text_embedding_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sphering transform on text embeddings\n",
    "\n",
    "This is a demonstration, just to get the idea across. We used a VERY simple document representation: the average word2vec embedding of the words of the document. This is sufficient to detect the difference between the two corpora that we used, but probably not much else. \n",
    "\n",
    "All the data used in this example (preprocessed and ready to go) can be found [in this zip file](https://drive.google.com/drive/folders/1sXtDqNF-D0mypORzfevir_eH9FdG2668)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict\n",
    "from numpy.linalg import norm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import polars as pl\n",
    "import re\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# our modules\n",
    "from w2v_text_embed import W2VEmbed\n",
    "from sphering_transform import SpheringTransform\n",
    "from distribution_diff import DistCalculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the word2vec embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr > th,\n",
       ".dataframe > tbody > tr > td {\n",
       "  text-align: right;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3_000_000, 301)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>word</th><th>v00000</th><th>v00001</th><th>v00002</th><th>v00003</th><th>v00004</th><th>v00005</th><th>v00006</th><th>v00007</th><th>v00008</th><th>v00009</th><th>v00010</th><th>v00011</th><th>v00012</th><th>v00013</th><th>v00014</th><th>v00015</th><th>v00016</th><th>v00017</th><th>v00018</th><th>v00019</th><th>v00020</th><th>v00021</th><th>v00022</th><th>v00023</th><th>v00024</th><th>v00025</th><th>v00026</th><th>v00027</th><th>v00028</th><th>v00029</th><th>v00030</th><th>v00031</th><th>v00032</th><th>v00033</th><th>v00034</th><th>v00035</th><th>&hellip;</th><th>v00263</th><th>v00264</th><th>v00265</th><th>v00266</th><th>v00267</th><th>v00268</th><th>v00269</th><th>v00270</th><th>v00271</th><th>v00272</th><th>v00273</th><th>v00274</th><th>v00275</th><th>v00276</th><th>v00277</th><th>v00278</th><th>v00279</th><th>v00280</th><th>v00281</th><th>v00282</th><th>v00283</th><th>v00284</th><th>v00285</th><th>v00286</th><th>v00287</th><th>v00288</th><th>v00289</th><th>v00290</th><th>v00291</th><th>v00292</th><th>v00293</th><th>v00294</th><th>v00295</th><th>v00296</th><th>v00297</th><th>v00298</th><th>v00299</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;&lt;/s&gt;&quot;</td><td>0.001129</td><td>-0.000896</td><td>0.000319</td><td>0.001534</td><td>0.001106</td><td>-0.001404</td><td>-0.000031</td><td>-0.00042</td><td>-0.000576</td><td>0.001076</td><td>-0.001022</td><td>-0.000618</td><td>-0.000755</td><td>0.001404</td><td>-0.00164</td><td>-0.000633</td><td>0.001633</td><td>-0.001007</td><td>-0.001266</td><td>0.000652</td><td>-0.000416</td><td>-0.001076</td><td>0.001526</td><td>-0.000275</td><td>0.00014</td><td>0.001572</td><td>0.001358</td><td>-0.000832</td><td>-0.001404</td><td>0.001579</td><td>0.000254</td><td>-0.000732</td><td>-0.000105</td><td>-0.001167</td><td>0.001579</td><td>0.000656</td><td>&hellip;</td><td>0.000591</td><td>-0.001465</td><td>0.000965</td><td>-0.001282</td><td>0.001602</td><td>0.001091</td><td>-0.001312</td><td>0.001091</td><td>-0.000511</td><td>0.000345</td><td>0.001045</td><td>-0.000207</td><td>0.000904</td><td>0.000668</td><td>0.001106</td><td>-0.000874</td><td>-0.000376</td><td>-0.000257</td><td>-0.000092</td><td>0.001434</td><td>-0.001183</td><td>-0.000087</td><td>0.001328</td><td>-0.000158</td><td>0.001289</td><td>-0.000984</td><td>-0.000549</td><td>-0.001549</td><td>0.001373</td><td>-0.000061</td><td>-0.000824</td><td>0.001328</td><td>0.00116</td><td>0.000568</td><td>-0.001564</td><td>-0.000123</td><td>-0.000086</td></tr><tr><td>&quot;in&quot;</td><td>0.0703125</td><td>0.086914</td><td>0.087891</td><td>0.0625</td><td>0.069336</td><td>-0.108887</td><td>-0.081543</td><td>-0.154297</td><td>0.020752</td><td>0.131836</td><td>-0.11377</td><td>-0.037354</td><td>0.069336</td><td>0.078125</td><td>-0.103027</td><td>-0.097656</td><td>0.044189</td><td>0.102539</td><td>-0.060791</td><td>-0.036133</td><td>-0.04541</td><td>0.047363</td><td>-0.120605</td><td>-0.063965</td><td>0.002258</td><td>0.037109</td><td>-0.002914</td><td>0.117676</td><td>0.061768</td><td>0.063965</td><td>0.081055</td><td>-0.068848</td><td>-0.021362</td><td>0.055176</td><td>-0.085449</td><td>0.068848</td><td>&hellip;</td><td>0.170898</td><td>0.173828</td><td>0.055664</td><td>0.091309</td><td>-0.037354</td><td>0.049805</td><td>-0.039307</td><td>0.044189</td><td>0.0625</td><td>0.048584</td><td>-0.053223</td><td>0.048828</td><td>-0.130859</td><td>-0.028931</td><td>-0.036133</td><td>-0.060791</td><td>-0.057373</td><td>0.123047</td><td>-0.08252</td><td>-0.011902</td><td>0.125</td><td>0.001358</td><td>0.063965</td><td>-0.106445</td><td>-0.143555</td><td>-0.042236</td><td>0.024048</td><td>-0.168945</td><td>-0.088867</td><td>-0.080566</td><td>0.064941</td><td>0.061279</td><td>-0.047363</td><td>-0.058838</td><td>-0.047607</td><td>0.014465</td><td>-0.0625</td></tr><tr><td>&quot;for&quot;</td><td>-0.01178</td><td>-0.047363</td><td>0.044678</td><td>0.063477</td><td>-0.018188</td><td>-0.063965</td><td>-0.001312</td><td>-0.072266</td><td>0.064453</td><td>0.086426</td><td>-0.169922</td><td>-0.039795</td><td>0.071289</td><td>-0.025879</td><td>0.018188</td><td>0.136719</td><td>0.144531</td><td>-0.033691</td><td>-0.097656</td><td>-0.120117</td><td>-0.07959</td><td>0.0625</td><td>-0.066895</td><td>0.074219</td><td>0.022705</td><td>0.033447</td><td>-0.180664</td><td>0.052002</td><td>0.013855</td><td>0.092773</td><td>0.00351</td><td>-0.009094</td><td>-0.097168</td><td>0.067871</td><td>-0.008789</td><td>0.044189</td><td>&hellip;</td><td>0.046387</td><td>0.047852</td><td>0.098145</td><td>-0.040283</td><td>0.094238</td><td>-0.034668</td><td>-0.042236</td><td>0.0703125</td><td>-0.013672</td><td>0.106445</td><td>0.016479</td><td>0.131836</td><td>-0.001694</td><td>-0.008484</td><td>-0.142578</td><td>-0.046631</td><td>-0.109863</td><td>0.082031</td><td>-0.041016</td><td>-0.018921</td><td>0.087891</td><td>-0.002808</td><td>0.238281</td><td>-0.047119</td><td>-0.022949</td><td>0.040771</td><td>0.029297</td><td>-0.022583</td><td>0.003723</td><td>-0.08252</td><td>0.081543</td><td>0.007935</td><td>0.000477</td><td>0.018433</td><td>0.071289</td><td>-0.034912</td><td>0.02417</td></tr><tr><td>&quot;that&quot;</td><td>-0.015747</td><td>-0.02832</td><td>0.083496</td><td>0.050293</td><td>-0.110352</td><td>0.031738</td><td>-0.014221</td><td>-0.089844</td><td>0.117676</td><td>0.118164</td><td>-0.071777</td><td>-0.077148</td><td>-0.068848</td><td>0.077148</td><td>-0.138672</td><td>0.0065</td><td>0.010986</td><td>-0.015137</td><td>-0.000961</td><td>-0.030273</td><td>-0.000158</td><td>0.03833</td><td>-0.02417</td><td>-0.045898</td><td>0.094727</td><td>-0.055176</td><td>-0.064