fnielsen/Nielsen2018Visual_analyze.ipynb

## Nielsen2018Visual_analyze.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from os.path import expanduser\n",
    "import json\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "from keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions\n",
    "from keras.preprocessing.image import load_img, img_to_array\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "import seaborn\n",
    "\n",
    "from pandas.io.json import json_normalize\n",
    "from SPARQLWrapper import SPARQLWrapper, JSON\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_directory = expanduser('~/data/imagesim-353/')\n",
    "scorings_filename = expanduser('~/fnielsen/data/Nielsen2018Visual_scorings-2018-03-09T22:05:36.762236.json')\n",
    "pairs_filename = expanduser('~/fnielsen/xls/Nielsen2018Visual_pairs.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairs = pd.read_excel(pairs_filename)\n",
    "annotation = json.load(open(scorings_filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n1   n2\n",
       "1   1    2\n",
       "2   1  253\n",
       "3   3    4\n",
       "4   1    3\n",
       "5   1    5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n1   n2  score\n",
       "1    1    2      8\n",
       "2    1  253      7\n",
       "3    3    4      9\n",
       "4    1    3      7\n",
       "5    1    5      8\n",
       "6    2    6      1\n",
       "7    4   37      6\n",
       "8    6   34      5\n",
       "9    6   29      5\n",
       "10   6   59      6"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = []\n",
    "for n in range(1, 354):\n",
    "    scores.append(annotation[str(n)]['score'])\n",
    "    \n",
    "pairs['score'] = scores\n",
    "pairs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ResNet50()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.zeros((353, 1000))\n",
    "X = np.zeros((353, np.prod(model.input_shape[1:3]) * 3))\n",
    "for n in range(1, 354):\n",
    "    filename = data_directory + 'image-{:03d}.jpg'.format(n)\n",
    "    img = load_img(filename, target_size=model.input_shape[1:3])\n",
    "    x = img_to_array(img)\n",
    "    X[n-1, :] = x.flatten()\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    y = model.predict(x)\n",
    "    Y[n-1, :] = y[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FA587ADC250>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation = np.corrcoef(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa58572dd90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa587b68350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "plt.matshow(np.corrcoef(Y), fignum=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.018807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.804770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>0.106226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.002257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0.001459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.001304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n1   n2  score  correlation\n",
       "1    1    2      8    -0.000504\n",
       "2    1  253      7    -0.000981\n",
       "3    3    4      9     0.018807\n",
       "4    1    3      7    -0.000432\n",
       "5    1    5      8     0.804770\n",
       "6    2    6      1    -0.002310\n",
       "7    4   37      6     0.106226\n",
       "8    6   34      5    -0.002257\n",
       "9    6   29      5     0.001459\n",
       "10   6   59      6    -0.001304"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations = []\n",
    "for n, row in pairs.iterrows():\n",
    "    n1, n2 = row.astype(int).values[:2]\n",
    "    correlations.append(correlation[n1-1][n2-1])\n",
    "    \n",
    "pairs['correlation'] = correlations\n",
    "pairs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa58654a750>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa586ece490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(pairs['score'], pairs['correlation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.000504</td>\n",
       "      <td>0.429619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>0.606894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.018807</td>\n",
       "      <td>0.538667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000432</td>\n",
       "      <td>0.092262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.804770</td>\n",
       "      <td>0.708531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002310</td>\n",
       "      <td>-0.112746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>0.106226</td>\n",
       "      <td>0.509231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>0.516821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.549673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>0.615558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n1   n2  score  correlation  correlation_on_log\n",
       "1    1    2      8    -0.000504            0.429619\n",
       "2    1  253      7    -0.000981            0.606894\n",
       "3    3    4      9     0.018807            0.538667\n",
       "4    1    3      7    -0.000432            0.092262\n",
       "5    1    5      8     0.804770            0.708531\n",
       "6    2    6      1    -0.002310           -0.112746\n",
       "7    4   37      6     0.106226            0.509231\n",
       "8    6   34      5    -0.002257            0.516821\n",
       "9    6   29      5     0.001459            0.549673\n",
       "10   6   59      6    -0.001304            0.615558"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_on_log = np.corrcoef(np.log(Y))\n",
    "correlations = []\n",
    "for n, row in pairs.iterrows():\n",
    "    n1, n2 = row.astype(int).values[:2]\n",
    "    correlations.append(correlation_on_log[n1-1][n2-1])\n",
    "    \n",
    "pairs['correlation_on_log'] = correlations\n",
    "pairs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa58649a2d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa586e18410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pairs['score'], pairs['correlation_on_log'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa5864ad350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "seaborn.swarmplot(pairs['score'], pairs['correlation_on_log'])\n",
    "plt.savefig('Nielsen2018Visual_resnet50scatter.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa585c13250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('Nielsen2018Visual_resnet50scatter.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.625810</td>\n",
       "      <td>0.023039</td>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.090583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n2</th>\n",
       "      <td>0.625810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>-0.095936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.023039</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>0.672063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation</th>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log</th>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          n1        n2     score  correlation  \\\n",
       "n1                  1.000000  0.625810  0.023039    -0.040134   \n",
       "n2                  0.625810  1.000000  0.012842    -0.072894   \n",
       "score               0.023039  0.012842  1.000000     0.615508   \n",
       "correlation        -0.040134 -0.072894  0.615508     1.000000   \n",
       "correlation_on_log -0.090583 -0.095936  0.672063     0.716846   \n",
       "\n",
       "                    correlation_on_log  \n",
       "n1                           -0.090583  \n",
       "n2                           -0.095936  \n",
       "score                         0.672063  \n",
       "correlation                   0.716846  \n",
       "correlation_on_log            1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.000504</td>\n",
       "      <td>0.429619</td>\n",
       "      <td>-0.019529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>0.606894</td>\n",
       "      <td>-0.203929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.018807</td>\n",
       "      <td>0.538667</td>\n",
       "      <td>0.031156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000432</td>\n",
       "      <td>0.092262</td>\n",
       "      <td>0.043735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.804770</td>\n",
       "      <td>0.708531</td>\n",
       "      <td>0.031601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002310</td>\n",
       "      <td>-0.112746</td>\n",
       "      <td>0.233721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>0.106226</td>\n",
       "      <td>0.509231</td>\n",
       "      <td>0.081999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>0.516821</td>\n",
       "      <td>0.105973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.549673</td>\n",
       "      <td>-0.048356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>0.615558</td>\n",
       "      <td>0.180264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n1   n2  score  correlation  correlation_on_log  correlation_on_raw\n",
       "1    1    2      8    -0.000504            0.429619           -0.019529\n",
       "2    1  253      7    -0.000981            0.606894           -0.203929\n",
       "3    3    4      9     0.018807            0.538667            0.031156\n",
       "4    1    3      7    -0.000432            0.092262            0.043735\n",
       "5    1    5      8     0.804770            0.708531            0.031601\n",
       "6    2    6      1    -0.002310           -0.112746            0.233721\n",
       "7    4   37      6     0.106226            0.509231            0.081999\n",
       "8    6   34      5    -0.002257            0.516821            0.105973\n",
       "9    6   29      5     0.001459            0.549673           -0.048356\n",
       "10   6   59      6    -0.001304            0.615558            0.180264"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_on_raw = np.corrcoef(X)\n",
