2_Optimization_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab921c71",
   "metadata": {},
   "source": [
    "# 0- Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa54aaa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in dir():\n",
    "    if not name.startswith('_'):\n",
    "        del globals()[name]\n",
    "        \n",
    "        \n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "#magic commands "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f5d8d6",
   "metadata": {},
   "source": [
    "- Naming convention:\n",
    "  - g German dataset \n",
    "  - int: Initial dataset without anomalies removal\n",
    "  - GHF: dataset containing onservables \n",
    "  - grid: grid containing observables \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ebe301",
   "metadata": {},
   "source": [
    "# 1- Importing Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "21003ba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logging and Warnings\n",
    "import warnings  # Provides a way to handle warning messages that may occur during the execution of the code.\n",
    "import logging  # Module for flexible logging of messages and errors.\n",
    "\n",
    "\n",
    "#Visualization\n",
    "import matplotlib.pyplot as plt  # Plotting library for creating various types of plots and visualizations.\n",
    "import seaborn as sns  # Library for creating statistical graphics, such as heatmaps and scatterplots.\n",
    "\n",
    "\n",
    "#File and Path Operations\n",
    "from pathlib import Path  # Object-oriented filesystem paths.\n",
    "import joblib  # Module for saving and loading Python objects.\n",
    "import pickle  # Module for serializing and deserializing Python objects.\n",
    "\n",
    "\n",
    "#Data Manipulation and Analysis\n",
    "import pandas as pd  # Data manipulation and analysis library.\n",
    "import numpy as np  # Fundamental package for scientific computing with Python.\n",
    "\n",
    "\n",
    "#Machine Learning\n",
    "from sklearn import (\n",
    "    compose,  \n",
    "    model_selection,\n",
    ")\n",
    "from skopt import BayesSearchCV  # For hyperparameter optimization using Bayesian search.\n",
    "\n",
    "from uq360.utils.misc import  make_sklearn_compatible_scorer\n",
    "\n",
    "from QRF.wrapper.qrf_wrapper import QuantileRegressionForest\n",
    "\n",
    "import GQ_utils as utils  # Custom module for AFQ utilities.\n",
    "\n",
    "#Directory and Styling\n",
    "DIR = Path().resolve()  # Current working directory.\n",
    "sns.set_theme(style=\"white\")  # Set the default theme/style for Seaborn.\n",
    "\n",
    "#Logging Configuration and Ignoring Warnings\n",
    "# Set up logging\n",
    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
    "\n",
    "# Ignore warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96acb65a",
   "metadata": {},
   "source": [
    "# 2- Variables and constants"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd89aabc",
   "metadata": {},
   "source": [
    "### Constants relative to datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1569405b",
   "metadata": {
    "scrolled": true
   },
   "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>REF_CODE_UNTRANS</th>\n",
       "      <th>LABELS</th>\n",
       "      <th>LABELS_gmt</th>\n",
       "      <th>LABELS_gmt_UNTRANS</th>\n",
       "      <th>UNITS</th>\n",
       "      <th>UNITS_gmt</th>\n",
       "      <th>UNITS_gmt_UNTRANS</th>\n",
       "      <th>V_RANGE_W</th>\n",
       "      <th>V_RANGE_G</th>\n",
       "      <th>V_RANGE_G_UNTRANS</th>\n",
       "      <th>CMAPS_gmt</th>\n",
