Task Similarity.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "from sklearn.model_selection import KFold, RepeatedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Ridge, RidgeCV, Lasso\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from scipy.stats import pearsonr\n",
    "from scipy import spatial\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "import plotly.figure_factory as ff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pairwise_accuracy(actual, predicted):\n",
    "    result1 = []\n",
    "    for i in range(0,len(actual)):\n",
    "        result1.append(spatial.distance.cosine(actual[i], predicted[i]))\n",
    "    return np.mean(result1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pearcorr(actual, predicted):\n",
    "    corr = []\n",
    "    r2 = []\n",
    "    for i in range(0, len(actual)):\n",
    "        corr.append(np.corrcoef(actual[i],predicted[i])[0][1])\n",
    "        #r2.append(np.corrcoef(actual[i],predicted[i])[0][1]**2)\n",
    "    return np.mean(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "core = np.load('coreferencetask_pred.npy')\n",
    "ner = np.load('ner_pred.npy')\n",
    "nli = np.load('nli_pred.npy')\n",
    "sa = np.load('sa_pred.npy')\n",
    "qa = np.load('qa_pred.npy')\n",
    "summary = np.load('summary_pred.npy')\n",
    "paraphrase = np.load('paraphrase_pred.npy')\n",
    "wsd = np.load('wsd_pred.npy')\n",
    "ss = np.load('ss_pred.npy')\n",
    "srl = np.load('srl_pred.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "tasks = [paraphrase, summary, qa, sa, ner, wsd, nli, srl, core, ss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pairwise = []\n",
    "corr = []\n",
    "for i in tasks:\n",
    "    temp1 = []\n",
    "    temp2 = []\n",
    "    for j in tasks:\n",
    "        temp1.append(pairwise_accuracy(i,j))\n",
    "        temp2.append(pearcorr(i,j))\n",
    "    pairwise.append(temp1)\n",
    "    corr.append(temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(corr).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('pieman_task_corr', np.array(corr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11, 11)\n"
     ]
    }
   ],
   "source": [
    "corr = np.load('pieman_task_corr.npy')\n",
    "print(corr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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\"zerolinewidth\": 2}}}, \"width\": 750, \"xaxis\": {\"dtick\": 1, \"gridcolor\": \"rgb(0, 0, 0)\", \"showgrid\": false, \"showticklabels\": true, \"side\": \"bottom\", \"tickangle\": 25, \"tickfont\": {\"color\": \"black\", \"size\": 12}, \"ticks\": \"\", \"title\": {\"font\": {\"size\": 12}, \"text\": \"\"}}, \"yaxis\": {\"dtick\": 1, \"showgrid\": false, \"showticklabels\": true, \"tickfont\": {\"color\": \"black\", \"size\": 12}, \"ticks\": \"\", \"ticksuffix\": \"  \", \"title\": {\"text\": \"\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('66cc78b6-fac7-4f3b-9ed5-5498e39bf339');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ROIS = ['PD', 'Sum','QA','SA', 'NER', 'WSD', 'NLI','SRL','CR', 'SS']\n",
    "fig = ff.create_annotated_heatmap(z=np.round(np.array(corr),3),\n",
    "                   x=['PD', 'Sum','QA','SA', 'NER', 'WSD', 'NLI','SRL','CR', 'SS'],\n",
