>206</td>\n",
       "      <td>2130</td>\n",
       "      <td>2570</td>\n",
       "      <td>0.808551</td>\n",
       "      <td>2.019797</td>\n",
       "      <td>13.432176</td>\n",
       "      <td>14.680088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>29</td>\n",
       "      <td>FOOT</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>420</td>\n",
       "      <td>938</td>\n",
       "      <td>2300</td>\n",
       "      <td>1.257333</td>\n",
       "      <td>4.201176</td>\n",
       "      <td>8.147633</td>\n",
       "      <td>13.944883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type gender  speaker  vowel  repetition   F0   F1    F2    F3        z0  \\\n",
       "1508    c      f       76  STRUT           1  310  930  1540  3120  3.131278   \n",
       "1087    w      f       55   TRAP           2  224  896  2040  3000  2.219744   \n",
       "279     m      m       14  NURSE           2  111  420  1300  1570  0.906944   \n",
       "143     m      m        8    KIT           2  103  206  2130  2570  0.808551   \n",
       "575     m      m       29   FOOT           2  140  420   938  2300  1.257333   \n",
       "\n",
       "            z1         z2         z3  \n",
       "1508  8.097439  11.266400  15.935984  \n",
       "1087  7.880980  13.143100  15.685726  \n",
       "279   4.201176  10.161104  11.393994  \n",
       "143   2.019797  13.432176  14.680088  \n",
       "575   4.201176   8.147633  13.944883  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb52 = pd.read_csv('pb52_data//pb52.csv')\n",
    "pbcm = pd.read_csv('pb52_data//pbcm.csv').drop([0]).set_index('vowelChoice').rename_axis(None)\n",
    "pb52 = HzToBark(pb52, [\"F0\", \"F1\", \"F2\", \"F3\"])\n",
    "pb52.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "406e026d",
   "metadata": {},
   "source": [
    "## Set default parameters (based on previous work)\n",
    "- dimensions *m* & attention weights *w*\n",
    "- list of categories\n",
    "- exemplar sensitivity *c*\n",
    "- set to be categorized \n",
    "- exemplar cloud used to categorize against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9175605",
   "metadata": {},
   "outputs": [],
   "source": [
    "dimsvals={'z0' : 1,\n",
    "          'z1' : 2.56,\n",
    "          'z2' : 1.985,\n",
    "          'z3' : 1.34}\n",
    "\n",
    "dimslist = list(dimsvals.keys())\n",
    "\n",
    "catslist = ['type', 'vowel']           # man, woman, or child; lexical set notation\n",
    "\n",
    "cval = 25\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bd1962c",
   "metadata": {},
   "source": [
    "## Categorize one stimulus\n",
    "You can also use the function xm.categorize() to do these steps all at once! (Note, however, that you won't be able to visualize and verify at each step of the process!)\n",
    "\n",
    "### Set up\n",
    "- Randomly select a stimulus\n",
    "- Add an resting activation *N* value of 1to all stored exemplars\n",
    "- Exclude the stimulus from the exemplar cloud\n",
    "    - This way, the stimulus will not be compared to itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba997931",
   "metadata": {},
   "outputs": [
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       "      <th>type</th>\n",
       "      <th>gender</th>\n",
       "      <th>speaker</th>\n",
       "      <th>vowel</th>\n",
       "      <th>repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
       "      <th>z0</th>\n",
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       "      <th>z3</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>38</td>\n",
       "      <td>DRESS</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>600</td>\n",
       "      <td>2400</td>\n",
       "      <td>3150</td>\n",
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      "text/plain": [
       "    type gender  speaker  vowel  repetition   F0   F1    F2    F3        z0  \\\n",
       "745    w      f       38  DRESS           2  200  600  2400  3150  1.952407   \n",
       "\n",
       "           z1         z2         z3  \n",
       "745  5.753594  14.227798  15.996712  "
      ]
     },
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   ],
   "source": [
    "stim = pb52.sample()\n",
    "stim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73fa17ff",
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    {
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       "      <th>speaker</th>\n",
       "      <th>vowel</th>\n",
       "      <th>repetition</th>\n",
       "      <th>F0</th>\n",
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       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
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       "      <th>N</th>\n",
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       "      <th>88</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>5.0</td>\n",
       "      <td>STRUT</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>1950.0</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   type gender  speaker  vowel  repetition     F0     F1      F2      F3  \\\n",
       "88    m      m      5.0  STRUT         1.0  136.0  630.0  1300.0  1950.0   \n",
       "\n",
       "         z0        z1         z2         z3  N  \n",
       "88  1.20958  5.991351  10.161104  12.840716  1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "exemplars = reset_N(pb52, N = 1)\n",
    "exemplars = exclude(exemplars, stim, exclude_self = True)\n",
    "exemplars.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "455e7f32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is stim contained within exemplars?\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>gender</th>\n",
       "      <th>speaker</th>\n",
       "      <th>vowel</th>\n",
       "      <th>repetition</th>\n",
       "      <th>F0</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>F3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "      type  gender  speaker  vowel  repetition     F0     F1     F2     F3  \\\n",
