Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "96000128",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import math\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import LeaveOneOut, LeavePOut\n",
    "from sklearn.metrics import f1_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363cb7ff",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c1ac9f1b",
   "metadata": {
    "scrolled": true
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>vs0</th>\n",
       "      <th>PSLV_v2</th>\n",
       "      <th>PSLV_v3</th>\n",
       "      <th>PSLV_v4</th>\n",
       "      <th>PSLV_v5</th>\n",
       "      <th>PSLV_v6</th>\n",
       "      <th>PSLV_v7</th>\n",
       "      <th>PSLV_v8</th>\n",
       "      <th>...</th>\n",
       "      <th>O700_t24</th>\n",
       "      <th>CFLX_t24</th>\n",
       "      <th>CSST_t24</th>\n",
       "      <th>V20C_t24</th>\n",
       "      <th>E000_t24</th>\n",
       "      <th>DELV-12</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>date-time</th>\n",
       "      <th>dvs24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EP011982</td>\n",
       "      <td>1982</td>\n",
       "      <td>25</td>\n",
       "      <td>546</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3511.0</td>\n",
       "      <td>0.786585</td>\n",
       "      <td>20-05-1982</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>20-05-1982 00:00</td>\n",
       "      <td>5.004258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EP011982</td>\n",
       "      <td>1982</td>\n",
       "      <td>25</td>\n",
       "      <td>621</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>0.786585</td>\n",
       "      <td>20-05-1982</td>\n",
       "      <td>06:00:00</td>\n",
       "      <td>20-05-1982 06:00</td>\n",
       "      <td>5.004258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EP011982</td>\n",
       "      <td>1982</td>\n",
       "      <td>25</td>\n",
       "      <td>567</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3497.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20-05-1982</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>20-05-1982 12:00</td>\n",
       "      <td>5.004258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 127 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  year  vs0  PSLV_v2  PSLV_v3  PSLV_v4  PSLV_v5  PSLV_v6  PSLV_v7  \\\n",
       "0  EP011982  1982   25      546    -20.0     10.0    -28.0     22.0    -23.0   \n",
       "1  EP011982  1982   25      621    -15.0     10.0    -31.0     21.0    -21.0   \n",
       "2  EP011982  1982   25      567    -20.0     10.0    -21.0     18.0    -20.0   \n",
       "\n",
       "   PSLV_v8  ...  O700_t24  CFLX_t24  CSST_t24  V20C_t24  E000_t24   DELV-12  \\\n",
       "0     20.0  ...     -50.0      56.0     293.0      77.0    3511.0  0.786585   \n",
       "1     19.0  ...     -58.0      53.0     293.0      67.0    3503.0  0.786585   \n",
       "2     17.0  ...      -7.0      69.0     293.0      64.0    3497.0  0.000000   \n",
       "\n",
       "         date      hour         date-time     dvs24  \n",
       "0  20-05-1982  00:00:00  20-05-1982 00:00  5.004258  \n",
       "1  20-05-1982  06:00:00  20-05-1982 06:00  5.004258  \n",
       "2  20-05-1982  12:00:00  20-05-1982 12:00  5.004258  \n",
       "\n",
       "[3 rows x 127 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(pd.read_csv(\"global.csv\"))\n",
    "display(df.shape)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a2d53ae4",
   "metadata": {
    "scrolled": false
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       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>vs0</th>\n",
       "      <th>PSLV_v2</th>\n",
       "      <th>PSLV_v3</th>\n",
       "      <th>PSLV_v4</th>\n",
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       "      <th>PSLV_v7</th>\n",
       "      <th>PSLV_v8</th>\n",
       "      <th>...</th>\n",
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       "      <th>O500_t24</th>\n",
       "      <th>O700_t24</th>\n",
       "      <th>CFLX_t24</th>\n",
       "      <th>CSST_t24</th>\n",
       "      <th>V20C_t24</th>\n",
       "      <th>E000_t24</th>\n",
       "      <th>DELV-12</th>\n",
       "      <th>dvs24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>EP011982</td>\n",
       "      <td>1982</td>\n",
       "      <td>25</td>\n",
       "      <td>546</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-67.0</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>3511.0</td>\n",
       "      <td>0.786585</td>\n",
       "      <td>5.004258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EP011982</td>\n",
       "      <td>1982</td>\n",
       "      <td>25</td>\n",
       "      <td>621</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-69.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>0.786585</td>\n",
       "      <td>5.004258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EP011982</td>\n",
       "      <td>1982</td>\n",
       "      <td>25</td>\n",
       "      <td>567</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3497.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.004258</td>\n",
