7_ ResultAnalyser.ipynb

{
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   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model_result(file):\n",
    "    \n",
    "    import pandas as pd\n",
    "\n",
    "    negation_df=pd.read_excel(\"../Fast_Context_French/conf/\"+file,sheet_name='Negation')\n",
    "    experiencer_df=pd.read_excel(\"../Fast_Context_French/conf/\"+file,sheet_name='Experiencer')\n",
    "    temporality_df=pd.read_excel(\"../Fast_Context_French/conf/\"+file,sheet_name='Temporality')\n",
    "\n",
    "\n",
    "    import sklearn.metrics as skm\n",
    "\n",
    "    negation_df[\"gold\"]=negation_df[\"Gold Value\"]==\"Negated\"\n",
    "    negation_df[\"pred\"]=negation_df[\"FastContext Value\"]!=\"Not negated\"\n",
    "\n",
    "    precision_neg_true=skm.precision_score(negation_df[\"gold\"], negation_df[\"pred\"],pos_label=True)\n",
    "    recall_neg_true=skm.recall_score(negation_df[\"gold\"], negation_df[\"pred\"],pos_label=True)\n",
    "    f1_neg_true=skm.f1_score(negation_df[\"gold\"], negation_df[\"pred\"],pos_label=True)\n",
    "    \n",
    "    \n",
    "    precision_neg_false=skm.precision_score(negation_df[\"gold\"], negation_df[\"pred\"],pos_label=False)\n",
    "    recall_neg_false=skm.recall_score(negation_df[\"gold\"], negation_df[\"pred\"],pos_label=False)\n",
    "    f1_neg_false=skm.f1_score(negation_df[\"gold\"], negation_df[\"pred\"],pos_label=False)\n",
    "\n",
    "\n",
    "    experiencer_df[\"gold\"]=experiencer_df[\"Gold Value\"]==\"Patient\"\n",
    "    experiencer_df[\"pred\"]=experiencer_df[\"FastContext Value\"]==\"patient\"\n",
    "\n",
    "    precision_exp_true=skm.precision_score(experiencer_df[\"gold\"], experiencer_df[\"pred\"],pos_label=True)\n",
    "    recall_exp_true=skm.recall_score(experiencer_df[\"gold\"], experiencer_df[\"pred\"],pos_label=True)\n",
    "    f1_exp_true=skm.f1_score(experiencer_df[\"gold\"], experiencer_df[\"pred\"],pos_label=True)\n",
    "\n",
    "    precision_exp_false=skm.precision_score(experiencer_df[\"gold\"], experiencer_df[\"pred\"],pos_label=False)\n",
    "    recall_exp_false=skm.recall_score(experiencer_df[\"gold\"], experiencer_df[\"pred\"],pos_label=False)\n",
    "    f1_exp_false=skm.f1_score(experiencer_df[\"gold\"], experiencer_df[\"pred\"],pos_label=False)\n",
    "\n",
    "    temporality_df[\"gold\"]=temporality_df[\"Gold Value\"]==\"Historical\"\n",
    "    temporality_df[\"pred\"]=temporality_df[\"FastContext Value\"]!=\"Not historical\"\n",
    "\n",
    "    precision_his_true=skm.precision_score(temporality_df[\"gold\"], temporality_df[\"pred\"],pos_label=True)\n",
    "    recall_his_true=skm.recall_score(temporality_df[\"gold\"], temporality_df[\"pred\"],pos_label=True)\n",
    "    f1_his_true=skm.f1_score(temporality_df[\"gold\"], temporality_df[\"pred\"],pos_label=True)\n",
    "    \n",
    "    precision_his_false=skm.precision_score(temporality_df[\"gold\"], temporality_df[\"pred\"],pos_label=False)\n",
    "    recall_his_false=skm.recall_score(temporality_df[\"gold\"], temporality_df[\"pred\"],pos_label=False)\n",
    "    f1_his_false=skm.f1_score(temporality_df[\"gold\"], temporality_df[\"pred\"],pos_label=False)\n",
    "\n",
    "    data = {'Precision':[precision_neg_true,precision_neg_false,precision_exp_true,precision_exp_false,precision_his_true,precision_his_false], \n",
    "            'Recall':[recall_neg_true,recall_neg_false,recall_exp_true,recall_exp_false,recall_his_true,recall_his_false],\n",
    "            'F1':[f1_neg_true,f1_neg_false,f1_exp_true,f1_exp_false,f1_his_true,f1_his_false]\n",
    "            } \n",
    "    model_outcome = pd.DataFrame(data, index=[\"Negated\",\"Not Negated\",\"Patient\",\"Not Patient\",\"Historical\",\"Not Historical\"]) \n",
    "\n",
    "    return(model_outcome)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Negated</th>\n",
       "      <td>0.733871</td>\n",
       "      <td>0.957895</td>\n",
       "      <td>0.831050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Negated</th>\n",
       "      <td>0.991597</td>\n",
       "      <td>0.934653</td>\n",
       "      <td>0.962283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient</th>\n",
       "      <td>0.816547</td>\n",
       "      <td>0.741830</td>\n",
       "      <td>0.777397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Patient</th>\n",
       "      <td>0.556180</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.603659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Historical</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.746114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Historical</th>\n",
       "      <td>0.878229</td>\n",
       "      <td>0.937008</td>\n",
       "      <td>0.906667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Precision    Recall        F1\n",
       "Negated          0.733871  0.957895  0.831050\n",
       "Not Negated      0.991597  0.934653  0.962283\n",
       "Patient          0.816547  0.741830  0.777397\n",
       "Not Patient      0.556180  0.660000  0.603659\n",
       "Historical       0.818182  0.685714  0.746114\n",
       "Not Historical   0.878229  0.937008  0.906667"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_model_result(\"HEGP_dataset_output.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Negated</th>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.911392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Negated</th>\n",
       "      <td>0.995964</td>\n",
       "      <td>0.996970</td>\n",
       "      <td>0.996466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient</th>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Patient</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Historical</th>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.896000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not Historical</th>\n",
       "      <td>0.920455</td>\n",
       "      <td>0.931034</td>\n",
       "      <td>0.925714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Precision    Recall        F1\n",
       "Negated          0.923077  0.900000  0.911392\n",
       "Not Negated      0.995964  0.996970  0.996466\n",
       "Patient          0.980000  0.980000  0.980000\n",
       "Not Patient      0.900000  0.900000  0.900000\n",
       "Historical       0.903226  0.888889  0.896000\n",
       "Not Historical   0.920455  0.931034  0.925714"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_model_result(\"CepiDC_dataset_output.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rules_id=[146,195,197,329,330,331,332,335,336,340,341,345,378,379,380,394,407,408,411,414,421,422,423,424,425,426,462,464,506,507,527,579,581,583,637,675,677]\n",
    "\n",
    "# rules_list=(open(\"../Fast_Context_French/conf/\"+\"context_amine.txt\", \"r\")\n",
    "#            .read()\n",
    "#            .split(\"\\n\"))\n",
    "\n",
    "# fired_rules=[rules_list[rule_id-1] for rule_id in rules_id]\n",
    "# fired_rules.sort()\n",
    "\n",
    "# outfile=open(\"../Fast_Context_French/conf/\"+\"context_mehdi.txt\", \"w\")\n",
    "# outfile.write(\"\\n\".join(fired_rules))\n",
    "# outfile.close()"
   ]
  }
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