    "correlations = []\n",
    "for n, row in pairs.iterrows():\n",
    "    n1, n2 = row.astype(int).values[:2]\n",
    "    correlations.append(correlation_on_raw[n1-1][n2-1])\n",
    "    \n",
    "pairs['correlation_on_raw'] = correlations\n",
    "pairs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.625810</td>\n",
       "      <td>0.023039</td>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.198678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n2</th>\n",
       "      <td>0.625810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>-0.191183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.023039</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.121592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation</th>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>0.205652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log</th>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <td>-0.198678</td>\n",
       "      <td>-0.191183</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          n1        n2     score  correlation  \\\n",
       "n1                  1.000000  0.625810  0.023039    -0.040134   \n",
       "n2                  0.625810  1.000000  0.012842    -0.072894   \n",
       "score               0.023039  0.012842  1.000000     0.615508   \n",
       "correlation        -0.040134 -0.072894  0.615508     1.000000   \n",
       "correlation_on_log -0.090583 -0.095936  0.672063     0.716846   \n",
       "correlation_on_raw -0.198678 -0.191183  0.121592     0.205652   \n",
       "\n",
       "                    correlation_on_log  correlation_on_raw  \n",
       "n1                           -0.090583           -0.198678  \n",
       "n2                           -0.095936           -0.191183  \n",
       "score                         0.672063            0.121592  \n",
       "correlation                   0.716846            0.205652  \n",
       "correlation_on_log            1.000000            0.263991  \n",
       "correlation_on_raw            0.263991            1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/SuLab/sparql_to_pandas/blob/master/SPARQL_pandas.ipynb\n",
    "def sparql_to_dataframe(query):\n",
    "    endpoint = SPARQLWrapper(\"https://query.wikidata.org/sparql\")\n",
    "    endpoint.setQuery(query)\n",
    "    endpoint.setReturnFormat(JSON)\n",
    "    return json_normalize(endpoint.query().convert()['results']['bindings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "SELECT\n",
    "  ?item\n",
    "  (SAMPLE(?synset_) AS ?synset)\n",
    "  (GROUP_CONCAT(?pid) AS ?properties)\n",
    "WITH {\n",
    "  SELECT ?item ?uri WHERE {\n",
    "    ?item wdt:P2888 ?uri .\n",
    "    FILTER STRSTARTS(STR(?uri),\n",
    "      \"http://wordnet-rdf.princeton.edu/wn30/\")\n",
    "  }\n",
    "} AS %items\n",
    "WHERE {\n",
    "  INCLUDE %items \n",
    "  ?item ?p [] .  \n",
    "  ?property wikibase:directClaim ?p .\n",
    "  BIND(SUBSTR(STR(?p), 37) AS ?pid)\n",
    "  BIND(CONCAT('n', SUBSTR(STR(?uri), 39, 8)) AS ?synset_)\n",
    "}\n",
    "GROUP BY ?item\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item.type</th>\n",
       "      <th>item.value</th>\n",
       "      <th>properties.type</th>\n",
       "      <th>properties.value</th>\n",
       "      <th>synset.type</th>\n",
       "      <th>synset.value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>uri</td>\n",
       "      <td>http://www.wikidata.org/entity/Q4504</td>\n",
       "      <td>literal</td>\n",
       "      <td>P10 P18 P31 P105 P138 P141 P171 P181 P183 P225...</td>\n",
       "      <td>literal</td>\n",
       "      <td>n01695060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>uri</td>\n",
       "      <td>http://www.wikidata.org/entity/Q5843</td>\n",
       "      <td>literal</td>\n",
       "      <td>P18 P227 P279 P373 P646 P935 P1014 P1296 P2167...</td>\n",
       "      <td>literal</td>\n",
       "      <td>n02769748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>uri</td>\n",
       "      <td>http://www.wikidata.org/entity/Q1420</td>\n",
       "      <td>literal</td>\n",
       "      <td>P18 P18 P18 P61 P227 P279 P349 P366 P366 P366 ...</td>\n",
       "      <td>literal</td>\n",
       "      <td>n02958343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uri</td>\n",
       "      <td>http://www.wikidata.org/entity/Q6573</td>\n",
       "      <td>literal</td>\n",
       "      <td>P18 P18 P31 P105 P171 P171 P225 P227 P349 P373...</td>\n",
       "      <td>literal</td>\n",
       "      <td>n02342885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>uri</td>\n",
       "      <td>http://www.wikidata.org/entity/Q2811</td>\n",
       "      <td>literal</td>\n",
       "      <td>P18 P31 P227 P244 P268 P279 P279 P349 P373 P46...</td>\n",
       "      <td>literal</td>\n",
       "      <td>n04347754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  item.type                            item.value properties.type  \\\n",
       "0       uri  http://www.wikidata.org/entity/Q4504         literal   \n",
       "1       uri  http://www.wikidata.org/entity/Q5843         literal   \n",
       "2       uri  http://www.wikidata.org/entity/Q1420         literal   \n",
       "3       uri  http://www.wikidata.org/entity/Q6573         literal   \n",
       "4       uri  http://www.wikidata.org/entity/Q2811         literal   \n",
       "\n",
       "                                    properties.value synset.type synset.value  \n",
       "0  P10 P18 P31 P105 P138 P141 P171 P181 P183 P225...     literal    n01695060  \n",
       "1  P18 P227 P279 P373 P646 P935 P1014 P1296 P2167...     literal    n02769748  \n",
       "2  P18 P18 P18 P61 P227 P279 P349 P366 P366 P366 ...     literal    n02958343  \n",
       "3  P18 P18 P31 P105 P171 P171 P225 P227 P349 P373...     literal    n02342885  \n",
       "4  P18 P31 P227 P244 P268 P279 P279 P349 P373 P46...     literal    n04347754  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_and_properties = sparql_to_dataframe(query)\n",
    "items_and_properties.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# items_and_properties.to_csv('Nielsen2018Visual_wikidata.csv')\n",
    "# items_and_properties.to_pickle('Nielsen2018Visual_wikidata.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "items_and_properties = pd.read_csv('Nielsen2018Visual_wikidata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'n02398521', u'n01440764', u'n02408429', u'n02410509', u'n02391049']"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = expanduser('~/.keras/models/imagenet_class_index.json')\n",
    "index_to_synset = [value[0] for value in json.load(open(filename)).values()]\n",
    "index_to_synset[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['P10', 'P1014', 'P1034', 'P1036', 'P105', 'P1051', 'P1056', 'P1070']"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find all properties\n",
    "properties = []\n",
    "for n, row in items_and_properties.iterrows():\n",
    "    properties.extend(row['properties.value'].split())\n",
    "properties = sorted(list(set(properties) - set(['P2888'])))\n",
    "properties[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bag-of-properties matrix\n",
    "class_property_matrix = np.zeros((len(index_to_synset), len(properties)))\n",
    "synsets = items_and_properties['synset.value']\n",
    "for n, synset in enumerate(index_to_synset):\n",
    "    matched = synsets.index[synsets == synset]\n",
    "    if len(matched) == 0:\n",
    "        continue\n",
    "    elif len(matched) == 1:\n",
    "        row_index = matched[0]\n",
    "    else:\n",
    "        raise Exception(matched)\n",
    "    properties_for_item = items_and_properties['properties.value'][row_index].split()\n",
    "    for prop in properties_for_item:\n",
    "        if prop not in properties:\n",
    "            continue\n",
    "        index = properties.index(prop)\n",
    "        if index:\n",
    "            class_property_matrix[n, index] += 1\n",
    "    \n",
    "class_property_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_property_matrix.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 262)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_property_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_property_matrix = class_property_matrix[:, class_property_matrix.sum(axis=0) > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_transformer = TfidfTransformer()   # norm=None, sublinear_tf=True)\n",
    "transformed = tfidf_transformer.fit_transform(class_property_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 192)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled = transformed / transformed.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = np.log(Y * transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.000504</td>\n",
       "      <td>0.429619</td>\n",
       "      <td>-0.019529</td>\n",
       "      <td>0.738903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.602987</td>\n",
       "      <td>0.513555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>0.606894</td>\n",
       "      <td>-0.203929</td>\n",
       "      <td>0.703106</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>0.685570</td>\n",
       "      <td>0.498922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.018807</td>\n",
       "      <td>0.538667</td>\n",
       "      <td>0.031156</td>\n",
       "      <td>0.791672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.681945</td>\n",
       "      <td>0.642740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000432</td>\n",
       "      <td>0.092262</td>\n",
       "      <td>0.043735</td>\n",
       "      <td>0.648706</td>\n",
       "      <td>0.227341</td>\n",
       "      <td>0.397585</td>\n",
       "      <td>0.356174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.804770</td>\n",
       "      <td>0.708531</td>\n",
       "      <td>0.031601</td>\n",
       "      <td>0.903989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.798834</td>\n",