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       "       name  year  vs0  PSLV_v2  PSLV_v3  PSLV_v4  PSLV_v5  PSLV_v6  PSLV_v7  \\\n",
       "0  EP011982  1982   25      546    -20.0     10.0    -28.0     22.0    -23.0   \n",
       "1  EP011982  1982   25      621    -15.0     10.0    -31.0     21.0    -21.0   \n",
       "2  EP011982  1982   25      567    -20.0     10.0    -21.0     18.0    -20.0   \n",
       "\n",
       "   PSLV_v8  ...  HE07_t24  HE05_t24  O500_t24  O700_t24  CFLX_t24  CSST_t24  \\\n",
       "0     20.0  ...      12.0      14.0     -67.0     -50.0      56.0     293.0   \n",
       "1     19.0  ...      30.0      50.0     -69.0     -58.0      53.0     293.0   \n",
       "2     17.0  ...      27.0      43.0     -11.0      -7.0      69.0     293.0   \n",
       "\n",
       "   V20C_t24  E000_t24   DELV-12     dvs24  \n",
       "0      77.0    3511.0  0.786585  5.004258  \n",
       "1      67.0    3503.0  0.786585  5.004258  \n",
       "2      64.0    3497.0  0.000000  5.004258  \n",
       "\n",
       "[3 rows x 124 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the unnecessary columns\n",
    "df.drop(columns = ['date', 'hour', 'date-time'], axis = 1, inplace = True)\n",
    "display(df.shape)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bdc86841",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "124"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a list of all features\n",
    "# Target is dvs24\n",
    "feat = df.columns.to_list()\n",
    "len(feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "312513b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select ocean basin: Indian\n"
     ]
    }
   ],
   "source": [
    "# Select a basin to run the model\n",
    "\n",
    "select_basin = str(input('Select ocean basin: '))\n",
    "\n",
    "# Dictionary of cell range index of the basins\n",
    "basins = {\n",
    "    'Western Pacific': '26616:44660',\n",
    "    'Atlantic': '44660:55370',\n",
    "    'Indian': '13399:15778',\n",
    "    'Eastern Pacific': '0:13397'\n",
    "}\n",
    "\n",
    "if select_basin not in basins:\n",
    "    raise ValueError(\"Invalid basin selection!\")\n",
    "\n",
    "# Extracting start and end indices from the dictionary based on the selected basin\n",
    "start_idx, end_idx = map(int, basins[select_basin].split(':'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5bc75c48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected basin: Indian\n"
     ]
    },
    {
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       "      <th>year</th>\n",
       "      <th>vs0</th>\n",
       "      <th>PSLV_v2</th>\n",
       "      <th>PSLV_v3</th>\n",
       "      <th>PSLV_v4</th>\n",
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       "    <tr>\n",
       "      <th>13399</th>\n",
       "      <td>IO021990</td>\n",
       "      <td>1990</td>\n",
       "      <td>30</td>\n",
       "      <td>476</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>-143.0</td>\n",
       "      <td>-116.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>3564.0</td>\n",
       "      <td>5.004258</td>\n",
       "      <td>15.012775</td>\n",
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       "    <tr>\n",
       "      <th>13400</th>\n",
       "      <td>IO021990</td>\n",
       "      <td>1990</td>\n",
       "      <td>35</td>\n",
       "      <td>484</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-170.0</td>\n",
       "      <td>-150.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3571.0</td>\n",
       "      <td>10.008517</td>\n",
       "      <td>15.012775</td>\n",
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       "    <tr>\n",
       "      <th>13401</th>\n",
       "      <td>IO021990</td>\n",
       "      <td>1990</td>\n",
       "      <td>35</td>\n",
       "      <td>494</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-130.0</td>\n",
       "      <td>-101.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>3582.0</td>\n",
       "      <td>5.004258</td>\n",
       "      <td>20.017033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13402</th>\n",
       "      <td>IO021990</td>\n",
       "      <td>1990</td>\n",
       "      <td>40</td>\n",
       "      <td>543</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-173.0</td>\n",
       "      <td>-144.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3572.0</td>\n",
       "      <td>5.004258</td>\n",
       "      <td>20.017033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13403</th>\n",
       "      <td>IO021990</td>\n",
       "      <td>1990</td>\n",
       "      <td>45</td>\n",
       "      <td>556</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-160.0</td>\n",
       "      <td>-127.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>3564.0</td>\n",
       "      <td>10.008517</td>\n",
       "      <td>25.021292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  year  vs0  PSLV_v2  PSLV_v3  PSLV_v4  PSLV_v5  PSLV_v6  \\\n",
       "13399  IO021990  1990   30      476    -25.0      5.0    -10.0      8.0   \n",
       "13400  IO021990  1990   35      484    -28.0     15.0    -13.0      6.0   \n",