       "      <th>LABELS_REDUCED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VP</th>\n",
       "      <td>VP_VELOCITY</td>\n",
       "      <td>Δ$P_v$ @150Km tomography</td>\n",
       "      <td>@~D@~P@_v@\\t@@150km\\ttomography</td>\n",
       "      <td>P@_v@\\t@@150km\\ttomography</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>ms@+-2@</td>\n",
       "      <td>(-0.02, 0.02)</td>\n",
       "      <td>(8.1, 8.4)</td>\n",
       "      <td>(8.1, 8.4)</td>\n",
       "      <td>SCM/roma</td>\n",
       "      <td>P wave tomogr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VS</th>\n",
       "      <td>VS_VELOCITY</td>\n",
       "      <td>Δ$S_v$ @150km tomography</td>\n",
       "      <td>@~D@~S@_v@\\t@@150km\\ttomography</td>\n",
       "      <td>S@_v@\\t@@150km\\ttomography</td>\n",
       "      <td>%</td>\n",
       "      <td>%</td>\n",
       "      <td>ms@+-2@</td>\n",
       "      <td>(-0.1, 0.1)</td>\n",
       "      <td>(4.2, 4.7)</td>\n",
       "      <td>(4.2, 4.7)</td>\n",
       "      <td>SCM/roma</td>\n",
       "      <td>S wave tomogr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOLCS_DIST_W</th>\n",
       "      <td>VOLCS_DIST</td>\n",
       "      <td>Proximity to Volcano</td>\n",
       "      <td>Proximity\\tto\\tvolcano</td>\n",
       "      <td>Distance\\tto\\tVolcano</td>\n",
       "      <td>f(Km)</td>\n",
       "      <td>f(Km)</td>\n",
       "      <td>Km</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(0, 300)</td>\n",
       "      <td>SCM/broc</td>\n",
       "      <td>Prox. to Volcanos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STRESS_DIST_W</th>\n",
       "      <td>STRESS_DIST</td>\n",
       "      <td>Proximity to Faults</td>\n",
       "      <td>Proximity\\tto\\tfaults</td>\n",
       "      <td>Distance\\tto\\tfaults</td>\n",
       "      <td>f(km)</td>\n",
       "      <td>f(km)</td>\n",
       "      <td>km</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(0, 300)</td>\n",
       "      <td>SCM/broc</td>\n",
       "      <td>Prox. to Faults</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAG_CLASS</th>\n",
       "      <td>MAG</td>\n",
       "      <td>Magnetic Anomaly</td>\n",
       "      <td>Magnetic\\tAnomaly</td>\n",
       "      <td>Magnetic\\tAnomaly</td>\n",
       "      <td>f(nT)</td>\n",
       "      <td>f(nT)</td>\n",
       "      <td>nT</td>\n",
       "      <td>(-0.4, 0.4)</td>\n",
       "      <td>(-0.4, 0.4)</td>\n",
       "      <td>(-200, 200)</td>\n",
       "      <td>SCM/bilbao</td>\n",
       "      <td>Magnetic Anom.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RHO_L</th>\n",
       "      <td>RHO_L</td>\n",
       "      <td>Litho. ρ</td>\n",
       "      <td>Lith.\\t@~r@</td>\n",
       "      <td>Lith.\\t@~r@</td>\n",
       "      <td>kgm$^{-3}$</td>\n",
       "      <td>kgm@+-3@</td>\n",
       "      <td>kgm@+-3@</td>\n",
       "      <td>(3.26, 3.36)</td>\n",
       "      <td>(3.26, 3.36)</td>\n",
       "      <td>(3.26, 3.36)</td>\n",
       "      <td>SCM/batlow</td>\n",
       "      <td>Litho. ρ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTD_vb35</th>\n",
       "      <td>CTD_vb35</td>\n",
       "      <td>Curie Depth</td>\n",
       "      <td>Curie\\tDepth</td>\n",
       "      <td>Curie\\tDepth</td>\n",
       "      <td>km</td>\n",
       "      <td>km</td>\n",
       "      <td>km</td>\n",
       "      <td>(10, 40)</td>\n",
       "      <td>(10, 40)</td>\n",
       "      <td>(10, 40)</td>\n",
       "      <td>SCM/bamako</td>\n",
       "      <td>Curie Depth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAB</th>\n",