    "                y=ROIS, annotation_text=np.round(np.array(corr),3), showscale=True, zmin =0, zmax=1)\n",
    "fig['layout']['xaxis']['side'] = 'bottom'\n",
    "fig.layout.update(width=750,height=450,\n",
    "  xaxis=dict(\n",
    "        title='',\n",
    "        showgrid=False,\n",
    "        titlefont=dict(\n",
    "           # family='Gill sans, monospace',\n",
    "            size=12,\n",
    "            #color='#7f7f7f'\n",
    "        ),\n",
    "        showticklabels=True,\n",
    "        tickangle=25,\n",
    "        tickfont=dict(\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='',\n",
    "        showgrid=False,\n",
    "        titlefont=dict(\n",
    "            #family='Gill sans',\n",
    "            #size=12,\n",
    "            #color='#7f7f7f'\n",
    "        ),\n",
    "        showticklabels=True,\n",
    "        tickfont=dict(\n",
    "            size=12,\n",
    "            color='black'\n",
    "        ),\n",
    ")\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rf6QLrbsREdwKfCaT1wxd+1ngy8B1mWwYXHsE8PfAtzNpe//e1wMfz+TLg693pWzr+NXBcOjwvUcBZ2ZyUvOVblfHEZQJQo+jNFy/Spk5vjfwM8DDKJNcjs/k7wd/59WZi86EnXStfwOsy9zxgRIRXA1szeSXJl/ZsnVcig3Bh6yp80DPprTQRhcb/ynwauDfKB82ewMfin4c3VBdy3khMJu9/P3s5dWDPXzPogzn3gw8q1PhWbyGshn7HZRndFuAZ0PzHyoV7qacyvPblNnaDxCxdeRPF3bK2Z+Fs8k3UFrFF89dGDxHmqUclNC284GZoa/3ocy8XWwjjScDvzH5kpY2WLpyFfBwyvthn0wOzeR5mRxGGfn5T5SZuFdFsHcEr6U0HttwOGW/23F8hvkN8dv0TOCvhy8MGoLPpTQEn5fJOZSh3K9Duw2qrmpqEtFzgL/O3vwOOYNniCdTFqYfmb28O/pxIKXH9DuUodE27U+ZDTps7uv3Df8uHXIS8BXgWWT+EICIDwKnELEPOd/776AP061JTUvZnYUNwWcO/ne0QfVP0O56v2V0Ya31Us6hvG5HLbacZtA4eX8En6ecgnIDZTewaxqtct5jGH/DjLsG97dt7IZgBLOUz+TWRfDJFf6VzORlEymG5gL0iZT1RMOOpWwndWH28m6A7OXXox+XA7/aUF3L2Y2FuyfNfX0H3fQzwJseDM/iv1Bm0R3Ewq3muiPzlLZLGNO3WLh143Mp0/xHz1ndA9o9amtKHUc5T3PZtaiZfCmCP6fMwv04ZelFG35IOclmHI9l/F3ZJmlaG4LHrfD+iTbKmxrC3QcWPOw/gvLLjU6++Qplc+YumLbtuR4J88dsDXxn6Ht66K4HTorgEIAITqAM6y/W+zmE8pxOK7Oe8Q85+DvKv8dfHV2L26BbgV8Y894XDe5v21Q2BDPZZUd/gBcwvznIRA+0b6oH+l1KL3TYUcC9lMkXwxK4r4mixvC26McfDn29K4MhjujH6H6tmb08rLnSljQa7nNfd3nIDiJ+eqz7MtveVPytlGeEmyP4HqVH8RPYfs3nYKLO8bi3aI2tlBGgcawD7m15icUVwDsjeFkmn1jqpgiOpwTo7zVW2dLmGoKXZLJlqCF46SL3TkVDMIKDKWew/nvKqMB5lHN6J6apAN0CvDL68Y7s5b3Rj4Mos3I/lb0FMy0PZMKthjF9ixI+o/vFfovSc+/OPrLbeykRw6fZ7EH5PX6NiJmRe5PMCxqrbHm3M17PvtUdZzK5bXDQc48yM/vzwPmZC3oVxwDfg6U/UBu22Gvb1ZGUr1Nev/eNce+Gwf1t+gBlS8SPDvZI/mAmt899M4L1lJnx51CeO3ZhUt+aaQhG8ETgzZTfZytlN7PzM/nexH92Q8tYjqZsUHw75Zy8oynDtMdnL/9m5N6bgS3Zy1ZnAk6lWPmWiR3aym8jCz/Q1wFPoWybtwW4hsw1tatOEwZb+d3H9hvK70l5Bja6KcE6YPc2t8aL4DxKA+W4zO327x2978WU2bobM/njpupbopanDmo5iPI+/iHwA0pDe2/KCNBXKb9TJ9ZmR/AM5huC36CEzudG7nkhZanhazPHnmnciMG5tm+gzMjeHfgL4I3DjZeJ19DUUrvox5nAWygPo/8V2Ji9fNfIPXNBe2oX1oIuJfoxvFPHrdnL0WHodkQ8f8V/pwN7y+5QxJMps7NPI7dvcGnHurb4fEci2IuyVGx/yhDcxZnz5wlH8GRKj+61lGf8hy1ylFzjBkfb/RZlEuTTKMH5A8rRgR+j/B6jE3e0QoMDHc4GzqXMr/kfwLmZzR9111QP9DHZy3uiH7tSdhz6f0vc9wgGD6yzl61u1xX92MDcTh29vGPo+uI7dfSyEzt1rFkRbwJeSmYX1tBpwiI4iLJO8UDme3RzGynM9ei+RtlIYZxTnrQGRHAaZQvVf0c5BencTD7dWj0NBehWSotyE2Uh8XXDOxJ1UfTjUuCY7OUBI9eX3qmjw73mqRfxauACMvdouxQ1Y7Cb029ij04DQycL3UQ5g3dHAZaZTGyeR1MB+inKrNs9mT+o9WZKmG6iBOq4B0E3IvpRtmzr5WuGrs1v2dbLDYNr81u29bLVLdvWtIhrgEPJfELbpUybri0+l2oNAnQlJnrUXSOzcLOXLx4M3z6TMmvuGEqgzlDGsrdFPzYz30O9PnvbbQbQhvF26ujlj6MfndmpY2pF/NES33kMZV3XwcCfNFfQmtKpxefSQ9Cpg8ibWsbC4Jnm5wZ/3jYI1CMoobSBsmfrz1PWSD1A+2drTutOHdNq4zLfu4Ny5urbmyllbRksLl/W4HSOt1Pe411YRiYtkLngs7dVjQXoqEGg3gjcGP24kNKy+APKEpfW6hqy9E4dvezsTh1TbLFD1xO4h8zWZ1iuVW0sPpfWilaCavDc8DnMD+c+Y1DLPZQZrl1oZZSdOvpxSfZyS/Rj6nfq6LTMb273dTy4VOhIIm4lO7JUaI1oc/G5tFY0EqCDcz+fzXxgPpNynt+dlFNXZoFrs5e3NFHPmOZ36ujH0jt1lKHoTu/U0VkRGxgsFSLnlwoRiywViriMdKnQQ9WFxefSWtFUD/T7lMD8LqVn9xFgU/by/zb081cse3lb9GPhlm297PqWbdPkFOAYMn935PpllF798FKhk4m4lnSpUI0uLT6X1oqmlrFso0wM+lvK8Oy1wE3ZyzY3gFbboiwVIueXChHzS4XIslSImF8qRLpUaKW6tvhcWiua6oHODd9uoExSeBtwb/Tjs8wH6heylw8s9X+gNWmspUJk/phwqdBDcDHbLz6fiWBmmfsnuvhcWisa2wv3wR+4/fKV51PCdU/gR5RZuddSnode32hhal7EvcDZZH5w6NollKHd9eTQbOeIU4EPkPmwhqucel1bfC6tFY3Pwh1evgK8dWSDheMoQ03ZRm1q3JJLhbYLz8KlQvU6tfhcWitaDanBcpbnMj8793C6fvCzVtP1wElEXELmFsKlQpPQtcXn0lrRaIAOlrOMrv/cjRKa9wE3ULby+0yTdak1Dy4VIpZeKkS4VEhS9zS1DrRPCcwjmA/Mn1CWKMwF5o3Zy580UY86IvO2wRmm2y0VIl0qJKn7ml7G8gXmA/Oz2cv7Jv7DJUmagKaGcF8C3JC9vLehnydJ0kQ1voxFkqS1YIfHHEmSpIUMUEmSKhigkiRVMEAlSapggEqSVOH/A3C2xrdYs7XIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as shc\n",
    "fig = plt.figure(figsize=(8, 3))  \n",
    "#plt.title(\"Lucy: Neural Language Taskonomy\")  \n",
    "shc.set_link_color_palette(['black', 'black', 'black', 'black'])\n",
    "#shc.set_link_color_palette(['red', 'green'])\n",
    "dend = shc.dendrogram(shc.linkage(np.round(np.array(corr),3), method='ward'), labels=ROIS,\n",
    "    leaf_rotation=90.,leaf_font_size=18., above_threshold_color='black', p=0.5)\n",
    "# Assignment of colors to labels: 'a' is red, 'b' is green, etc.\n",
    "label_colors = {'PD': 'r', 'Sum': 'r', 'NER': 'b', 'CR': 'b',\n",
    "               'SS': 'b', 'WSD': 'g', 'SRL': 'g', 'SA': 'b', 'QA': 'b', 'NLI': 'b'}\n",
    "plt.tick_params(axis='y', which='major', labelsize=12, color='black')\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "xlbls = ax.get_xmajorticklabels()\n",
    "for lbl in xlbls:\n",
    "    lbl.set_color(label_colors[lbl.get_text()])\n",
    "ax.set_yticklabels([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as shc\n",
    "ROIS = ['Paraphrase', 'Summarization','QA', 'SA', 'NER', 'WSD', 'NLI','SRL','CR', 'SS']\n",
    "fig = plt.figure(figsize=(8, 3))  \n",
    "#plt.title(\"Lucy: Neural Language Taskonomy\")  \n",
    "shc.set_link_color_palette(['black', 'black', 'black', 'black'])\n",
    "#shc.set_link_color_palette(['red', 'green'])\n",
    "dend = shc.dendrogram(shc.linkage(np.round(np.array(pairwise),3), method='ward'), labels=ROIS,\n",
    "    leaf_rotation=90.,leaf_font_size=18., above_threshold_color='black', p=0.5)\n",
    "# Assignment of colors to labels: 'a' is red, 'b' is green, etc.\n",
    "label_colors = {'Paraphrase': 'r', 'Summarization': 'r', 'NER': 'b', 'CR': 'b',\n",
    "               'SS': 'b', 'WSD': 'b', 'SRL': 'b', 'SA': 'b', 'QA': 'g', 'NLI': 'g'}\n",
    "plt.tick_params(axis='y', which='major', labelsize=12, color='black')\n",
    "\n",
    "ax = plt.gca()\n",
    "xlbls = ax.get_xmajorticklabels()\n",
    "for lbl in xlbls:\n",
    "    lbl.set_color(label_colors[lbl.get_text()])\n",
    "ax.set_yticklabels([])\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}