941</td><td>0.006104</td><td>0.000854</td><td>0.062012</td><td>-0.054443</td><td>0.014099</td><td>0.022217</td><td>-0.044922</td><td>0.111328</td><td>-0.038574</td><td>&hellip;</td><td>0.106445</td><td>-0.039795</td><td>0.116211</td><td>-0.009705</td><td>-0.0354</td><td>-0.06543</td><td>0.058838</td><td>0.162109</td><td>0.05127</td><td>0.15918</td><td>0.095215</td><td>0.076172</td><td>-0.091797</td><td>0.025146</td><td>-0.078613</td><td>0.089355</td><td>-0.058594</td><td>-0.040039</td><td>0.045898</td><td>0.031006</td><td>0.0390625</td><td>0.035645</td><td>-0.105957</td><td>-0.037109</td><td>-0.161133</td><td>0.021362</td><td>0.001221</td><td>-0.011292</td><td>-0.015625</td><td>-0.033447</td><td>-0.02063</td><td>-0.019409</td><td>0.063965</td><td>0.020142</td><td>0.006866</td><td>0.061035</td><td>-0.148438</td></tr><tr><td>&quot;is&quot;</td><td>0.00705</td><td>-0.073242</td><td>0.171875</td><td>0.022583</td><td>-0.132812</td><td>0.198242</td><td>0.112793</td><td>-0.10791</td><td>0.071777</td><td>0.020874</td><td>-0.123047</td><td>-0.059082</td><td>0.101074</td><td>0.010742</td><td>0.143555</td><td>0.259766</td><td>-0.036377</td><td>0.185547</td><td>-0.078613</td><td>-0.022705</td><td>-0.120605</td><td>0.177734</td><td>0.049561</td><td>0.017212</td><td>0.07959</td><td>-0.045654</td><td>-0.188477</td><td>0.189453</td><td>-0.023193</td><td>0.062988</td><td>0.097656</td><td>-0.019043</td><td>-0.079102</td><td>0.152344</td><td>0.173828</td><td>0.1015625</td><td>&hellip;</td><td>0.072266</td><td>0.04126</td><td>0.425781</td><td>-0.103027</td><td>-0.160156</td><td>-0.090332</td><td>-0.063965</td><td>-0.048096</td><td>0.144531</td><td>0.06543</td><td>0.049316</td><td>0.054199</td><td>0.135742</td><td>-0.019287</td><td>-0.21582</td><td>-0.074219</td><td>-0.146484</td><td>0.011475</td><td>-0.165039</td><td>-0.10498</td><td>0.003204</td><td>0.134766</td><td>-0.003967</td><td>-0.103516</td><td>-0.139648</td><td>0.104492</td><td>-0.012573</td><td>-0.233398</td><td>-0.036377</td><td>-0.09375</td><td>0.182617</td><td>0.0271</td><td>0.12793</td><td>-0.02478</td><td>0.01123</td><td>0.1640625</td><td>0.106934</td></tr><tr><td>&quot;on&quot;</td><td>0.026733</td><td>-0.09082</td><td>0.027832</td><td>0.204102</td><td>0.006226</td><td>-0.090332</td><td>0.022583</td><td>-0.161133</td><td>0.1328125</td><td>0.061035</td><td>-0.015747</td><td>0.088379</td><td>0.013794</td><td>0.046387</td><td>-0.055908</td><td>-0.066895</td><td>0.012268</td><td>0.136719</td><td>0.154297</td><td>-0.046143</td><td>-0.039307</td><td>-0.154297</td><td>-0.165039</td><td>0.10791</td><td>0.033203</td><td>-0.051025</td><td>0.037109</td><td>0.1015625</td><td>0.110352</td><td>0.020508</td><td>0.006775</td><td>0.001183</td><td>-0.012512</td><td>-0.125</td><td>0.014832</td><td>-0.026855</td><td>&hellip;</td><td>-0.124512</td><td>0.035889</td><td>0.145508</td><td>-0.105469</td><td>0.022949</td><td>-0.008362</td><td>0.004639</td><td>0.219727</td><td>-0.049561</td><td>0.238281</td><td>-0.05835</td><td>0.04834</td><td>0.060547</td><td>-0.037354</td><td>-0.177734</td><td>0.044922</td><td>-0.042236</td><td>0.08252</td><td>0.110352</td><td>-0.109375</td><td>0.094238</td><td>-0.072266</td><td>0.049072</td><td>-0.158203</td><td>0.078125</td><td>0.029541</td><td>-0.121094</td><td>0.026855</td><td>-0.027954</td><td>0.030884</td><td>0.040527</td><td>-0.130859</td><td>0.083008</td><td>0.015747</td><td>-0.116699</td><td>-0.029419</td><td>-0.070801</td></tr><tr><td>&quot;##&quot;</td><td>0.168945</td><td>-0.062988</td><td>-0.000265</td><td>0.063477</td><td>0.087402</td><td>-0.030029</td><td>0.00145</td><td>-0.077637</td><td>0.199219</td><td>-0.00177</td><td>-0.166992</td><td>-0.040527</td><td>0.024414</td><td>0.018921</td><td>-0.083008</td><td>-0.05542</td><td>0.049316</td><td>-0.032959</td><td>-0.169922</td><td>0.066406</td><td>-0.255859</td><td>-0.203125</td><td>-0.05127</td><td>-0.102539</td><td>0.163086</td><td>0.08252</td><td>-0.054688</td><td>0.027832</td><td>0.143555</td><td>0.018555</td><td>-0.126953</td><td>-0.191406</td><td>-0.035889</td><td>-0.25</td><td>-0.077637</td><td>0.013916</td><td>&hellip;</td><td>0.000368</td><td>0.163086</td><td>-0.063477</td><td>-0.029175</td><td>0.083008</td><td>-0.03125</td><td>-0.009888</td><td>0.21582</td><td>-0.018433</td><td>0.107422</td><td>-0.049072</td><td>0.220703</td><td>-0.005524</td><td>-0.259766</td><td>0.048584</td><td>0.049316</td><td>-0.040039</td><td>-0.015076</td><td>0.024658</td><td>-0.121582</td><td>0.005554</td><td>0.043457</td><td>-0.197266</td><td>0.011902</td><td>0.1796875</td><td>0.027344</td><td>0.140625</td><td>0.021118</td><td>0.001526</td><td>-0.098633</td><td>0.056641</td><td>-0.091309</td><td>0.103516</td><td>0.002304</td><td>0.023804</td><td>-0.123535</td><td>0.016357</td></tr><tr><td>&quot;The&quot;</td><td>-0.172852</td><td>0.279297</td><td>0.106934</td><td>-0.158203</td><td>-0.084473</td><td>0.059082</td><td>0.040771</td><td>0.002548</td><td>0.259766</td><td>0.180664</td><td>0.097656</td><td>-0.081055</td><td>-0.010498</td><td>0.098145</td><td>0.000603</td><td>0.070801</td><td>-0.015625</td><td>-0.095215</td><td>-0.081055</td><td>-0.028687</td><td>-0.033203</td><td>0.165039</td><td>0.039795</td><td>-0.037109</td><td>0.041016</td><td>-0.126953</td><td>-0.128906</td><td>0.123535</td><td>0.049805</td><td>0.012573</td><td>0.057861</td><td>-0.008301</td><td>-0.02832</td><td>-0.033203</td><td>0.161133</td><td>0.075195</td><td>&hellip;</td><td>-0.013794</td><td>0.018555</td><td>0.155273</td><td>-0.181641</td><td>0.033691</td><td>-0.000805</td><td>0.066895</td><td>-0.018433</td><td>0.141602</td><td>0.064453</td><td>-0.007324</td><td>0.07959</td><td>-0.027954</td><td>0.125</td><td>-0.237305</td><td>0.036377</td><td>-0.046875</td><td>-0.046875</td><td>0.054932</td><td>0.07959</td><td>0.271484</td><td>0.225586</td><td>-0.205078</td><td>-0.205078</td><td>-0.064453</td><td>-0.077637</td><td>-0.069824</td><td>-0.0177</td><td>-0.128906</td><td>0.021973</td><td>0.014771</td><td>-0.052979</td><td>-0.203125</td><td>0.061768</td><td>0.123047</td><td>0.129883</td><td>-0.182617</td></tr><tr><td>&quot;with&quot;</td><td>-0.024902</td><td>0.021973</td><td>-0.0354</td><td>0.136719</td><td>0.016357</td><td>-0.027954</td><td>-0.024292</td><td>-0.139648</td><td>0.057373</td><td>0.062256</td><td>-0.117676</td><td>-0.097168</td><td>-0.174805</td><td>-0.02063</td><td>-0.119141</td><td>0.099121</td><td>0.060303</td><td>0.140625</td><td>-0.062988</td><td>-0.048828</td><td>-0.04541</td><td>0.03418</td><td>0.011292</td><td>-0.045166</td><td>0.1953125</td><td>-0.03833</td><td>-0.131836</td><td>0.063477</td><td>-0.018799</td><td>0.1328125</td><td>0.057129</td><td>0.022217</td><td>-0.046875</td><td>0.09082</td><td>-0.175781</td><td>0.024658</td><td>&hellip;</td><td>0.049316</td><td>0.023804</td><td>0.078125</td><td>0.01239</td><td>-0.032227</td><td>0.06543</td><td>0.05835</td><td>0.137695</td><td>-0.031982</td><td>-0.056396</td><td>-0.071