       "      <td>LAB</td>\n",
       "      <td>LAB Depth</td>\n",
       "      <td>LAB\\tDepth</td>\n",
       "      <td>LAB\\tDepth</td>\n",
       "      <td>km</td>\n",
       "      <td>km</td>\n",
       "      <td>km</td>\n",
       "      <td>(0, 300)</td>\n",
       "      <td>(50, 250)</td>\n",
       "      <td>(50, 250)</td>\n",
       "      <td>SCM/bamako</td>\n",
       "      <td>LAB Depth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOHO</th>\n",
       "      <td>MOHO</td>\n",
       "      <td>Moho Depth</td>\n",
       "      <td>Moho\\tDepth</td>\n",
       "      <td>Moho\\tDepth</td>\n",
       "      <td>km</td>\n",
       "      <td>km</td>\n",
       "      <td>km</td>\n",
       "      <td>(15, 60)</td>\n",
       "      <td>(20, 50)</td>\n",
       "      <td>(20, 50)</td>\n",
       "      <td>SCM/bamako</td>\n",
       "      <td>Moho Depth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEDIMENT</th>\n",
       "      <td>SEDIMENT</td>\n",
       "      <td>Sediment Thickness</td>\n",
       "      <td>Sediment\\tThickness</td>\n",
       "      <td>Sediment\\tThickness</td>\n",
       "      <td>km</td>\n",
       "      <td>km</td>\n",
       "      <td>km</td>\n",
       "      <td>(0, 10)</td>\n",
       "      <td>(0, 10)</td>\n",
       "      <td>(0, 10)</td>\n",
       "      <td>SCM/davos</td>\n",
       "      <td>Sediment Thick.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>DEM</td>\n",
       "      <td>DEM</td>\n",
       "      <td>DEM</td>\n",
       "      <td>DEM</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>(-2200, 2200)</td>\n",
       "      <td>(-2200, 2200)</td>\n",
       "      <td>(-2200, 2200)</td>\n",
       "      <td>gmt/geo</td>\n",
       "      <td>Topography</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEOID_A</th>\n",
       "      <td>GEOID_A</td>\n",
       "      <td>Geoid</td>\n",
       "      <td>Geoid</td>\n",
       "      <td>Geoid</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>(-30, 30)</td>\n",
       "      <td>(-30, 30)</td>\n",
       "      <td>(-30, 30)</td>\n",
       "      <td>SCM/vik</td>\n",
       "      <td>Geoid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>BG</td>\n",
       "      <td>Bouguer Anomaly</td>\n",
       "      <td>Bouguer\\tAnomaly</td>\n",
       "      <td>Bouguer\\tAnomaly</td>\n",
       "      <td>mGal</td>\n",
       "      <td>mGal</td>\n",
       "      <td>mGal</td>\n",
       "      <td>(-100, 100)</td>\n",
       "      <td>(-100, 100)</td>\n",
       "      <td>(-100, 100)</td>\n",
       "      <td>SCM/vik</td>\n",
       "      <td>Bouguer Anom.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FA</th>\n",
       "      <td>FA</td>\n",
       "      <td>Free Air Anomaly</td>\n",
       "      <td>Free\\tair\\tanomaly</td>\n",
       "      <td>Free\\tair\\tAnomaly</td>\n",
       "      <td>mGal</td>\n",
       "      <td>mGal</td>\n",
       "      <td>mGal</td>\n",
       "      <td>(-100, 100)</td>\n",
       "      <td>(-100, 100)</td>\n",
       "      <td>(-100, 100)</td>\n",
       "      <td>SCM/vik</td>\n",
       "      <td>Free Air Anom.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>SI</td>\n",
       "      <td>Shape Index</td>\n",
       "      <td>Shape\\tindex</td>\n",
       "      <td>Shape\\tindex</td>\n",
       "      <td>si</td>\n",
       "      <td>si</td>\n",
       "      <td>si</td>\n",
       "      <td>(-1, 1)</td>\n",
       "      <td>(-1, 1)</td>\n",
       "      <td>(-1, 1)</td>\n",
       "      <td>SCM/broc</td>\n",