       "13401  IO021990  1990   35      494    -23.0     18.0    -11.0      5.0   \n",
       "13402  IO021990  1990   40      543    -18.0     13.0    -15.0     10.0   \n",
       "13403  IO021990  1990   45      556    -20.0      5.0    -16.0     13.0   \n",
       "\n",
       "       PSLV_v7  PSLV_v8  ...  HE07_t24  HE05_t24  O500_t24  O700_t24  \\\n",
       "13399    -14.0      6.0  ...     -12.0     -20.0    -143.0    -116.0   \n",
       "13400    -17.0      8.0  ...     -19.0     -17.0    -170.0    -150.0   \n",
       "13401    -15.0     10.0  ...     -18.0     -11.0    -130.0    -101.0   \n",
       "13402    -15.0     11.0  ...     -17.0     -16.0    -173.0    -144.0   \n",
       "13403    -17.0      9.0  ...      -3.0      -8.0    -160.0    -127.0   \n",
       "\n",
       "       CFLX_t24  CSST_t24  V20C_t24  E000_t24    DELV-12      dvs24  \n",
       "13399      52.0     300.0      67.0    3564.0   5.004258  15.012775  \n",
       "13400      23.0     300.0      81.0    3571.0  10.008517  15.012775  \n",
       "13401      -9.0     300.0     106.0    3582.0   5.004258  20.017033  \n",
       "13402      48.0     301.0      85.0    3572.0   5.004258  20.017033  \n",
       "13403      46.0     301.0     123.0    3564.0  10.008517  25.021292  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.iloc[start_idx : end_idx][feat]\n",
    "print(\"Selected basin:\", select_basin)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e92513c5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Range of years: 1990 - 2017'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2379, 124)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(f'Range of years: {df1[\"year\"].min()} - {df1[\"year\"].max()}')\n",
    "display(df1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9e1c87",
   "metadata": {},
   "source": [
    "#### Loading the best beatures file! \n",
    "\n",
    "#### The file contains the top features obtained after running random forest ranking or permutation feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "85a39e91",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DELV-12',\n",
       " 'RSST_t24',\n",
       " 'SHGC_t24',\n",
       " 'SHDC_t24',\n",
       " 'SHRD_t24',\n",
       " 'vs0',\n",
       " 'SHRG_t24',\n",
       " 'VMPI_t24',\n",
       " 'V300_t24',\n",
       " 'MTPW_v10',\n",
       " 'DTL_t24',\n",
       " 'U200_t24',\n",
       " 'TWXC_t24',\n",
       " 'CSST_t24',\n",
       " 'ENEG_t24',\n",
       " 'TWAC_t24',\n",
       " 'SHTS_t24',\n",
       " 'MTPW_v18',\n",
       " 'E000_t24',\n",
       " 'U20C_t24',\n",
       " 'dvs24']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_feat = pd.read_csv('best_20_rf_indian.csv')  # change csv file name here\n",
    "best_feat = list(best_feat.iloc[:, 1].values)\n",
    "best_feat += ['dvs24']    # appending the target label\n",
    "best_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0f414b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of input features + target (use this variable in place of best_feat IF you want to use all 121 features)\n",
    "all_feat = feat[2 :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fd9c14c",
   "metadata": {},
   "source": [
    "### Leave-three-year-out cross validation folds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4827640",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "years = df1['year'].unique()  # Get unique years from the 'year' column\n",
    "\n",
    "train_test_data = []  # List to store training and testing datasets\n",
    "\n",
    "for i in range(0, len(years) - 2, 3):  # Loop over the range up to len(years) - 2\n",
    "    test_years = years[i : i + 3]      # Three consecutive years as testing years\n",
    "    train_years = np.setdiff1d(years, test_years)  # All other years as training years\n",
    "\n",
    "    train_data = df1[df1['year'].isin(train_years)]  # Training data \n",
    "    test_data = df1[df1['year'].isin(test_years)]    # Testing data             \n",
    "\n",
    "    train_data = train_data.drop(['name', 'year'], axis = 1)  \n",
    "    test_data = test_data.drop(['name', 'year'], axis = 1)\n",
    "    \n",
    "    train_test_data.append((train_data, test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d9914e",
   "metadata": {},
   "source": [
    "### Statistical data analysis (descriptive statistics) of folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "44219536",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOLD #1 \n",
      "           count        mean         std    min     25%         50%    75%  \\\n",
      "SHRG_t24  292.0  274.557395  101.186202  110.0  197.25  255.523574  340.0   \n",
      "RSST_t24  292.0  289.397405   11.127252  244.0  284.00  289.000000  297.0   \n",
      "V300_t24  292.0    9.226666   34.956949 -100.0  -12.25    7.000000   29.0   \n",
      "\n",
      "            max  Fold  \n",
      "SHRG_t24  611.0     1  \n",
      "RSST_t24  308.0     1  \n",
      "V300_t24  118.0     1  \n",
      "\n",
      "FOLD #2 \n",
      "           count        mean         std    min    25%    50%    75%    max  \\\n",
      "SHRG_t24  183.0  275.215176  126.774045   85.0  196.5  254.0  308.5  901.0   \n",