777</td><td>0.085449</td><td>0.028198</td><td>0.002014</td><td>-0.070801</td><td>0.034668</td><td>-0.040039</td><td>-0.03125</td><td>0.089355</td><td>-0.040283</td><td>0.045898</td><td>0.109863</td><td>0.080566</td><td>-0.087891</td><td>-0.033203</td><td>0.049072</td><td>0.013977</td><td>-0.020752</td><td>0.003082</td><td>-0.138672</td><td>-0.024414</td><td>-0.093262</td><td>-0.047607</td><td>-0.075684</td><td>0.010803</td><td>-0.018799</td><td>-0.068848</td></tr><tr><td>&quot;said&quot;</td><td>-0.009094</td><td>-0.044189</td><td>0.099609</td><td>-0.076172</td><td>-0.056641</td><td>0.061523</td><td>0.255859</td><td>-0.158203</td><td>0.016602</td><td>-0.09668</td><td>-0.063477</td><td>-0.151367</td><td>0.074707</td><td>0.138672</td><td>-0.230469</td><td>0.062256</td><td>0.176758</td><td>0.216797</td><td>-0.25</td><td>-0.041992</td><td>-0.201172</td><td>0.04834</td><td>0.119629</td><td>0.027832</td><td>0.112305</td><td>-0.134766</td><td>-0.109375</td><td>-0.059082</td><td>-0.207031</td><td>0.08252</td><td>-0.123047</td><td>0.046387</td><td>0.056396</td><td>0.032959</td><td>0.074219</td><td>-0.031738</td><td>&hellip;</td><td>0.043213</td><td>0.100586</td><td>0.3046875</td><td>0.269531</td><td>-0.09082</td><td>-0.088867</td><td>0.194336</td><td>0.235352</td><td>-0.208008</td><td>0.032227</td><td>0.018799</td><td>-0.138672</td><td>-0.026001</td><td>0.015747</td><td>-0.028687</td><td>0.078613</td><td>-0.115234</td><td>-0.181641</td><td>0.165039</td><td>-0.115723</td><td>-0.117188</td><td>0.170898</td><td>0.125977</td><td>-0.012512</td><td>-0.032959</td><td>0.052246</td><td>0.027222</td><td>-0.193359</td><td>0.029907</td><td>-0.093262</td><td>0.053711</td><td>-0.117676</td><td>0.069824</td><td>0.105957</td><td>0.144531</td><td>0.180664</td><td>-0.086914</td></tr><tr><td>&quot;was&quot;</td><td>0.026001</td><td>-0.001892</td><td>0.185547</td><td>-0.051758</td><td>0.005127</td><td>-0.109863</td><td>-0.008179</td><td>-0.088379</td><td>0.09668</td><td>0.04834</td><td>0.011047</td><td>-0.363281</td><td>0.082031</td><td>-0.02124</td><td>0.158203</td><td>0.044189</td><td>-0.01178</td><td>0.212891</td><td>-0.057373</td><td>0.056641</td><td>-0.107422</td><td>0.185547</td><td>0.077148</td><td>0.000145</td><td>0.152344</td><td>-0.06543</td><td>-0.152344</td><td>0.225586</td><td>0.081055</td><td>0.088867</td><td>0.073242</td><td>-0.103516</td><td>-0.066895</td><td>0.176758</td><td>0.212891</td><td>0.140625</td><td>&hellip;</td><td>0.1328125</td><td>0.039795</td><td>0.4296875</td><td>-0.018799</td><td>-0.147461</td><td>0.061035</td><td>0.090332</td><td>0.086914</td><td>-0.068848</td><td>0.11084</td><td>0.098145</td><td>0.150391</td><td>0.161133</td><td>-0.080566</td><td>-0.174805</td><td>-0.033203</td><td>-0.128906</td><td>0.122559</td><td>-0.014465</td><td>-0.163086</td><td>-0.035889</td><td>0.027832</td><td>-0.063477</td><td>-0.079102</td><td>-0.114746</td><td>0.018433</td><td>0.029175</td><td>-0.300781</td><td>-0.045898</td><td>-0.174805</td><td>0.233398</td><td>0.022583</td><td>0.110352</td><td>-0.103516</td><td>-0.121582</td><td>0.22168</td><td>-0.021973</td></tr><tr><td>&quot;the&quot;</td><td>0.080078</td><td>0.10498</td><td>0.049805</td><td>0.053467</td><td>-0.067383</td><td>-0.120605</td><td>0.035156</td><td>-0.118652</td><td>0.043945</td><td>0.030151</td><td>-0.056885</td><td>-0.076172</td><td>0.012878</td><td>0.049805</td><td>-0.084961</td><td>-0.063477</td><td>0.006287</td><td>-0.043213</td><td>0.020264</td><td>0.013306</td><td>-0.019531</td><td>0.092773</td><td>-0.171875</td><td>-0.00132</td><td>0.06543</td><td>0.05835</td><td>-0.08252</td><td>0.0859375</td><td>-0.003189</td><td>0.058594</td><td>-0.034912</td><td>-0.012329</td><td>-0.048096</td><td>-0.003021</td><td>0.056396</td><td>0.014954</td><td>&hellip;</td><td>0.066406</td><td>0.12207</td><td>0.123535</td><td>0.003998</td><td>0.045166</td><td>-0.018555</td><td>0.04834</td><td>0.045166</td><td>0.086914</td><td>0.029419</td><td>0.037598</td><td>0.034424</td><td>-0.07373</td><td>-0.040283</td><td>-0.146484</td><td>-0.024414</td><td>-0.019531</td><td>0.006592</td><td>-0.001816</td><td>-0.010925</td><td>0.093262</td><td>0.06543</td><td>0.018433</td><td>-0.093262</td><td>-0.015747</td><td>-0.071289</td><td>-0.089355</td><td>-0.071289</td><td>-0.030151</td><td>-0.013</td><td>0.016357</td><td>-0.018311</td><td>0.014832</td><td>0.005005</td><td>0.003662</td><td>0.047607</td><td>-0.068848</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;chairman_Ken_D…</td><td>-0.0354</td><td>-0.022339</td><td>-0.124023</td><td>-0.015198</td><td>0.010925</td><td>-0.101562</td><td>-0.031738</td><td>-0.015869</td><td>0.007416</td><td>0.000481</td><td>0.016846</td><td>0.067383</td><td>-0.045166</td><td>-0.029541</td><td>-0.017212</td><td>0.074219</td><td>0.032471</td><td>0.026245</td><td>-0.047852</td><td>-0.04541</td><td>-0.051514</td><td>0.047607</td><td>0.009949</td><td>0.078125</td><td>0.022583</td><td>0.059326</td><td>-0.051514</td><td>-0.00116</td><td>0.120605</td><td>0.037109</td><td>0.002899</td><td>0.080078</td><td>0.038574</td><td>0.015442</td><td>0.069824</td><td>-0.002869</td><td>&hellip;</td><td>0.137695</td><td>-0.052979</td><td>0.049805</td><td>-0.126953</td><td>0.062256</td><td>-0.017944</td><td>-0.06543</td><td>0.048828</td><td>-0.049561</td><td>-0.067871</td><td>0.01062</td><td>-0.052734</td><td>-0.007507</td><td>0.05957</td><td>-0.052002</td><td>0.057617</td><td>-0.019287</td><td>-0.019165</td><td>-0.027222</td><td>0.05127</td><td>-0.0354</td><td>0.100586</td><td>0.088867</td><td>0.065918</td><td>-0.015991</td><td>0.07959</td><td>0.04126</td><td>-0.058594</td><td>0.043213</td><td>-0.112793</td><td>0.039795</td><td>-0.00528</td><td>-0.023926</td><td>0.008301</td><td>0.021118</td><td>0.072266</td><td>-0.028809</td></tr><tr><td>&quot;USA_TODAY_Reid…</td><td>-0.028809</td><td>-0.05957</td><td>0.021851</td><td>-0.088867</td><td>-0.03418</td><td>0.05127</td><td>0.020752</td><td>-0.028198</td><td>0.001381</td><td>-0.06543</td><td>-0.033447</td><td>-0.08252</td><td>0.031982</td><td>0.09082</td><td>-0.086426</td><td>0.034912</td><td>0.088379</td><td>0.0177</td><td>0.005829</td><td>-0.04834</td><td>0.02771</td><td>0.014099</td><td>0.071777</td><td>-0.007996</td><td>-0.046143</td><td>-0.010803</td><td>0.016968</td><td>0.087891</td><td>-0.017456</td><td>0.016602</td><td>-0.037354</td><td>0.064453</td><td>0.02124</td><td>-0.01416</td><td>0.014893</td><td>-0.036377</td><td>&hellip;</td><td>-0.019531</td><td>-0.019287</td><td>0.066895</td><td>0.034424</td><td>-0.012085</td><td>0.078125</td><td>-0.042236</td><td>0.014771</td><td>0.134766</td><td>-0.034912</td><td>-0.06543</td><td>-0.018555</td><td>0.00161</td><td>0.04248</td><td>0.068848</td><td>0.115234</td><td>-0.044189</td><td>0.036133</td><td>-0.004791</td><td>-0.057373</td><td>0.002289</td><td>0.025391</td><td>0.015381</td><td>0.010071</td><td>-0.033203</td><td>-0.015564</td><td>0.081543</td><td>0.058594</td><td>-0.039307</