Statistics on FoodMine Training data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def food_statistics(filename_scoring,filename_chemicals):\n",
    "    \n",
    "    food_scoring_data = pd.read_csv(filename_scoring, encoding='latin1')[['PMID', 'abstract', 'paper', 'mesh_terms', 'qual_terms', 'is_useful']]\n",
    "\n",
    "    food_scoring_data_usefulness = food_scoring_data['is_useful'].tolist()\n",
    "    nonuseful_count = 0\n",
    "    useful_count = 0\n",
    "\n",
    "    for elem in food_scoring_data_usefulness:\n",
    "        if elem == 0.0:\n",
    "            nonuseful_count = nonuseful_count + 1\n",
    "        else:\n",
    "            useful_count = useful_count + 1\n",
    "\n",
    "    food_usefulness_dict = dict()\n",
    "    food_usefulness_dict['Useful'] = useful_count\n",
    "    food_usefulness_dict['Not useful'] = nonuseful_count\n",
    "    \n",
    "    print(food_usefulness_dict)\n",
    "\n",
    "    plt.figure(figsize=(20, 10)) \n",
    "    plt.bar(food_usefulness_dict.keys(),food_usefulness_dict.values())\n",
    "    plt.ylabel('Paper count')\n",
    "    plt.show()\n",
    "    \n",
    "    food_chemical_data = pd.read_csv(filename_chemicals, encoding='latin1')[['chemical','method']]\n",
    "    \n",
    "    print('Distinct chemicals found: ', len(set(food_chemical_data['chemical'].tolist())))\n",
    "    print('Distinct measurement methods found: ', len(set(food_chemical_data['method'].tolist())))\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Useful': 193, 'Not useful': 222}\n"
     ]
    },
    {
     "data": {
      "image/png": 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l1YMBAAAAsF4mIemnk7whyd8tj3OS/HxVXVdVX1rlcAAAAACsj8ld2+68FYMAAAAAsN4m10hKVd01yfFJbrdnrbvfs6qhAAAAAFg/Bw1JVfXsJM9Pcq8kFyd5WJL3JXnUakcDAAAAYJ1MrpH0/CQ/kOTT3f3DSR6U5NqVTgUAAADA2pmEpL/t7r9Nkqq6bXd/PMn9VzsWAAAAAOtmco2kK6vqLknelOSdVfWFJFetdiwAAAAA1s3krm0/trx8UVVdkOQ7krxtpVMBAAAAsHb2G5Kq6nZJfibJ9yT5SJJXd/efbNVgAAAAAKyXA10j6ewku7IRkR6X5GVbMhEAAAAAa+lAp7ad0N3/IEmq6tVJPrA1IwEAAACwjg50RNLX9rzo7uu3YBYAAAAA1tiBjkh6YFV9aXldSW6/bFeS7u6/t/LpAAAAAFgb+w1J3X3UVg4CAAAAwHo70KltAAAAAPANQhIAAAAAI0ISAAAAACNCEgAAAAAjKwtJVfWaqrqmqi7ZtHZMVb2zqv5yeb7rsl5V9YqqurSqdlfVg1c1FwAAAAA3zSqPSHptksfutXZmkvO7+/gk5y/bSfK4JMcvj9OTvHKFcwEAAABwE6wsJHX3e5J8fq/lk5Ocvbw+O8kpm9Zf1xven+QuVXWPVc0GAAAAwKHb6msk3b27r06S5fluy/o9k1yxab8rlzUAAAAA1sS6XGy79rHW+9yx6vSqurCqLrz22mtXPBYAAAAAe2x1SPrMnlPWludrlvUrkxy3ab97JblqX1/Q3Wd1967u3rVjx46VDgsAAADAN211SDovyWnL69OSvHnT+jOWu7c9LMkX95wCBwAAAMB6OHpVX1xVb0xyUpJjq+rKJL+c5CVJzq2qZyW5PMmTl93fmuTxSS5N8jdJnrmquQAAAAC4aVYWkrr7qft569H72LeTnLGqWQAAAAC4+dblYtsAAAAArDkhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAICRo7fjj1bVZUmuS3JDkuu7e1dVHZPk95PsTHJZkqd09xe2Yz4AAAAAvtV2HpH0w919YnfvWrbPTHJ+dx+f5PxlGwAAAIA1sU6ntp2c5Ozl9dlJTtnGWQAAAADYy3aFpE7yjqq6qKpOX9bu3t1XJ8nyfLdtmg0AAACAfdiWayQleXh3X1VVd0vyzqr6+PSDS3g6PUnufe97r2o+AAAAAPayLUckdfdVy/M1Sf4oyUOTfKaq7pEky/M1+/nsWd29q7t37dixY6tGBgAAADjibXlIqqo7VtWd97xO8pgklyQ5L8lpy26nJXnzVs8GAAAAwP5tx6ltd0/yR1W15++/obvfVlUfTHJuVT0ryeVJnrwNswEAAACwH1sekrr7k0keuI/1zyV59FbPAwAAAMDMdt21DQAAAIBbGCEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAIARIQkAAACAESEJAAAAgBEhCQAAAICRtQtJVfXYqvpEVV1aVWdu9zwAAAAAbFirkFRVRyX57SSPS3JCkqdW1QnbOxUAAAAAyZqFpCQPTXJpd3+yu/8uyTlJTt7mmQAAAADI+oWkeya5YtP2lcsaAAAAANvs6O0eYC+1j7W+0Q5Vpyc5fdn8clV9YuVTAUeCY5N8druHgCNZ/dp2TwBwi+f3DGyjw+y3zH3298a6haQrkxy3afteSa7avEN3n5XkrK0cCjj8VdWF3b1ru+cAALip/J