       "745  False   False    False  False       False  False  False  False  False   \n",
       "\n",
       "        z0     z1     z2     z3  \n",
       "745  False  False  False  False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o = stim.isin(exemplars)\n",
    "print('Is stim contained within exemplars?')\n",
    "o\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "321b4ff1",
   "metadata": {},
   "source": [
    "### Calculate activation of each exemplar wrt the stimulus\n",
    "- For exemplars that share categories with stimulus,\n",
    "    - activation (a) *a* is higher\n",
    "    - distance (dist) *d* is lower\n",
    "- Plot activation:\n",
    "    - exemplars plotted in F2, F1 (Bark) space\n",
    "    - stimulus plotted in a blue X\n",
    "    - color = exemplar category\n",
    "    - size = activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9f7325b",
   "metadata": {},
   "outputs": [],
   "source": [
    "activation_df = activation(\n",
    "    testset = stim,\n",
    "    cloud = reset_N(exemplars, N=1),\n",
    "    dimsdict = dimsvals,\n",
    "    c = cval\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4500099d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---- Stimulus info ----\n",
      "    type  vowel  speaker\n",
      "745    w  DRESS       38\n",
      "\n",
      "      F0   F1    F2    F3\n",
      "745  200  600  2400  3150\n",
      "-----------------------\n"
     ]
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       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>dist</th>\n",
       "      <th>type_ex</th>\n",
       "      <th>vowel_ex</th>\n",
       "      <th>F0_ex</th>\n",
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       "      <th>1399</th>\n",
       "      <td>7.117773e-19</td>\n",
       "      <td>1.671461</td>\n",
       "      <td>c</td>\n",
       "      <td>FLEECE</td>\n",
       "      <td>230.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>3140.0</td>\n",
       "      <td>3830.0</td>\n",
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       "      <th>980</th>\n",
       "      <td>2.775662e-09</td>\n",
       "      <td>0.788095</td>\n",
       "      <td>w</td>\n",
       "      <td>FLEECE</td>\n",
       "      <td>200.0</td>\n",
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       "      <td>3100.0</td>\n",
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       "      <th>462</th>\n",
       "      <td>1.350367e-13</td>\n",
       "      <td>1.185329</td>\n",
       "      <td>m</td>\n",
       "      <td>KIT</td>\n",
       "      <td>160.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>2530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1242</th>\n",
       "      <td>9.522063e-12</td>\n",
       "      <td>1.015096</td>\n",
       "      <td>c</td>\n",
       "      <td>KIT</td>\n",
       "      <td>325.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1.186355e-13</td>\n",
       "      <td>1.190509</td>\n",
       "      <td>m</td>\n",
       "      <td>DRESS</td>\n",
       "      <td>114.0</td>\n",
       "      <td>570.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>2570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>1.951825e-07</td>\n",
       "      <td>0.617973</td>\n",
       "      <td>w</td>\n",
       "      <td>DRESS</td>\n",
       "      <td>217.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>2160.0</td>\n",
       "      <td>2770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>7.825822e-13</td>\n",
       "      <td>1.115047</td>\n",
       "      <td>m</td>\n",
       "      <td>TRAP</td>\n",
       "      <td>111.0</td>\n",
       "      <td>622.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>2560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>1.574450e-17</td>\n",
       "      <td>1.547602</td>\n",
       "      <td>w</td>\n",
       "      <td>TRAP</td>\n",
       "      <td>212.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>2820.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>1.645684e-24</td>\n",
       "      <td>2.190555</td>\n",
       "      <td>w</td>\n",
       "      <td>STRUT</td>\n",
       "      <td>200.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>6.751693e-31</td>\n",
       "      <td>2.778814</td>\n",
       "      <td>m</td>\n",
       "      <td>STRUT</td>\n",
       "      <td>126.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>2330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>6.401876e-25</td>\n",
       "      <td>2.228321</td>\n",
       "      <td>c</td>\n",
       "      <td>PALM</td>\n",
       "      <td>299.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>3750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>5.792939e-29</td>\n",
       "      <td>2.600733</td>\n",
       "      <td>w</td>\n",
       "      <td>PALM</td>\n",
       "      <td>256.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>2940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>3.582502e-30</td>\n",
       "      <td>2.712060</td>\n",
       "      <td>c</td>\n",
       "      <td>THOUGHT</td>\n",
       "      <td>270.0</td>\n",
       "      <td>540.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>4.187956e-30</td>\n",
       "      <td>2.705814</td>\n",
       "      <td>c</td>\n",
       "      <td>THOUGHT</td>\n",
       "      <td>219.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>3850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954</th>\n",
       "      <td>9.748857e-39</td>\n",
       "      <td>3.500947</td>\n",
       "      <td>w</td>\n",
       "      <td>FOOT</td>\n",
       "      <td>214.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>860.0</td>\n",
       "      <td>2680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1.729308e-29</td>\n",
       "      <td>2.649090</td>\n",
       "      <td>m</td>\n",
       "      <td>FOOT</td>\n",