      "RSST_t24  183.0  285.771821    7.490892  258.0  282.0  286.0  291.0  301.0   \n",
      "V300_t24  183.0    0.055134   30.941577 -109.0  -19.0    8.0   20.0   66.0   \n",
      "\n",
      "          Fold  \n",
      "SHRG_t24     2  \n",
      "RSST_t24     2  \n",
      "V300_t24     2  \n",
      "\n",
      "FOLD #3 \n",
      "           count        mean        std    min    25%    50%    75%    max  \\\n",
      "SHRG_t24  395.0  289.507165  79.208854  105.0  239.0  281.0  340.5  536.0   \n",
      "RSST_t24  395.0  288.399906  12.807476  242.0  281.0  289.0  297.0  311.0   \n",
      "V300_t24  395.0    2.547667  29.906587 -106.0  -14.5    2.0   17.0   90.0   \n",
      "\n",
      "          Fold  \n",
      "SHRG_t24     3  \n",
      "RSST_t24     3  \n",
      "V300_t24     3  \n",
      "\n",
      "FOLD #4 \n",
      "           count        mean        std    min         25%    50%    75%  \\\n",
      "SHRG_t24  240.0  247.011301  85.759268   79.0  186.750000  235.0  290.5   \n",
      "RSST_t24  240.0  286.911508  10.293511  262.0  277.640203  288.0  295.0   \n",
      "V300_t24  240.0   -1.914894  25.146926  -72.0  -18.250000   -3.5   13.0   \n",
      "\n",
      "            max  Fold  \n",
      "SHRG_t24  523.0     4  \n",
      "RSST_t24  308.0     4  \n",
      "V300_t24   58.0     4  \n",
      "\n",
      "FOLD #5 \n",
      "           count        mean         std    min    25%    50%    75%    max  \\\n",
      "SHRG_t24  253.0  314.720113  103.458425   87.0  240.0  304.0  376.0  698.0   \n",
      "RSST_t24  253.0  290.735659   11.870698  262.0  281.0  289.0  302.0  314.0   \n",
      "V300_t24  253.0   -4.387175   23.839027  -64.0  -21.0   -2.0   10.0   58.0   \n",
      "\n",
      "          Fold  \n",
      "SHRG_t24     5  \n",
      "RSST_t24     5  \n",
      "V300_t24     5  \n",
      "\n",
      "FOLD #6 \n",
      "           count        mean        std    min         25%         50%     75%  \\\n",
      "SHRG_t24  232.0  279.179569  81.417887  139.0  216.000000  255.523574  323.75   \n",
      "RSST_t24  232.0  285.491059  10.713652  259.0  277.640203  283.000000  292.25   \n",
      "V300_t24  232.0    1.041623  26.533256  -72.0  -16.000000    4.500000   13.25   \n",
      "\n",
      "            max  Fold  \n",
      "SHRG_t24  530.0     6  \n",
      "RSST_t24  309.0     6  \n",
      "V300_t24   80.0     6  \n",
      "\n",
      "FOLD #7 \n",
      "           count        mean        std    min     25%    50%     75%    max  \\\n",
      "SHRG_t24  220.0  256.903451  69.148471  115.0  212.75  255.0  289.00  581.0   \n",
      "RSST_t24  220.0  292.709283  10.163405  260.0  285.00  291.5  301.00  315.0   \n",
      "V300_t24  220.0   13.928121  25.039201  -70.0    0.00   13.0   32.25   70.0   \n",
      "\n",
      "          Fold  \n",
      "SHRG_t24     7  \n",
      "RSST_t24     7  \n",
      "V300_t24     7  \n",
      "\n",
      "FOLD #8 \n",
      "           count        mean        std    min         25%    50%    75%  \\\n",
      "SHRG_t24  235.0  274.615596  80.646707   88.0  220.000000  271.0  345.5   \n",
      "RSST_t24  235.0  284.687155   9.874461  262.0  277.640203  284.0  290.0   \n",
      "V300_t24  235.0    2.832613  34.604629  -60.0  -29.000000    4.0   29.0   \n",
      "\n",
      "            max  Fold  \n",
      "SHRG_t24  462.0     8  \n",
      "RSST_t24  313.0     8  \n",
      "V300_t24  101.0     8  \n",
      "\n",
      "FOLD #9 \n",
      "           count        mean         std    min     25%         50%     75%  \\\n",
      "SHRG_t24  280.0  261.808995  103.133549  100.0  186.00  255.523574  308.25   \n",
      "RSST_t24  280.0  289.453873   10.127911  264.0  284.75  290.000000  295.25   \n",
      "V300_t24  280.0   13.222761   29.014291  -67.0   -4.25    9.380594   32.00   \n",
      "\n",
      "            max  Fold  \n",
      "SHRG_t24  710.0     9  \n",
      "RSST_t24  313.0     9  \n",
      "V300_t24   87.0     9  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "years = df1['year'].unique()\n",
    "total_stats = []\n",
    "\n",
    "for i in range(0, len(years) - 2, 3):\n",
    "    fold_years = years[i : i + 3]\n",
    "    fold_data = df1[df1['year'].isin(fold_years)][best_feat]\n",
    "    fold_stats = fold_data.describe().transpose()\n",
    "    total_stats.append(fold_stats)  \n",
    "\n",
    "\n",
    "features_of_interest = ['SHRG_t24', 'RSST_t24', 'V300_t24']  \n",
    "fold_dfs = []\n",
    "\n",
    "for i, stats in enumerate(total_stats, start = 1):\n",
    "    filtered_stats = stats.loc[features_of_interest]\n",
    "    filtered_stats['Fold'] = i\n",
    "    fold_dfs.append(filtered_stats)\n",
    "    print(f'FOLD #{i} \\n {filtered_stats}')\n",
    "    print()\n",
    "\n",
    "all_folds_df = pd.concat(fold_dfs, axis = 1)  \n",
    "all_folds_df.reset_index(inplace = True)\n",
    "\n",
    "# Optional: Rename the index column if needed\n",
    "all_folds_df.rename(columns = {'index': 'Statistic'}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bddde20f",
   "metadata": {},
   "source": [