td><td>0.025391</td><td>0.014771</td><td>0.024414</td><td>-0.106934</td><td>-0.025757</td><td>0.016968</td><td>-0.043945</td><td>-0.080078</td></tr><tr><td>&quot;Mauri_Liberati…</td><td>0.001328</td><td>0.015259</td><td>-0.06543</td><td>0.027954</td><td>0.07666</td><td>-0.081543</td><td>0.007446</td><td>-0.021729</td><td>-0.009827</td><td>0.027954</td><td>0.012817</td><td>-0.01239</td><td>-0.048096</td><td>0.034912</td><td>0.0390625</td><td>-0.02002</td><td>0.050049</td><td>-0.007141</td><td>-0.007111</td><td>-0.008545</td><td>-0.026245</td><td>-0.000755</td><td>0.058594</td><td>-0.005676</td><td>-0.033447</td><td>-0.026733</td><td>-0.058838</td><td>0.037354</td><td>0.027466</td><td>-0.055664</td><td>0.011597</td><td>-0.077637</td><td>0.019287</td><td>-0.081055</td><td>-0.028076</td><td>-0.07373</td><td>&hellip;</td><td>-0.050537</td><td>0.037354</td><td>0.021118</td><td>-0.017334</td><td>0.081543</td><td>-0.013306</td><td>-0.001785</td><td>-0.044434</td><td>0.028931</td><td>-0.055664</td><td>-0.004395</td><td>0.063965</td><td>0.016235</td><td>-0.044434</td><td>-0.027222</td><td>0.021851</td><td>0.017944</td><td>-0.048584</td><td>-0.01709</td><td>-0.013855</td><td>-0.023315</td><td>0.024292</td><td>-0.100586</td><td>0.086914</td><td>-0.01709</td><td>0.06543</td><td>0.004822</td><td>0.009094</td><td>-0.026611</td><td>-0.047607</td><td>0.0625</td><td>-0.038818</td><td>-0.056885</td><td>-0.009277</td><td>0.00415</td><td>-0.011292</td><td>0.016235</td></tr><tr><td>&quot;Shilpa_Goenka&quot;</td><td>-0.04834</td><td>0.037842</td><td>0.098145</td><td>0.070801</td><td>-0.071777</td><td>0.025879</td><td>0.023926</td><td>-0.050049</td><td>0.014465</td><td>0.057861</td><td>0.044922</td><td>-0.063965</td><td>-0.072754</td><td>0.066406</td><td>-0.041016</td><td>0.009155</td><td>0.0234375</td><td>-0.015991</td><td>-0.059082</td><td>-0.020874</td><td>0.044434</td><td>0.002258</td><td>0.100098</td><td>-0.05957</td><td>0.007996</td><td>-0.05957</td><td>-0.07959</td><td>-0.039307</td><td>0.079102</td><td>-0.029053</td><td>-0.008789</td><td>-0.106445</td><td>-0.050293</td><td>-0.014954</td><td>-0.079102</td><td>-0.035156</td><td>&hellip;</td><td>-0.022949</td><td>0.050537</td><td>0.167969</td><td>-0.094238</td><td>0.053467</td><td>0.04541</td><td>-0.025635</td><td>-0.017212</td><td>0.019409</td><td>-0.049805</td><td>0.04248</td><td>0.103027</td><td>-0.029663</td><td>-0.05127</td><td>0.030029</td><td>-0.010254</td><td>-0.007416</td><td>-0.022827</td><td>-0.051514</td><td>-0.110352</td><td>0.087402</td><td>0.023315</td><td>-0.037354</td><td>-0.010132</td><td>0.087402</td><td>-0.035645</td><td>0.022461</td><td>0.037598</td><td>-0.043945</td><td>-0.036865</td><td>-0.016357</td><td>-0.040039</td><td>-0.083008</td><td>-0.017822</td><td>-0.034912</td><td>0.041992</td><td>0.023926</td></tr><tr><td>&quot;divider_piston…</td><td>-0.033447</td><td>-0.121582</td><td>0.007538</td><td>-0.161133</td><td>0.003967</td><td>-0.087891</td><td>-0.074707</td><td>0.09082</td><td>-0.003937</td><td>0.04834</td><td>-0.014343</td><td>-0.075684</td><td>-0.084961</td><td>-0.084473</td><td>-0.055664</td><td>0.040527</td><td>-0.111816</td><td>-0.075684</td><td>-0.011963</td><td>0.04126</td><td>0.099121</td><td>-0.077637</td><td>-0.111816</td><td>-0.067871</td><td>0.182617</td><td>-0.045654</td><td>-0.080078</td><td>0.145508</td><td>0.100098</td><td>0.002518</td><td>-0.125</td><td>-0.0177</td><td>0.046387</td><td>0.026001</td><td>0.024902</td><td>-0.207031</td><td>&hellip;</td><td>-0.02832</td><td>0.081055</td><td>-0.050293</td><td>-0.00013</td><td>-0.170898</td><td>0.111328</td><td>-0.061035</td><td>-0.069824</td><td>-0.032959</td><td>-0.069336</td><td>0.006317</td><td>-0.020874</td><td>0.066406</td><td>-0.001862</td><td>0.016113</td><td>-0.143555</td><td>0.00528</td><td>0.025635</td><td>0.032959</td><td>0.055664</td><td>-0.01416</td><td>0.054199</td><td>-0.089355</td><td>0.009155</td><td>-0.047852</td><td>0.001846</td><td>0.000429</td><td>-0.006958</td><td>0.029419</td><td>-0.066406</td><td>0.171875</td><td>-0.002518</td><td>-0.001694</td><td>-0.090332</td><td>0.030518</td><td>0.015747</td><td>-0.022949</td></tr><tr><td>&quot;Thirsty_Owl&quot;</td><td>-0.025024</td><td>-0.044434</td><td>-0.037598</td><td>0.002045</td><td>0.055908</td><td>-0.02832</td><td>-0.000542</td><td>0.0177</td><td>0.050293</td><td>0.017578</td><td>-0.038574</td><td>-0.006378</td><td>-0.027588</td><td>-0.023438</td><td>-0.078613</td><td>0.032227</td><td>-0.004242</td><td>0.062012</td><td>-0.019897</td><td>0.027588</td><td>0.001968</td><td>0.056152</td><td>0.007477</td><td>-0.02356</td><td>-0.01532</td><td>0.037109</td><td>0.031494</td><td>0.040283</td><td>-0.016724</td><td>-0.041016</td><td>-0.003052</td><td>-0.046387</td><td>0.004822</td><td>0.056152</td><td>0.0078125</td><td>0.010437</td><td>&hellip;</td><td>0.006958</td><td>-0.011658</td><td>0.044189</td><td>-0.120117</td><td>-0.043945</td><td>0.0625</td><td>0.047607</td><td>0.013062</td><td>0.037109</td><td>-0.049072</td><td>0.029541</td><td>0.1328125</td><td>0.081543</td><td>-0.077148</td><td>-0.033691</td><td>-0.078125</td><td>-0.013428</td><td>-0.088867</td><td>0.041504</td><td>0.092773</td><td>-0.00322</td><td>0.010925</td><td>0.014771</td><td>-0.018677</td><td>0.070801</td><td>0.0078125</td><td>0.053955</td><td>0.003967</td><td>-0.034912</td><td>-0.07666</td><td>0.010437</td><td>-0.035645</td><td>-0.067383</td><td>-0.03064</td><td>-0.069824</td><td>0.080566</td><td>0.035156</td></tr><tr><td>&quot;righthander_Ky…</td><td>0.030029</td><td>0.05249</td><td>0.046143</td><td>0.07666</td><td>0.029297</td><td>-0.009521</td><td>-0.013855</td><td>-0.053223</td><td>0.029785</td><td>0.031128</td><td>0.042725</td><td>-0.037842</td><td>-0.030273</td><td>0.038818</td><td>-0.003296</td><td>0.032227</td><td>0.046143</td><td>0.022705</td><td>-0.023071</td><td>0.017212</td><td>-0.002502</td><td>0.049316</td><td>-0.020874</td><td>-0.016357</td><td>0.004364</td><td>-0.02771</td><td>-0.018311</td><td>0.071289</td><td>-0.019043</td><td>0.03833</td><td>0.071289</td><td>0.064941</td><td>0.03833</td><td>0.021118</td><td>-0.050049</td><td>0.025146</td><td>&hellip;</td><td>0.05835</td><td>-0.010742</td><td>0.000923</td><td>-0.040039</td><td>-0.003891</td><td>0.023804</td><td>0.024536</td><td>0.021851</td><td>0.009888</td><td>-0.030151</td><td>-0.026001</td><td>-0.012207</td><td>0.062256</td><td>-0.016602</td><td>0.074707</td><td>-0.030029</td><td>0.031128</td><td>0.06543</td><td>0.015198</td><td>0.022705</td><td>0.014709</td><td>0.002396</td><td>0.047119</td><td>-0.020752</td><td>0.028564</td><td>0.044434</td><td>-0.050781</td><td>0.033691</td><td>-0.010681</td><td>-0.063477</td><td>0.000076</td><td>0.009827</td><td>-0.073242</td><td>-0.016602</td><td>-0.053223</td><td>0.053467</td><td>-0.067383</td></tr><tr><td>&quot;RAFFAELE&quot;</td><td>0.009277</td><td>-0.050537</td><td>-0.018799</td><td>0.029785</td><td>0.037354</td><td>0.008911</td><td>-0.057617