4BtsK6ndr2wSTHV9V9q+o2SU5Nct42zwQAAABA1uyIpO6+vqqem+TtSY5K8pru/ug2jwUAAABA1iwkJUl3vzXJW7d7DuCI45RZAOCWzu8ZYOWquw++FwAAAABHvHW7RhIAAAAAa0pIAg4bVbWzqi7Za+1FVfWCm/Bdb6yq3VX1Lw6wz0lV9cc3ZVYA4MhSVV1VL9u0/YKqetFBPnNKVZ2w4rkeUVUfraqLq+r2B9jv3VXljnCAkASwt6r6riQ/2N0P6O6Xb/c8AMBh4atJfryqjj2Ez5ySZKUhKcnTkvx6d5/Y3f9vxX8LOAwIScARoaqeV1V/sRxldM6ydseqek1VfbCqPlxVJy+7vyPJ3ZZ/mXvE5n+Bq6pjq+qybfrPAABuua7PxsWwv+Vo56q6T1Wdv/xOOb+q7l1VP5jkR5O8dPlNcr+9PvPaqnrSpu0vL8/3qKr3LJ+5pKoesaw/pqreV1Ufqqo/qKo7VdWzkzwlyb+rqtfvfbR1Vf1WVf3ECv5fALdga3fXNoAVOTPJfbv7q1V1l2Xt3yR5V3f/5LL2gar639n40fbH3X1iklTV9kwMABxufjvJ7qr6T3ut/1aS13X32VX1k0le0d2nVNV52fhN8j8O4W/8syRv7+4XV9VRSe6wHAX1wiQ/0t1fqapfTPLz3f3vq+of7/kbVXXSzf4vBA57QhJwONnfbSg7ye4kr6+qNyV507L+mCQ/uukaSrdLcu8kDusGAL7tuvtLVfW6JM/LjX9v/KMkP768/r0ke4emQ/HBJK+pqlsneVN3X1xVP5SNU+Teu/wD2W2SvO9m/A3gCCYkAYeTzyW5615rxyT5VJInJHlkNo42+rdV9f1JKskTu/sTmz9QVTv3+o7r881TgW/37R0ZADjC/OckH0ry3w6wz/7+cWyzb/w+qY06dJsk6e73VNUjs/Hb5/eq6qVJvpDknd391Ol3LvzuAb6FayQBh43u/nKSq6vq0UlSVcckeWySP01yXHdfkORfJblLkjsleXuSf778+EpVPWg/X31Zkocsr5+0n30AAA6quz+f5Nwkz9q0/GdJTl1ePy0bv12S5Lokd97PV12Wb/4+OTnJrZON6y0luaa7X5Xk1UkenOT9SR5eVd+z7HOHqvrefXznp5OcUFW3rarvSPLoQ/4PBA57QhJwuHlGkhdW1cVJ3pXkV5JcnuS/V9VHknw4ycu7+6+T/Go2fnTtrqpLlu19+fUkz6mqP0tyKHdaAQDYl5flxr8pnpfkmVW1O8nTkzx/WT8nyS8sNwW5317f8aokP1RVH0jyD5N8ZVk/KcnFVfXhJE9M8pvdfW2Sn0jyxuVvvD/J9+09VHdfkY3ItTvJ67PxuwngRqp7ctQkAAAAAEc6RyQBAAAAMCIkAQAAADAiJAEAAAAwIiQBAAAAMCIkAQAAADAiJAEAAAAwIiQBAAAAMCIkAQAAADDy/wGORK15z5XIyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distinct chemicals found:  371\n",
      "Distinct measurement methods found:  72\n"
     ]
    }
   ],
   "source": [
    "food_statistics(\"garlic_scoring.csv\",\"garlic_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Useful': 244, 'Not useful': 231}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distinct chemicals found:  737\n",
      "Distinct measurement methods found:  96\n"
     ]
    }
   ],
   "source": [
    "food_statistics(\"cocoa_scoring.csv\",\"cocoa_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def food_statistics_usefulenss_only(filename_scoring):\n",
    "    \n",
    "    food_scoring_data = pd.read_excel(filename_scoring, encoding='latin1')[['PMID', 'abstract', 'mesh_terms', 'qual_terms', 'is_useful']]\n",
    "\n",
    "    food_scoring_data_usefulness = food_scoring_data['is_useful'].tolist()\n",
    "    nonuseful_count = 0\n",
    "    useful_count = 0\n",
    "\n",
    "    for elem in food_scoring_data_usefulness:\n",
    "        if elem == 0.0:\n",
    "            nonuseful_count = nonuseful_count + 1\n",
    "        else:\n",
    "            useful_count = useful_count + 1\n",
    "\n",
    "    food_usefulness_dict = dict()\n",
    "    food_usefulness_dict['Useful'] = useful_count\n",
    "    food_usefulness_dict['Not useful'] = nonuseful_count\n",
    "    \n",
    "    print(food_usefulness_dict)\n",
    "\n",
    "    plt.figure(figsize=(20, 10)) \n",
    "    plt.bar(food_usefulness_dict.keys(),food_usefulness_dict.values())\n",
    "    plt.ylabel('Paper count')\n",
    "    plt.show()\n",
    "    \n",
    "    #food_chemical_data = pd.read_csv(filename_chemicals, encoding='latin1')[['chemical','method']]\n",
    "    \n",
    "    #print('Distinct chemicals found: ', len(set(food_chemical_data['chemical'].tolist())))\n",