       "      <td>0.816243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002310</td>\n",
       "      <td>-0.112746</td>\n",
       "      <td>0.233721</td>\n",
       "      <td>0.666448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.202972</td>\n",
       "      <td>0.229644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>0.106226</td>\n",
       "      <td>0.509231</td>\n",
       "      <td>0.081999</td>\n",
       "      <td>0.793753</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.654387</td>\n",
       "      <td>0.644958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>0.516821</td>\n",
       "      <td>0.105973</td>\n",
       "      <td>0.811937</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646399</td>\n",
       "      <td>0.625618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.549673</td>\n",
       "      <td>-0.048356</td>\n",
       "      <td>0.705481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.639823</td>\n",
       "      <td>0.511715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>0.615558</td>\n",
       "      <td>0.180264</td>\n",
       "      <td>0.849305</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.715012</td>\n",
       "      <td>0.650566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n1   n2  score  correlation  correlation_on_log  correlation_on_raw  \\\n",
       "1    1    2      8    -0.000504            0.429619           -0.019529   \n",
       "2    1  253      7    -0.000981            0.606894           -0.203929   \n",
       "3    3    4      9     0.018807            0.538667            0.031156   \n",
       "4    1    3      7    -0.000432            0.092262            0.043735   \n",
       "5    1    5      8     0.804770            0.708531            0.031601   \n",
       "6    2    6      1    -0.002310           -0.112746            0.233721   \n",
       "7    4   37      6     0.106226            0.509231            0.081999   \n",
       "8    6   34      5    -0.002257            0.516821            0.105973   \n",
       "9    6   29      5     0.001459            0.549673           -0.048356   \n",
       "10   6   59      6    -0.001304            0.615558            0.180264   \n",
       "\n",
       "    correlation_on_log_with_wikidata  correlation_on_max_with_wikidata  \\\n",
       "1                           0.738903                          0.000000   \n",
       "2                           0.703106                          0.217900   \n",
       "3                           0.791672                          0.000000   \n",
       "4                           0.648706                          0.227341   \n",
       "5                           0.903989                          1.000000   \n",
       "6                           0.666448                          0.000000   \n",
       "7                           0.793753                          0.000000   \n",
       "8                           0.811937                          0.000000   \n",
       "9                           0.705481                          0.000000   \n",
       "10                          0.849305                          0.000000   \n",
       "\n",
       "    correlation_on_log_extended  correlation_on_log_wikidata  \n",
       "1                      0.602987                     0.513555  \n",
       "2                      0.685570                     0.498922  \n",
       "3                      0.681945                     0.642740  \n",
       "4                      0.397585                     0.356174  \n",
       "5                      0.798834                     0.816243  \n",
       "6                      0.202972                     0.229644  \n",
       "7                      0.654387                     0.644958  \n",
       "8                      0.646399                     0.625618  \n",
       "9                      0.639823                     0.511715  \n",
       "10                     0.715012                     0.650566  "
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation = np.corrcoef(W)\n",
    "correlations = []\n",
    "for n, row in pairs.iterrows():\n",
    "    n1, n2 = row.astype(int).values[:2]\n",
    "    correlations.append(correlation[n1-1, n2-1])\n",
    "    \n",
    "pairs['correlation_on_log_with_wikidata'] = correlations\n",
    "pairs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n1</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.023039</td>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.198678</td>\n",
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       "      <td>-0.058164</td>\n",
       "      <td>-0.076210</td>\n",
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       "    <tr>\n",
       "      <th>n2</th>\n",
       "      <td>0.625810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>-0.095936</td>\n",
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       "      <td>-0.062840</td>\n",
       "      <td>-0.085600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.023039</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.672063</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.596764</td>\n",
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       "      <td>0.699799</td>\n",
       "      <td>0.649696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation</th>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.739166</td>\n",
       "      <td>0.082027</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>0.757876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log</th>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>0.831936</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.983169</td>\n",
       "      <td>0.871093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <td>-0.198678</td>\n",
       "      <td>-0.191183</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>0.026166</td>\n",
       "      <td>0.251687</td>\n",
       "      <td>0.266719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <td>-0.138980</td>\n",
       "      <td>-0.161546</td>\n",
       "      <td>0.596764</td>\n",
       "      <td>0.739166</td>\n",
       "      <td>0.831936</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>0.861518</td>\n",
       "      <td>0.949386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <td>-0.023621</td>\n",
       "      <td>0.020928</td>\n",
       "      <td>0.110537</td>\n",
       "      <td>0.082027</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.026166</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060069</td>\n",
       "      <td>0.089880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <td>-0.058164</td>\n",
       "      <td>-0.062840</td>\n",
       "      <td>0.699799</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>0.983169</td>\n",
       "      <td>0.251687</td>\n",
       "      <td>0.861518</td>\n",
       "      <td>0.060069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "      <td>-0.076210</td>\n",
       "      <td>-0.085600</td>\n",
       "      <td>0.649696</td>\n",
       "      <td>0.757876</td>\n",
       "      <td>0.871093</td>\n",
       "      <td>0.266719</td>\n",
       "      <td>0.949386</td>\n",
       "      <td>0.089880</td>\n",
       "      <td>0.918661</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        n1        n2     score  correlation  \\\n",
       "n1                                1.000000  0.625810  0.023039    -0.040134   \n",
       "n2                                0.625810  1.000000  0.012842    -0.072894   \n",
       "score                             0.023039  0.012842  1.000000     0.615508   \n",
       "correlation                      -0.040134 -0.072894  0.615508     1.000000   \n",
       "correlation_on_log               -0.090583 -0.095936  0.672063     0.716846   \n",
       "correlation_on_raw               -0.198678 -0.191183  0.121592     0.205652   \n",
       "correlation_on_log_with_wikidata -0.138980 -0.161546  0.596764     0.739166   \n",
       "correlation_on_max_with_wikidata -0.023621  0.020928  0.110537     0.082027   \n",
       "correlation_on_log_extended      -0.058164 -0.062840  0.699799     0.743360   \n",
       "correlation_on_log_wikidata      -0.076210 -0.085600  0.649696     0.757876   \n",
       "\n",
       "                                  correlation_on_log  correlation_on_raw  \\\n",
       "n1                                         -0.090583           -0.198678   \n",
       "n2                                         -0.095936           -0.191183   \n",
       "score                                       0.672063            0.121592   \n",
       "correlation                                 0.716846            0.205652   \n",
       "correlation_on_log                          1.000000            0.263991   \n",
       "correlation_on_raw                          0.263991            1.000000   \n",
       "correlation_on_log_with_wikidata            0.831936            0.297468   \n",
       "correlation_on_max_with_wikidata            0.012970            0.026166   \n",
       "correlation_on_log_extended                 0.983169            0.251687   \n",
       "correlation_on_log_wikidata                 0.871093            0.266719   \n",
       "\n",
       "                                  correlation_on_log_with_wikidata  \\\n",
       "n1                                                       -0.138980   \n",
       "n2                                                       -0.161546   \n",
       "score                                                     0.596764   \n",
       "correlation                                               0.739166   \n",
       "correlation_on_log                                        0.831936   \n",
       "correlation_on_raw                                        0.297468   \n",
       "correlation_on_log_with_wikidata                          1.000000   \n",
       "correlation_on_max_with_wikidata                          0.066515   \n",
       "correlation_on_log_extended                               0.861518   \n",