       "      <td>120.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>3.874638e-38</td>\n",
       "      <td>3.445751</td>\n",
       "      <td>m</td>\n",
       "      <td>GOOSE</td>\n",
       "      <td>140.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2.542860e-40</td>\n",
       "      <td>3.646805</td>\n",
       "      <td>m</td>\n",
       "      <td>GOOSE</td>\n",
       "      <td>116.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>898.0</td>\n",
       "      <td>2140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>1.325562e-27</td>\n",
       "      <td>2.475518</td>\n",
       "      <td>w</td>\n",
       "      <td>NURSE</td>\n",
       "      <td>180.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>1835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1.809238e-27</td>\n",
       "      <td>2.463076</td>\n",
       "      <td>c</td>\n",
       "      <td>NURSE</td>\n",
       "      <td>242.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>1790.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 a      dist type_ex vowel_ex  F0_ex   F1_ex   F2_ex   F3_ex\n",
       "1399  7.117773e-19  1.671461       c   FLEECE  230.0   280.0  3140.0  3830.0\n",
       "980   2.775662e-09  0.788095       w   FLEECE  200.0   400.0  2600.0  3100.0\n",
       "462   1.350367e-13  1.185329       m      KIT  160.0   400.0  2080.0  2530.0\n",
       "1242  9.522063e-12  1.015096       c      KIT  325.0   650.0  3000.0  3800.0\n",
       "105   1.186355e-13  1.190509       m    DRESS  114.0   570.0  1830.0  2570.0\n",
       "725   1.951825e-07  0.617973       w    DRESS  217.0   540.0  2160.0  2770.0\n",
       "507   7.825822e-13  1.115047       m     TRAP  111.0   622.0  1890.0  2560.0\n",
       "706   1.574450e-17  1.547602       w     TRAP  212.0  1000.0  1830.0  2820.0\n",
       "748   1.645684e-24  2.190555       w    STRUT  200.0   740.0  1280.0  2900.0\n",
       "349   6.751693e-31  2.778814       m    STRUT  126.0   620.0  1120.0  2330.0\n",
       "1309  6.401876e-25  2.228321       c     PALM  299.0   990.0  1410.0  3750.0\n",
       "1150  5.792939e-29  2.600733       w     PALM  256.0   850.0  1150.0  2940.0\n",
       "1452  3.582502e-30  2.712060       c  THOUGHT  270.0   540.0  1070.0  3000.0\n",
       "1231  4.187956e-30  2.705814       c  THOUGHT  219.0   660.0  1100.0  3850.0\n",
       "954   9.748857e-39  3.500947       w     FOOT  214.0   380.0   860.0  2680.0\n",
       "115   1.729308e-29  2.649090       m     FOOT  120.0   450.0  1140.0  2600.0\n",
       "537   3.874638e-38  3.445751       m    GOOSE  140.0   280.0  1000.0  2160.0\n",
       "497   2.542860e-40  3.646805       m    GOOSE  116.0   350.0   898.0  2140.0\n",
       "1037  1.325562e-27  2.475518       w    NURSE  180.0   504.0  1565.0  1835.0\n",
       "1278  1.809238e-27  2.463076       c    NURSE  242.0   580.0  1620.0  1790.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"---- Stimulus info ----\")\n",
    "print(stim[['type', 'vowel', 'speaker']])\n",
    "print(\"\")\n",
    "print(stim[['F0', 'F1', 'F2', 'F3']])\n",
    "print(\"-----------------------\")\n",
    "smallactivdf = activation_df[['a',\n",
    "                      'dist',\n",
    "                      'type_ex',\n",
    "                      'vowel_ex',\n",
    "                      'F0_ex',\n",
    "                      \"F1_ex\",\n",
    "                      \"F2_ex\",\n",
    "                      \"F3_ex\"]]\n",
    "gettestset(smallactivdf, \"vowel_ex\", n = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a2d0cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "act = getactiv(activation_df, 'z2', 'z1', 'vowel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cfc881b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='z2', ylabel='z1'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "activplot(act, 'z2', 'z1', 'vowel', stim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a0d871",
   "metadata": {},
   "source": [
    "### Calculate probabilities & choose\n",
    "Use Luce's choice rule to calculate probabilities & choose\n",
    "- Calculate probability for categorization as each label\n",
    "    - Add up activation by category\n",
    "    - Add up total amount of activation\n",
    "    - Divide category activation by total activation\n",
    "        - Probability of being categorized as that\n",
    "- Choose the label with the highest probability\n",
    "        \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6157e93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type':   type   probability\n",
       " 0    c  8.140478e-02\n",
       " 1    m  3.148200e-08\n",
       " 2    w  9.185952e-01,\n",
       " 'vowel':      vowel   probability\n",
       " 0    DRESS  9.856831e-01\n",
       " 1   FLEECE  6.387907e-08\n",
       " 2     FOOT  4.662267e-15\n",
       " 3    GOOSE  1.975256e-13\n",
       " 4      KIT  4.413582e-03\n",
       " 5    NURSE  1.224322e-03\n",
       " 6     PALM  4.099465e-19\n",
       " 7    STRUT  5.375933e-13\n",
       " 8  THOUGHT  4.291118e-24\n",
       " 9     TRAP  8.678917e-03}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr = probs(activation_df, catslist)\n",
    "pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c6b5cbc",
   "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>typeChoice</th>\n",
       "      <th>typeProb</th>\n",
       "      <th>vowelChoice</th>\n",
       "      <th>vowelProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>w</td>\n",
       "      <td>0.918595</td>\n",
       "      <td>DRESS</td>\n",
       "      <td>0.985683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    typeChoice  typeProb vowelChoice  vowelProb\n",
       "745          w  0.918595       DRESS   0.985683"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choices = choose(pr, stim, catslist)\n",
    "choices[['typeChoice','typeProb','vowelChoice','vowelProb']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d27ee7a",
   "metadata": {},
   "source": [
