Fuzzy Python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bcac37c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from statistics import mean, stdev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "089906dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Directory variables\n",
    "\n",
    "PROTEIN_SCORE_DIR = 'fuzzy_python_results'\n",
    "IEEE_DIR = 'IEEE'\n",
    "IEEE_PROTEIN_SCORE_REF = 'ieee_ref.pkl'\n",
    "INSTR_LIB = 'py'\n",
    "THRESHOLD = 0.3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9513efa",
   "metadata": {},
   "source": [
    "## Calculate stats across proteins and metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "989dbf90",
   "metadata": {},
   "outputs": [],
   "source": [
    "#protein stats\n",
    "docker_pred = {}\n",
    "for i in range(1,11):\n",
    "#     for j in range(0,646):\n",
    "    f = open(PROTEIN_SCORE_DIR + '/%s.pkl'%(i), 'rb')\n",
    "#         print(i,j)\n",
    "    pkl_dict = pickle.load(f)\n",
    "\n",
    "        pkl_dict = {}\n",
    "        for prot in temp:\n",
    "            pkl_dict[prot] = {}\n",
    "            for go_id in temp[prot]:\n",
    "                if temp[prot][go_id] >= THRESHOLD:\n",
    "                    pkl_dict[prot][go_id] = np.float64(temp[prot][go_id])\n",
    "\n",
    "    for protein in pkl_dict.keys():\n",
    "        if i == 1:\n",
    "            docker_pred[protein] = {}\n",
    "            for go_id in pkl_dict[protein]:\n",
    "                docker_pred[protein][go_id] = np.array([pkl_dict[protein][go_id]])\n",
    "        else:\n",
    "\n",
    "            for go_id in pkl_dict[protein]:\n",
    "                docker_pred[protein][go_id] = np.append(docker_pred[protein][go_id], pkl_dict[protein][go_id])\n",
    "\n",
    "original_file = open(IEEE_DIR + IEEE_PROTEIN_SCORE_REF, 'rb')\n",
    "pkl_dict = pickle.load(original_file)\n",
    "original_results = {}\n",
    "\n",
    "for protein in pkl_dict:\n",
    "    original_results[protein] = pkl_dict[protein]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f10234d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#protein stats\n",
    "stats = {}\n",
    "dfs = {}\n",
    "proteins = []\n",
    "for name in docker_pred.keys():\n",
    "    proteins.append(name)\n",
    "#     print(name, len(docker_pred[name]))\n",
    "    stats[name] = {'GO term':[], 'Standard deviation':[], 'Mean': [], 'Standard error': [], 'Original Result':[]}\n",
    "    for i in docker_pred[name].keys():\n",
    "        stats[name]['GO term'] = np.append(stats[name]['GO term'], i)\n",
    "        stats[name]['Standard deviation'] = np.append(stats[name]['Standard deviation'], np.std(docker_pred[name][i].astype(np.float64), dtype=np.float64))\n",
    "        stats[name]['Mean'] = np.append(stats[name]['Mean'], np.mean(docker_pred[name][i].astype(np.float64), dtype=np.float64))\n",
    "        stats[name]['Standard error'] = np.append(stats[name]['Standard error'], (np.std(docker_pred[name][i].astype(np.float64), dtype=np.float64) / np.sqrt(len(docker_pred[name][i])).astype(np.float64)))\n",
    "        if i in original_results[name].keys():\n",
    "            stats[name]['Original Result'] = np.append(stats[name]['Original Result'], original_results[name][i])\n",
    "        \n",
    "    dfs[name] = pd.DataFrame(stats[name])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2eea1b2",
   "metadata": {},
   "source": [
    "### Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f51ea5b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#metric stats\n",
    "metrics ={'bp': {'threshold':[], 'Smin':[], 'Fmax':[], 'AUPR':[]}, 'cc':{'threshold':[], 'Smin':[], 'Fmax':[], 'AUPR':[]}, 'mf':{'threshold':[], 'Smin':[], 'Fmax':[], 'AUPR':[]}}\n",
    "metric_stats ={'bp': {'threshold':{}, 'Smin':{}, 'Fmax':{}, 'AUPR':{}}, 'cc':{'threshold':{}, 'Smin':{}, 'Fmax':{}, 'AUPR':{}}, 'mf':{'threshold':{}, 'Smin':{}, 'Fmax':{}, 'AUPR':{}}}\n",
    "df_metric = {'bp':{}, 'cc':{}, 'mf':{}}\n",
    "\n",
    "for category in ['bp', 'cc', 'mf']:\n",
    "    for i in range(1,11):\n",
    "\n",
    "        metrics_file = open(PROTEIN_SCORE_DIR + \"/%s_%s.txt\"%(category, i))\n",
    "        read_file = csv.reader(metrics_file)\n",
    "        next(read_file)\n",
    "        \n",
    "        for row in read_file: \n",
    "#             if 'Smin threshold' in row[0] or 'Precision' in row[0] or 'Recall' in row[0] or 'Positive' in row[0] or 'Negative' in row[0]: continue\n",
    "            if 'Length' in row[0]: continue\n",
    "            metric, score = row[0].split(': ')\n",
    "            metrics[category][metric].append(np.float64(score))\n",
    "    \n",
    "    metrics_file = csv.reader(open(IEEE_DIR + \"/%s.txt\"%category))\n",
    "    next(metrics_file)\n",
    "    for row in metrics_file:\n",
    "#         if 'Smin threshold' in row[0] or 'Precision' in row[0] or 'Recall' in row[0] or 'Positive' in row[0] or 'Negative' in row[0]: continue\n",
    "        if 'Length' in row[0]: continue       \n",
    "        term, score = row[0].split(': ')\n",
    "        metric_stats[category][term]['Original Result'] = [np.float64(score)]\n",
    "\n",
    "    for i in metrics[category]: \n",
    "        metric_stats[category][i]['Standard Deviation'] = [stdev(metrics[category][i])]\n",
    "        metric_stats[category][i]['Mean'] = [mean(metrics[category][i])]\n",
    "        metric_stats[category][i]['Standard Error'] = [stdev(metrics[category][i]) / np.sqrt(len(metrics[category][i]), dtype=np.float64)]\n",
    "\n",
    "        df_metric[category]['%s'%i] = pd.DataFrame(metric_stats[category][i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0936ca",
   "metadata": {},
   "source": [
    "## Significant Digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "faa3fd54",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = 0\n",
    "sig_file = open('References/sig_proteins_%s.txt'%INSTR_LIB)\n",
    "protein_sig = {}\n",
    "for row in sig_file:\n",
    "    if 'NEW PROTEIN' in row:\n",