       "correlation_on_log_wikidata                               0.949386   \n",
       "\n",
       "                                  correlation_on_max_with_wikidata  \\\n",
       "n1                                                       -0.023621   \n",
       "n2                                                        0.020928   \n",
       "score                                                     0.110537   \n",
       "correlation                                               0.082027   \n",
       "correlation_on_log                                        0.012970   \n",
       "correlation_on_raw                                        0.026166   \n",
       "correlation_on_log_with_wikidata                          0.066515   \n",
       "correlation_on_max_with_wikidata                          1.000000   \n",
       "correlation_on_log_extended                               0.060069   \n",
       "correlation_on_log_wikidata                               0.089880   \n",
       "\n",
       "                                  correlation_on_log_extended  \\\n",
       "n1                                                  -0.058164   \n",
       "n2                                                  -0.062840   \n",
       "score                                                0.699799   \n",
       "correlation                                          0.743360   \n",
       "correlation_on_log                                   0.983169   \n",
       "correlation_on_raw                                   0.251687   \n",
       "correlation_on_log_with_wikidata                     0.861518   \n",
       "correlation_on_max_with_wikidata                     0.060069   \n",
       "correlation_on_log_extended                          1.000000   \n",
       "correlation_on_log_wikidata                          0.918661   \n",
       "\n",
       "                                  correlation_on_log_wikidata  \n",
       "n1                                                  -0.076210  \n",
       "n2                                                  -0.085600  \n",
       "score                                                0.649696  \n",
       "correlation                                          0.757876  \n",
       "correlation_on_log                                   0.871093  \n",
       "correlation_on_raw                                   0.266719  \n",
       "correlation_on_log_with_wikidata                     0.949386  \n",
       "correlation_on_max_with_wikidata                     0.089880  \n",
       "correlation_on_log_extended                          0.918661  \n",
       "correlation_on_log_wikidata                          1.000000  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(353, 192)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa5235ccc50>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa523619c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "seaborn.swarmplot(pairs['score'], pairs['correlation_on_log_with_wikidata'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.zeros(Y.shape)\n",
    "indices = np.arange(353), np.argmax(Y, axis=1)\n",
    "Z[indices] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "V = Z * transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.000504</td>\n",
       "      <td>0.429619</td>\n",
       "      <td>-0.019529</td>\n",
       "      <td>0.738903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.602987</td>\n",
       "      <td>0.513555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>0.606894</td>\n",
       "      <td>-0.203929</td>\n",
       "      <td>0.703106</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>0.685570</td>\n",
       "      <td>0.498922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.018807</td>\n",
       "      <td>0.538667</td>\n",
       "      <td>0.031156</td>\n",
       "      <td>0.791672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.681945</td>\n",
       "      <td>0.642740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000432</td>\n",
       "      <td>0.092262</td>\n",
       "      <td>0.043735</td>\n",
       "      <td>0.648706</td>\n",
       "      <td>0.227341</td>\n",
       "      <td>0.397585</td>\n",
       "      <td>0.356174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.804770</td>\n",
       "      <td>0.708531</td>\n",
       "      <td>0.031601</td>\n",
       "      <td>0.903989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.798834</td>\n",
       "      <td>0.816243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002310</td>\n",
       "      <td>-0.112746</td>\n",
       "      <td>0.233721</td>\n",
       "      <td>0.666448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.202972</td>\n",
       "      <td>0.229644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>0.106226</td>\n",
       "      <td>0.509231</td>\n",
       "      <td>0.081999</td>\n",
       "      <td>0.793753</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.654387</td>\n",
       "      <td>0.644958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>0.516821</td>\n",
       "      <td>0.105973</td>\n",
       "      <td>0.811937</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646399</td>\n",
       "      <td>0.625618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.549673</td>\n",
       "      <td>-0.048356</td>\n",
       "      <td>0.705481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.639823</td>\n",
       "      <td>0.511715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>0.615558</td>\n",
       "      <td>0.180264</td>\n",
       "      <td>0.849305</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.715012</td>\n",
       "      <td>0.650566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n1   n2  score  correlation  correlation_on_log  correlation_on_raw  \\\n",
       "1    1    2      8    -0.000504            0.429619           -0.019529   \n",
       "2    1  253      7    -0.000981            0.606894           -0.203929   \n",
       "3    3    4      9     0.018807            0.538667            0.031156   \n",
       "4    1    3      7    -0.000432            0.092262            0.043735   \n",
       "5    1    5      8     0.804770            0.708531            0.031601   \n",
       "6    2    6      1    -0.002310           -0.112746            0.233721   \n",
       "7    4   37      6     0.106226            0.509231            0.081999   \n",
       "8    6   34      5    -0.002257            0.516821            0.105973   \n",
       "9    6   29      5     0.001459            0.549673           -0.048356   \n",
       "10   6   59      6    -0.001304            0.615558            0.180264   \n",
       "\n",
       "    correlation_on_log_with_wikidata  correlation_on_max_with_wikidata  \\\n",
       "1                           0.738903                          0.000000   \n",
       "2                           0.703106                          0.217900   \n",
       "3                           0.791672                          0.000000   \n",
       "4                           0.648706                          0.227341   \n",
       "5                           0.903989                          1.000000   \n",
       "6                           0.666448                          0.000000   \n",
       "7                           0.793753                          0.000000   \n",
       "8                           0.811937                          0.000000   \n",
       "9                           0.705481                          0.000000   \n",
       "10                          0.849305                          0.000000   \n",
       "\n",
       "    correlation_on_log_extended  correlation_on_log_wikidata  \n",
       "1                      0.602987                     0.513555  \n",
       "2                      0.685570                     0.498922  \n",
       "3                      0.681945                     0.642740  \n",
       "4                      0.397585                     0.356174  \n",
       "5                      0.798834                     0.816243  \n",
       "6                      0.202972                     0.229644  \n",
       "7                      0.654387                     0.644958  \n",
       "8                      0.646399                     0.625618  \n",
       "9                      0.639823                     0.511715  \n",
       "10                     0.715012                     0.650566  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation = np.corrcoef(V)\n",
    "correlation[np.isnan(correlation)] = 0\n",
    "correlations = []\n",
    "for n, row in pairs.iterrows():\n",
    "    n1, n2 = row.astype(int).values[:2]\n",
    "    correlations.append(correlation[n1-1, n2-1])\n",
    "    \n",
    "pairs['correlation_on_max_with_wikidata'] = correlations\n",
    "pairs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.625810</td>\n",
       "      <td>0.023039</td>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.198678</td>\n",
       "      <td>-0.138980</td>\n",
       "      <td>-0.023621</td>\n",
       "      <td>-0.058164</td>\n",
       "      <td>-0.076210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n2</th>\n",
       "      <td>0.625810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>-0.191183</td>\n",
       "      <td>-0.161546</td>\n",
       "      <td>0.020928</td>\n",
       "      <td>-0.062840</td>\n",
       "      <td>-0.085600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.023039</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.596764</td>\n",
       "      <td>0.110537</td>\n",
       "      <td>0.699799</td>\n",
       "      <td>0.649696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation</th>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.739166</td>\n",
       "      <td>0.082027</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>0.757876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log</th>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>0.831936</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.983169</td>\n",
