zooniverse_consensus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "continental-complex",
   "metadata": {},
   "source": [
    "# WARNING: This file is a work in progress and its results should not be trusted!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "regulated-senegal",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib\n",
    "import pandas\n",
    "import seaborn\n",
    "\n",
    "import swasputils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cheap-hollywood",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anm83\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3338: DtypeWarning: Columns (8,9) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n",
      "C:\\Users\\anm83\\Anaconda3\\lib\\site-packages\\numpy\\lib\\arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "classifications = swasputils.ZooniverseClassifications()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "diagnostic-theorem",
   "metadata": {},
   "source": [
    "The consensus function here certainly needs more thought, but this is hopefully a decent starting point.\n",
    "\n",
    "This calculates consensus as the max proportion of classifications any annotation value has:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "embedded-reasoning",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_consensus(series):\n",
    "    sum_values = sum(series.values)\n",
    "    return max([v/sum_values for v in series.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "micro-myrtle",
   "metadata": {},
   "outputs": [],
   "source": [
    "SKIP_WORKFLOWS = {5214,17339,17444,17445}\n",
    "workflow_consensus = {}\n",
    "for workflow_id, workflow_classifications in classifications.workflows.items():\n",
    "    if workflow_id in SKIP_WORKFLOWS:\n",
    "        continue\n",
    "    workflow_counts = pandas.read_csv(\n",
    "        os.path.join(swasputils.DATA_LOCATION, 'aggregated-workflow-{}.csv'.format(workflow_id)),\n",
    "        index_col='subject_ids',\n",
    "    )\n",
    "    workflow_consensus[workflow_id] = {\n",
    "        'Classification Count': workflow_counts.apply(sum, 'columns'),\n",
