05_cnn_validation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8602fea2-5434-42ee-9c1c-df909878847e",
   "metadata": {},
   "source": [
    "# CNN Validation\n",
    "Check the performance of the CNN on the validation set\n",
    "\n",
    "- Compute the precision and recall curves\n",
    "- find the threshold score that achieves 99% precision on the validation set\n",
    "- check precision and recall for various thresholds (for testing robustness of temporal patterns to change of threshold)\n",
    "\n",
    "\n",
    "This notebook is part of a series of notebooks and scripts in the [repository](https://github.com/kitzeslab/rana-sierrae-cnn):\n",
    "\n",
    "- `01_explore_annotated_data.ipynb` Explore annotated dataset of Rana sierrae call types\n",
    "\n",
    "- `02_prep_training_data.ipynb` Prepare annotated files for training a CNN machine learning model\n",
    "\n",
    "- `03_train_cnn.ipynb` Train a CNN to recognize Rana sierrae vocaliztaions\n",
    "\n",
    "- `04_cnn_prediction.ipynb` Use the cnn to detect Rana sierrae in audio recordings\n",
    "\n",
    "- `05_cnn_validation.ipynb` Analyze the accuracy and performance of the CNN\n",
    "\n",
    "- `06_aggregate_scores.py` Aggregate scores from CNN prediction across dates and times of day\n",
    "\n",
    "- `07_explore_results.ipynb` Analyze temporal patterns of vocal activity using the CNN detections\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95fcdead-53c3-4a15-a08f-409504c1d508",
   "metadata": {},
   "source": [
    "### imports\n",
    "import required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1925f876-483e-479d-8eb5-f036d232affa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from opensoundscape.audio import Audio\n",
    "from opensoundscape.spectrogram import Spectrogram\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['figure.figsize']=[15,5] #for big visuals\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0197d39-76b1-49c4-b318-324a421d0cf3",
   "metadata": {
    "tags": []
   },
   "source": [
    "### plotting settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acad5087-d9d0-4b23-882f-62a64dcaeff6",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# create style sheet?\n",
    "# plt.style.use('./styles/sam.mplstyle')\n",
    "\n",
    "small_txt = 7\n",
    "med_txt = 8\n",
    "big_txt = 8\n",
    "# set text sizes\n",
    "plt.rcParams['font.size'] = small_txt\n",
    "plt.rcParams['axes.labelsize'] = med_txt\n",
    "plt.rcParams['axes.titlesize'] = big_txt\n",
    "plt.rcParams['xtick.labelsize'] = small_txt\n",
    "plt.rcParams['ytick.labelsize'] = small_txt\n",
    "plt.rcParams['legend.fontsize'] = med_txt\n",
    "plt.rcParams['figure.titlesize'] = big_txt\n",
    "# opaque legend (<1 is not fully opaque)\n",
    "plt.rcParams[\"legend.framealpha\"] = 1.0\n",
    "# set figure size\n",
    "plt.rcParams['figure.figsize']=[6,2.5]\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "# set default font\n",
    "# plt.rcParams['font.sans-serif'] = \"Gill Sans\"\n",
    "# plt.rcParams['font.family'] = \"sans-serif\"\n",