    "        p_name = proteins[count]\n",
    "        protein_sig[p_name] = {}\n",
    "        count += 1\n",
    "        continue\n",
    "        \n",
    "    if len(row.split(',')) == 1:\n",
    "        name = row[:-2]\n",
    "\n",
    "    else:\n",
    "        if 'Relative' not in name and 'CNH' not in name:\n",
    "            scores = row.split(',')[:-1]\n",
    "            protein_sig[p_name][name + ' Mean'] = np.mean(np.array(scores).astype(np.float64), dtype=np.float64)\n",
    "            protein_sig[p_name][name + ' Standard Deviation'] = np.std(np.array(scores).astype(np.float64), dtype=np.float64)\n",
    "            \n",
    "protein_sig = pd.DataFrame(protein_sig).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d4f4f416",
   "metadata": {},
   "outputs": [],
   "source": [
    "sig_metrics = {'bp': {'threshold':{}, 'Smin':{}, 'Fmax':{}, 'AUPR':{}}, 'cc':{'threshold':{}, 'Smin':{}, 'Fmax':{}, 'AUPR':{}}, 'mf':{'threshold':{}, 'Smin':{}, 'Fmax':{}, 'AUPR':{}}}\n",
    "\n",
    "for category in ['bp', 'cc', 'mf']:\n",
    "    sig_file = open('References/sig_%s_%s.txt'%(category, INSTR_LIB))\n",
    "\n",
    "    for row in sig_file:\n",
    "        if len(row.split(',')) == 1:\n",
    "            name = row[:-1]\n",
    "        else:\n",
    "            if 'Relative' not in name and 'CNH' not in name:\n",
    "                scores = row.split(',')\n",
    "                sig_metrics[category]['threshold'][name] = scores[0]\n",
    "                sig_metrics[category]['Smin'][name] = scores[1]\n",
    "                sig_metrics[category]['Fmax'][name] = scores[2]\n",
    "                sig_metrics[category]['AUPR'][name] = scores[3]\n",
    "\n",
    "for category in ['bp', 'cc', 'mf']:\n",
    "    for metric in sig_metrics[category]:\n",
    "        for column in sig_metrics[category][metric]:\n",
    "#             print(column)\n",
    "            df_metric[category][metric][column] = sig_metrics[category][metric][column]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a40933",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9532d027",
   "metadata": {},
   "source": [
    "#### Significant Digits of Test Proteins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a276021",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>[General] Absolute  significant Mean</th>\n",
       "      <th>[General] Absolute  significant Standard Deviation</th>\n",
       "      <th>[General] Absolute contributing Mean</th>\n",
       "      <th>[General] Absolute contributing Standard Deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.874000e+03</td>\n",
       "      <td>3.874000e+03</td>\n",
       "      <td>3.874000e+03</td>\n",
       "      <td>3.874000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>6.774825e-16</td>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>6.774825e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.328037e-15</td>\n",
       "      <td>8.129865e-16</td>\n",
       "      <td>1.328037e-15</td>\n",
       "      <td>8.129865e-16</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <th>25%</th>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <th>50%</th>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>8.881784e-16</td>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>8.881784e-16</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>8.881784e-16</td>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>8.881784e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>3.552714e-15</td>\n",
       "      <td>6.923690e+00</td>\n",
       "      <td>3.552714e-15</td>\n",
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      "text/plain": [
       "       [General] Absolute  significant Mean  \\\n",
       "count                          3.874000e+03   \n",
       "mean                           6.923690e+00   \n",
       "std                            1.328037e-15   \n",
       "min                            6.923690e+00   \n",
       "25%                            6.923690e+00   \n",
       "50%                            6.923690e+00   \n",
       "75%                            6.923690e+00   \n",
       "max                            6.923690e+00   \n",
       "\n",
       "       [General] Absolute  significant Standard Deviation  \\\n",
       "count                                       3.874000e+03    \n",
       "mean                                        6.774825e-16    \n",
       "std                                         8.129865e-16    \n",
       "min                                         0.000000e+00    \n",
       "25%                                         0.000000e+00    \n",
       "50%                                         8.881784e-16    \n",
       "75%                                         8.881784e-16    \n",
       "max                                         3.552714e-15    \n",
       "\n",
       "       [General] Absolute contributing Mean  \\\n",
       "count                          3.874000e+03   \n",
       "mean                           6.923690e+00   \n",
       "std                            1.328037e-15   \n",
       "min                            6.923690e+00   \n",
       "25%                            6.923690e+00   \n",
       "50%                            6.923690e+00   \n",
       "75%                            6.923690e+00   \n",
       "max                            6.923690e+00   \n",
       "\n",
       "       [General] Absolute contributing Standard Deviation  \n",
       "count                                       3.874000e+03   \n",
       "mean                                        6.774825e-16   \n",
       "std                                         8.129865e-16   \n",
       "min                                         0.000000e+00   \n",
       "25%                                         0.000000e+00   \n",
       "50%                                         8.881784e-16   \n",
       "75%                                         8.881784e-16   \n",