    "### Check for \"veridical perception\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a26cda1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Was vowel categorized accurately?          y\n",
      "Was speaker type categorized accurately?   y\n"
     ]
    }
   ],
   "source": [
    "accu = checkaccuracy(choices, catslist)\n",
    "print('Was vowel categorized accurately?          ' + accu.iloc[0]['vowelAcc'])\n",
    "print('Was speaker type categorized accurately?   ' + accu.iloc[0]['typeAcc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde76b59",
   "metadata": {},
   "source": [
    "## Categorize dataset with respect to itself\n",
    "In the past sections we:\n",
    "- Set some parameters for categorization, including\n",
    "    - Dimensions & their attention weights\n",
    "    - Category types to categorize for\n",
    "- Calculate the activation of each exemplar wrt the stimulus\n",
    "- Calculate probabilities\n",
    "- Choose the most probable category label for each category type\n",
    "- Evaluate the stimulus\n",
    "\n",
    "Now, let's do the same process with <b> multiple </b> exemplars. In fact, let's simulate the perception component of Peterson and Barney 1952 by having our \"perceiver\" categorize every exemplar.\n",
    "\n",
    "### First, let's re-set our variables\n",
    "As a reminder or in case something got wonky!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "220d798c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dimsvals={'z0' : 1,\n",
    "          'z1' : 2.56,\n",
    "          'z2' : 1.985,\n",
    "          'z3' : 1.34}\n",
    "\n",
    "dimslist = list(dimsvals.keys())\n",
    "\n",
    "catslist = ['type', 'vowel']           # man, woman, or child; lexical set notation\n",
    "\n",
    "cval = 25\n",
    "\n",
    "exemplars = pb52\n",
    "test = pb52"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ded5149",
   "metadata": {},
   "source": [
    "### Next, let's categorize!\n",
    "This may take a minute!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "799eb804",
   "metadata": {},
   "outputs": [],
   "source": [
    "choices = multicat(\n",
    "    testset = test,\n",
    "    cloud = exemplars,\n",
    "    cats = catslist,\n",
    "    dimsdict = dimsvals,\n",
    "    c = cval,\n",
    "    N = 1,\n",
    "    exclude_self = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5261c195",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>gender</th>\n",
       "      <th>speaker</th>\n",
       "      <th>vowel</th>\n",
       "      <th>repetition</th>\n",
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       "      <th>z3</th>\n",
       "      <th>typeChoice</th>\n",
       "      <th>typeProb</th>\n",
       "      <th>vowelChoice</th>\n",
       "      <th>vowelProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
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       "      <td>FLEECE</td>\n",
       "      <td>1</td>\n",
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       "      <td>FLEECE</td>\n",
       "      <td>0.999925</td>\n",
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       "      <th>1</th>\n",
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       "      <td>FLEECE</td>\n",
       "      <td>2</td>\n",
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       "      <td>280</td>\n",
       "      <td>2400</td>\n",
       "      <td>2790</td>\n",
       "      <td>1.793700</td>\n",
       "      <td>2.821250</td>\n",
       "      <td>14.227798</td>\n",
       "      <td>15.217347</td>\n",
       "      <td>m</td>\n",
       "      <td>0.994734</td>\n",
       "      <td>FLEECE</td>\n",
       "      <td>0.998462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>KIT</td>\n",
       "      <td>1</td>\n",
       "      <td>203</td>\n",
       "      <td>390</td>\n",
       "      <td>2030</td>\n",
       "      <td>2640</td>\n",
       "      <td>1.986149</td>\n",
       "      <td>3.919319</td>\n",
       "      <td>13.110175</td>\n",
       "      <td>14.856609</td>\n",
       "      <td>m</td>\n",
       "      <td>0.982181</td>\n",
       "      <td>KIT</td>\n",
       "      <td>0.973843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m</td>\n",
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       "      <td>1</td>\n",
       "      <td>KIT</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>310</td>\n",
       "      <td>1980</td>\n",
       "      <td>2550</td>\n",
       "      <td>1.861970</td>\n",
       "      <td>3.131278</td>\n",
       "      <td>12.943046</td>\n",
       "      <td>14.628647</td>\n",
       "      <td>m</td>\n",
       "      <td>0.999517</td>\n",
       "      <td>KIT</td>\n",
       "      <td>0.996075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>DRESS</td>\n",
       "      <td>1</td>\n",
       "      <td>161</td>\n",
       "      <td>490</td>\n",
       "      <td>1870</td>\n",
       "      <td>2420</td>\n",
       "      <td>1.505083</td>\n",
       "      <td>4.832000</td>\n",
       "      <td>12.560000</td>\n",
       "      <td>14.282831</td>\n",
       "      <td>m</td>\n",
       "      <td>0.739049</td>\n",
       "      <td>DRESS</td>\n",
       "      <td>0.572512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1515</th>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>FOOT</td>\n",
       "      <td>2</td>\n",
       "      <td>322</td>\n",
       "      <td>610</td>\n",
       "      <td>1550</td>\n",
       "      <td>3400</td>\n",
       "      <td>3.253006</td>\n",
       "      <td>5.833463</td>\n",
       "      <td>11.309174</td>\n",
       "      <td>16.476343</td>\n",
       "      <td>c</td>\n",
       "      <td>0.999919</td>\n",
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       "      <td>0.983871</td>\n",
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       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>c</td>\n",