    "    #print('Distinct measurement methods found: ', len(set(food_chemical_data['method'].tolist())))\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Useful': 61, 'Not useful': 32}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "food_statistics_usefulenss_only('basil_scoring.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Useful': 474, 'Not useful': 1191}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "food_statistics_usefulenss_only('apple_scoring.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def food_statistics_milk(filename_scoring):\n",
    "    \n",
    "    food_scoring_data = pd.read_csv(filename_scoring, encoding='latin1')[['PMID', 'abstract', 'paper', 'mesh_terms', 'qual_terms', 'is_useful', 'measmethod', 'chemicals']]\n",
    "\n",
    "    food_scoring_data_usefulness = food_scoring_data['is_useful'].tolist()\n",
    "    nonuseful_count = 0\n",
    "    useful_count = 0\n",
    "\n",
    "    for elem in food_scoring_data_usefulness:\n",
    "        if elem == 0.0:\n",
    "            nonuseful_count = nonuseful_count + 1\n",
    "        else:\n",
    "            useful_count = useful_count + 1\n",
    "\n",
    "    food_usefulness_dict = dict()\n",
    "    food_usefulness_dict['Useful'] = useful_count\n",
    "    food_usefulness_dict['Not useful'] = nonuseful_count\n",
    "    \n",
    "    print(food_usefulness_dict)\n",
    "\n",
    "    plt.figure(figsize=(20, 10)) \n",
    "    plt.bar(food_usefulness_dict.keys(),food_usefulness_dict.values())\n",
    "    plt.ylabel('Paper count')\n",
    "    plt.show()\n",
    "    \n",
    "    chemial_list = []\n",
    "    \n",
    "    for chem_list in food_scoring_data['chemicals'].tolist():\n",
    "        for chem in chem_list:\n",
    "            chemial_list.append(chem)\n",
    "            \n",
    "    measmethod_list = []\n",
    "    \n",
    "    for mm_list in food_scoring_data['measmethod'].tolist():\n",
    "        for m in mm_list:\n",
    "            measmethod_list.append(m)\n",
    "    \n",
    "    print('Distinct chemicals found: ', len(set(chemial_list)))\n",
    "    print('Distinct measurement methods found: ', len(set(measmethod_list)))\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Useful': 131, 'Not useful': 106}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distinct chemicals found:  81\n",
      "Distinct measurement methods found:  55\n"
     ]
    }
   ],
   "source": [
    "food_statistics_milk('mBase_15Aug_abstract[chemical_gen].csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combined Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ranking based on total paper count: Apple, Cocoa, Garlic, Human Milk and Basil "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    " \n",
    "# set height of bar # First bar is useful and second bar is not useful\n",
    "useful_bar = [462,93,77,57]\n",
    "not_useful_bar = [1191,231,222,36]\n",
    "\n",
    "\n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(useful_bar))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "# Make the plot\n",
    "plt.bar(r1, useful_bar, color='#2ca25f', width=barWidth, edgecolor='white', label='Useful')\n",
    "plt.bar(r2, not_useful_bar, color='#fc9272', width=barWidth, edgecolor='white', label='Not useful')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('Food')\n",
    "plt.ylabel('Paper Count')\n",
    "plt.xticks([r + barWidth for r in range(len(useful_bar))], ['Apple','Cocoa','Garlic','Basil'])\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}