    "# plt.rcParams['font.sans-serif'] = \"gillsans\"\n",
    "\n",
    "\n",
    "# TIP use these 2 lines to list all available fonts:\n",
    "# from matplotlib import font_manager\n",
    "# font_manager.findSystemFonts(fontpaths=None, fontext=\"ttf\")\n",
    "\n",
    "# to add a font\n",
    "# on linux, add .ttf font files to /usr/share/fonts/truetype/\n",
    "# then follow these instructions https://scentellegher.github.io/visualization/2018/05/02/custom-fonts-matplotlib.html \n",
    "\n",
    "# tell matplotlib to create vector fonts \n",
    "# so you can edit them later! eg in Illustrator\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "# plt.rcParams['lines.markersize'] = 1\n",
    "\n",
    "\n",
    "#color and line style cycles #\n",
    "# TIP: use https://coolers.co to create nice color palettes\n",
    "#or modify this one https://coolors.co/35aad8-f3b61f-ba3b54-45b69c-8a96d7\n",
    "from cycler import cycler\n",
    "default_cycler = (cycler(color=['#35aad8','#f3b61f','#8a96d7','#45b69c','#ba3b54']) \n",
    "                  # + cycler(linestyle=['-', '--', '-.', ':','-'])\n",
    "                 )\n",
    "plt.rc('lines', linewidth=0.5)\n",
    "plt.rc('axes', prop_cycle=default_cycler)\n",
    "\n",
    "#use appropriate resolution for retina screens\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757493ef-a2bf-48d5-971e-f8b9f448c72f",
   "metadata": {},
   "source": [
    "## Load the labels and CNN prediction scores for the validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c91308a6-d193-4d06-a7cc-4ca58048e18b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>rana_sierrae</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./rana_sierrae_2022/mp3/sine2022a_MSD-0558_202...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027761</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./rana_sierrae_2022/mp3/sine2022a_MSD-0558_202...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.023385</td>\n",
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      "text/plain": [
       "                                                file  start_time  end_time  \\\n",
       "0  ./rana_sierrae_2022/mp3/sine2022a_MSD-0558_202...         0.0       2.0   \n",
       "1  ./rana_sierrae_2022/mp3/sine2022a_MSD-0558_202...         0.5       2.5   \n",
       "\n",
       "   rana_sierrae     score  \n",
       "0             0  0.027761  \n",
       "1             0  0.023385  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_df = pd.read_csv('./resources/validation_labels_and_scores.csv')\n",
    "val_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc0d8c4-2591-49af-905e-1b5aeab00f0f",
   "metadata": {},
   "source": [
    "## Calculate metrics and visualize performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1aa550e1-61ee-4c4a-9cbb-2c7fc85dc873",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve, average_precision_score, roc_auc_score, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "507cf0e9-9203-46b1-b38b-d3b6f138e86e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision: 0.9187549944446871\n",