       "      <td>0.871093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <td>-0.198678</td>\n",
       "      <td>-0.191183</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>0.026166</td>\n",
       "      <td>0.251687</td>\n",
       "      <td>0.266719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <td>-0.138980</td>\n",
       "      <td>-0.161546</td>\n",
       "      <td>0.596764</td>\n",
       "      <td>0.739166</td>\n",
       "      <td>0.831936</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>0.861518</td>\n",
       "      <td>0.949386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <td>-0.023621</td>\n",
       "      <td>0.020928</td>\n",
       "      <td>0.110537</td>\n",
       "      <td>0.082027</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.026166</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060069</td>\n",
       "      <td>0.089880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <td>-0.058164</td>\n",
       "      <td>-0.062840</td>\n",
       "      <td>0.699799</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>0.983169</td>\n",
       "      <td>0.251687</td>\n",
       "      <td>0.861518</td>\n",
       "      <td>0.060069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "      <td>-0.076210</td>\n",
       "      <td>-0.085600</td>\n",
       "      <td>0.649696</td>\n",
       "      <td>0.757876</td>\n",
       "      <td>0.871093</td>\n",
       "      <td>0.266719</td>\n",
       "      <td>0.949386</td>\n",
       "      <td>0.089880</td>\n",
       "      <td>0.918661</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        n1        n2     score  correlation  \\\n",
       "n1                                1.000000  0.625810  0.023039    -0.040134   \n",
       "n2                                0.625810  1.000000  0.012842    -0.072894   \n",
       "score                             0.023039  0.012842  1.000000     0.615508   \n",
       "correlation                      -0.040134 -0.072894  0.615508     1.000000   \n",
       "correlation_on_log               -0.090583 -0.095936  0.672063     0.716846   \n",
       "correlation_on_raw               -0.198678 -0.191183  0.121592     0.205652   \n",
       "correlation_on_log_with_wikidata -0.138980 -0.161546  0.596764     0.739166   \n",
       "correlation_on_max_with_wikidata -0.023621  0.020928  0.110537     0.082027   \n",
       "correlation_on_log_extended      -0.058164 -0.062840  0.699799     0.743360   \n",
       "correlation_on_log_wikidata      -0.076210 -0.085600  0.649696     0.757876   \n",
       "\n",
       "                                  correlation_on_log  correlation_on_raw  \\\n",
       "n1                                         -0.090583           -0.198678   \n",
       "n2                                         -0.095936           -0.191183   \n",
       "score                                       0.672063            0.121592   \n",
       "correlation                                 0.716846            0.205652   \n",
       "correlation_on_log                          1.000000            0.263991   \n",
       "correlation_on_raw                          0.263991            1.000000   \n",
       "correlation_on_log_with_wikidata            0.831936            0.297468   \n",
       "correlation_on_max_with_wikidata            0.012970            0.026166   \n",
       "correlation_on_log_extended                 0.983169            0.251687   \n",
       "correlation_on_log_wikidata                 0.871093            0.266719   \n",
       "\n",
       "                                  correlation_on_log_with_wikidata  \\\n",
       "n1                                                       -0.138980   \n",
       "n2                                                       -0.161546   \n",
       "score                                                     0.596764   \n",
       "correlation                                               0.739166   \n",
       "correlation_on_log                                        0.831936   \n",
       "correlation_on_raw                                        0.297468   \n",
       "correlation_on_log_with_wikidata                          1.000000   \n",
       "correlation_on_max_with_wikidata                          0.066515   \n",
       "correlation_on_log_extended                               0.861518   \n",
       "correlation_on_log_wikidata                               0.949386   \n",
       "\n",
       "                                  correlation_on_max_with_wikidata  \\\n",
       "n1                                                       -0.023621   \n",
       "n2                                                        0.020928   \n",
       "score                                                     0.110537   \n",
       "correlation                                               0.082027   \n",
       "correlation_on_log                                        0.012970   \n",
       "correlation_on_raw                                        0.026166   \n",
       "correlation_on_log_with_wikidata                          0.066515   \n",
       "correlation_on_max_with_wikidata                          1.000000   \n",
       "correlation_on_log_extended                               0.060069   \n",
       "correlation_on_log_wikidata                               0.089880   \n",
       "\n",
       "                                  correlation_on_log_extended  \\\n",
       "n1                                                  -0.058164   \n",
       "n2                                                  -0.062840   \n",
       "score                                                0.699799   \n",
       "correlation                                          0.743360   \n",
       "correlation_on_log                                   0.983169   \n",
       "correlation_on_raw                                   0.251687   \n",
       "correlation_on_log_with_wikidata                     0.861518   \n",
       "correlation_on_max_with_wikidata                     0.060069   \n",
       "correlation_on_log_extended                          1.000000   \n",
       "correlation_on_log_wikidata                          0.918661   \n",
       "\n",
       "                                  correlation_on_log_wikidata  \n",
       "n1                                                  -0.076210  \n",
       "n2                                                  -0.085600  \n",
       "score                                                0.649696  \n",
       "correlation                                          0.757876  \n",
       "correlation_on_log                                   0.871093  \n",
       "correlation_on_raw                                   0.266719  \n",
       "correlation_on_log_with_wikidata                     0.949386  \n",
       "correlation_on_max_with_wikidata                     0.089880  \n",
       "correlation_on_log_extended                          0.918661  \n",
       "correlation_on_log_wikidata                          1.000000  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(353, 1192)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = np.concatenate((np.log(Y), np.log(Y * transformed)), axis=1)\n",
    "C.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.000504</td>\n",
       "      <td>0.429619</td>\n",
       "      <td>-0.019529</td>\n",
       "      <td>0.738903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.602987</td>\n",
       "      <td>0.513555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>0.606894</td>\n",
       "      <td>-0.203929</td>\n",
       "      <td>0.703106</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>0.685570</td>\n",
       "      <td>0.498922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.018807</td>\n",
       "      <td>0.538667</td>\n",
       "      <td>0.031156</td>\n",
       "      <td>0.791672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.681945</td>\n",
       "      <td>0.642740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000432</td>\n",
       "      <td>0.092262</td>\n",
       "      <td>0.043735</td>\n",
       "      <td>0.648706</td>\n",
       "      <td>0.227341</td>\n",
       "      <td>0.397585</td>\n",
       "      <td>0.356174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.804770</td>\n",
       "      <td>0.708531</td>\n",
       "      <td>0.031601</td>\n",
       "      <td>0.903989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.798834</td>\n",
       "      <td>0.816243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002310</td>\n",
       "      <td>-0.112746</td>\n",
       "      <td>0.233721</td>\n",
       "      <td>0.666448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.202972</td>\n",
       "      <td>0.229644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>0.106226</td>\n",
       "      <td>0.509231</td>\n",
       "      <td>0.081999</td>\n",
       "      <td>0.793753</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.654387</td>\n",
       "      <td>0.644958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>0.516821</td>\n",
       "      <td>0.105973</td>\n",
       "      <td>0.811937</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646399</td>\n",
       "      <td>0.625618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.549673</td>\n",
       "      <td>-0.048356</td>\n",
       "      <td>0.705481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.639823</td>\n",
       "      <td>0.511715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>0.615558</td>\n",
       "      <td>0.180264</td>\n",
       "      <td>0.849305</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.715012</td>\n",
       "      <td>0.650566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n1   n2  score  correlation  correlation_on_log  correlation_on_raw  \\\n",
       "1    1    2      8    -0.000504            0.429619           -0.019529   \n",
       "2    1  253      7    -0.000981            0.606894           -0.203929   \n",