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       "      <td>76</td>\n",
       "      <td>GOOSE</td>\n",
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       "      <td>345</td>\n",
       "      <td>520</td>\n",
       "      <td>1250</td>\n",
       "      <td>3460</td>\n",
       "      <td>3.482777</td>\n",
       "      <td>5.091452</td>\n",
       "      <td>9.910031</td>\n",
       "      <td>16.584871</td>\n",
       "      <td>c</td>\n",
       "      <td>0.993950</td>\n",
       "      <td>FOOT</td>\n",
       "      <td>0.934974</td>\n",
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       "    <tr>\n",
       "      <th>1517</th>\n",
       "      <td>c</td>\n",
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       "      <td>GOOSE</td>\n",
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       "      <td>334</td>\n",
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       "      <td>1140</td>\n",
       "      <td>3380</td>\n",
       "      <td>3.373461</td>\n",
       "      <td>4.919187</td>\n",
       "      <td>9.329161</td>\n",
       "      <td>16.439625</td>\n",
       "      <td>c</td>\n",
       "      <td>0.998422</td>\n",
       "      <td>FOOT</td>\n",
       "      <td>0.825487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1518</th>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>NURSE</td>\n",
       "      <td>1</td>\n",
       "      <td>308</td>\n",
       "      <td>740</td>\n",
       "      <td>1850</td>\n",
       "      <td>2160</td>\n",
       "      <td>3.110864</td>\n",
       "      <td>6.817926</td>\n",
       "      <td>12.487979</td>\n",
       "      <td>13.525728</td>\n",
       "      <td>c</td>\n",
       "      <td>0.495870</td>\n",
       "      <td>NURSE</td>\n",
       "      <td>0.503462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1519</th>\n",
       "      <td>c</td>\n",
       "      <td>f</td>\n",
       "      <td>76</td>\n",
       "      <td>NURSE</td>\n",
       "      <td>2</td>\n",
       "      <td>328</td>\n",
       "      <td>660</td>\n",
       "      <td>1830</td>\n",
       "      <td>2200</td>\n",
       "      <td>3.313392</td>\n",
       "      <td>6.223664</td>\n",
       "      <td>12.415198</td>\n",
       "      <td>13.648365</td>\n",
       "      <td>c</td>\n",
       "      <td>0.855784</td>\n",
       "      <td>NURSE</td>\n",
       "      <td>0.898766</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1520 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     type gender  speaker   vowel  repetition   F0   F1    F2    F3        z0  \\\n",
       "0       m      m        1  FLEECE           1  160  240  2280  2850  1.493396   \n",
       "1       m      m        1  FLEECE           2  186  280  2400  2790  1.793700   \n",
       "2       m      m        1     KIT           1  203  390  2030  2640  1.986149   \n",
       "3       m      m        1     KIT           2  192  310  1980  2550  1.861970   \n",
       "4       m      m        1   DRESS           1  161  490  1870  2420  1.505083   \n",
       "...   ...    ...      ...     ...         ...  ...  ...   ...   ...       ...   \n",
       "1515    c      f       76    FOOT           2  322  610  1550  3400  3.253006   \n",
       "1516    c      f       76   GOOSE           1  345  520  1250  3460  3.482777   \n",
       "1517    c      f       76   GOOSE           2  334  500  1140  3380  3.373461   \n",
       "1518    c      f       76   NURSE           1  308  740  1850  2160  3.110864   \n",
       "1519    c      f       76   NURSE           2  328  660  1830  2200  3.313392   \n",
       "\n",
       "            z1         z2         z3 typeChoice  typeProb vowelChoice  \\\n",
       "0     2.394727  13.886698  15.355343          m  0.999809      FLEECE   \n",
       "1     2.821250  14.227798  15.217347          m  0.994734      FLEECE   \n",
       "2     3.919319  13.110175  14.856609          m  0.982181         KIT   \n",
       "3     3.131278  12.943046  14.628647          m  0.999517         KIT   \n",
       "4     4.832000  12.560000  14.282831          m  0.739049       DRESS   \n",
       "...        ...        ...        ...        ...       ...         ...   \n",
       "1515  5.833463  11.309174  16.476343          c  0.999919        FOOT   \n",
       "1516  5.091452   9.910031  16.584871          c  0.993950        FOOT   \n",
       "1517  4.919187   9.329161  16.439625          c  0.998422        FOOT   \n",
       "1518  6.817926  12.487979  13.525728          c  0.495870       NURSE   \n",
       "1519  6.223664  12.415198  13.648365          c  0.855784       NURSE   \n",
       "\n",
       "      vowelProb  \n",
       "0      0.999925  \n",
       "1      0.998462  \n",
       "2      0.973843  \n",
       "3      0.996075  \n",
       "4      0.572512  \n",
       "...         ...  \n",
       "1515   0.983871  \n",
       "1516   0.934974  \n",
       "1517   0.825487  \n",
       "1518   0.503462  \n",
       "1519   0.898766  \n",
       "\n",
       "[1520 rows x 17 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a842c58",
   "metadata": {},
   "source": [
    "### Check accuracy\n",
    "- We'll get a dataframe where each exemplar is labeled with whether or not it was categorized accurately (\\[CATNAME\\]Acc)\n",
    "\n",
    "Then, for each category type:\n",
    "- We'll tabulate and plot accuracy by category label\n",
    "- And then look at overall accuracy as a proportion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "59ae9b32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>gender</th>\n",
       "      <th>speaker</th>\n",
       "      <th>vowel</th>\n",
       "      <th>repetition</th>\n",
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       "      <th>typeProb</th>\n",
       "      <th>vowelChoice</th>\n",
       "      <th>vowelProb</th>\n",
       "      <th>typeAcc</th>\n",
       "      <th>vowelAcc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>50</td>\n",
