{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "57e279da-2992-48d5-ace4-ae421f69e8d2",
   "metadata": {},
   "source": [
    "# Analyzing classification result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "1e5597b1-b1a0-4015-b2b2-3f539435a3dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from mne.decoding import CSP\n",
    "\n",
    "mne_logger = logging.getLogger('mne')\n",
    "mne_logger.setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "8eb6d6b1-a92b-41ed-8446-29f165c00329",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RepeatedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "74f509ab-bfdd-42b8-abab-4507b0602133",
   "metadata": {},
   "outputs": [],
   "source": [
    "import conf\n",
    "import utils\n",
    "import transforms as trans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e3368f3-cd01-432e-a374-673c4c938735",
   "metadata": {},
   "source": [
    "## Building and running the best pipeline (`csp_svm`)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b26b767-862e-4a01-b7d5-42864230292a",
   "metadata": {},
   "source": [
    "Next, we run the best pipeline once which we have developed which provided 85.3%\n",
    "accuracy previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "30009cd7-f4d9-4a04-854a-25d36c40beef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load our dataset\n",
    "with (conf.DATA_DIR / \"filteredX.pickle\").open(\"rb\") as f:\n",
    "  filteredX = pickle.load(f)\n",
    "  \n",
    "with (conf.DATA_DIR / \"y.npy\").open(\"rb\") as f:\n",
    "  y = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "3e519585-58ce-4860-9b65-c468c8b18adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_start = conf.dT - 0.1\n",
    "\n",
    "window_length = 0.3\n",
    "\n",
    "clf = make_pipeline(\n",
    "  trans.GetWindow(best_start, best_start + window_length),\n",
    "  trans.NpArrayTransform,\n",
    "  CSP(),\n",
    "  SVC(gamma='auto'),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "2d89ca26-098b-4a3d-b1ff-2e4e1df2884a",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf.fit(filteredX, y); pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "b1ea3a08-d790-4e5f-bfd5-ec44ea2703b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = clf.predict(filteredX)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca9d56b4-eaf5-4e17-8308-5d8d9e2cf217",
   "metadata": {},
   "source": [
    "## Obtaining GWOSC Parameter Estimations for detected coalescenses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "4791c190-9cdc-45c7-bbc7-da1e521cdca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the GWOSC catalog containing estimated parameters\n",
    "\n",
    "confident = pd.read_csv(\"https://www.gw-openscience.org/eventapi/csv/allevents/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "e1579bec-f004-4084-8fac-01950724d6b0",
   "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>id</th>\n",
       "      <th>commonName</th>\n",
       "      <th>version</th>\n",
       "      <th>catalog.shortName</th>\n",
       "      <th>GPS</th>\n",
       "      <th>reference</th>\n",
       "      <th>jsonurl</th>\n",
       "      <th>mass_1_source</th>\n",
       "      <th>mass_1_source_lower</th>\n",
       "      <th>mass_1_source_upper</th>\n",
       "      <th>...</th>\n",
       "      <th>redshift_upper</th>\n",
       "      <th>far</th>\n",
       "      <th>far_lower</th>\n",
       "      <th>far_upper</th>\n",
       "      <th>p_astro</th>\n",
       "      <th>p_astro_lower</th>\n",
       "      <th>p_astro_upper</th>\n",
       "      <th>final_mass_source</th>\n",
       "      <th>final_mass_source_lower</th>\n",
       "      <th>final_mass_source_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151008-v1</td>\n",
       "      <td>151008</td>\n",
       "      <td>1</td>\n",
       "      <td>GWTC-1-marginal</td>\n",
       "      <td>1.128349e+09</td>\n",
       "      <td>https://doi.org/10.7935/82H3-HH23</td>\n",
       "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151012.2-v1</td>\n",
       "      <td>151012.2</td>\n",
       "      <td>1</td>\n",
       "      <td>GWTC-1-marginal</td>\n",
       "      <td>1.128667e+09</td>\n",
       "      <td>https://doi.org/10.7935/82H3-HH23</td>\n",
       "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151116-v1</td>\n",
       "      <td>151116</td>\n",
       "      <td>1</td>\n",
       "      <td>GWTC-1-marginal</td>\n",
       "      <td>1.131749e+09</td>\n",
       "      <td>https://doi.org/10.7935/82H3-HH23</td>\n",
       "      <td>https://www.gw-openscience.org/eventapi/json/G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id commonName  version catalog.shortName           GPS  \\\n",
       "0    151008-v1     151008        1   GWTC-1-marginal  1.128349e+09   \n",
       "1  151012.2-v1   151012.2        1   GWTC-1-marginal  1.128667e+09   \n",
       "2    151116-v1     151116        1   GWTC-1-marginal  1.131749e+09   \n",
       "\n",
       "                           reference  \\\n",
       "0  https://doi.org/10.7935/82H3-HH23   \n",