</td><td>0.035645</td><td>-0.05249</td><td>0.078125</td><td>-0.066895</td><td>0.016602</td><td>-0.013184</td><td>0.028564</td><td>-0.0177</td><td>0.056152</td><td>0.016479</td><td>0.043701</td><td>-0.013977</td><td>0.000126</td><td>-0.009521</td><td>0.040283</td><td>0.0546875</td><td>-0.041992</td><td>-0.012878</td><td>-0.043457</td><td>0.01355</td><td>0.0354</td><td>-0.038574</td><td>0.025024</td><td>0.041504</td><td>-0.007141</td><td>0.029053</td><td>-0.052002</td><td>0.056641</td><td>-0.024048</td><td>&hellip;</td><td>-0.057617</td><td>0.036377</td><td>0.006958</td><td>0.07373</td><td>0.038086</td><td>-0.041016</td><td>-0.030762</td><td>-0.00053</td><td>0.04248</td><td>-0.025146</td><td>0.044434</td><td>0.055908</td><td>0.03125</td><td>-0.006104</td><td>-0.004486</td><td>-0.000656</td><td>0.114258</td><td>-0.088867</td><td>0.064941</td><td>-0.05835</td><td>0.081543</td><td>-0.006958</td><td>0.027832</td><td>0.043701</td><td>-0.004456</td><td>0.001579</td><td>0.033691</td><td>-0.028076</td><td>-0.002853</td><td>-0.059326</td><td>-0.007385</td><td>-0.008606</td><td>-0.02356</td><td>0.016968</td><td>-0.012634</td><td>0.01062</td><td>0.026978</td></tr><tr><td>&quot;Bim_Skala_Bim&quot;</td><td>0.012573</td><td>0.04541</td><td>-0.043213</td><td>-0.001495</td><td>0.026489</td><td>0.049561</td><td>-0.059326</td><td>-0.083496</td><td>-0.033691</td><td>0.099609</td><td>-0.000637</td><td>0.067383</td><td>0.004669</td><td>0.028198</td><td>0.036377</td><td>-0.043457</td><td>-0.012939</td><td>0.051514</td><td>0.011292</td><td>-0.040039</td><td>-0.019043</td><td>0.048096</td><td>0.085449</td><td>0.058838</td><td>-0.066406</td><td>0.063965</td><td>-0.034668</td><td>0.041748</td><td>0.030762</td><td>-0.046387</td><td>-0.000136</td><td>0.09375</td><td>-0.009888</td><td>0.018311</td><td>-0.017578</td><td>-0.035156</td><td>&hellip;</td><td>0.013672</td><td>-0.028931</td><td>0.071777</td><td>-0.106445</td><td>-0.019897</td><td>-0.000984</td><td>0.012512</td><td>0.020386</td><td>0.001068</td><td>-0.042236</td><td>-0.013306</td><td>0.092285</td><td>0.031128</td><td>-0.03833</td><td>0.019287</td><td>-0.00041</td><td>0.039551</td><td>-0.048828</td><td>-0.0354</td><td>-0.025513</td><td>-0.015442</td><td>0.025757</td><td>0.002533</td><td>0.034912</td><td>0.031738</td><td>-0.002396</td><td>-0.027344</td><td>0.086914</td><td>-0.041504</td><td>-0.036865</td><td>-0.023315</td><td>0.012451</td><td>-0.112305</td><td>-0.01062</td><td>-0.051514</td><td>-0.014282</td><td>0.051514</td></tr><tr><td>&quot;Mezze_Cafe&quot;</td><td>-0.019653</td><td>-0.09082</td><td>-0.019409</td><td>0.019653</td><td>-0.00023</td><td>0.040283</td><td>-0.050293</td><td>-0.012695</td><td>0.02417</td><td>0.013123</td><td>0.021606</td><td>0.002258</td><td>0.102051</td><td>-0.038574</td><td>0.086426</td><td>0.032471</td><td>0.06543</td><td>0.07373</td><td>0.080078</td><td>-0.010986</td><td>0.033203</td><td>-0.047852</td><td>0.074219</td><td>-0.032227</td><td>0.003387</td><td>-0.015442</td><td>0.074707</td><td>0.016357</td><td>0.04834</td><td>-0.038086</td><td>-0.040039</td><td>-0.081055</td><td>0.0177</td><td>-0.007568</td><td>-0.014648</td><td>-0.003128</td><td>&hellip;</td><td>0.019897</td><td>-0.023071</td><td>0.025635</td><td>0.025146</td><td>-0.095703</td><td>-0.082031</td><td>-0.016235</td><td>-0.019897</td><td>-0.008423</td><td>-0.078125</td><td>0.02771</td><td>0.069824</td><td>0.055176</td><td>-0.100098</td><td>-0.023926</td><td>-0.101562</td><td>-0.006622</td><td>-0.074707</td><td>0.071289</td><td>0.05249</td><td>-0.012756</td><td>0.018188</td><td>0.010254</td><td>0.033447</td><td>0.025513</td><td>0.040527</td><td>0.04248</td><td>-0.046387</td><td>0.001076</td><td>-0.136719</td><td>-0.004547</td><td>0.022827</td><td>0.042969</td><td>-0.046875</td><td>-0.016357</td><td>-0.013428</td><td>0.046631</td></tr><tr><td>&quot;pulverizes_bou…</td><td>0.032715</td><td>-0.032227</td><td>0.036133</td><td>0.001175</td><td>-0.036621</td><td>-0.084961</td><td>0.036865</td><td>0.032471</td><td>0.063477</td><td>0.114746</td><td>-0.05127</td><td>0.001053</td><td>-0.008728</td><td>0.09375</td><td>-0.062012</td><td>0.037109</td><td>0.026489</td><td>0.086914</td><td>-0.067383</td><td>-0.010193</td><td>-0.00238</td><td>-0.013062</td><td>0.022949</td><td>0.005371</td><td>-0.055176</td><td>-0.112305</td><td>-0.024292</td><td>0.006683</td><td>-0.029297</td><td>-0.107422</td><td>-0.129883</td><td>-0.052246</td><td>-0.032715</td><td>0.046631</td><td>-0.053223</td><td>-0.077148</td><td>&hellip;</td><td>-0.013611</td><td>0.005096</td><td>0.090332</td><td>-0.045898</td><td>-0.120117</td><td>0.0354</td><td>-0.027832</td><td>-0.118164</td><td>0.064941</td><td>-0.02124</td><td>0.072754</td><td>0.097168</td><td>0.010071</td><td>0.009399</td><td>-0.066895</td><td>-0.099121</td><td>0.048584</td><td>-0.049072</td><td>-0.031982</td><td>-0.01416</td><td>0.072266</td><td>0.011597</td><td>-0.078613</td><td>0.033691</td><td>-0.007507</td><td>0.098145</td><td>0.112793</td><td>0.02002</td><td>-0.070312</td><td>-0.167969</td><td>0.003082</td><td>-0.060303</td><td>-0.007294</td><td>-0.009827</td><td>-0.00885</td><td>0.026978</td><td>0.019043</td></tr><tr><td>&quot;snowcapped_Cau…</td><td>0.045166</td><td>-0.045166</td><td>-0.003937</td><td>0.048828</td><td>0.088379</td><td>-0.123535</td><td>-0.033691</td><td>-0.142578</td><td>-0.121094</td><td>0.201172</td><td>-0.028809</td><td>0.047607</td><td>0.041016</td><td>-0.003159</td><td>0.133789</td><td>-0.140625</td><td>-0.123535</td><td>0.115723</td><td>-0.081055</td><td>-0.004395</td><td>-0.046387</td><td>-0.032227</td><td>0.036133</td><td>0.043945</td><td>-0.033936</td><td>-0.05127</td><td>-0.115234</td><td>0.030884</td><td>-0.003448</td><td>-0.097656</td><td>-0.054199</td><td>0.061035</td><td>0.060791</td><td>0.032227</td><td>-0.061523</td><td>-0.029053</td><td>&hellip;</td><td>0.017578</td><td>0.201172</td><td>0.141602</td><td>-0.005188</td><td>-0.081055</td><td>0.002487</td><td>-0.056641</td><td>-0.001694</td><td>-0.012207</td><td>-0.001999</td><td>0.064941</td><td>-0.03833</td><td>0.067871</td><td>0.074219</td><td>-0.12793</td><td>-0.060791</td><td>-0.091797</td><td>0.0703125</td><td>-0.007812</td><td>0.092285</td><td>-0.035645</td><td>-0.161133</td><td>-0.028442</td><td>0.032227</td><td>0.038574</td><td>-0.079102</td><td>-0.027222</td><td>0.015991</td><td>0.100586</td><td>-0.062988</td><td>-0.11377</td><td>0.070801</td><td>-0.012939</td><td>-0.041504</td><td>0.07959</td><td>0.072266</td><td>0.013</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3_000_000, 301)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ word      ┆ v00000    ┆ v00001    ┆ v00002    ┆ … ┆ v00296    ┆ v00297    ┆ v00298    ┆ v00299   │\n",
       "│ ---       ┆ ---       ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
       "│ str       ┆ f64       ┆ f64       ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ </s>      ┆ 0.001129  ┆ -0.000896 ┆ 0.000319  ┆ … ┆ 0.000568  ┆ -0.001564 ┆ -0.000123 ┆ -0.00008 │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 6        │\n",