       "      <td>TRAP</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
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       "      <td>w</td>\n",
       "      <td>0.999158</td>\n",
       "      <td>TRAP</td>\n",
       "      <td>0.999963</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
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       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>18</td>\n",
       "      <td>THOUGHT</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "      <td>470</td>\n",
       "      <td>690</td>\n",
       "      <td>2200</td>\n",
       "      <td>0.992416</td>\n",
       "      <td>4.655473</td>\n",
       "      <td>6.450717</td>\n",
       "      <td>13.648365</td>\n",
       "      <td>m</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>THOUGHT</td>\n",
       "      <td>0.996497</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
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       "      <td>FOOT</td>\n",
       "      <td>1</td>\n",
       "      <td>223</td>\n",
       "      <td>335</td>\n",
       "      <td>1049</td>\n",
       "      <td>2470</td>\n",
       "      <td>2.208722</td>\n",
       "      <td>3.383442</td>\n",
       "      <td>8.816524</td>\n",
       "      <td>14.418239</td>\n",
       "      <td>w</td>\n",
       "      <td>0.796194</td>\n",
       "      <td>GOOSE</td>\n",
       "      <td>0.820404</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>w</td>\n",
       "      <td>f</td>\n",
       "      <td>61</td>\n",
       "      <td>STRUT</td>\n",
       "      <td>1</td>\n",
       "      <td>230</td>\n",
       "      <td>710</td>\n",
       "      <td>1340</td>\n",
       "      <td>2780</td>\n",
       "      <td>2.285662</td>\n",
       "      <td>6.599251</td>\n",
       "      <td>10.356485</td>\n",
       "      <td>15.194008</td>\n",
       "      <td>w</td>\n",
       "      <td>0.986470</td>\n",
       "      <td>STRUT</td>\n",
       "      <td>0.992748</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>21</td>\n",
       "      <td>GOOSE</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>290</td>\n",
       "      <td>1000</td>\n",
       "      <td>2300</td>\n",
       "      <td>1.316770</td>\n",
       "      <td>2.925511</td>\n",
       "      <td>8.527432</td>\n",
       "      <td>13.944883</td>\n",
       "      <td>m</td>\n",
       "      <td>0.998588</td>\n",
       "      <td>GOOSE</td>\n",
       "      <td>0.999260</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     type gender  speaker    vowel  repetition   F0   F1    F2    F3  \\\n",
       "986     w      f       50     TRAP           1  189  850  1853  2685   \n",
       "353     m      m       18  THOUGHT           2  118  470   690  2200   \n",
       "1074    w      f       54     FOOT           1  223  335  1049  2470   \n",
       "1208    w      f       61    STRUT           1  230  710  1340  2780   \n",
       "417     m      m       21    GOOSE           2  145  290  1000  2300   \n",
       "\n",
       "            z0        z1         z2         z3 typeChoice  typeProb  \\\n",
       "986   1.827883  7.579786  12.498830  14.967277          w  0.999158   \n",
       "353   0.992416  4.655473   6.450717  13.648365          m  0.999999   \n",
       "1074  2.208722  3.383442   8.816524  14.418239          w  0.796194   \n",
       "1208  2.285662  6.599251  10.356485  15.194008          w  0.986470   \n",
       "417   1.316770  2.925511   8.527432  13.944883          m  0.998588   \n",
       "\n",
       "     vowelChoice  vowelProb typeAcc vowelAcc  \n",
       "986         TRAP   0.999963       y        y  \n",
       "353      THOUGHT   0.996497       y        y  \n",
       "1074       GOOSE   0.820404       y        n  \n",
       "1208       STRUT   0.992748       y        y  \n",
       "417        GOOSE   0.999260       y        y  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = checkaccuracy(choices,catslist)\n",
    "acc.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecfd1773",
   "metadata": {},
   "source": [
    "#### For vowel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d83c01a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='vowel', ylabel='Proportion accurate of 1520 trials'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accplot(acc, 'vowel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c974a2e6",
   "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>vowel</th>\n",
       "      <th>propcorr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRESS</td>\n",
       "      <td>0.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FLEECE</td>\n",
       "      <td>0.967105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOOT</td>\n",
       "      <td>0.815789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GOOSE</td>\n",
       "      <td>0.822368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KIT</td>\n",
       "      <td>0.881579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NURSE</td>\n",
       "      <td>0.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PALM</td>\n",
       "      <td>0.848684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>STRUT</td>\n",
       "      <td>0.901316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>THOUGHT</td>\n",
       "      <td>0.848684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TRAP</td>\n",
       "      <td>0.901316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     vowel  propcorr\n",
       "0    DRESS  0.842105\n",
       "1   FLEECE  0.967105\n",
       "2     FOOT  0.815789\n",
       "3    GOOSE  0.822368\n",
       "4      KIT  0.881579\n",
       "5    NURSE  0.894737\n",
       "6     PALM  0.848684\n",
       "7    STRUT  0.901316\n",
       "8  THOUGHT  0.848684\n",
       "9     TRAP  0.901316"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propcorr(acc,'vowel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0894f5cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall accuracy: 0.8723684210526316\n"