    "### Correlation of selected features in the last fold of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e64fe1e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract features from test_data\n",
    "features = df1[best_feat].values\n",
    "\n",
    "correlation_matrix = np.corrcoef(features, rowvar = False)\n",
    "\n",
    "plt.figure(figsize = (11, 8))\n",
    "sns.heatmap(correlation_matrix, annot = True,\n",
    "            cmap = 'coolwarm', fmt = \".2f\",\n",
    "            xticklabels = best_feat, yticklabels = best_feat)\n",
    "plt.title('Correlation Matrix of Features (Last Fold)')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.yticks(rotation = 0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af78ab1",
   "metadata": {},
   "source": [
    "### Removing correlated features (optional) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "79064b07",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 0,  1,  1,  1,  1,  1,  2,  2,  2,  2,  3,  3,  3,  3,  4,  4,  4,\n",
      "        4,  5,  6,  6,  6,  6,  7,  7,  7,  8,  8,  9,  9,  9,  9, 10, 11,\n",
      "       11, 11, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 15, 15,\n",
      "       15, 16, 16, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 19, 19, 20],\n",
      "      dtype=int64), array([ 0,  1,  7, 13, 14, 18,  2,  3,  4,  6,  2,  3,  4,  6,  2,  3,  4,\n",
      "        6,  5,  2,  3,  4,  6,  1,  7, 13,  8, 15,  9, 14, 17, 18, 10, 11,\n",
      "       16, 19, 12, 15,  1,  7, 13, 14, 18,  1,  9, 13, 14, 17, 18,  8, 12,\n",
      "       15, 11, 16,  9, 14, 17, 18,  1,  9, 13, 14, 17, 18, 11, 19, 20],\n",
      "      dtype=int64))\n",
      "Features after removing highly correlated features: ['DELV-12', 'RSST_t24', 'SHGC_t24', 'vs0', 'V300_t24', 'MTPW_v10', 'DTL_t24', 'U200_t24', 'TWXC_t24', 'dvs24']\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "indices_to_remove = np.where(np.abs(correlation_matrix) > threshold)     \n",
    "print(indices_to_remove)\n",
    "indices_to_remove = [(i, j) for i, j in zip(indices_to_remove[0], indices_to_remove[1]) if i < j]\n",
    "\n",
    "# Collect indices of features to remove\n",
    "features_to_remove = set()\n",
    "for i, j in indices_to_remove:\n",
    "    if i not in features_to_remove: \n",
    "        features_to_remove.add(j)\n",
    "\n",
    "# Create a new feature list without the removed features\n",
    "truncated_best_feat = [feature for i, feature in enumerate(best_feat) if i not in features_to_remove]\n",
    "print(\"Features after removing highly correlated features:\", truncated_best_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8c2a4c77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features after removing highly correlated features: ['DELV-12', 'RSST_t24', 'SHGC_t24', 'vs0', 'V300_t24', 'MTPW_v10', 'DTL_t24', 'U200_t24', 'TWXC_t24', 'dvs24']\n"
     ]
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "indices_to_remove = np.where(np.abs(correlation_matrix) > threshold)     \n",
    "indices_to_remove = [(i, j) for i, j in zip(indices_to_remove[0], indices_to_remove[1]) if i < j]\n",
    "\n",
    "# Collect indices of features to remove\n",
    "features_to_remove = set()\n",
    "for i, j in indices_to_remove:\n",
    "    features_to_remove.add(j)\n",
    "\n",
    "# Create a new feature list without the removed features\n",
    "truncated_best_feat = [feature for i, feature in enumerate(best_feat) if i not in features_to_remove]\n",
    "print(\"Features after removing highly correlated features:\", truncated_best_feat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9e5ea46f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(truncated_best_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7f7041",
   "metadata": {},
   "source": [
    "### Correlation matrix after removing correlated features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "76d1c175",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming train_test_data is a list of tuples where each tuple contains (train_data, test_data)\n",
    "last_fold = train_test_data[-1]\n",
    "train_data, test_data = last_fold\n",
    "\n",
    "# Extract features from test_data\n",
    "features = test_data[truncated_best_feat].values\n",
    "\n",
    "# Calculate correlation matrix\n",
    "correlation_matrix = np.corrcoef(features, rowvar = False)\n",
    "\n",
    "# Plot heatmap\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.heatmap(correlation_matrix,\n",
    "            annot = True, cmap = 'coolwarm',\n",
    "            fmt = \".2f\", xticklabels = truncated_best_feat,\n",
    "            yticklabels = truncated_best_feat)\n",
    "plt.title('Correlation Matrix of Retained Features')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.yticks(rotation = 0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891ca664",
   "metadata": {},
   "source": [
    "## Model training and testing \n",
    "#### Change RI threshold (*`ri_threshold`*) as intended. \n",
    "#### Assign *`features_to_use`*  as either *`best_feat`* (to use selected features), *`truncated_best_feat`* (to use features with corr < 0.5) or *`all_feat`* to use all the 121 features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bb511352",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fold 1 F1: 0.36 POD: 1.0 FAR: 0.7804878048780488 HSS: 0.32592699466166497 PSS: 0.8869257950530035 BS: 0.08643887671232876\n",