    "        'Consensus': workflow_counts.apply(calc_consensus, 'columns')\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "contained-scholarship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6IXByJ7OpXlfttP0E8Pv2DgtFxFRgKkClUmmvDzMzWwu5EoGkmdTZiEfEPh3MtgQYUTU9HFjaTtuJ+LCQmVmPyHuvoS9VvV8POATo7C7ls4FRkrYGnibb2B9Z20jSJsBewGdyxmJmZg2U99DQnJqi30uaVbfxW/OskHQiMAPoB1yRzi8cn+qnpKYHA7+OiL92LXQzM2uEvIeGqq/tXwd4H7B5Z/NFxHRgek3ZlJrpK8nxnQQzMytG3kNDc8jOEYjskNCTwOeKCsrKo/rh6L73vVnPyHtoaOuiA7HyqU4CbdNOBmbdL++9hg6TtFF6f7akGyTtWmxo1pfVJoHOys2sOHm/UPaViHhV0p7AvsBVwI+KC8vMzLpL3kSwMv08APhRRNwEDCwmJDMz6055E8HTki4FDgemS1q3C/OaraG9cwE+R2DW/fJuzA8n+z7A+Ih4CXgHcEZRQVk51G70nQTMekbeq4aWS7oJGCppy1T8WHFhWVl442/W8/J+oewk4D+A54C/p+IAdi4oLjMz6yZ5v1B2CvDuiHihyGDMzKz75T1HsBh4uchAzMysZ+QdESwE7pR0K/C3tsKI+G4hUZmZWbfJmwieSq+B+PsDZmZ9St6rhr4GIGkD3y7azKxvyXuvoT0kPQI8mqZ3kXRJoZGZmVm3yHuy+EKyewy9ABARDwIfLigmMzPrRrlvExERi2uKVtZtaGZmTSXvyeLFkj4AhKSBwMmkw0RmZtbc8o4IjgdOAIYBS4AxabpDksZLelzSAkmT22mzt6S5kuZ39hxkM7OymvbAYo69ajbTHqg9OPP2KSIa3imApH7AH4GPkSWP2cAREfFIVZtNgT+Q3czuKUn/EBHPd9RvpVKJlpaWQmI2M+uNdv/Gb3j2lTdWTW+x8UDuOetjXepD0pyIqNSry3vV0LckbSxpgKQ7JC2T9JlOZhsLLIiIhRHxBnAdMKGmzZHADRHxFEBnScDMrGymPbB4tSQA8MwrbzR0ZJD30NDHI+IV4ECyvfvt6fw21MPIbk3RZkkqq7Y9sJmkOyXNkTSpXkeSjpPUIqmltbU1Z8hmZs3vloee7VL52sibCAakn/sD10bEiznmUZ2y2uNQ/YH3kT35bF/gK5K2X2OmiKkRUYmIypAhQ3KGbGbW/A7cafMula+NvIngV5IeAyrAHZKGAP/XyTxLgBFV08OBpXXa3B4Rf42IZcBdwC45YzIz6/MO2nUEW2y8+p19tth4IAftOqKdObou98liSZsBr0TESkmDgI0jot2xiaT+ZCeLPwI8TXay+MiImF/VZgfgIrLRwEDgfmBiRDzcXr8+WWxmZTTtgcXc8tCzHLjT5muVBDo6WZz3ewQAOwAj0wa+zdXtNY6IFZJOJHvEZT/gioiYL+n4VD8lIh6VdDswj+yBN5d3lATMzMrqoF1HNHQUUC3XiEDST4Ftgbm89Y3iiIiTC4mqAx4RmJl1XSNGBBVgdBT1pQMzM+sxeU8WPww07hS1mZn1GnlHBIOBRyTdz+pPKPtkIVGZmVm3yZsIvlpkEGZm1nPyPqFslqShwG6p6H7fDsLMrG/Ie6+hw8mu8T8MOBy4T9KhRQZmZmbdI++hoX8HdmsbBaRvFv8P8MuiAjMzs+6R96qhdWoOBb3QhXnNzKwXyzsiuF3SDODaNP1pYHoxIZmZWXfqMBFI2g4YGhFnSPoUsCfZXUXvAa7phvjMzKxgnR3euRB4FSAiboiIL0bEaWSjgQuLDc3MzLpDZ4lgZETMqy2MiBZgZCERmZlZt+osEazXQd36jQzEzMx6RmeJYLakz9cWSvocMKeYkMzMrDt1dtXQqcCNko7irQ1/hewhMgcXGJeZmXWTDhNBRDwHfEDSOGDHVHxrRPy28MjMzKxb5L3X0ExgZsGxmJlZD+jKoyq7TNJ44Ptkj6q8PCIuqKnfG7gJeDIV3RAR5xYRy8jJt656v+iCA4r4CLM+oXLu7SxbvpLBg/rRcs74ng7HukFht4mQ1A+4GNgPGA0cIWl0naZ3R8SY9Co8CdSbNrPMyMm3smx59jTaZctX+n+lJIq8X9BYYEFELIyIN4DrgAkFfl5d7f0h+w/cbHWVc2/vUrn1HUUmgmHA4qrpJams1h6SHpR0m6R/rNeRpOMktUhqaW1tLSJWs9JrGwnkLbe+o8hEoDplUTP9ALBVROwC/BCYVq+jiJgaEZWIqAwZMqSxUZoZAIMH9etSufUdRSaCJcCIqunhwNLqBhHxSkS8lt5PBwZIGtzIINo7MewTxmara+/EsE8Y931FJoLZwChJW0saCEwEbq5uIGlzSUrvx6Z4Xmh0ILUbfScBs/oWXXDAqhHA4EH9/L9SEoVdPhoRKySdCMwgu3z0ioiYL+n4VD8FOBT4F0krgNeBiRFRe/ioIfwHbZaPRwDlo4K2u4WpVCrR0tLS02GYmTUVSXMiolKvzo+bNDMrOScCM7OScyIwMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjAzKzknAjOzknMiMDMrOScCM7OScyIwMys5JwIzs5JzIjAzK7lCE