       "max                                         3.552714e-15   "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein_sig.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c3a566",
   "metadata": {},
   "source": [
    "#### Stats on Protein Metrics for 3 Principal Protein Ontology Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f98a923e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Original Result</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>[General] Absolute  significant:</th>\n",
       "      <th>[General] Absolute contributing:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Smin</th>\n",
       "      <td>10.448033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.448033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fmax</th>\n",
       "      <td>0.694438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.694438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUPR</th>\n",
       "      <td>0.737946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.737946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fmax threshold</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
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      "text/plain": [
       "                Original Result  Standard Deviation       Mean  \\\n",
       "Smin                  10.448033                 0.0  10.448033   \n",
       "Fmax                   0.694438                 0.0   0.694438   \n",
       "AUPR                   0.737946                 0.0   0.737946   \n",
       "Fmax threshold         0.240000                 0.0   0.240000   \n",
       "\n",
       "                Standard Error [General] Absolute  significant:  \\\n",
       "Smin                       0.0                6.923689900271567   \n",
       "Fmax                       0.0                6.923689900271567   \n",
       "AUPR                       0.0                6.923689900271567   \n",
       "Fmax threshold             0.0                6.923689900271567   \n",
       "\n",
       "               [General] Absolute contributing:  \n",
       "Smin                          6.923689900271567  \n",
       "Fmax                          6.923689900271567  \n",
       "AUPR                          6.923689900271567  \n",
       "Fmax threshold                6.923689900271567  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc = df_metric['cc']['Smin'].append(df_metric['cc']['Fmax']).append(df_metric['cc']['AUPR']).append(df_metric['cc']['threshold']).set_index([pd.Index(['Smin', 'Fmax', 'AUPR', 'Fmax threshold'])])\n",
    "# with pd.option_context('display.precision', 20): display(df_bp)\n",
    "df_cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "24d87c59",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Original Result</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Mean</th>\n",
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       "      <th>Smin</th>\n",
       "      <td>44.377911</td>\n",
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       "      <th>Fmax</th>\n",
       "      <td>0.530160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.530160</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>AUPR</th>\n",
       "      <td>0.495861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.495861</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fmax threshold</th>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Original Result  Standard Deviation       Mean  \\\n",
       "Smin                  44.377911                 0.0  44.377911   \n",
       "Fmax                   0.530160                 0.0   0.530160   \n",
       "AUPR                   0.495861                 0.0   0.495861   \n",
       "Fmax threshold         0.210000                 0.0   0.210000   \n",
       "\n",
       "                Standard Error [General] Absolute  significant:  \\\n",
       "Smin                       0.0                6.923689900271567   \n",
       "Fmax                       0.0                6.923689900271567   \n",
       "AUPR                       0.0                6.923689900271567   \n",
       "Fmax threshold             0.0                6.923689900271567   \n",
       "\n",
       "               [General] Absolute contributing:  \n",
       "Smin                          6.923689900271567  \n",
       "Fmax                          6.923689900271567  \n",
       "AUPR                          6.923689900271567  \n",
       "Fmax threshold                6.923689900271567  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bp = df_metric['bp']['Smin'].append(df_metric['bp']['Fmax']).append(df_metric['bp']['AUPR']).append(df_metric['bp']['threshold']).set_index([pd.Index(['Smin', 'Fmax', 'AUPR', 'Fmax threshold'])])\n",
    "# with pd.option_context('display.precision', 20): display(df_bp)\n",
    "df_bp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3511c276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Original Result</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>[General] Absolute  significant:</th>\n",
       "      <th>[General] Absolute contributing:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Smin</th>\n",
       "      <td>9.597233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.597233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fmax</th>\n",
       "      <td>0.663958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.663958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUPR</th>\n",
       "      <td>0.670708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.670708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fmax threshold</th>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.923689900271567</td>\n",
       "      <td>6.923689900271567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Original Result  Standard Deviation      Mean  Standard Error  \\\n",
       "Smin                   9.597233                 0.0  9.597233             0.0   \n",
       "Fmax                   0.663958                 0.0  0.663958             0.0   \n",