       "      <td>Shape Index</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              REF_CODE_UNTRANS                    LABELS   \n",
       "VP                 VP_VELOCITY  Δ$P_v$ @150Km tomography  \\\n",
       "VS                 VS_VELOCITY  Δ$S_v$ @150km tomography   \n",
       "VOLCS_DIST_W        VOLCS_DIST      Proximity to Volcano   \n",
       "STRESS_DIST_W      STRESS_DIST       Proximity to Faults   \n",
       "MAG_CLASS                  MAG          Magnetic Anomaly   \n",
       "RHO_L                    RHO_L                  Litho. ρ   \n",
       "CTD_vb35              CTD_vb35               Curie Depth   \n",
       "LAB                        LAB                 LAB Depth   \n",
       "MOHO                      MOHO                Moho Depth   \n",
       "SEDIMENT              SEDIMENT        Sediment Thickness   \n",
       "DEM                        DEM                       DEM   \n",
       "GEOID_A                GEOID_A                     Geoid   \n",
       "BG                          BG           Bouguer Anomaly   \n",
       "FA                          FA          Free Air Anomaly   \n",
       "SI                          SI               Shape Index   \n",
       "\n",
       "                                    LABELS_gmt          LABELS_gmt_UNTRANS   \n",
       "VP             @~D@~P@_v@\\t@@150km\\ttomography  P@_v@\\t@@150km\\ttomography  \\\n",
       "VS             @~D@~S@_v@\\t@@150km\\ttomography  S@_v@\\t@@150km\\ttomography   \n",
       "VOLCS_DIST_W            Proximity\\tto\\tvolcano       Distance\\tto\\tVolcano   \n",
       "STRESS_DIST_W            Proximity\\tto\\tfaults        Distance\\tto\\tfaults   \n",
       "MAG_CLASS                    Magnetic\\tAnomaly           Magnetic\\tAnomaly   \n",
       "RHO_L                             Lith.\\t@~r@                 Lith.\\t@~r@    \n",
       "CTD_vb35                          Curie\\tDepth                Curie\\tDepth   \n",
       "LAB                                 LAB\\tDepth                  LAB\\tDepth   \n",
       "MOHO                               Moho\\tDepth                 Moho\\tDepth   \n",
       "SEDIMENT                   Sediment\\tThickness         Sediment\\tThickness   \n",
       "DEM                                        DEM                         DEM   \n",
       "GEOID_A                                  Geoid                       Geoid   \n",
       "BG                            Bouguer\\tAnomaly            Bouguer\\tAnomaly   \n",
       "FA                          Free\\tair\\tanomaly          Free\\tair\\tAnomaly   \n",
       "SI                                Shape\\tindex                Shape\\tindex   \n",
       "\n",
       "                    UNITS UNITS_gmt UNITS_gmt_UNTRANS      V_RANGE_W   \n",
       "VP                      %         %           ms@+-2@  (-0.02, 0.02)  \\\n",
       "VS                      %         %           ms@+-2@    (-0.1, 0.1)   \n",
       "VOLCS_DIST_W        f(Km)     f(Km)                Km         (0, 1)   \n",
       "STRESS_DIST_W       f(km)     f(km)                km         (0, 1)   \n",
       "MAG_CLASS           f(nT)     f(nT)                nT    (-0.4, 0.4)   \n",
       "RHO_L          kgm$^{-3}$  kgm@+-3@          kgm@+-3@   (3.26, 3.36)   \n",
       "CTD_vb35               km        km                km       (10, 40)   \n",
       "LAB                    km        km                km       (0, 300)   \n",
       "MOHO                   km        km                km       (15, 60)   \n",
       "SEDIMENT               km        km                km        (0, 10)   \n",
       "DEM                     m         m                 m  (-2200, 2200)   \n",