      "Area under ROC curve: 0.951881297624947\n",
      "Area under P-R curve: 0.9186412082012038\n"
     ]
    }
   ],
   "source": [
    "p,r,t = precision_recall_curve(val_df['rana_sierrae'],val_df['score'])\n",
    "print(f\"Average precision: {average_precision_score(val_df['rana_sierrae'],val_df['score'])}\")\n",
    "print(f\"Area under ROC curve: {roc_auc_score(val_df['rana_sierrae'],val_df['score'])}\")\n",
    "print(f\"Area under P-R curve: {auc(r,p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11985f08-50a0-471d-b134-122fd3039e8c",
   "metadata": {},
   "source": [
    "### Plot precision and recall versus threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52bbb95b-7115-401e-bf66-f8f9f80860e1",
   "metadata": {},
   "outputs": [
    {
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Y7yerlZSU6JNPPnFoDw8P1+DBgy05x+bNmzVp0iStWLFCf/zxh44cOaKcnBwlJCSoXr16qlevnjp37qx+/fqpX79+9OwLMKF6L/3yyy9atmyZVqxYobS0NB09elQnT55U3bp1VbduXSUlJalr167q37+/+vTpo7p161pyXrP3u1WrVh7PX3dKs2bNFB0d7bA6aaC836EiVO8nV6SkpGjDhg0O7W3atFG/fv0sPx+fTQhEPDe5llOwo2AHBCCzX4SNGzeWzWbzOnaTJk0cvsTt2bNHhmFYEr8yZtclyesvp9Kf12Wmql/y6enppvM1WJFTw4YNZbfbVVpa6lZOsFaw3k9W++CDD0znCendu7cSExMtOceUKVNM248ePaqjR49Kkn7++We9+uqrstlsuvjiizVx4kT17t3bkvPDO6F6Lz300EOm7YcPH9bhw4e1detWLV++XP/3f/+n8PBwXXXVVXrwwQd19tlne3zOsrIy09UxrfhsstlsatSokdLS0sq189nkW6F6P7nCWe+6G2+8sVry57MJgYjnJtdyCnYMiQUCUEZGhkObVX/NM4tTXFyszMxMS+JXxuy6JGuuzVmMI0eO+C2nsLAwJSUluZ0TrBWs95OVsrKy9Oijj5puu/32232czZ8Mw9C3336rPn36aMiQITp06JBf8sD/x71UtZKSEs2aNUsdO3bUNddco5ycHI/inDhxwvShqDrfbz6bfIv7yVxhYaE++ugjh3a73a4bbrjBDxmVx2cTfIXnJtdyCnYU7IAAU1RUpNzcXIf2hIQES+I7i3Ps2DFL4lfG2TmsuLaoqChFRUW5fE5f5OQsji/ea/wpmO8nK40fP17p6ekO7Z06ddLll1/uh4zKW7BggTp37qwlS5b4O5WQxb3kvo8++khdunTxaP4df3w2ZWdnu7RCILzH/eTcV199pePHjzu0DxkyxJJePFbiswnViecm13IKdgyJBQKM2Rc4SapVq5Yl8ePj403bPe0F4A5fXFvFFcWqui5/vN++eK/xp2C+n6wya9YszZw506E9LCxMr7zyitfzZf1V48aN1aFDB51++umqXbu2YmNjlZmZqaNHj+rXX3/V1q1bnR576NAhXXjhhZozZ46GDRtmWU5wTajeSzabTc2bN9fZZ5+tZs2aKSEhQVFRUTp+/LiOHDmiNWvWVDpcZ8eOHerXr59+/PFHt1b/9Of7bdUQeDgXqveTK/yx2ASfTQhEPDe5llOwo2AHBBizJewlma745YmIiAi3zmslf1xbVdcViDnBOsF8P1khJSVFf//73023jR8/3uv5eex2u4YMGaJLLrlEw4YNU7NmzSrd/8iRI3r33Xf14osvmg4zKioq0pVXXqk1a9aoXbt2XuUG94TSvRQVFaURI0Zo+PDhGjp0qOrXr1/p/vv27dPrr7+u119/3XTIYVZWlkaMGKFff/1VDRs2dCmHUHq/QxH/vub27dunRYsWObQnJSVpxIgRlp2HzybUBIH4jBKIOQU7hsQCAaa4uNi0PTzcmvq6sy9xzs5rJX9cW1XXFYg5wTrBfD95Kz09XSNGjFBeXp7Dti5duujf//63V/EnTJigXbt2ae7cubrtttuqfCCS/nwomzhxov744w9dd911pvvk5ubq+uuvV1lZmVf5wT2hci9NmjRJ+/fv12effabrrruuymKdJJ122mn697//re3bt+uCCy4w3Sc9PV133HGHy3mEyvsdqvj3NTdz5kzT3+3XXnut02tyF59NqCkC8RklEHMKdhTsgADjbPiZVV8AnMWxctibM/64tqquKxBzgnWC+X7yRnZ2toYNG6b9+/c7bEtKStLs2bNN5zZxxz333KPmzZt7dGxcXJzef/99Pf7446bb161bZzopOapPqNxLEydOdKlIZyYpKUnz58/X2LFjTbd/+eWXWrFihUuxQuX9DlX8+zoyDMN0egbJ2uGwfDahpgjEZ5RAzCnYhfbVAwHI2V8QrZoI2lkcq7oyV8Yf11bVdQViTrBOMN9PniooKNDw4cO1ceNGh23x8fH6/vvv1apVKz9k5uipp57SVVddZbptypQpPs4mtHEvucZms+mtt95S3759Tbe7+nPL+x3c+Pd1tHTpUu3atcuhvVu3burQoYMfMnKOzyb4QiA+owRiTsGOgh0QYGJiYkzb8/PzLYlvNvxNkqKjoy2JXxl/XFtV1xWIOcE6wXw/eaK4uFijR4/W8uXLHbZFR0frm2++cWtifF+YOnWqYmNjHdo3bNigbdu2+SGj0MS95LqIiAhNmzZNNpvNYdu8efOUlZVVZQze7+DGv68jfyw24Q0+m1DdAvEZJRBzCnYU7IAAEx8fL7vd7tCenZ1tSXxnK+3UrVvXkviVcbbynFXXZrZyUVXXVd05mb3fvniv8adgvp/cVVZWpuuuu05z58512BYREaHPP/9cAwYM8H1iVWjUqJHTBzazyclRPbiX3PO3v/1NF110kUN7UVGRfvrppyqP98dnU0REhNPVRWEt7qfysrOz9cUXXzi0x8TE6Oqrr/ZDRlXjswnVjecm13IKdhTs2DJ2MQAALx1JREFUgABjs9lMfzEdO3bMkvhHjx41bffFL8N69eqZtltxbcePHzed96Cq66rOnCTz9zvUP3h8KZjvJ3cYhqFbbrlFn376qcO2sLAwzZo1y7S4ECic5bZu3TofZxK6uJfc583PrT8+m5w9iMF63E/lffzxx6Y9dEaPHq3atWv7ISPX8NmE6sRzk2s5BTsKdkAAatKkiUOb2TLynjCLU7duXaddnK1kdl2SNdfmLEZVq39VZ055eXmmfylyZUUyWCdY7yd33HPPPZoxY4ZDu81m0/Tp03XFFVf4ISvX9e7d27TdbL4jVB/uJfd483MbGxurhIQEh/bqfL/5bPIt7qf/r6YNhz2FzyZUJ56bXMsp2FGwAwKQ2YTvGRkZTuckcUdaWppL56sOzs6ze/dur2ObXVdl5zylTp06qlOnTkDlBGsF6/3kqgcffFCvvPKK6baXXnop4B+IpD+Hj5nNFWTVX3ThmlC/l9zVsGFD03ZXf27Nrt+Kz6aTJ0/qyJEjLp0P1Yf76U9btmzRmjVrHNpbt26t/v37+yEj1/HZhOrEc5NrOQU7CnZAADrzzDMd2gzD0I4dO7yKm5WVZfol3ex81SEhIUENGjRwaN++fbvXsZ29N65cm9k+3r7X3uYE6wTr/eSKJ554Qi+88ILptueee0533323jzPynNkXxIKCAt8nEsJC+V7yhLMhpq7+3Jpdf0ZGhkuLVlTmjz/+cPl8qD7cT39y1rvuxhtvNF24JdDw2YTqwnOT6zkFMwp2QADq3LmzafvGjRu9ipuSkuLW+aqD2blSU1NVXFzsVVxvrs1snxMnTmjv3r1+ywnWCeb7qTKTJ0/W008/bbrtscce00MPPeTjjLyTmZnp0Baow7uCVajeS546ceKEaburP7e838GNf98/Vy6fNWuWQ3tYWJhuuOEGP2TkPj6bUJ14bgrM312+RMEOCEDdu3c3bV+1apVXcZ0d361bN6/iusPs2oqKivTrr796Fdfs2lq2bKmkpCSPcnIW09ucoqOjdfbZZ3sVF+4J5vvJmZdfftlpQW7ChAlOC3mBKicnx3SYWKhPROxroXgveePw4cOm7a7+3Pr6/T733HO9igv3cD9J3333nTIyMhzahwwZUiPmreKzCdWN5yaemyjYAQGoVatWatmypUP7woULvYprdnxsbKx69uzpVVx3DBo0yLTdm2vbu3evaffwwYMH+y2ngoICrVixwqG9T58+io6O9jgu3BfM95OZ6dOn69577zXddvvtt2vKlCk+zsh7K1euNG03+3dF9Qm1e8lb3v7c9uzZ0/Tzojre7zZt2nA/+Rj3U81dbOIUPptQ3Xhu4rmJgh0QoIYNG+bQtnPnTqfdhaty+PBhLV++3KF98ODBioyM9CimJ3r16mU638fs2bM9junsWLP30EyLFi2UnJzs0P7VV1953OV87ty5pn91dTUnWCtY76eKZs2apXHjxpluGzt2rF599VUfZ2SNuXPnmrZ36dLFx5kgVO4lK3j7cxsdHW36YLRs2TLTOcpcsX79etPJwfls8o9Qvp8OHTqk+fPnO7TXr19fI0aM8ENG7uOzCdWN5yY+myjYAQFqzJgxpu1vvPGGR/HefvttlZaWunye6hIREaHRo0c7tG/atMmjrtSGYeitt95yaE9ISHDrl7zZ+3Ds2DF9/vnnbuckmf87hYWF6corr/QoHrwTrPfTX33xxRcaO3asysrKHLZdddVVeuedd2rEBN4VHTp0yGkvDGd/5UX1CYV7yQobN240fZgPDw9X3759XY5j9j6UlJTo7bff9igvZ/9ONf39rqlC+X567733VFJS4tB+7bXXBlxx0QyfTfAFnpt4bpIBICCVlZUZycnJhqRy/0VFRRm7d+92K1ZmZqaRlJTkEKtBgwZGfn5+9VxAJX755ReHXCQZQ4YMcTvWhx9+aBrr7rvvdivO/v37jYiICIc4ycnJRklJiVuxVqxYYZrT8OHD3YoD6wTz/WQYhjFv3jwjMjLS9Odu5MiRRnFxsV/yssKYMWNMrys5OdnfqYWkYL+XrFBcXGz079/f9Od22LBhbsU6efKkUa9ePYc4SUlJRlZWlluxdu3aZURFRTnE6tixo1txYJ1Qvp/OOuss03tk06ZN/k7NJXw21VwzZsww/bebMWOGv1MzxXNTaKNgBwQwZx8oQ4YMMcrKylyOc/PNN5vGeeaZZ9zK54YbbrDsA87Zw8yHH37ocoyMjAyjcePGDjHCw8Pd/qJrGM6v79lnn3U5Rl5entGhQwfTOCtWrHA7J1gnWO+nJUuWGDExMaaxLrzwQqOwsNCteN7atWuXZbGefvpp0+uSZEyfPt2y88A9wXgvWfVzW1ZW5vS6JBkLFy50O+YTTzxhGuvWW291K6/zzz/fNM6sWbPczgnWCcb7qSrOHtC7du1q2Tkq4rMJp/i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      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 513,
       "width": 630
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2,1.5)) \n",
    "plt.plot(t,p[1:],label='precision')\n",
    "plt.plot(t,r[1:],label='recall')\n",
    "plt.xlabel('threshold')\n",
    "plt.legend()\n",