       "AUPR                   0.670708                 0.0  0.670708             0.0   \n",
       "Fmax threshold         0.180000                 0.0  0.180000             0.0   \n",
       "\n",
       "               [General] Absolute  significant:  \\\n",
       "Smin                          6.923689900271567   \n",
       "Fmax                          6.923689900271567   \n",
       "AUPR                          6.923689900271567   \n",
       "Fmax threshold                6.923689900271567   \n",
       "\n",
       "               [General] Absolute contributing:  \n",
       "Smin                          6.923689900271567  \n",
       "Fmax                          6.923689900271567  \n",
       "AUPR                          6.923689900271567  \n",
       "Fmax threshold                6.923689900271567  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mf = df_metric['mf']['Smin'].append(df_metric['mf']['Fmax']).append(df_metric['mf']['AUPR']).append(df_metric['mf']['threshold']).set_index([pd.Index(['Smin', 'Fmax', 'AUPR', 'Fmax threshold'])])\n",
    "# with pd.option_context('display.precision', 20): display(df_bp)\n",
    "df_mf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d38c5b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Original Result</th>\n",
       "      <th>Standard Deviation</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>[General] Absolute  significant:</th>\n",
       "      <th>[General] Absolute contributing:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Smin</th>\n",
       "      <td>10.448033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.448033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.65355977452702</td>\n",
       "      <td>15.65355977452702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fmax</th>\n",
       "      <td>0.694438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.694438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.65355977452702</td>\n",
       "      <td>15.65355977452702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUPR</th>\n",
       "      <td>0.737946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.737946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.65355977452702</td>\n",
       "      <td>15.65355977452702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fmax threshold</th>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.65355977452702</td>\n",
       "      <td>15.65355977452702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Original Result  Standard Deviation       Mean  \\\n",
       "Smin                  10.448033                 0.0  10.448033   \n",
       "Fmax                   0.694438                 0.0   0.694438   \n",
       "AUPR                   0.737946                 0.0   0.737946   \n",
       "Fmax threshold         0.240000                 0.0   0.240000   \n",
       "\n",
       "                Standard Error [General] Absolute  significant:  \\\n",
       "Smin                       0.0                15.65355977452702   \n",
       "Fmax                       0.0                15.65355977452702   \n",
       "AUPR                       0.0                15.65355977452702   \n",
       "Fmax threshold             0.0                15.65355977452702   \n",
       "\n",
       "               [General] Absolute contributing:  \n",
       "Smin                          15.65355977452702  \n",
       "Fmax                          15.65355977452702  \n",
       "AUPR                          15.65355977452702  \n",
       "Fmax threshold                15.65355977452702  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc = df_metric['cc']['Smin'].append(df_metric['cc']['Fmax']).append(df_metric['cc']['AUPR']).append(df_metric['cc']['threshold']).set_index([pd.Index(['Smin', 'Fmax', 'AUPR', 'Fmax threshold'])])\n",
    "# with pd.option_context('display.precision', 20): display(df_bp)\n",
    "df_cc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49087585",
   "metadata": {},
   "source": [
    "#### Stats on Individual Protein Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0a095255",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_val = []\n",
    "for i in np.array(list(dfs.keys())):\n",
    "\n",
    "    if len(dfs[i]['Standard deviation']) != 0:\n",
    "        a = max(dfs[i]['Standard deviation'])\n",
    "        max_val.append(a)\n",
    "\n",
    "max(max_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c1df48ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GO term</th>\n",
       "      <th>Standard deviation</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Standard error</th>\n",
       "      <th>Original Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GO:0005575</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.776371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.776371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO:0110165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.776371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.776371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GO:0003674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.620349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.620349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GO:0008150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.933437</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.933437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GO:0043229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.530498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.530498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GO:0043231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.410556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.410556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GO:0016020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.468064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.468064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GO:0043227