4Gk8ZIel7RA0uQO2u0maaWkQ4uMx8zM1lRYIpDUD7gY2A8YDRwhaXQ77b5J9mxjMzPrZkWOCMYCCyJiYUS8AVwHTKjT7iTgeuD5AmMxM7N2FJkIhgGLq6aXpLJVJA0DDgamdNSRpOMktUhqaW1tbXigZmZlVmQiUJ2yqJm+EPhyRKzsqKOImBoRlYioDBkypFHxmZkZ0L/AvpcAI6qmhwNLa9pUgOskAQwG9pe0IiKmFRiXmZlVKTIRzAZGSdoaeBqYCBxZ3SAitm57L+lK4BYnATOz7lVYIoiIFZJOJLsaqB9wRUTMl3R8qu/wvICZmXWPIkcERMR0YHpNWd0EEBFHFxmLmZnV528Wm5mVnBOBmVnJORGYmZWcE4GZWck5EZiZlZwTgZlZyTkRmJmVnBOBmVnJORGYmZVcod8sNuvMyMm3rnq/6IIDejASs/LyiMB6THUSqDdtZt3DicB6RHsbfScDs+7nRGBmVnJOBGZmJedEYD2ivRPDPmFs1v2cCKzH1G70nQTMeoYvH7Ue5Y2/Wc/ziMDMrOQKTQSSxkt6XNICSZPr1E+QNE/SXEktkvYsMh4zM1tTYYeGJPUDLgY+BiwBZku6OSIeqWp2B3BzRISknYFfAO8pKiYzM1tTkSOCscCCiFgYEW8A1wETqhtExGsREWlyAyAwM7NuVWQiGAYsrppekspWI+lgSY8BtwL/XK8jScelQ0ctra2thQRrZlZWRSYC1SlbY48/Im6MiPcABwH/Wa+jiJgaEZWIqAwZMqSxUZqZlVyRiWAJMKJqejiwtL3GEXEXsK2kwQXGZGZmNYpMBLOBUZK2ljQQmAjcXN1A0naSlN7vCgwEXigwJjMzq1HYVUMRsULSicAMoB9wRUTMl3R8qp8CHAJMkvQm8Drw6aqTx2Zm1g3UbNvdSqUSLS0tPR2GmVlTkTQnIir16vzNYjOzknMiMDMrOScCM7OScyIwMys5JwIzW82C517lly2LWfDcqz0dinUTP4/AzFY5Z9pDXH3vU6umJ+2xJedO2KkHI7Lu4BGBmQHZSKA6CQBcfc9THhmUgBOBmQEwd/FLXSq3vsOJwMwAGDNi0y6VW9/hRGBmAGw3dCMm7bHlamWT9tiS7YZu1EMRWXfxyWIzW+XcCTsxafeRzF38EmNGbOokUBJOBGa2mu2GbuQEUDI+NGRmVnJOBGZmJedEYGZWck4EZmYl50RgZlZyTfeEMkmtwJ/XcvbBwLIGhtOTvCy9U19Zlr6yHOBlabNVRAypV9F0ieDtkNTS3qPamo2XpXfqK8vSV5YDvCx5+NCQmVnJORGYmZVc2RLB1J4OoIG8LL1TX1mWvrIc4GXpVKnOEZiZ2ZrKNiIwM7MaTgRmZiXX5xKBpBGSZkp6VNJ8SafUaSNJP5C0QNI8Sbv2RKydybkse0t6WdLc9DqnJ2LtjKT1JN0v6cG0LF+r06bXr5ecy9EU66SNpH6S/lfSLXXqev06adPJcjTbOlkk6aEUa0ud+oaul754G+oVwOkR8YCkjYA5kn4TEY9UtdkPGJVe7wd+lH72NnmWBeDuiDiwB+Lrir8B+0TEa5IGAL+TdFtE3FvVphnWS57lgOZYJ21OAR4FNq5T1wzrpE1HywHNtU4AxkVEe18ea+h66XMjgoh4JiIeSO9fJfvDGFbTbAJwdWTuBTaVtEU3h9qpnMvSFNLv+rU0OSC9aq9U6PXrJedyNA1Jw4EDgMvbadLr1wnkWo6+pqHrpc8lgmqSRgLvBe6rqRoGLK6aXkIv38B2sCwAe6RDFbdJ+sfujSy/NHSfCzwP/CYimnK95FgOaJJ1AlwI/Bvw93bqm2Kd0PlyQPOsE8h2Ln4taY6k4+rUN3S99NlEIGlD4Hrg1Ih4pba6ziy9dq+uk2V5gOweIrsAPwSmdXN4uUXEyogYAwwHxkrasaZJU6yXHMvRFOtE0oHA8xExp6Nmdcp61TrJuRxNsU6qfDAidiU7BHSCpA/X1Dd0vfTJRJCO3V4PXBMRN9RpsgQYUTU9HFjaHbF1VWfLEhGvtB2qiIjpwABJg7s5zC6JiJeAO4HxNVVNs16g/eVoonXyQeCTkhYB1wH7SPqvmjbNsE46XY4mWicARMTS9PN54EZgbE2Thq6XPpcIJAn4MfBoRHy3nWY3A5PSmffdgZcj4pluCzKnPMsiafPUDkljydbpC90XZT6ShkjaNL1fH/go8FhNs16/XvIsR7Osk4g4MyKGR8RIYCLw24j4TE2zXr9O8ixHs6wTAEkbpItDkLQB8HHg4ZpmDV0vffGqoQ8CnwUeSsdxAc4CtgSIiCnAdGB/YAGwHDim+8PMJc+yHAr8i6QVwOvAxOidXxffArhKUj+yf8JfRMQtko6HploveZajWdZJXU24Tupq4nUyFLgx5a3+wM8i4vYi14tvMWFmVnJ97tCQmZl1jROBmVnJORGYmZWcE4GZWck5EZiZlZwTgfUa