      "\n",
      "Fold 2 F1: 0.4444444444444444 POD: 0.7142857142857143 FAR: 0.6774193548387096 HSS: 0.37898737613682637 PSS: 0.5900253592561285 BS: 0.09834270491803279\n",
      "\n",
      "Fold 3 F1: 0.06818181818181819 POD: 0.16666666666666666 FAR: 0.9571428571428572 HSS: -0.004652605459057072 PSS: -0.011052166224580023 BS: 0.1441008683544304\n",
      "\n",
      "Fold 4 F1: 0.5625 POD: 0.6923076923076923 FAR: 0.5263157894736842 HSS: 0.497907949790795 PSS: 0.5988497483824586 BS: 0.07245174583333333\n",
      "\n",
      "Fold 5 F1: 0.27586206896551724 POD: 0.5714285714285714 FAR: 0.8181818181818182 HSS: 0.24413145539906103 PSS: 0.4982578397212543 BS: 0.07544665612648221\n",
      "\n",
      "Fold 6 F1: 0.6 POD: 1.0 FAR: 0.5714285714285714 HSS: 0.5512572533849129 PSS: 0.8878504672897196 BS: 0.07774915517241379\n",
      "\n",
      "Fold 7 F1: 0.4583333333333333 POD: 0.5238095238095238 FAR: 0.5925925925925926 HSS: 0.39316783365160196 PSS: 0.44340751375927256 BS: 0.08304763181818181\n",
      "\n",
      "Fold 8 F1: 0.358974358974359 POD: 1.0 FAR: 0.78125 HSS: 0.32602959733853387 PSS: 0.8903508771929824 BS: 0.07572727659574466\n",
      "\n",
      "Fold 9 F1: 0.41935483870967744 POD: 0.7647058823529411 FAR: 0.7111111111111111 HSS: 0.36323436512950097 PSS: 0.6430328785506597 BS: 0.09615600000000002\n"
     ]
    }
   ],
   "source": [
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "\n",
    "ri_threshold = 30\n",
    "features_to_use = best_feat\n",
    "\n",
    "list_f1, list_pod, list_far, list_hss, list_pss, list_bs  = [[] for _ in range(6)]\n",
    "\n",
    "for i in range(len(train_test_data)):\n",
    "    train_data, test_data = train_test_data[i]\n",
    "\n",
    "    # Access the training and testing datasets for the current iteration\n",
    "    X_train_initial = train_data[features_to_use].values     \n",
    "    X_train = np.delete(X_train_initial, list(features_to_use).index('dvs24'), axis = 1)  \n",
    "    y_train = train_data['dvs24'].values  \n",
    "\n",
    "    X_test_initial = test_data[features_to_use].values    \n",
    "    X_test = np.delete(X_test_initial, list(features_to_use).index('dvs24'), axis = 1)\n",
    "    y_test = test_data['dvs24'].values\n",
    "    \n",
    "    #FEATURE SCALING AND TARGET ENCODING\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)         # input data scaling\n",
    "    y_train = np.where(y_train >= ri_threshold, 1, 0)      # target feature (dvs24) encoding to 0 and 1\n",
    "\n",
    "    unri = np.where(y_train < 1)\n",
    "    ri = np.where(y_train > 0)\n",
    "    a, b = y_train[unri], y_train[ri]\n",
    "\n",
    "    X_test_scaled = scaler.fit_transform(X_test)           # input test data scaling\n",
    "    y_test = np.where(y_test >= ri_threshold, 1, 0)        # target label encoding\n",
    "\n",
    "\n",
    "    # SMOTE \n",
    "    sm = SMOTE(random_state = 42, k_neighbors = 10, sampling_strategy = {0: len(a), 1: round(0.60 * len(a))})                          \n",
    "    xs, ys = sm.fit_resample(X_train_scaled, y_train)\n",
    "\n",
    "    # SVC \n",
    "    if select_basin == 'Atlantic':\n",
    "        svc = SVC(kernel = 'rbf', gamma = 0.001, C = 15, verbose = False)\n",
    "    elif select_basin == 'Indian':\n",
    "        svc = SVC(kernel = 'rbf', gamma = 0.0005, C = 5, verbose = False)\n",
    "        \n",
    "    classifier = svc.fit(xs, ys)\n",
    "      \n",
    "    # GETTING PROBABILITIES BY SVC\n",
    "    calibrated_classifier = CalibratedClassifierCV(classifier, method = 'sigmoid', cv = 'prefit') # Calibration using Platt scaling\n",
    "    calibrated_classifier.fit(xs, ys)\n",
    "    probabilities = calibrated_classifier.predict_proba(X_test_scaled)[:, 1]     # Obtain calibrated probabilities\n",
    "    rounded_probabilities = np.round(probabilities, 3)\n",
    "    ypredict = (probabilities >= 0.5).astype(int)     # Threshold probabilities to get binary predictions\n",
    "    \n",
    "    # OUTPUT METRICS\n",
    "    f1 = f1_score(y_test, ypredict, zero_division = 0)    # Calculate F1 score with zero_division parameter\n",
    "    cm = confusion_matrix(y_test, ypredict)\n",
    "    tp, tn, fp, fn = cm[1, 1], cm[0, 0], cm[0, 1], cm[1, 0]\n",
    "    pod = tp / (tp + fn)                                              \n",
    "    far = fp / (tp + fp)\n",
    "    pofd = fp / (fp + tn)\n",
    "    hss = 2 * ((tp*tn) - (fn*fp)) / ((tp+fn)*(fn+tn)+(tp+fp)*(fp+tn))\n",
    "    pss = pod - pofd\n",
    "    o_i = np.array(y_test)\n",
    "    f_i = np.array(rounded_probabilities)\n",
    "    bs = (1 / len(o_i)) * np.sum((f_i - o_i) ** 2)\n",
    "\n",
    "    list_f1.append(f1)\n",
    "    list_pod.append(pod)\n",
    "    list_far.append(far)\n",
    "    list_hss.append(hss)\n",
    "    list_pss.append(pss)\n",
    "    list_bs.append(bs)\n",
    "    \n",
    "    print('\\nFold',i + 1, 'F1:', f1, 'POD:', pod, 'FAR:', far, 'HSS:', hss, 'PSS:', pss, 'BS:', bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "add9206e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean POD: 0.715\n",