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.421303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.421303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GO:0005622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.764394</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.764394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GO:0043226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.530498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.530498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GO:0005634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.362152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.362152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GO:0005737</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.540246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.540246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GO:0009987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.872470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.872470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GO:0005829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.332176</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.332176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GO:0008152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.678671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.678671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GO:0071840</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.448246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.448246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>GO:0016043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.448246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.448246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GO:0071704</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GO:0043170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.491758</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.491758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GO:1901564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GO:0006807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.503289</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.503289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GO:0044238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.557016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.557016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GO:0044237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.563157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.563157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>GO:0003824</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.372867</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.372867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GO term  Standard deviation      Mean  Standard error  Original Result\n",
       "0   GO:0005575                 0.0  0.776371             0.0         0.776371\n",
       "1   GO:0110165                 0.0  0.776371             0.0         0.776371\n",
       "2   GO:0003674                 0.0  0.620349             0.0         0.620349\n",
       "3   GO:0008150                 0.0  0.933437             0.0         0.933437\n",
       "4   GO:0043229                 0.0  0.530498             0.0         0.530498\n",
       "5   GO:0043231                 0.0  0.410556             0.0         0.410556\n",
       "6   GO:0016020                 0.0  0.468064             0.0         0.468064\n",
       "7   GO:0043227                 0.0  0.421303             0.0         0.421303\n",
       "8   GO:0005622                 0.0  0.764394             0.0         0.764394\n",
       "9   GO:0043226                 0.0  0.530498             0.0         0.530498\n",
       "10  GO:0005634                 0.0  0.362152             0.0         0.362152\n",
       "11  GO:0005737                 0.0  0.540246             0.0         0.540246\n",
       "12  GO:0009987                 0.0  0.872470             0.0         0.872470\n",
       "13  GO:0005829                 0.0  0.332176             0.0         0.332176\n",
       "14  GO:0008152                 0.0  0.678671             0.0         0.678671\n",
       "15  GO:0071840                 0.0  0.448246             0.0         0.448246\n",
       "16  GO:0016043                 0.0  0.448246             0.0         0.448246\n",
       "17  GO:0071704                 0.0  0.666036             0.0         0.666036\n",
       "18  GO:0043170                 0.0  0.491758             0.0         0.491758\n",
       "19  GO:1901564                 0.0  0.309629             0.0         0.309629\n",
       "20  GO:0006807                 0.0  0.503289             0.0         0.503289\n",
       "21  GO:0044238                 0.0  0.557016             0.0         0.557016\n",
       "22  GO:0044237                 0.0  0.563157             0.0         0.563157\n",
       "23  GO:0003824                 0.0  0.372867             0.0         0.372867"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs['ACK1_YEAST']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e8eb39",
   "metadata": {},
   "source": [
    "## Graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e137d30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "protein_class_std = []\n",
    "for protein in dfs.values():\n",
    "    for value in protein['Standard deviation']:\n",
    "        protein_class_std.append(value)\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "_, _, bars = plt.hist(protein_class_std, bins=10)\n",
    "plt.bar_label(bars, fontsize=15), plt.title('Standard Deviation Across All Protein Classes', fontsize=20)\n",
    "plt.xlabel('Standard Deviation', fontsize=15), plt.ylabel('Protein Class Count', fontsize=15)\n",
    "plt.yscale('log'); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1515c4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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