       "GEOID_A                 m         m                 m      (-30, 30)   \n",
       "BG                   mGal      mGal              mGal    (-100, 100)   \n",
       "FA                   mGal      mGal              mGal    (-100, 100)   \n",
       "SI                     si        si                si        (-1, 1)   \n",
       "\n",
       "                   V_RANGE_G V_RANGE_G_UNTRANS   CMAPS_gmt     LABELS_REDUCED  \n",
       "VP                (8.1, 8.4)        (8.1, 8.4)    SCM/roma     P wave tomogr.  \n",
       "VS                (4.2, 4.7)        (4.2, 4.7)    SCM/roma     S wave tomogr.  \n",
       "VOLCS_DIST_W          (0, 1)          (0, 300)    SCM/broc  Prox. to Volcanos  \n",
       "STRESS_DIST_W         (0, 1)          (0, 300)    SCM/broc    Prox. to Faults  \n",
       "MAG_CLASS        (-0.4, 0.4)       (-200, 200)  SCM/bilbao     Magnetic Anom.  \n",
       "RHO_L           (3.26, 3.36)      (3.26, 3.36)  SCM/batlow           Litho. ρ  \n",
       "CTD_vb35            (10, 40)          (10, 40)  SCM/bamako        Curie Depth  \n",
       "LAB                (50, 250)         (50, 250)  SCM/bamako          LAB Depth  \n",
       "MOHO                (20, 50)          (20, 50)  SCM/bamako         Moho Depth  \n",
       "SEDIMENT             (0, 10)           (0, 10)   SCM/davos    Sediment Thick.  \n",
       "DEM            (-2200, 2200)     (-2200, 2200)     gmt/geo         Topography  \n",
       "GEOID_A            (-30, 30)         (-30, 30)     SCM/vik              Geoid  \n",
       "BG               (-100, 100)       (-100, 100)     SCM/vik      Bouguer Anom.  \n",
       "FA               (-100, 100)       (-100, 100)     SCM/vik     Free Air Anom.  \n",
       "SI                   (-1, 1)           (-1, 1)    SCM/broc        Shape Index  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Initialize a dictionary called obs with different lists as values.\n",
    "#obs are organized alphabetically\n",
    "obs_dict = {\n",
    "'REF_CODE_UNTRANS': {'BG': 'BG',  'CTD_vb35': 'CTD_vb35',  'DEM': 'DEM',  'MAG_CLASS': 'MAG',  'FA': 'FA',  'GEOID_A': 'GEOID_A',  'LAB': 'LAB',  'MOHO': 'MOHO',  'RHO_L': 'RHO_L',  'SEDIMENT': 'SEDIMENT',  'SI': 'SI',  'VOLCS_DIST_W': 'VOLCS_DIST',  'VP': 'VP_VELOCITY',  'VS': 'VS_VELOCITY',  'STRESS_DIST_W': 'STRESS_DIST'},\n",
    "'LABELS': {'BG': 'Bouguer Anomaly',  'CTD_vb35': 'Curie Depth',  'DEM': 'DEM',  'MAG_CLASS': 'Magnetic Anomaly',  'FA': 'Free Air Anomaly',  'GEOID_A': 'Geoid',  'LAB': 'LAB Depth',  'MOHO': 'Moho Depth',  'RHO_L': 'Litho. ρ',  'SEDIMENT': 'Sediment Thickness',  'SI': 'Shape Index',  'VOLCS_DIST_W': 'Proximity to Volcano',  'VP': 'Δ$P_v$ @150Km tomography',  'VS': 'Δ$S_v$ @150km tomography',  'STRESS_DIST_W': 'Proximity to Faults'},\n",
    "'LABELS_gmt': {'BG': 'Bouguer\\tAnomaly',  'CTD_vb35': 'Curie\\tDepth',  'DEM': 'DEM',  'MAG_CLASS': 'Magnetic\\tAnomaly',  'FA': 'Free\\tair\\tanomaly',  'GEOID_A': 'Geoid',  'LAB': 'LAB\\tDepth',  'MOHO': 'Moho\\tDepth',  'RHO_L': 'Lith.\\t@~r@ ',  'SEDIMENT': 'Sediment\\tThickness',  'SI': 'Shape\\tindex',  'VOLCS_DIST_W': 'Proximity\\tto\\tvolcano',  'VP': '@~D@~P@_v@\\t@@150km\\ttomography',  'VS': '@~D@~S@_v@\\t@@150km\\ttomography',  'STRESS_DIST_W': 'Proximity\\tto\\tfaults'},\n",
    "'LABELS_gmt_UNTRANS': {'BG': 'Bouguer\\tAnomaly',  'CTD_vb35': 'Curie\\tDepth',  'DEM': 'DEM',  'MAG_CLASS': 'Magnetic\\tAnomaly',  'FA': 'Free\\tair\\tAnomaly',  'GEOID_A': 'Geoid',  'LAB': 'LAB\\tDepth',  'MOHO': 'Moho\\tDepth',  'RHO_L': 'Lith.\\t@~r@ ',  'SEDIMENT': 'Sediment\\tThickness',  'SI': 'Shape\\tindex',  'VOLCS_DIST_W': 'Distance\\tto\\tVolcano',  'VP': 'P@_v@\\t@@150km\\ttomography',  'VS': 'S@_v@\\t@@150km\\ttomography',  'STRESS_DIST_W': 'Distance\\tto\\tfaults'},\n",