       "3    3    4      9     0.018807            0.538667            0.031156   \n",
       "4    1    3      7    -0.000432            0.092262            0.043735   \n",
       "5    1    5      8     0.804770            0.708531            0.031601   \n",
       "6    2    6      1    -0.002310           -0.112746            0.233721   \n",
       "7    4   37      6     0.106226            0.509231            0.081999   \n",
       "8    6   34      5    -0.002257            0.516821            0.105973   \n",
       "9    6   29      5     0.001459            0.549673           -0.048356   \n",
       "10   6   59      6    -0.001304            0.615558            0.180264   \n",
       "\n",
       "    correlation_on_log_with_wikidata  correlation_on_max_with_wikidata  \\\n",
       "1                           0.738903                          0.000000   \n",
       "2                           0.703106                          0.217900   \n",
       "3                           0.791672                          0.000000   \n",
       "4                           0.648706                          0.227341   \n",
       "5                           0.903989                          1.000000   \n",
       "6                           0.666448                          0.000000   \n",
       "7                           0.793753                          0.000000   \n",
       "8                           0.811937                          0.000000   \n",
       "9                           0.705481                          0.000000   \n",
       "10                          0.849305                          0.000000   \n",
       "\n",
       "    correlation_on_log_extended  correlation_on_log_wikidata  \n",
       "1                      0.602987                     0.513555  \n",
       "2                      0.685570                     0.498922  \n",
       "3                      0.681945                     0.642740  \n",
       "4                      0.397585                     0.356174  \n",
       "5                      0.798834                     0.816243  \n",
       "6                      0.202972                     0.229644  \n",
       "7                      0.654387                     0.644958  \n",
       "8                      0.646399                     0.625618  \n",
       "9                      0.639823                     0.511715  \n",
       "10                     0.715012                     0.650566  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "correlation = np.corrcoef(C)\n",
    "correlations = []\n",
    "for n, row in pairs.iterrows():\n",
    "    n1, n2 = row.astype(int).values[:2]\n",
    "    correlations.append(correlation[n1-1, n2-1])\n",
    "    \n",
    "pairs['correlation_on_log_extended'] = correlations\n",
    "pairs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n1</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.058164</td>\n",
       "      <td>-0.076210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n2</th>\n",
       "      <td>0.625810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>-0.072894</td>\n",
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       "      <td>-0.062840</td>\n",
       "      <td>-0.085600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.023039</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.596764</td>\n",
       "      <td>0.110537</td>\n",
       "      <td>0.699799</td>\n",
       "      <td>0.649696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation</th>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.739166</td>\n",
       "      <td>0.082027</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>0.757876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log</th>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>0.831936</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.983169</td>\n",
       "      <td>0.871093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <td>-0.198678</td>\n",
       "      <td>-0.191183</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>0.026166</td>\n",
       "      <td>0.251687</td>\n",
       "      <td>0.266719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <td>-0.138980</td>\n",
       "      <td>-0.161546</td>\n",
       "      <td>0.596764</td>\n",
       "      <td>0.739166</td>\n",
       "      <td>0.831936</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>0.861518</td>\n",
       "      <td>0.949386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <td>-0.023621</td>\n",
       "      <td>0.020928</td>\n",
       "      <td>0.110537</td>\n",
       "      <td>0.082027</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.026166</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060069</td>\n",
       "      <td>0.089880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <td>-0.058164</td>\n",
       "      <td>-0.062840</td>\n",
       "      <td>0.699799</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>0.983169</td>\n",
       "      <td>0.251687</td>\n",
       "      <td>0.861518</td>\n",
       "      <td>0.060069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "      <td>-0.076210</td>\n",
       "      <td>-0.085600</td>\n",
       "      <td>0.649696</td>\n",
       "      <td>0.757876</td>\n",
       "      <td>0.871093</td>\n",
       "      <td>0.266719</td>\n",
       "      <td>0.949386</td>\n",
       "      <td>0.089880</td>\n",
       "      <td>0.918661</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        n1        n2     score  correlation  \\\n",
       "n1                                1.000000  0.625810  0.023039    -0.040134   \n",
       "n2                                0.625810  1.000000  0.012842    -0.072894   \n",
       "score                             0.023039  0.012842  1.000000     0.615508   \n",
       "correlation                      -0.040134 -0.072894  0.615508     1.000000   \n",
       "correlation_on_log               -0.090583 -0.095936  0.672063     0.716846   \n",
       "correlation_on_raw               -0.198678 -0.191183  0.121592     0.205652   \n",
       "correlation_on_log_with_wikidata -0.138980 -0.161546  0.596764     0.739166   \n",
       "correlation_on_max_with_wikidata -0.023621  0.020928  0.110537     0.082027   \n",
       "correlation_on_log_extended      -0.058164 -0.062840  0.699799     0.743360   \n",
       "correlation_on_log_wikidata      -0.076210 -0.085600  0.649696     0.757876   \n",
       "\n",
       "                                  correlation_on_log  correlation_on_raw  \\\n",
       "n1                                         -0.090583           -0.198678   \n",
       "n2                                         -0.095936           -0.191183   \n",
       "score                                       0.672063            0.121592   \n",
       "correlation                                 0.716846            0.205652   \n",
       "correlation_on_log                          1.000000            0.263991   \n",
       "correlation_on_raw                          0.263991            1.000000   \n",
       "correlation_on_log_with_wikidata            0.831936            0.297468   \n",
       "correlation_on_max_with_wikidata            0.012970            0.026166   \n",
       "correlation_on_log_extended                 0.983169            0.251687   \n",
       "correlation_on_log_wikidata                 0.871093            0.266719   \n",
       "\n",
       "                                  correlation_on_log_with_wikidata  \\\n",
       "n1                                                       -0.138980   \n",
       "n2                                                       -0.161546   \n",
       "score                                                     0.596764   \n",
       "correlation                                               0.739166   \n",
       "correlation_on_log                                        0.831936   \n",
       "correlation_on_raw                                        0.297468   \n",
       "correlation_on_log_with_wikidata                          1.000000   \n",
       "correlation_on_max_with_wikidata                          0.066515   \n",
       "correlation_on_log_extended                               0.861518   \n",
       "correlation_on_log_wikidata                               0.949386   \n",
       "\n",
       "                                  correlation_on_max_with_wikidata  \\\n",
       "n1                                                       -0.023621   \n",
       "n2                                                        0.020928   \n",
       "score                                                     0.110537   \n",
       "correlation                                               0.082027   \n",
       "correlation_on_log                                        0.012970   \n",
       "correlation_on_raw                                        0.026166   \n",
       "correlation_on_log_with_wikidata                          0.066515   \n",
       "correlation_on_max_with_wikidata                          1.000000   \n",
       "correlation_on_log_extended                               0.060069   \n",
       "correlation_on_log_wikidata                               0.089880   \n",
       "\n",
       "                                  correlation_on_log_extended  \\\n",
       "n1                                                  -0.058164   \n",
       "n2                                                  -0.062840   \n",
       "score                                                0.699799   \n",
       "correlation                                          0.743360   \n",
       "correlation_on_log                                   0.983169   \n",
       "correlation_on_raw                                   0.251687   \n",
       "correlation_on_log_with_wikidata                     0.861518   \n",
       "correlation_on_max_with_wikidata                     0.060069   \n",
       "correlation_on_log_extended                          1.000000   \n",