     ]
    }
   ],
   "source": [
    "print(\"Overall accuracy: \" + str(overallacc(acc,'vowel')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f73a0e",
   "metadata": {},
   "source": [
    "#### For speaker type (man, woman, child)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8de4d564",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='type', ylabel='Proportion accurate of 1520 trials'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accplot(acc,'type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd8a09aa",
   "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>type</th>\n",
       "      <th>propcorr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c</td>\n",
       "      <td>0.756667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m</td>\n",
       "      <td>0.962121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>w</td>\n",
       "      <td>0.851786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type  propcorr\n",
       "0    c  0.756667\n",
       "1    m  0.962121\n",
       "2    w  0.851786"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propcorr(acc,'type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9cb64b9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall accuracy: 0.8809210526315789\n"
     ]
    }
   ],
   "source": [
    "print(\"Overall accuracy: \" + str(overallacc(acc,'type')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c379c42",
   "metadata": {},
   "source": [
    "### Get confusion matrices\n",
    "- Calculate a confusion matrix for our \"perceiver\"\n",
    "- Look at the confusion matrix from the Peterson and Barney paper we read in earlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8139641b",
   "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>vowelChoice</th>\n",
       "      <th>DRESS</th>\n",
       "      <th>FLEECE</th>\n",
       "      <th>FOOT</th>\n",
       "      <th>GOOSE</th>\n",
       "      <th>KIT</th>\n",
       "      <th>NURSE</th>\n",
       "      <th>PALM</th>\n",
       "      <th>STRUT</th>\n",
       "      <th>THOUGHT</th>\n",
       "      <th>TRAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DRESS</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEECE</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOOT</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOSE</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIT</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NURSE</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PALM</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRUT</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOUGHT</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAP</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "vowelChoice  DRESS  FLEECE  FOOT  GOOSE   KIT  NURSE  PALM  STRUT  THOUGHT  \\\n",
       "DRESS         0.84    0.00  0.00   0.00  0.12   0.01  0.00   0.00     0.00   \n",
       "FLEECE        0.00    0.97  0.00   0.00  0.03   0.00  0.00   0.00     0.00   \n",
       "FOOT          0.00    0.00  0.82   0.13  0.00   0.00  0.00   0.03     0.02   \n",
       "GOOSE         0.00    0.00  0.14   0.82  0.00   0.00  0.00   0.00     0.04   \n",
       "KIT           0.07    0.05  0.00   0.00  0.88   0.00  0.00   0.00     0.00   \n",
       "NURSE         0.06    0.00  0.00   0.00  0.02   0.89  0.00   0.00     0.00   \n",
       "PALM          0.00    0.00  0.00   0.00  0.00   0.00  0.85   0.09     0.06   \n",
       "STRUT         0.00    0.00  0.00   0.00  0.00   0.00  0.09   0.90     0.01   \n",
       "THOUGHT       0.00    0.00  0.01   0.03  0.00   0.00  0.09   0.02     0.85   \n",
       "TRAP          0.10    0.00  0.00   0.00  0.00   0.00  0.00   0.00     0.00   \n",
       "\n",
       "vowelChoice  TRAP  \n",
       "DRESS        0.03  \n",
       "FLEECE       0.00  \n",
       "FOOT         0.00  \n",
       "GOOSE        0.00  \n",
       "KIT          0.00  \n",
       "NURSE        0.03  \n",
       "PALM         0.00  \n",
       "STRUT        0.00  \n",
       "THOUGHT      0.00  \n",
       "TRAP         0.90  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion(choices,catslist)['vowel']\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4b5c83f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DRESS</th>\n",
       "      <th>FLEECE</th>\n",
       "      <th>FOOT</th>\n",
       "      <th>GOOSE</th>\n",
       "      <th>KIT</th>\n",
       "      <th>NURSE</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DRESS</th>\n",
       "      <td>0.658</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.072</td>\n",
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       "    <tr>\n",
       "      <th>FLEECE</th>\n",
       "      <td>0.006</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>FOOT</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.620</td>\n",
       "      <td>0.284</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOSE</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIT</th>\n",
       "      <td>0.067</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NURSE</th>\n",
       "      <td>0.040</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PALM</th>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRUT</th>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOUGHT</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAP</th>\n",
       "      <td>0.280</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DRESS  FLEECE   FOOT  GOOSE    KIT  NURSE   PALM  STRUT  THOUGHT  \\\n",
       "DRESS    0.658   0.013  0.004  0.001  0.237  0.011  0.000  0.003    0.000   \n",
       "FLEECE   0.006   0.962  0.000  0.000  0.031  0.000  0.000  0.000    0.000   \n",
       "FOOT     0.001   0.000  0.620  0.284  0.002  0.009  0.001  0.052    0.031   \n",
       "GOOSE    0.000   0.002  0.090  0.891  0.002  0.002  0.000  0.007    0.007   \n",
       "KIT      0.067   0.251  0.006  0.000  0.670  0.001  0.000  0.001    0.000   \n",