      "Mean FAR: 0.713\n",
      "Mean F1: 0.394\n",
      "Mean HSS: 0.342\n",
      "Mean PSS: 0.603\n",
      "Mean BS: 0.09\n",
      "Mean BSS: 0.526\n"
     ]
    }
   ],
   "source": [
    "# Averaging the metrics over all folds \n",
    "\n",
    "new_list_pod = [item for item in list_pod if item is not None and not math.isnan(item)]\n",
    "new_list_far = [item for item in list_far if item is not None and not math.isnan(item)]\n",
    "new_list_hss = [item for item in list_hss if item is not None and not math.isnan(item)]\n",
    "new_list_pss = [item for item in list_pss if item is not None and not math.isnan(item)]\n",
    "\n",
    "print('Mean POD:', round(sum(new_list_pod) / len(new_list_pod), 3))    \n",
    "print('Mean FAR:', round(sum(new_list_far) / len(new_list_far), 3))\n",
    "print('Mean F1:', round(sum(list_f1) / len(list_f1), 3))\n",
    "print('Mean HSS:', round(sum(list_hss) / len(list_hss), 3))\n",
    "print('Mean PSS:', round(sum(list_pss) / len(list_pss), 3))\n",
    "\n",
    "bss_ref = 0.19     # 0.19 is the BSS for the SHIPS-RII `Consensus` model (DeMaria)\n",
    "avg_bs = round(sum(list_bs) / len(list_bs), 3)\n",
    "print('Mean BS:', avg_bs)\n",
    "print('Mean BSS:', round(1 - (avg_bs / bss_ref), 3))  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38ddc951",
   "metadata": {},
   "source": [
    "### Re-evaluation for 95% confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0d85c39a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run1 Avg F1: 0.387 Avg POD: 0.71 Avg FAR: 0.72 Avg HSS 0.333 Avg PSS 0.594 Avg BS 0.092\n",
      "Run2 Avg F1: 0.39 Avg POD: 0.711 Avg FAR: 0.717 Avg HSS 0.336 Avg PSS 0.598 Avg BS 0.092\n",
      "Run3 Avg F1: 0.383 Avg POD: 0.717 Avg FAR: 0.726 Avg HSS 0.329 Avg PSS 0.599 Avg BS 0.092\n",
      "Run4 Avg F1: 0.384 Avg POD: 0.7 Avg FAR: 0.721 Avg HSS 0.33 Avg PSS 0.588 Avg BS 0.091\n",
      "Run5 Avg F1: 0.389 Avg POD: 0.71 Avg FAR: 0.716 Avg HSS 0.336 Avg PSS 0.597 Avg BS 0.091\n",
      "Run6 Avg F1: 0.389 Avg POD: 0.717 Avg FAR: 0.719 Avg HSS 0.335 Avg PSS 0.602 Avg BS 0.092\n",
      "Run7 Avg F1: 0.383 Avg POD: 0.704 Avg FAR: 0.724 Avg HSS 0.329 Avg PSS 0.589 Avg BS 0.093\n",
      "Run8 Avg F1: 0.391 Avg POD: 0.711 Avg FAR: 0.717 Avg HSS 0.338 Avg PSS 0.597 Avg BS 0.092\n",
      "Run9 Avg F1: 0.385 Avg POD: 0.711 Avg FAR: 0.723 Avg HSS 0.331 Avg PSS 0.596 Avg BS 0.092\n",
      "Run10 Avg F1: 0.38 Avg POD: 0.705 Avg FAR: 0.726 Avg HSS 0.326 Avg PSS 0.589 Avg BS 0.092\n",
      "Run11 Avg F1: 0.39 Avg POD: 0.71 Avg FAR: 0.716 Avg HSS 0.337 Avg PSS 0.596 Avg BS 0.092\n",
      "Run12 Avg F1: 0.382 Avg POD: 0.699 Avg FAR: 0.723 Avg HSS 0.328 Avg PSS 0.584 Avg BS 0.092\n",
      "Run13 Avg F1: 0.382 Avg POD: 0.711 Avg FAR: 0.725 Avg HSS 0.328 Avg PSS 0.592 Avg BS 0.093\n",
      "Run14 Avg F1: 0.386 Avg POD: 0.712 Avg FAR: 0.719 Avg HSS 0.333 Avg PSS 0.598 Avg BS 0.092\n",
      "Run15 Avg F1: 0.39 Avg POD: 0.716 Avg FAR: 0.717 Avg HSS 0.337 Avg PSS 0.602 Avg BS 0.092\n",
      "Run16 Avg F1: 0.386 Avg POD: 0.711 Avg FAR: 0.721 Avg HSS 0.333 Avg PSS 0.597 Avg BS 0.093\n",
      "Run17 Avg F1: 0.38 Avg POD: 0.705 Avg FAR: 0.726 Avg HSS 0.326 Avg PSS 0.589 Avg BS 0.092\n",
      "Run18 Avg F1: 0.392 Avg POD: 0.716 Avg FAR: 0.715 Avg HSS 0.339 Avg PSS 0.603 Avg BS 0.092\n",
      "Run19 Avg F1: 0.382 Avg POD: 0.7 Avg FAR: 0.722 Avg HSS 0.328 Avg PSS 0.587 Avg BS 0.092\n",
      "Run20 Avg F1: 0.382 Avg POD: 0.699 Avg FAR: 0.723 Avg HSS 0.328 Avg PSS 0.585 Avg BS 0.093\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import t\n",
    "\n",
    "num_runs = 20\n",
    "ri_threshold = 30\n",
    "num_folds = len(train_test_data)\n",
    "\n",
    "list_avg_f1, list_avg_pod, list_avg_far, list_avg_hss, list_avg_pss, list_avg_bs = [[] for _ in range(6)]\n",
    "\n",
    "for run in range(num_runs):\n",
    "    list_f1, list_pod, list_far, list_hss, list_pss, list_bs = [[] for _ in range(6)]\n",
    "\n",
    "    for i in range(num_folds):\n",
    "        train_data, test_data = train_test_data[i]\n",
    "\n",
    "        # Access the training and testing datasets for the current iteration\n",
    "        X_train_initial = train_data[features_to_use].values  \n",
    "        X_train = np.delete(X_train_initial, list(features_to_use).index('dvs24'), axis = 1)  \n",
    "        y_train = train_data['dvs24'].values  \n",
    "\n",
    "        X_test_initial = test_data[features_to_use].values    \n",
    "        X_test = np.delete(X_test_initial, list(features_to_use).index('dvs24'), axis = 1)   \n",
    "        y_test = test_data['dvs24'].values\n",
    "\n",
    "        # FEATURE SCALING AND TEARGET ENCODING\n",
    "        scaler = StandardScaler()\n",
    "        X_train_scaled = scaler.fit_transform(X_train)  \n",
    "        y_train = np.where(y_train >= ri_threshold, 1, 0)  \n",
    "\n",
    "        unri = np.where(y_train < 1)\n",
    "        ri = np.where(y_train > 0)\n",
    "        a, b = y_train[unri], y_train[ri]\n",
    "\n",
    "        X_test_scaled = scaler.fit_transform(X_test)  \n",
    "        y_test = np.where(y_test >= ri_threshold, 1, 0)\n",
    "\n",
    "\n",