       "1  https://doi.org/10.7935/82H3-HH23   \n",
       "2  https://doi.org/10.7935/82H3-HH23   \n",
       "\n",
       "                                             jsonurl  mass_1_source  \\\n",
       "0  https://www.gw-openscience.org/eventapi/json/G...            NaN   \n",
       "1  https://www.gw-openscience.org/eventapi/json/G...            NaN   \n",
       "2  https://www.gw-openscience.org/eventapi/json/G...            NaN   \n",
       "\n",
       "   mass_1_source_lower  mass_1_source_upper  ...  redshift_upper    far  \\\n",
       "0                  NaN                  NaN  ...             NaN  10.17   \n",
       "1                  NaN                  NaN  ...             NaN   8.56   \n",
       "2                  NaN                  NaN  ...             NaN   4.77   \n",
       "\n",
       "   far_lower  far_upper  p_astro  p_astro_lower  p_astro_upper  \\\n",
       "0        NaN        NaN      NaN            NaN            NaN   \n",
       "1        NaN        NaN      NaN            NaN            NaN   \n",
       "2        NaN        NaN      NaN            NaN            NaN   \n",
       "\n",
       "   final_mass_source  final_mass_source_lower  final_mass_source_upper  \n",
       "0                NaN                      NaN                      NaN  \n",
       "1                NaN                      NaN                      NaN  \n",
       "2                NaN                      NaN                      NaN  \n",
       "\n",
       "[3 rows x 43 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confident.loc[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a250423d-9fef-47da-a958-94b5859fb0e9",
   "metadata": {},
   "source": [
    "## Preparing the extended catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28195a48-ee62-47a9-a59a-64edfc60b790",
   "metadata": {},
   "source": [
    "Next, we merge our catalog, the parameter estimations and the predictions to\n",
    "form an extended catalog, `xcatalog`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "eca61201-5dba-4fc1-94ac-890474823eb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "catalog = pd.read_csv(conf.DATA_DIR / \"catalog.csv\")\n",
    "\n",
    "# Extended catalog to include confident information\n",
    "xcatalog = catalog.reset_index().merge(confident, left_on=\"event\", right_on=\"id\", indicator=True)\n",
    "del xcatalog[\"id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "b0322327-6f8e-4296-9939-809541a845ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "xcatalog[\"prediction\"] = y_hat[xcatalog[\"index\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41611716-9563-4bd1-b5ea-2e50756a7fa1",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "467e7b71-4fd8-4d33-8539-cad6474a6741",
   "metadata": {},
   "source": [
    "### Signal to noise ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "03c0d36d-5d6f-4ae9-86d5-3293a51a8c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = \"network_matched_filter_snr\"\n",
    "yerr = xcatalog[[f\"{y}_lower\", f\"{y}_upper\"]]\n",
    "yerr = yerr.to_numpy().T\n",
    "\n",
    "xcatalog.plot.scatter(\n",
    "  x=\"index\",\n",
    "  y=y,\n",
    "  c=\"prediction\",\n",
    "  yerr=yerr,\n",
    "  title=\"Signal to Noise ratio of classified events\",\n",
    "  colormap='viridis'); pass\n",
    "\n",
    "plt.savefig(conf.SCREENSHOTS_DIR / \"snr_pred.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c716be59-72ae-4dba-ac80-d942c0c0704b",
   "metadata": {},
   "source": [
    "Clearly, most of the errors of our classifier are events with SNR <= 11."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "37c2e9ad-2898-40cf-bbac-3e6d4563f3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = xcatalog[[\"network_matched_filter_snr\", \"prediction\"]].dropna()\n",
    "df[\"snr\"] = df[\"network_matched_filter_snr\"].apply(lambda x: round(x, 0))\n",
    "del df[\"network_matched_filter_snr\"]\n",
    "\n",
    "ratios = {}\n",
    "for snr, rows in df.groupby(\"snr\"):\n",
    "  ratios[snr] = 100 * sum(rows[\"prediction\"]) / len(rows[\"prediction\"])\n",
    "\n",
    "plt.plot(ratios.keys(), ratios.values())\n",
    "plt.title(\"Sensitivity of csp_svm classifier vs SNR\")\n",
    "plt.ylabel(\"Sensitivity (%)\")\n",
    "plt.xlabel(\"Signal to Noise Ratio provided by GWOSC\")\n",
    "plt.savefig(conf.SCREENSHOTS_DIR / \"sensitivity-snr.jpg\")\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b770c0cc-86d2-4939-b357-68bc4a872644",
   "metadata": {},
   "source": [
    "### Mass ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "81b549df-8e38-49b4-ba94-0b3c3dd79725",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"mass_1_source\"\n",
    "y = \"mass_2_source\"\n",
    "\n",
    "xcatalog.plot.scatter(\n",
    "  x=x,\n",
    "  y=y,\n",
    "  c=\"prediction\",\n",
    "  colormap='viridis'); pass\n",
    "\n",
    "plt.rcParams['errorbar.capsize']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d3c12b6-ae50-4384-b487-b39b749f6374",
   "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": "3.9.9+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}