       "correlation_on_log_wikidata                          0.918661   \n",
       "\n",
       "                                  correlation_on_log_wikidata  \n",
       "n1                                                  -0.076210  \n",
       "n2                                                  -0.085600  \n",
       "score                                                0.649696  \n",
       "correlation                                          0.757876  \n",
       "correlation_on_log                                   0.871093  \n",
       "correlation_on_raw                                   0.266719  \n",
       "correlation_on_log_with_wikidata                     0.949386  \n",
       "correlation_on_max_with_wikidata                     0.089880  \n",
       "correlation_on_log_extended                          0.918661  \n",
       "correlation_on_log_wikidata                          1.000000  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.000504</td>\n",
       "      <td>0.429619</td>\n",
       "      <td>-0.019529</td>\n",
       "      <td>0.738903</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.602987</td>\n",
       "      <td>0.513555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>253</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>0.606894</td>\n",
       "      <td>-0.203929</td>\n",
       "      <td>0.703106</td>\n",
       "      <td>0.217900</td>\n",
       "      <td>0.685570</td>\n",
       "      <td>0.498922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.018807</td>\n",
       "      <td>0.538667</td>\n",
       "      <td>0.031156</td>\n",
       "      <td>0.791672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.681945</td>\n",
       "      <td>0.642740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.000432</td>\n",
       "      <td>0.092262</td>\n",
       "      <td>0.043735</td>\n",
       "      <td>0.648706</td>\n",
       "      <td>0.227341</td>\n",
       "      <td>0.397585</td>\n",
       "      <td>0.356174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0.804770</td>\n",
       "      <td>0.708531</td>\n",
       "      <td>0.031601</td>\n",
       "      <td>0.903989</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.798834</td>\n",
       "      <td>0.816243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.002310</td>\n",
       "      <td>-0.112746</td>\n",
       "      <td>0.233721</td>\n",
       "      <td>0.666448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.202972</td>\n",
       "      <td>0.229644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>37</td>\n",
       "      <td>6</td>\n",
       "      <td>0.106226</td>\n",
       "      <td>0.509231</td>\n",
       "      <td>0.081999</td>\n",
       "      <td>0.793753</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.654387</td>\n",
       "      <td>0.644958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.002257</td>\n",
       "      <td>0.516821</td>\n",
       "      <td>0.105973</td>\n",
       "      <td>0.811937</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646399</td>\n",
       "      <td>0.625618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>0.001459</td>\n",
       "      <td>0.549673</td>\n",
       "      <td>-0.048356</td>\n",
       "      <td>0.705481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.639823</td>\n",
       "      <td>0.511715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>0.615558</td>\n",
       "      <td>0.180264</td>\n",
       "      <td>0.849305</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.715012</td>\n",
       "      <td>0.650566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n1   n2  score  correlation  correlation_on_log  correlation_on_raw  \\\n",
       "1    1    2      8    -0.000504            0.429619           -0.019529   \n",
       "2    1  253      7    -0.000981            0.606894           -0.203929   \n",
       "3    3    4      9     0.018807            0.538667            0.031156   \n",
       "4    1    3      7    -0.000432            0.092262            0.043735   \n",
       "5    1    5      8     0.804770            0.708531            0.031601   \n",
       "6    2    6      1    -0.002310           -0.112746            0.233721   \n",
       "7    4   37      6     0.106226            0.509231            0.081999   \n",
       "8    6   34      5    -0.002257            0.516821            0.105973   \n",
       "9    6   29      5     0.001459            0.549673           -0.048356   \n",
       "10   6   59      6    -0.001304            0.615558            0.180264   \n",
       "\n",
       "    correlation_on_log_with_wikidata  correlation_on_max_with_wikidata  \\\n",
       "1                           0.738903                          0.000000   \n",
       "2                           0.703106                          0.217900   \n",
       "3                           0.791672                          0.000000   \n",
       "4                           0.648706                          0.227341   \n",
       "5                           0.903989                          1.000000   \n",
       "6                           0.666448                          0.000000   \n",
       "7                           0.793753                          0.000000   \n",
       "8                           0.811937                          0.000000   \n",
       "9                           0.705481                          0.000000   \n",
       "10                          0.849305                          0.000000   \n",
       "\n",
       "    correlation_on_log_extended  correlation_on_log_wikidata  \n",
       "1                      0.602987                     0.513555  \n",
       "2                      0.685570                     0.498922  \n",
       "3                      0.681945                     0.642740  \n",
       "4                      0.397585                     0.356174  \n",
       "5                      0.798834                     0.816243  \n",
       "6                      0.202972                     0.229644  \n",
       "7                      0.654387                     0.644958  \n",
       "8                      0.646399                     0.625618  \n",
       "9                      0.639823                     0.511715  \n",
       "10                     0.715012                     0.650566  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = np.log(Y * scaled)\n",
    "correlation = np.corrcoef(C)\n",
    "correlations = []\n",
    "for n, row in pairs.iterrows():\n",
    "    n1, n2 = row.astype(int).values[:2]\n",
    "    correlations.append(correlation[n1-1, n2-1])\n",
    "    \n",
    "pairs['correlation_on_log_wikidata'] = correlations\n",
    "pairs.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n1</th>\n",
       "      <th>n2</th>\n",
       "      <th>score</th>\n",
       "      <th>correlation</th>\n",
       "      <th>correlation_on_log</th>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.625810</td>\n",
       "      <td>0.023039</td>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.198678</td>\n",
       "      <td>-0.138980</td>\n",
       "      <td>-0.023621</td>\n",
       "      <td>-0.058164</td>\n",
       "      <td>-0.076210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n2</th>\n",
       "      <td>0.625810</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>-0.191183</td>\n",
       "      <td>-0.161546</td>\n",
       "      <td>0.020928</td>\n",
       "      <td>-0.062840</td>\n",
       "      <td>-0.085600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>0.023039</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.596764</td>\n",
       "      <td>0.110537</td>\n",
       "      <td>0.699799</td>\n",
       "      <td>0.649696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation</th>\n",
       "      <td>-0.040134</td>\n",
       "      <td>-0.072894</td>\n",
       "      <td>0.615508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.739166</td>\n",
       "      <td>0.082027</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>0.757876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log</th>\n",
       "      <td>-0.090583</td>\n",
       "      <td>-0.095936</td>\n",
       "      <td>0.672063</td>\n",
       "      <td>0.716846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>0.831936</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.983169</td>\n",
       "      <td>0.871093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_raw</th>\n",
       "      <td>-0.198678</td>\n",
       "      <td>-0.191183</td>\n",
       "      <td>0.121592</td>\n",
       "      <td>0.205652</td>\n",
       "      <td>0.263991</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>0.026166</td>\n",
       "      <td>0.251687</td>\n",
       "      <td>0.266719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_with_wikidata</th>\n",
       "      <td>-0.138980</td>\n",
       "      <td>-0.161546</td>\n",
       "      <td>0.596764</td>\n",
       "      <td>0.739166</td>\n",
       "      <td>0.831936</td>\n",
       "      <td>0.297468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>0.861518</td>\n",
       "      <td>0.949386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_max_with_wikidata</th>\n",
       "      <td>-0.023621</td>\n",
       "      <td>0.020928</td>\n",
       "      <td>0.110537</td>\n",
       "      <td>0.082027</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.026166</td>\n",
       "      <td>0.066515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060069</td>\n",
       "      <td>0.089880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_extended</th>\n",
       "      <td>-0.058164</td>\n",
       "      <td>-0.062840</td>\n",
       "      <td>0.699799</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>0.983169</td>\n",
       "      <td>0.251687</td>\n",
       "      <td>0.861518</td>\n",
       "      <td>0.060069</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>correlation_on_log_wikidata</th>\n",