       "│ in        ┆ 0.0703125 ┆ 0.086914  ┆ 0.087891  ┆ … ┆ -0.058838 ┆ -0.047607 ┆ 0.014465  ┆ -0.0625  │\n",
       "│ for       ┆ -0.01178  ┆ -0.047363 ┆ 0.044678  ┆ … ┆ 0.018433  ┆ 0.071289  ┆ -0.034912 ┆ 0.02417  │\n",
       "│ that      ┆ -0.015747 ┆ -0.02832  ┆ 0.083496  ┆ … ┆ 0.020142  ┆ 0.006866  ┆ 0.061035  ┆ -0.14843 │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 8        │\n",
       "│ …         ┆ …         ┆ …         ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …        │\n",
       "│ Bim_Skala ┆ 0.012573  ┆ 0.04541   ┆ -0.043213 ┆ … ┆ -0.01062  ┆ -0.051514 ┆ -0.014282 ┆ 0.051514 │\n",
       "│ _Bim      ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ Mezze_Caf ┆ -0.019653 ┆ -0.09082  ┆ -0.019409 ┆ … ┆ -0.046875 ┆ -0.016357 ┆ -0.013428 ┆ 0.046631 │\n",
       "│ e         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ pulverize ┆ 0.032715  ┆ -0.032227 ┆ 0.036133  ┆ … ┆ -0.009827 ┆ -0.00885  ┆ 0.026978  ┆ 0.019043 │\n",
       "│ s_boulder ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ s         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ snowcappe ┆ 0.045166  ┆ -0.045166 ┆ -0.003937 ┆ … ┆ -0.041504 ┆ 0.07959   ┆ 0.072266  ┆ 0.013    │\n",
       "│ d_Caucasu ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ s         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use Polars to read word2vec TSV\n",
    "d = pl.read_csv(\n",
    "    'data/word2vec/word2vec.tsv.gz', \n",
    "    separator='\\t',\n",
    "    comment_char=None,\n",
    "    quote_char=None,\n",
    "    )\n",
    "\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the embedder\n",
    "\n",
    "This will take a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "emb = W2VEmbed(d)\n",
    "del d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>290</th>\n",
       "      <th>291</th>\n",
       "      <th>292</th>\n",
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       "      <th>294</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005127</td>\n",
       "      <td>0.095744</td>\n",
       "      <td>0.021718</td>\n",
       "      <td>0.072266</td>\n",
       "      <td>-0.135152</td>\n",
       "      <td>-0.106222</td>\n",
       "      <td>-0.067057</td>\n",
       "      <td>-0.098796</td>\n",
       "      <td>0.053019</td>\n",
       "      <td>0.281576</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050659</td>\n",
       "      <td>0.12439</td>\n",
       "      <td>-0.056559</td>\n",
       "      <td>-0.134359</td>\n",
       "      <td>0.029093</td>\n",
       "      <td>-0.018555</td>\n",
       "      <td>-0.022371</td>\n",
       "      <td>-0.209635</td>\n",
       "      <td>-0.000366</td>\n",
       "      <td>-0.01888</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  0.005127  0.095744  0.021718  0.072266 -0.135152 -0.106222 -0.067057   \n",
       "\n",
       "        7         8         9    ...       290      291       292       293  \\\n",
       "0 -0.098796  0.053019  0.281576  ... -0.050659  0.12439 -0.056559 -0.134359   \n",
       "\n",
       "        294       295       296       297       298      299  \n",
       "0  0.029093 -0.018555 -0.022371 -0.209635 -0.000366 -0.01888  \n",
       "\n",
       "[1 rows x 300 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quick demo\n",
    "emb.transform_str(\"hello there, boys\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the text corpora\n",
    "\n",
    "IMDB and SMS Spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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       "    }\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>fname</th>\n",
       "      <th>text</th>\n",
       "      <th>what</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train/pos/4715_9.txt</td>\n",
       "      <td>\"For a movie that gets no respect there sure a...</td>\n",
       "      <td>train/pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train/pos/12390_8.txt</td>\n",
       "      <td>\"Bizarre horror movie filled with famous faces...</td>\n",
       "      <td>train/pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train/pos/8329_7.txt</td>\n",
       "      <td>A solid, if unremarkable film. Matthau, as Ein...</td>\n",
       "      <td>train/pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train/pos/9063_8.txt</td>\n",
       "      <td>\"It's a strange feeling to sit alone in a thea...</td>\n",
       "      <td>train/pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train/pos/3092_10.txt</td>\n",
       "      <td>You probably all already know this by now, but...</td>\n",
       "      <td>train/pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>test/neg/11513_1.txt</td>\n",
       "      <td>With actors like Depardieu and Richard it is r...</td>\n",
       "      <td>test/neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>test/neg/5409_1.txt</td>\n",
       "      <td>\"If you like to get a couple of fleeting glimp...</td>\n",
       "      <td>test/neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>test/neg/11187_1.txt</td>\n",
       "      <td>When something can be anything you want it to ...</td>\n",
       "      <td>test/neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>test/neg/9359_3.txt</td>\n",
       "      <td>\"I had heard good things about \"\"States of Gra...</td>\n",
       "      <td>test/neg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>test/neg/11556_1.txt</td>\n",
       "      <td>\"Well, this movie actually did have one redeem...</td>\n",
       "      <td>test/neg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       fname  \\\n",
       "0       train/pos/4715_9.txt   \n",
       "1      train/pos/12390_8.txt   \n",
       "2       train/pos/8329_7.txt   \n",
       "3       train/pos/9063_8.txt   \n",
       "4      train/pos/3092_10.txt   \n",
       "...                      ...   \n",
       "49995   test/neg/11513_1.txt   \n",
       "49996    test/neg/5409_1.txt   \n",
       "49997   test/neg/11187_1.txt   \n",
       "49998    test/neg/9359_3.txt   \n",
       "49999   test/neg/11556_1.txt   \n",
       "\n",
       "                                                    text       what  \n",
       "0      \"For a movie that gets no respect there sure a...  train/pos  \n",
       "1      \"Bizarre horror movie filled with famous faces...  train/pos  \n",
       "2      A solid, if unremarkable film. Matthau, as Ein...  train/pos  \n",
       "3      \"It's a strange feeling to sit alone in a thea...  train/pos  \n",
       "4      You probably all already know this by now, but...  train/pos  \n",
       "...                                                  ...        ...  \n",
       "49995  With actors like Depardieu and Richard it is r...   test/neg  \n",
