Youden_index_generate_report_demo.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "90450fcc",
   "metadata": {},
   "source": [
    "Purpose of this notebook is to demonstrate the process of getting optimal cutoff (based on Youden index) from training ROC, and generating performance metrics based on the selected cutoff for test predictions. This can be used for  CV fold or holdout testing in the similar way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "39175638",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from generate_report import get_optimal_cutoff, get_pred_report, evalplots, class_report\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ccabaef",
   "metadata": {},
   "source": [
    "Load the true labels and predicted probabilities in the train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5ffb3ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#with open('training coyote label.csv','rb') as f:\n",
    "    #data = pickle.load(f,encoding='bytes')\n",
    "data1 = pd.read_csv('C:/EEG_project/coyote/training coyote label.csv')\n",
    "data2 = pd.read_csv('C:/EEG_project/coyote/training coyote prob.csv')\n",
    "data3 = pd.read_csv('C:/EEG_project/coyote/testing coyote label.csv')\n",
    "data4 = pd.read_csv('C:/EEG_project/coyote/testing coyote prob.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cb38c11a",
   "metadata": {},
   "outputs": [
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       "Name: mange, Length: 218, dtype: float64"
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   "source": [
    "data4['mange']"
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       "      mange\n",
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     "execution_count": 42,
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   ],
   "source": [
    "#data['train_y_true']\n",
    "pd.read_csv('C:/EEG_project/coyote/training coyote label.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a9008275",
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   "outputs": [
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       "      mange\n",
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   "source": [
    "train_y_true = data1\n",
    "train_y_prob = data2\n",
    "test_y_true = data3\n",
    "test_y_prob = data4\n",
    "text_labels = train_y_true.columns.tolist()\n",
    "test_y_prob.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e2ab9d3c",
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       "      mange\n",
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       "...     ...\n",
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     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "train_y_true"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e61696",
   "metadata": {},
   "source": [
    "Get the optimal cutoff point and generate the pred report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "577bebe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**mange**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cutoff Probability based on Training ROC:  0.7344801\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Use the train_y to generate the optimal cutoff point\n",
    "_,_, _, _, roc_j_thr_dict = get_optimal_cutoff(train_y_true.to_numpy(),train_y_prob.to_numpy(), text_labels)\n",
    "# Use the test_y to generate report\n",
    "class_df_dict, creport_dict = get_pred_report(test_y_true.to_numpy(),test_y_prob.to_numpy(),text_labels,roc_j_thr_dict, verbose=False) # verbose = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65928c04",
   "metadata": {},
   "source": [
    "class_df_dict provides binarized test predictions using test probabilities based on optimal cutoff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c4ad724a",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "     true  pred_proba  pred\n",
       "0       0    0.009146     0\n",
       "1       0    0.011844     0\n",
       "2       0    0.697892     0\n",
       "3       0    0.007382     0\n",
       "4       0    0.010674     0\n",
       "..    ...         ...   ...\n",
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       "\n",
       "[218 rows x 3 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_df_dict['mange']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a0bcab",
   "metadata": {},
   "source": [
    "creport_dict provides a dictionary of various performance metrics for the test predictions (accuracy, auroc, auprc, f1_score, prec, rec, spec, aprec, br_score, tn, fp, fn, tp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "560922dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.963302752293578,\n",
       " 'auroc': 0.704225352112676,\n",
       " 'auprc': 0.20917431192660552,\n",
       " 'f1_score': 0.20000000000000004,\n",
       " 'prec': 0.2,\n",
       " 'rec': 0.2,\n",
       " 'spec': 0.9812206572769953,\n",
       " 'aprec': 0.2513437146538966,\n",
       " 'br_score': 0.05413551672942252,\n",
       " 'tn': 209,\n",
       " 'fp': 4,\n",
       " 'fn': 4,\n",
       " 'tp': 1}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "creport_dict['mange']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3464516c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**mange**"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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     "metadata": {},
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     "text": [
      "Cutoff Probability based on Training ROC:  0.7344801\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.98      0.98       213\n",
      "           1       0.20      0.20      0.20         5\n",
      "\n",
      "    accuracy                           0.96       218\n",
      "   macro avg       0.59      0.59      0.59       218\n",
      "weighted avg       0.96      0.96      0.96       218\n",
      "\n",
      "AUROC score: 0.7042\n",
      "AUPRC score: 0.2092\n",
      "Average precision-recall score: 0.2513\n",
      "Accuracy score: 0.9633\n",
      "Sensitivity / Recall score: 0.2000\n",
      "Specificity score: 0.9812\n",
      "Positive predictive value / Precision score: 0.2000\n",
      "f1 score: 0.2000\n",
      "Brier score: 0.0541\n",
      "\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "class_df_dict, creport_dict = get_pred_report(test_y_true.to_numpy(),test_y_prob.to_numpy(),text_labels,roc_j_thr_dict, verbose=True) # verbose = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dcc398e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}