    "plt.savefig('./figures/precision_and_recall_vs_threshold.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54dcadfc-764c-48bb-8a9e-3f96af5d5f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 564,
       "width": 661
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2,1.5)) \n",
    "plt.plot(r,p)\n",
    "plt.xlabel('recall')\n",
    "plt.ylabel('precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.savefig('./figures/precision_recall_curve.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b291b9cf-79f0-4bb9-8a42-25557eb6cc14",
   "metadata": {},
   "source": [
    "## Find the threshold that achieves 99% precision:\n",
    "\n",
    "For >99% precision, we choose the threshold 0.99933 for the analyses shown in the manuscript,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5ef8655-15bd-4f94-a6ec-811dd70bbdcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold for 99% precision: 0.9993486\n",
      "precision: 0.9914529914529915\n",
      "recall: 0.3036649214659686\n"
     ]
    }
   ],
   "source": [
    "first_threshold_w_prec099 = np.argmax((p>0.99)[1:]) + 1\n",
    "print(f\"threshold for 99% precision: {t[first_threshold_w_prec099]}\")\n",
    "print(f\"precision: {p[first_threshold_w_prec099]}\")\n",
    "print(f\"recall: {r[first_threshold_w_prec099]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983ec79c-3cc0-4e43-938a-53a7ba8b676b",
   "metadata": {},
   "source": [
    "## precision and recall at thresholds shown in figures\n",
    "We chose a range of thresholds to test the robustness of observed temporal patterns to threshold score (and therefore robustness to varying precision and recall). \n",
    "\n",
    "We chose thresholds for logit(score) in {0,2,4,6,8,10}\n",
    "\n",
    "We also evaluate the threshold logit(0.9993342)=7.3139 which is the threshold used in the main body results of the manuscript for 99% precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a11b26a-9cae-4a5a-9f18-108891646e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support, precision_score, recall_score\n",
    "from torch import tensor, sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba221f85-ccba-44d3-ad7e-aae8ccab3506",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold: 0 precision: 0.82 recall: 0.87\n",
      "threshold: 2 precision: 0.89 recall: 0.74\n",
      "threshold: 4 precision: 0.94 recall: 0.58\n",
      "threshold: 6 precision: 0.97 recall: 0.40\n",
      "threshold: 7.3139 precision: 0.98 recall: 0.30\n",
      "threshold: 8 precision: 1.00 recall: 0.25\n",
      "threshold: 10 precision: 1.00 recall: 0.12\n"
     ]
    }
   ],
   "source": [
    "thresholds = [0,2,4,6,7.3139,8,10]\n",
    "for t in thresholds:\n",
    "    th = float(sigmoid(tensor(t)))\n",
    "    print(f\"threshold: {t} precision: {precision_score(val_df['rana_sierrae'], (val_df.score>th)):0.2f} recall: {recall_score(val_df['rana_sierrae'], (val_df.score>th)):0.2f}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "opso080",
   "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": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}