       "49996  \"If you like to get a couple of fleeting glimp...   test/neg  \n",
       "49997  When something can be anything you want it to ...   test/neg  \n",
       "49998  \"I had heard good things about \"\"States of Gra...   test/neg  \n",
       "49999  \"Well, this movie actually did have one redeem...   test/neg  \n",
       "\n",
       "[50000 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb_frame = pd.read_csv(\n",
    "    \"data/IMDB/IMDB.tsv.gz\",\n",
    "    sep='\\t',\n",
    "    quoting=csv.QUOTE_NONE,\n",
    "    quotechar=None,\n",
    "    comment=None,\n",
    ")\n",
    "\n",
    "imdb_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>class</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>spam</td>\n",
       "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5570</th>\n",
       "      <td>ham</td>\n",
       "      <td>Will ü b going to esplanade fr home?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5571</th>\n",
       "      <td>ham</td>\n",
       "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5572</th>\n",
       "      <td>ham</td>\n",
       "      <td>The guy did some bitching but I acted like i'd...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5573</th>\n",
       "      <td>ham</td>\n",
       "      <td>Rofl. Its true to its name</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5574 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     class                                               text\n",
       "0      ham  Go until jurong point, crazy.. Available only ...\n",
       "1      ham                      Ok lar... Joking wif u oni...\n",
       "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3      ham  U dun say so early hor... U c already then say...\n",
       "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
       "...    ...                                                ...\n",
       "5569  spam  This is the 2nd time we have tried 2 contact u...\n",
       "5570   ham               Will ü b going to esplanade fr home?\n",
       "5571   ham  Pity, * was in mood for that. So...any other s...\n",
       "5572   ham  The guy did some bitching but I acted like i'd...\n",
       "5573   ham                         Rofl. Its true to its name\n",
       "\n",
       "[5574 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_texts = pd.read_csv(\n",
    "    \"data/smsspamcollection/SMSSpamCollection.tsv.gz\", \n",
    "    sep=\"\\t\",\n",
    "    quotechar=None,\n",
    "    quoting=csv.QUOTE_NONE,\n",
    "    comment=None,\n",
    "    )\n",
    "\n",
    "spam_texts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First task - distinguish IMDB reviews from spam texts\n",
    "\n",
    "We'll use a sample of positive IMDB reviews as the reference distribution, and compare it to\n",
    "\n",
    "* another sample of positive IMDB reviews\n",
    "* a sample of negative IMDB reviews\n",
    "* a sample of SMS spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first, get the embeddings of all the sample sets\n",
    "\n",
    "train_frame = emb.transform(imdb_frame.loc[imdb_frame[\"what\"] == \"train/pos\", \"text\"])\n",
    "test_frame_pos = emb.transform(imdb_frame.loc[imdb_frame[\"what\"] == \"test/pos\", \"text\"])\n",
    "test_frame_neg = emb.transform(imdb_frame.loc[imdb_frame[\"what\"] == \"test/neg\", \"text\"])\n",
    "spam_frame = emb.transform(spam_texts[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sphering_transform.SpheringTransform at 0x5f809db50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now fit the sphering transform on the reference set\n",
    "st = SpheringTransform()\n",
    "st.fit(train_frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sphere-transform all the data sets\n",
    "xformed_train = st.transform(train_frame)\n",
    "xformed_test_pos = st.transform(test_frame_pos)\n",
    "xformed_test_neg = st.transform(test_frame_neg)\n",
    "xformed_spam_frame = st.transform(spam_frame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will compare the different text embedding distributions (in transform space) by comparing the distributions of norms of each transformed embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the norms\n",
    "\n",
    "imdb_train_pos = norm(xformed_train, axis=1)  # the reference distribution\n",
    "imdb_test_neg = norm(xformed_test_neg, axis=1)\n",
    "imdb_test_pos = norm(xformed_test_pos, axis=1)\n",
    "sms_text = norm(xformed_spam_frame, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare visually\n",
    "\n",
    "We can distinguish the SMS messages from the IMDB reviews. This document representation is not sensitive enough to distinguish positive reviews from negative ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison_frame = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        'norm': imdb_train_pos,\n",
    "        'what': \"IDMB_train_pos\",\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'norm': imdb_test_neg,\n",
    "        'what': \"IMDB_test_neg\",\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'norm': imdb_test_pos,\n",
    "        'what': \"IMDB_test_pos\",\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'norm': sms_text,\n",
    "        'what': \"SMS_text\",\n",
    "    }),\n",
    "])\n",
    "\n",
    "sns.kdeplot(\n",
    "    data=comparison_frame,\n",
    "    x=\"norm\",\n",
    "    hue=\"what\",\n",
    "    common_norm=False,\n",
    ")\n",
    "plt.title(\"distribution of sphered norm on training and different test data sets\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also measure the difference quantitatively using the Population Stability Index (or your preferred distribution divergence metric).\n",
    "\n",
    "This PSI calculator uses decile binning. A common rule of thumb:\n",
    "\n",
    "* PSI < 0.1: distributions are similar\n",
    "* PSI between 0.1 and 0.25: distributions are somewhat different\n",
    "* PSI > 0.25: distributions are very different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'psi test pos': 0.0120601197683604,\n",
       " 'psi test neg': 0.0007532764568599546,\n",
       " 'psi sms text': 7.889785670522413}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psiCalc = DistCalculator(imdb_train_pos)\n",
    "\n",
    "{\n",
    "    'psi test pos': psiCalc.PSI(imdb_test_pos),\n",
    "    'psi test neg': psiCalc.PSI(imdb_test_neg),\n",
    "    'psi sms text': psiCalc.PSI(sms_text)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring the embeddings in the original space\n",
    "\n",
    "For comparison, let's compare the embedding norms in the original space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imdb_train_pos = norm(train_frame, axis=1)\n",
    "imdb_test_neg = norm(test_frame_neg, axis=1)\n",
    "imdb_test_pos = norm(test_frame_pos, axis=1)\n",