6Vrv6yT9SdIjkqZL2l7SSEm111G/nc85V9JH0/sPKbuL6FxJwyT9ci37PFrSu6qmL5c0ukHx7iepRdldaB+T9J1G9FvzGadKGtTofq05+PJR6xXSl33+AFyVrpNG0hhgI7J7qtwSEbW3cmjE504B7ouIn7zNfu4EvhQRa9wy+G32uyNwE3BARDwmqT9wXERc0uDPWQRUOrjbpfVhHhFYbzEOeLMtCQBExNyIuLu6URod3C3pgfT6QCrfQtJdac/+4bSn30/SlWn6IUmnpbZXSjpU0rHA4cA5kq6pHnmkeb+T5psn6aRUfo6k2anPqembnYcCFeCa9PnrS7pTUiXNc0Tq52FJ36xaltckfV3ZjdDulTS0zu/l34CvR8Rj6Xeyoi0JSNpK0h0pvjskbVm9fNWfk37uneL6ZRpZXJPiPxl4FzBT0sy3sQ6tSTkRWG+xI9DRTcPaPA98LN2Q69PAD1L5kcCMdDO4XYC5wBhgWETsGBE7Aavt9UfE5WRf1T8jIo6q+ZzjgK2B90bEzsA1qfyiiNgtjU7WBw6MiF8CLcBRETEmIl5v6yQdLvomsE+KZzdJB6XqDYB7043Q7gI+38Xfy0VktyJui+8H7bSr9l7gVGA0sA3Zzc1+QHafmnERMS5HH9bHOBFYsxkAXCbpIeC/yTZoALOBYyR9FdgpPb9hIbCNpB9KGg/U3rm1Ix8FpkTECoCIeDGVj5N0X/r8fYDObme8G3BnRLSmvq4B2u4k+QbQ9jStOcDILsQHsAfws/T+p8CeOea5PyKWRMTfyZJlVz/T+iAnAust5gPvy9HuNOA5sr3+CjAQICLuItvAPg38VNKkiPhLancncAJde2iJqLmtr6T1gEuAQ9MI4zJgvRz9tOfNqvvdrKT+vb/y/l7grXhXkP6307mXgVVt/lb1vr3PtJJxIrDe4rfAupJWHR6RtJukvWrabQI8k/ZoPwv0S223Irsn/WVkd2zdVdlthteJiOuBrwBdea7rr4Hj08lZJL2Dtzb6y5Q9I+LQqvavkp3YrnUfsJekwcpuVHcEMKsLcXwbOEvS9imOdSR9MdX9gexumwBHAb9L7xfxVvKYQDaK6kx78VsJeG/AeoWICEkHAxdKmgz8H9kG7dSappcA10s6DJgJ/DWV7w2cIelN4DVgEtkTm34iqW2H58wuhHQ5sD0wL/V5WURcJOky4KEU2+yq9lcCUyS9TnbIpm25npF0ZopVwPSIuClvEBExT9KpwLXp8s4Abk3VJwNXSDoDaOWtO1BeBtwk6X7gDt76HXVkKnCbpGd8nqB8fPmomVnJ+dCQmVnJORGYmZWcE4GZWck5EZiZlZwTgZlZyTkRmJmVnBOBmVnJ/X8a2dBCVH3GlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for workflow_id, consensus in workflow_consensus.items():\n",
    "    consensus_df = pandas.DataFrame(consensus)\n",
    "    consensus_df[consensus_df['Classification Count'] > 1].plot.scatter(\n",
    "        title='Consensus in workflow {}'.format(workflow_id),\n",
    "        x='Classification Count',\n",
    "        y='Consensus',\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "printable-spirit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for workflow_id, consensus in workflow_consensus.items():\n",
    "    consensus_df = pandas.DataFrame(consensus)\n",
    "    seaborn.catplot(\n",
    "        data=consensus_df[consensus_df['Classification Count'] > 1],\n",
    "        x='Classification Count',\n",
    "        y='Consensus',\n",
    "        kind='bar',\n",
    "    )\n",
    "    matplotlib.pyplot.title(\"Consensus for workflow {}\".format(workflow_id))\n",
    "    matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "impressed-sierra",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": 5
}