    "        # SMOTE (without random seed!)\n",
    "        sm = SMOTE(k_neighbors = 10, sampling_strategy = {0: len(a), 1: round(0.60 * len(a))})                          \n",
    "        xs, ys = sm.fit_resample(X_train_scaled, y_train)\n",
    "\n",
    "        # SVC \n",
    "        if select_basin == 'Atlantic':\n",
    "            svc = SVC(kernel = 'rbf', gamma = 0.001, C = 15, verbose = False)\n",
    "        elif select_basin == 'Indian':\n",
    "            svc = SVC(kernel = 'rbf', gamma = 0.0005, C = 5, verbose = False)\n",
    "            \n",
    "        classifier = svc.fit(xs, ys)\n",
    "        \n",
    "        # GETTING PROBABILITIES BY SVC\n",
    "        calibrated_classifier = CalibratedClassifierCV(classifier, method = 'sigmoid', cv = 'prefit') \n",
    "        calibrated_classifier.fit(xs, ys)\n",
    "        probabilities = calibrated_classifier.predict_proba(X_test_scaled)[:, 1]  \n",
    "        rounded_probabilities = np.round(probabilities, 3)\n",
    "        ypredict = (probabilities >= 0.5).astype(int)  \n",
    "\n",
    "        # OUTPUT METRICS\n",
    "        f1 = f1_score(y_test, ypredict, zero_division = 0) \n",
    "        cm = confusion_matrix(y_test, ypredict)\n",
    "        \n",
    "        tp, tn, fp, fn = cm[1, 1], cm[0, 0], cm[0, 1], cm[1, 0]\n",
    "        pod = tp / (tp + fn)\n",
    "        far = fp / (tp + fp)\n",
    "        pofd = fp / (fp + tn)\n",
    "        hss = 2 * ((tp*tn) - (fn*fp)) / ((tp+fn)*(fn+tn)+(tp+fp)*(fp+tn))\n",
    "        pss = pod - pofd\n",
    "        o_i = np.array(y_test)\n",
    "        f_i = np.array(rounded_probabilities)\n",
    "        bs = (1 / len(o_i)) * np.sum((f_i - o_i) ** 2)\n",
    "\n",
    "        list_f1.append(f1)\n",
    "        list_pod.append(pod)\n",
    "        list_far.append(far)\n",
    "        list_hss.append(hss)\n",
    "        list_pss.append(pss)\n",
    "        list_bs.append(bs)\n",
    "\n",
    "    # Calculate averages for the current run\n",
    "    avg_f1 = np.mean(list_f1)\n",
    "    avg_pod = np.mean(list_pod)\n",
    "    avg_far = np.mean(list_far)\n",
    "    avg_hss = np.mean(list_hss)\n",
    "    avg_pss = np.mean(list_pss)\n",
    "    avg_bs = np.mean(list_bs)\n",
    "\n",
    "    list_avg_f1.append(avg_f1)\n",
    "    list_avg_pod.append(avg_pod)\n",
    "    list_avg_far.append(avg_far)\n",
    "    list_avg_hss.append(avg_hss)\n",
    "    list_avg_pss.append(avg_pss)\n",
    "    list_avg_bs.append(avg_bs)\n",
    "\n",
    "    print(f'Run{run + 1} Avg F1: {round(avg_f1, 3)} Avg POD: {round(avg_pod, 3)} Avg FAR: {round(avg_far, 3)} Avg HSS {round(avg_hss, 3)} Avg PSS {round(avg_pss, 3)} Avg BS {round(avg_bs, 3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f5ea4eca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "F1: 0.386 ± 0.002\n",
      "POD: 0.709 ± 0.003\n",
      "FAR: 0.721 ± 0.002\n",
      "HSS: 0.332 ± 0.002\n",
      "PSS: 0.594 ± 0.003\n",
      "BS: 0.092 ± 0.000\n"
     ]
    }
   ],
   "source": [
    "# CONFIDENCE INTERVALS COMPUTATION \n",
    "\n",
    "# Calculate overall averages\n",
    "overall_avg_f1 = np.mean(list_avg_f1)\n",
    "overall_avg_pod = np.mean(list_avg_pod)\n",
    "overall_avg_far = np.mean(list_avg_far)\n",
    "overall_avg_hss = np.mean(list_avg_hss)\n",
    "overall_avg_pss = np.mean(list_avg_pss)\n",
    "overall_avg_bs = np.mean(list_avg_bs)\n",
    "\n",
    "# Calculate confidence intervals (95%) for overall averages\n",
    "confidence = 0.95\n",
    "n = num_runs\n",
    "t_value = t.ppf((1 + confidence) / 2, n - 1)\n",
    "ci_avg_f1 = t_value * np.std(list_avg_f1, ddof=1) / np.sqrt(n)\n",
    "ci_avg_pod = t_value * np.std(list_avg_pod, ddof=1) / np.sqrt(n)\n",
    "ci_avg_far = t_value * np.std(list_avg_far, ddof=1) / np.sqrt(n)\n",
    "ci_avg_hss = t_value * np.std(list_avg_hss, ddof=1) / np.sqrt(n)\n",
    "ci_avg_pss = t_value * np.std(list_avg_pss, ddof=1) / np.sqrt(n)\n",
    "ci_avg_bs = t_value * np.std(list_avg_bs, ddof=1) / np.sqrt(n)\n",
    "\n",
    "# Calculate the confidence interval as mean value plus/minus uncertainty\n",
    "ci_avg_f1_interval = (overall_avg_f1 - ci_avg_f1, overall_avg_f1 + ci_avg_f1)\n",
    "ci_avg_pod_interval = (overall_avg_pod - ci_avg_pod, overall_avg_pod + ci_avg_pod)\n",
    "ci_avg_far_interval = (overall_avg_far - ci_avg_far, overall_avg_far + ci_avg_far)\n",
    "ci_avg_hss_interval = (overall_avg_hss - ci_avg_hss, overall_avg_hss + ci_avg_hss)\n",
    "ci_avg_pss_interval = (overall_avg_pss - ci_avg_pss, overall_avg_pss + ci_avg_pss)\n",
    "ci_avg_bs_interval = (overall_avg_bs - ci_avg_bs, overall_avg_bs + ci_avg_bs)\n",
    "\n",
    "# Print overall averages and confidence intervals\n",
    "print('\\nF1: {:.3f} ± {:.3f}'.format(overall_avg_f1, ci_avg_f1))\n",
    "print('POD: {:.3f} ± {:.3f}'.format(overall_avg_pod, ci_avg_pod))\n",
    "print('FAR: {:.3f} ± {:.3f}'.format(overall_avg_far, ci_avg_far))\n",
    "print('HSS: {:.3f} ± {:.3f}'.format(overall_avg_hss, ci_avg_hss))\n",
    "print('PSS: {:.3f} ± {:.3f}'.format(overall_avg_pss, ci_avg_pss))\n",
    "print('BS: {:.3f} ± {:.3f}'.format(overall_avg_bs, ci_avg_bs))"
   ]
  }
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