    "sms_text = norm(spam_frame, axis=1)\n",
    "\n",
    "comparison_frame = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        'norm': imdb_train_pos,\n",
    "        'what': \"IDMB_train_pos\",\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'norm': imdb_test_neg,\n",
    "        'what': \"IMDB_test_neg\",\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'norm': imdb_test_pos,\n",
    "        'what': \"IMDB_test_pos\",\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'norm': sms_text,\n",
    "        'what': \"SMS_text\",\n",
    "    }),\n",
    "])\n",
    "\n",
    "sns.kdeplot(\n",
    "    data=comparison_frame,\n",
    "    x=\"norm\",\n",
    "    hue=\"what\",\n",
    "    common_norm=False,\n",
    ")\n",
    "plt.title(\"distribution of non-sphered norm on training and different test data sets\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'psi test pos': 0.0042318510403948585,\n",
       " 'psi test neg': 0.09829329121230966,\n",
       " 'psi sms text': 6.806431055468874}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psiCalc = DistCalculator(imdb_train_pos)\n",
    "\n",
    "{\n",
    "    'psi test pos': psiCalc.PSI(imdb_test_pos),\n",
    "    'psi test neg': psiCalc.PSI(imdb_test_neg),\n",
    "    'psi sms text': psiCalc.PSI(sms_text)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, with this text representation, comparing norms in the original embedding space is probably good enough, though detecting differences in transform space may be slightly more sensitive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Task: detect changes in population mixes\n",
    "\n",
    "In this task, we'll start with reference corpus with a 50-50 split of IMDB reviews and SMS spam. We will compare that corpus with \n",
    "* another corpus with the same population mix\n",
    "* a corpus that is 75-25 IMDB to SMS\n",
    "\n",
    "\n",
    "### Build the reference and comparison sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gp\n",
       "train       2255\n",
       "samepop     2224\n",
       "shiftpop    1095\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# need to split the spam first, because there's not too much of it.\n",
    "nsamples = spam_texts.shape[0]\n",
    "setlabels = ['train', 'samepop', 'shiftpop']\n",
    "\n",
    "group = rng.choice(setlabels, size=nsamples, replace=True, p=[0.4, 0.4, 0.2])\n",
    "spam_texts['gp'] = group\n",
    "counts = spam_texts['gp'].value_counts()\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# draw the text samples and create their embeddings\n",
    "\n",
    "spam_train = emb.transform(spam_texts.loc[spam_texts['gp'] == 'train', \"text\"])\n",
    "spam_samepop = emb.transform(spam_texts.loc[spam_texts['gp'] == 'samepop', \"text\"])\n",
    "spam_shiftpop = emb.transform(spam_texts.loc[spam_texts['gp'] == 'shiftpop', \"text\"])\n",
    "\n",
    "imdb_train = emb.transform(rng.choice(imdb_frame[\"text\"], size=spam_train.shape[0], replace=False))\n",
    "imdb_samepop = emb.transform(rng.choice(imdb_frame[\"text\"], size=spam_samepop.shape[0], replace=False))\n",
    "imdb_shiftpop = emb.transform(rng.choice(imdb_frame[\"text\"], size=3*spam_shiftpop.shape[0], replace=False))\n",
    "\n",
    "# final data sets\n",
    "dtrain = pd.concat([spam_train, imdb_train], ignore_index=True)\n",
    "dsamepop = pd.concat([spam_samepop, imdb_samepop], ignore_index=True)\n",
    "dshiftpop = pd.concat([spam_shiftpop, imdb_shiftpop], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the sphering transform and transform the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = SpheringTransform()\n",
    "st.fit(dtrain)\n",
    "\n",
    "xformed_train = st.transform(dtrain)\n",
    "xformed_samepop = st.transform(dsamepop)\n",
    "xformed_shiftpop = st.transform(dshiftpop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the norm distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in transform space\n",
    "norm_xformed_train = norm(xformed_train, axis=1)\n",
    "norm_xformed_samepop = norm(xformed_samepop, axis=1)\n",
    "norm_xformed_shiftpop = norm(xformed_shiftpop, axis=1)\n",
    "\n",
    "# in real space\n",
    "norm_train = norm(dtrain, axis=1)\n",
    "norm_samepop = norm(dsamepop, axis=1)\n",
    "norm_shiftpop = norm(dshiftpop, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare\n",
    "\n",
    "### In transform space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison_frame = pd.concat([\n",
    "    pd.DataFrame({\n",
    "        'norm': norm_xformed_train,\n",
    "        'what': \"training mix (50-50)\",\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'norm': norm_xformed_samepop,\n",
    "        'what': \"test mix (50-50)\",\n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'norm': norm_xformed_shiftpop,\n",
    "        'what': \"test mix (75-25)\",\n",
    "    }),\n",
    "])\n",
    "\n",
    "sns.kdeplot(\n",
    "    data=comparison_frame,\n",
    "    x=\"norm\",\n",
    "    hue=\"what\",\n",
    "    common_norm=False,\n",
    ")\n",
    "plt.title(\"distribution of sphered norm on training and different test data sets\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Difference of Distributions (PSI)\n",
    "\n",
    "We can also measure the difference quantitatively using the Population Stability Index (or your preferred distribution divergence metric).\n",
    "\n",
    "This PSI calculator uses decile binning. A common rule of thumb:\n",
    "\n",
    "* PSI < 0.1: distributions are similar\n",
    "* PSI between 0.1 and 0.25: distributions are somewhat different\n",
    "* PSI > 0.25: distributions are very different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'psi 50-50 mix': 0.01968997708819725, 'psi 75-25 mix': 0.22287692999430095}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## create a PSI calculator to measure differences from reference norm distribution (transformed)\n",
    "psiCalc = DistCalculator(norm_xformed_train)\n",
    "\n",
    "{\n",
    "    'psi 50-50 mix': psiCalc.PSI(norm_xformed_samepop),\n",
    "    'psi 75-25 mix': psiCalc.PSI(norm_xformed_shiftpop)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PSI measurement flags the shifted distribution as somewhat different. As in the first task, the real space distributions show the same difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'psi 50-50 mix': 0.0015433218214826992, 'psi 75-25 mix': 0.2418655261070141}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## create a PSI calculator to measure differences from reference norm distribution (original space)\n",
    "psiCalc = DistCalculator(norm_train)\n",
    "\n",
    "{\n",
    "    'psi 50-50 mix': psiCalc.PSI(norm_samepop),\n",
    "    'psi 75-25 mix': psiCalc.PSI(norm_shiftpop)\n",
    "}"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tc_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}