    "'UNITS': {'BG': 'mGal',  'CTD_vb35': 'km',  'DEM': 'm',  'MAG_CLASS': 'f(nT)',  'FA': 'mGal',  'GEOID_A': 'm',  'LAB': 'km',  'MOHO': 'km',  'RHO_L': 'kgm$^{-3}$',  'SEDIMENT': 'km',  'SI': 'si',  'VOLCS_DIST_W': 'f(Km)',  'VP': '%',  'VS': '%',  'STRESS_DIST_W': 'f(km)'},\n",
    "'UNITS_gmt': {'BG': 'mGal',  'CTD_vb35': 'km',  'DEM': 'm',  'MAG_CLASS': 'f(nT)',  'FA': 'mGal',  'GEOID_A': 'm',  'LAB': 'km',  'MOHO': 'km',  'RHO_L': 'kgm@+-3@',  'SEDIMENT': 'km',  'SI': 'si',  'VOLCS_DIST_W': 'f(Km)',  'VP': '%',  'VS': '%',  'STRESS_DIST_W': 'f(km)'},\n",
    "'UNITS_gmt_UNTRANS': {'BG': 'mGal',  'CTD_vb35': 'km',  'DEM': 'm',  'MAG_CLASS': 'nT',  'FA': 'mGal',  'GEOID_A': 'm',  'LAB': 'km',  'MOHO': 'km',  'RHO_L': 'kgm@+-3@',  'SEDIMENT': 'km',  'SI': 'si',  'VOLCS_DIST_W': 'Km',  'VP': 'ms@+-2@',  'VS': 'ms@+-2@',  'STRESS_DIST_W': 'km'},\n",
    "'V_RANGE_W': {'BG': (-100, 100),  'CTD_vb35': (10, 40),  'DEM': (-2200, 2200),  'MAG_CLASS': (-0.4, 0.4),  'FA': (-100, 100),  'GEOID_A': (-30, 30),  'LAB': (0, 300),  'MOHO': (15, 60),  'RHO_L': (3.26, 3.36),  'SEDIMENT': (0, 10),  'SI': (-1, 1),  'VOLCS_DIST_W': (0, 1),  'VP': (-0.02, 0.02),  'VS': (-0.1, 0.1),  'STRESS_DIST_W': (0, 1)}, 'V_RANGE_G': {'BG': (-100, 100),  'CTD_vb35': (10, 40),  'DEM': (-2200, 2200),  'MAG_CLASS': (-0.4, 0.4),  'FA': (-100, 100),  'GEOID_A': (-30, 30),  'LAB': (50, 250),  'MOHO': (20, 50),  'RHO_L': (3.26, 3.36),  'SEDIMENT': (0, 10),  'SI': (-1, 1),\n",
    "'VOLCS_DIST_W': (0, 1),  'VP': (8.1, 8.4),  'VS': (4.2, 4.7),  'STRESS_DIST_W': (0, 1)},\n",
    "'V_RANGE_G_UNTRANS': {'BG': (-100, 100),  'CTD_vb35': (10, 40),  'DEM': (-2200, 2200),  'MAG_CLASS': (-200, 200),  'FA': (-100, 100),  'GEOID_A': (-30, 30),  'LAB': (50, 250),  'MOHO': (20, 50),  'RHO_L': (3.26, 3.36),  'SEDIMENT': (0, 10),  'SI': (-1, 1),  'VOLCS_DIST_W': (0, 300),  'VP': (8.1, 8.4),  'VS': (4.2, 4.7),  'STRESS_DIST_W': (0, 300)},\n",
    "'CMAPS_gmt': {'BG': 'SCM/vik',  'CTD_vb35': 'SCM/bamako',  'DEM': 'gmt/geo',  'MAG_CLASS': 'SCM/bilbao',  'FA': 'SCM/vik',  'GEOID_A': 'SCM/vik',  'LAB': 'SCM/bamako',  'MOHO': 'SCM/bamako',  'RHO_L': 'SCM/batlow',  'SEDIMENT': 'SCM/davos',  'SI': 'SCM/broc',  'VOLCS_DIST_W': 'SCM/broc',  'VP': 'SCM/roma',  'VS': 'SCM/roma',  'STRESS_DIST_W': 'SCM/broc'},\n",
    "'LABELS_REDUCED': {'BG': 'Bouguer Anom.',  'CTD_vb35': 'Curie Depth',  'DEM': 'Topography',  'MAG_CLASS': 'Magnetic Anom.',  'FA': 'Free Air Anom.',  'GEOID_A': 'Geoid',\n",
    "'LAB': 'LAB Depth',  'MOHO': 'Moho Depth',  'RHO_L': 'Litho. ρ',  'SEDIMENT': 'Sediment Thick.',  'SI': 'Shape Index',  'VOLCS_DIST_W': 'Prox. to Volcanos',  'VP': 'P wave tomogr.',  'VS': 'S wave tomogr.',  'STRESS_DIST_W': 'Prox. to Faults'}\n",
    "}\n",
    "\n",
    "\n",
    "# Set OBS_REF as index, making it easy to access by referencing observable\n",
    "obs_df = pd.DataFrame(obs_dict)\n",
    "\n",
    "# Set OBS_REF as index, making it easy to access by referencing observable\n",
    "obs_df.sort_values(by='UNITS_gmt', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cbe67da0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read CSV file and convert columns to appropriate data types\n",
    "ghf_f = DIR / 'Dataset' / 'Preprocessed' / f'ghf_6_int.csv' \n",
    "ghf_df = pd.read_csv(ghf_f, sep='\\t')\n",
    "\n",
    "# Set variable values\n",
    "TARGET       = 'GHF'\n",
    "TARGET_LABEL = 'Heat Flow'\n",
    "COORDs       = ['lon', 'lat']\n",
    "GRID_INDEX_G = 'grid_index'\n",
    "GHF_BOUNDS   = (int(ghf_df[TARGET].min()), int(np.ceil(ghf_df[TARGET].max())))\n",