       "      <td>-0.076210</td>\n",
       "      <td>-0.085600</td>\n",
       "      <td>0.649696</td>\n",
       "      <td>0.757876</td>\n",
       "      <td>0.871093</td>\n",
       "      <td>0.266719</td>\n",
       "      <td>0.949386</td>\n",
       "      <td>0.089880</td>\n",
       "      <td>0.918661</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        n1        n2     score  correlation  \\\n",
       "n1                                1.000000  0.625810  0.023039    -0.040134   \n",
       "n2                                0.625810  1.000000  0.012842    -0.072894   \n",
       "score                             0.023039  0.012842  1.000000     0.615508   \n",
       "correlation                      -0.040134 -0.072894  0.615508     1.000000   \n",
       "correlation_on_log               -0.090583 -0.095936  0.672063     0.716846   \n",
       "correlation_on_raw               -0.198678 -0.191183  0.121592     0.205652   \n",
       "correlation_on_log_with_wikidata -0.138980 -0.161546  0.596764     0.739166   \n",
       "correlation_on_max_with_wikidata -0.023621  0.020928  0.110537     0.082027   \n",
       "correlation_on_log_extended      -0.058164 -0.062840  0.699799     0.743360   \n",
       "correlation_on_log_wikidata      -0.076210 -0.085600  0.649696     0.757876   \n",
       "\n",
       "                                  correlation_on_log  correlation_on_raw  \\\n",
       "n1                                         -0.090583           -0.198678   \n",
       "n2                                         -0.095936           -0.191183   \n",
       "score                                       0.672063            0.121592   \n",
       "correlation                                 0.716846            0.205652   \n",
       "correlation_on_log                          1.000000            0.263991   \n",
       "correlation_on_raw                          0.263991            1.000000   \n",
       "correlation_on_log_with_wikidata            0.831936            0.297468   \n",
       "correlation_on_max_with_wikidata            0.012970            0.026166   \n",
       "correlation_on_log_extended                 0.983169            0.251687   \n",
       "correlation_on_log_wikidata                 0.871093            0.266719   \n",
       "\n",
       "                                  correlation_on_log_with_wikidata  \\\n",
       "n1                                                       -0.138980   \n",
       "n2                                                       -0.161546   \n",
       "score                                                     0.596764   \n",
       "correlation                                               0.739166   \n",
       "correlation_on_log                                        0.831936   \n",
       "correlation_on_raw                                        0.297468   \n",
       "correlation_on_log_with_wikidata                          1.000000   \n",
       "correlation_on_max_with_wikidata                          0.066515   \n",
       "correlation_on_log_extended                               0.861518   \n",
       "correlation_on_log_wikidata                               0.949386   \n",
       "\n",
       "                                  correlation_on_max_with_wikidata  \\\n",
       "n1                                                       -0.023621   \n",
       "n2                                                        0.020928   \n",
       "score                                                     0.110537   \n",
       "correlation                                               0.082027   \n",
       "correlation_on_log                                        0.012970   \n",
       "correlation_on_raw                                        0.026166   \n",
       "correlation_on_log_with_wikidata                          0.066515   \n",
       "correlation_on_max_with_wikidata                          1.000000   \n",
       "correlation_on_log_extended                               0.060069   \n",
       "correlation_on_log_wikidata                               0.089880   \n",
       "\n",
       "                                  correlation_on_log_extended  \\\n",
       "n1                                                  -0.058164   \n",
       "n2                                                  -0.062840   \n",
       "score                                                0.699799   \n",
       "correlation                                          0.743360   \n",
       "correlation_on_log                                   0.983169   \n",
       "correlation_on_raw                                   0.251687   \n",
       "correlation_on_log_with_wikidata                     0.861518   \n",
       "correlation_on_max_with_wikidata                     0.060069   \n",
       "correlation_on_log_extended                          1.000000   \n",
       "correlation_on_log_wikidata                          0.918661   \n",
       "\n",
       "                                  correlation_on_log_wikidata  \n",
       "n1                                                  -0.076210  \n",
       "n2                                                  -0.085600  \n",
       "score                                                0.649696  \n",
       "correlation                                          0.757876  \n",
       "correlation_on_log                                   0.871093  \n",
       "correlation_on_raw                                   0.266719  \n",
       "correlation_on_log_with_wikidata                     0.949386  \n",
       "correlation_on_max_with_wikidata                     0.089880  \n",
       "correlation_on_log_extended                          0.918661  \n",
       "correlation_on_log_wikidata                          1.000000  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs.corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa52354d850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "seaborn.swarmplot(pairs['score'], pairs['correlation_on_log_extended'])\n",
    "plt.xlabel('Human scoring')\n",
    "plt.ylabel('Machine-based similarity')\n",
    "plt.savefig('Nielsen2018Visual_resnet50scatter_wikidata.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1                                  48.000000\n",
       "n2                                  95.000000\n",
       "score                                5.000000\n",
       "correlation                         -0.005510\n",
       "correlation_on_log                  -0.107367\n",
       "correlation_on_raw                  -0.046122\n",
       "correlation_on_log_with_wikidata     0.130107\n",
       "correlation_on_max_with_wikidata     0.296874\n",
       "correlation_on_log_extended          0.238114\n",
       "correlation_on_log_wikidata          0.293983\n",
       "Name: 63, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = ((pairs['score'] == 5) & (pairs['correlation_on_log_extended'] < 0.3)).nonzero()[0][0]\n",
    "pairs.iloc[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def two_images(one, two):\n",
    "    plt.figure(figsize=(5, 2.5))\n",
    "    for i, n in enumerate([one, two], 1):\n",
    "        plt.subplot(1, 2, i)\n",
    "        filename = data_directory + 'image-{:03d}.jpg'.format(n)\n",
    "        img = load_img(filename, target_size=model.input_shape[1:3])\n",
    "        im = plt.imshow(img)\n",
    "        im.set_cmap('hot')\n",
    "        plt.axis('off')\n",
    "        plt.text(4, 215, str(n), fontsize=14, color='r', backgroundcolor='w')\n",
    "        plt.grid(False)\n",
    "        # plt.tight_layout(pad=0)\n",
    "        plt.subplots_adjust(0.01, 0.01, 0.99, 0.99, 0.02, 0.02)\n",
    "    plt.savefig('Nielsen2018Visual_resnet50scatter_{}_{}.png'.format(one, two))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa521b9df50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "two_images(48, 95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1                                   85.000000\n",
       "n2                                  102.000000\n",
       "score                                 1.000000\n",
       "correlation                           0.028534\n",
       "correlation_on_log                    0.649325\n",
       "correlation_on_raw                    0.344088\n",
       "correlation_on_log_with_wikidata      0.555938\n",
       "correlation_on_max_with_wikidata      0.000000\n",
       "correlation_on_log_extended           0.740696\n",
       "correlation_on_log_wikidata           0.678754\n",
       "Name: 91, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = ((pairs['score'] == 1) & (pairs['correlation_on_log_extended'] > 0.7)).nonzero()[0][0]\n",
    "pairs.iloc[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa521b9d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "two_images(85, 102)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n1                                  3.000000\n",
       "n2                                  4.000000\n",
       "score                               9.000000\n",
       "correlation                         0.018807\n",
       "correlation_on_log                  0.538667\n",
       "correlation_on_raw                  0.031156\n",
       "correlation_on_log_with_wikidata    0.610738\n",
       "correlation_on_max_with_wikidata    0.000000\n",
       "correlation_on_log_extended         0.681945\n",
       "correlation_on_log_wikidata         0.642740\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = ((pairs['score'] == 9) & (pairs['correlation_on_log_extended'] < 0.7)).nonzero()[0][0]\n",
    "pairs.iloc[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAADRCAYAAAAnvn4gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzsvGeUXWXd9//Zfe9Tp9dkJr13CDGBEDpIlaYIIkWpiiCioN7cIjbKrd7YwIKKiIB0CUhvKUAKCemZzGRmMjOZPqfvXv4vToi3f9ftel48a/G8mM9aZ83aa82ac+Y61/6173dfQhRFjDPOOOOMM87/RPy4P8A444wzzjj/7zGeHMYZZ5xxxvkXxpPDOOOMM844/8J4chhnnHHGGedfGE8O44wzzjjj/AvjyWGcccYZZ5x/YTw5jDPOOOOM8y+MJ4dxxhlnnHH+hfHkMM4444wzzr8gf5xvfvl1n44UOUEsBgsWHMmkqpX85tHvMTJ4gLQYQxYLVCxqZf7UKXhRjNqK