       "NURSE    0.040   0.003  0.030  0.007  0.041  0.866  0.000  0.009    0.000   \n",
       "PALM     0.002   0.000  0.006  0.001  0.000  0.000  0.550  0.136    0.305   \n",
       "STRUT    0.009   0.000  0.027  0.001  0.001  0.012  0.128  0.747    0.068   \n",
       "THOUGHT  0.000   0.000  0.130  0.059  0.000  0.000  0.059  0.080    0.672   \n",
       "TRAP     0.280   0.001  0.003  0.000  0.006  0.019  0.040  0.020    0.000   \n",
       "\n",
       "          TRAP  \n",
       "DRESS    0.072  \n",
       "FLEECE   0.000  \n",
       "FOOT     0.000  \n",
       "GOOSE    0.000  \n",
       "KIT      0.003  \n",
       "NURSE    0.003  \n",
       "PALM     0.001  \n",
       "STRUT    0.007  \n",
       "THOUGHT  0.000  \n",
       "TRAP     0.632  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbcm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c98ef74e",
   "metadata": {},
   "source": [
    "### Next, let's analyze the confusion matrices in a couple different ways\n",
    "- Take a simple difference\n",
    "- Set up a dataframe to look at correlations\n",
    "    - Convert matrices to stacksw\n",
    "    - Join together into dataframe\n",
    "- Calculate root mean square\n",
    "- Get correlation using df.corr\n",
    "- Set up a dataframe where all 0s are converted to NaN\n",
    "    - Afterall, 0 correlates real good with 0!\n",
    "- Get RMS\n",
    "- Get correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "02af8072",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>vowelChoice</th>\n",
       "      <th>DRESS</th>\n",
       "      <th>FLEECE</th>\n",
       "      <th>FOOT</th>\n",
       "      <th>GOOSE</th>\n",
       "      <th>KIT</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DRESS</th>\n",
       "      <td>0.182</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEECE</th>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>FOOT</th>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>-0.154</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOSE</th>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIT</th>\n",
       "      <td>0.003</td>\n",
       "      <td>-0.201</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.210</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NURSE</th>\n",
       "      <td>0.020</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>-0.007</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PALM</th>\n",
       "      <td>-0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.300</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>-0.245</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRUT</th>\n",
       "      <td>-0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.012</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>0.153</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>-0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THOUGHT</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.031</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRAP</th>\n",
       "      <td>-0.180</td>\n",
       "      <td>-0.001</td>\n",
       "      <td>-0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>-0.040</td>\n",
       "      <td>-0.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "vowelChoice  DRESS  FLEECE   FOOT  GOOSE    KIT  NURSE   PALM  STRUT  THOUGHT  \\\n",
       "DRESS        0.182  -0.013 -0.004 -0.001 -0.117 -0.001  0.000 -0.003    0.000   \n",
       "FLEECE      -0.006   0.008  0.000  0.000 -0.001  0.000  0.000  0.000    0.000   \n",
       "FOOT        -0.001   0.000  0.200 -0.154 -0.002 -0.009 -0.001 -0.022   -0.011   \n",
       "GOOSE        0.000  -0.002  0.050 -0.071 -0.002 -0.002  0.000 -0.007    0.033   \n",
       "KIT          0.003  -0.201 -0.006  0.000  0.210 -0.001  0.000 -0.001    0.000   \n",
       "NURSE        0.020  -0.003 -0.030 -0.007 -0.021  0.024  0.000 -0.009    0.000   \n",
       "PALM        -0.002   0.000 -0.006 -0.001  0.000  0.000  0.300 -0.046   -0.245   \n",
       "STRUT       -0.009   0.000 -0.027 -0.001 -0.001 -0.012 -0.038  0.153   -0.058   \n",
       "THOUGHT      0.000   0.000 -0.120 -0.029  0.000  0.000  0.031 -0.060    0.178   \n",
       "TRAP        -0.180  -0.001 -0.003  0.000 -0.006 -0.019 -0.040 -0.020    0.000   \n",
       "\n",
       "vowelChoice   TRAP  \n",
       "DRESS       -0.042  \n",
       "FLEECE       0.000  \n",
       "FOOT         0.000  \n",
       "GOOSE        0.000  \n",
       "KIT         -0.003  \n",
       "NURSE        0.027  \n",
       "PALM        -0.001  \n",
       "STRUT       -0.007  \n",
       "THOUGHT      0.000  \n",
       "TRAP         0.268  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm - pbcm #differences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bbc25d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# flatten and combine confusion matrices\n",
    "pbcmfl = pd.Series(pbcm.stack(), name = \"PB\")\n",
    "cmfl = pd.Series(cm.stack(), name = \"GCM\")\n",
    "cms = pd.concat([pbcmfl, cmfl], axis = 1)\n",
    "\n",
    "#Remove 0s (which correlate with each other)\n",
    "cmsnan = cms.replace(0, np.nan) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b53369b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0s included\n",
      "RMS =   0.07411052556823491\n",
      "r =     0.9645813339553275\n",
      "\n",
      "0s removed\n",
      "RMS =   0.12843085629429107\n",
      "r =     0.9519894540508785\n"
     ]
    }
   ],
   "source": [
    "print(\"0s included\")\n",
    "print(\"RMS =  \", (((cms.PB-cms.GCM) ** 2).mean()) ** .5)\n",
    "print(\"r =    \", (cms['PB'].corr(cms['GCM'])))\n",
    "print(\"\")\n",
    "print(\"0s removed\")\n",
    "print(\"RMS =  \",(((cmsnan.PB-cmsnan.GCM) ** 2).mean()) ** .5)\n",
    "print(\"r =    \", cmsnan['PB'].corr(cmsnan['GCM']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "884bb86b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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