    "                \n",
    "\n",
    "# Create full header names with short labels of observables for World and Germany\n",
    "observables_lst   = obs_df.index.tolist()\n",
    "observables_g_lst = observables_lst + COORDs + [GRID_INDEX_G, TARGET]\n",
    "\n",
    "# Create header names with short labels of observables\n",
    "observables_ghf_lst = observables_lst + [TARGET]\n",
    "\n",
    "# Create full header names with long labels of observables for World and germnay\n",
    "labels_lst   = obs_df.loc[observables_lst, 'LABELS_REDUCED'].values.tolist()\n",
    "labels_g_lst = labels_lst + ['lon', 'lat', GRID_INDEX_G, TARGET]\n",
    "\n",
    "# Create header names with long labels of observables\n",
    "labels_ghf_lst = labels_lst + [TARGET_LABEL]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fa5432f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def remove_highly_correlated_features(df, threshold=0.8):\n",
    "    corr_matrix = df.corr().abs()\n",
    "    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
    "    to_drop = [column for column in upper.columns if any(upper[column] > threshold)] \n",
    "    #to_drop.remove('LAB')\n",
    "    to_drop.remove('VP')\n",
    "    df_reduced = df.drop(columns=to_drop)\n",
    "    return df_reduced, to_drop\n",
    "\n",
    "# Assuming df is your DataFrame containing the features\n",
    "ghf_non_cor_df, to_drop = remove_highly_correlated_features(ghf_df[observables_lst], threshold=0.8)\n",
    "\n",
    "correlation_matrix = ghf_df[observables_lst].corr().abs()\n",
    "correlation_matrix_keep = ghf_non_cor_df.corr().abs()\n",
    "correlation_matrix_drop= ghf_df[to_drop].corr().abs()\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(3,1, figsize=(8, 15))\n",
    "sns.heatmap(correlation_matrix, ax=axes[0], annot=True, vmin=0, vmax=1, cmap='coolwarm', linewidths=0.5)\n",
    "sns.heatmap(correlation_matrix_drop, ax=axes[1], annot=True, vmin=0, vmax=1, cmap='coolwarm', linewidths=0.5)\n",
    "sns.heatmap(correlation_matrix_keep, ax=axes[2], annot=True, vmin=0, vmax=1, cmap='coolwarm', linewidths=0.5)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f64d5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "obs_df = obs_df.drop(labels=to_drop)\n",
    "\n",
    "# Create full header names with short labels of observables for World and Germany\n",
    "observables_lst   = obs_df.index.tolist()\n",
    "observables_g_lst = observables_lst + COORDs + [GRID_INDEX_G, TARGET]\n",
    "\n",
    "# Create header names with short labels of observables\n",
    "observables_ghf_lst = observables_lst + [TARGET]\n",
    "\n",
    "# Create full header names with long labels of observables for World and Germany\n",
    "labels_lst   = obs_df.loc[observables_lst, 'LABELS'].values.tolist()\n",
    "labels_g_lst = labels_lst + ['lon', 'lat', GRID_INDEX_G, TARGET]\n",
    "\n",
    "# Create header names with long labels of observables\n",
    "labels_ghf_lst = labels_lst + [TARGET_LABEL]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e7f488c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features from target variable\n",
    "X = ghf_df[observables_lst].values\n",
    "y = ghf_df[TARGET].values.ravel()\n",
    "weights = ghf_df['normalized_weights'].values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8c063f",
   "metadata": {},
   "source": [
    "# 3. Determining the hyperparameter serach space"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52f30ef",
   "metadata": {},
   "source": [
    "n_estimators: the number of trees in the forest. A higher number of trees can improve the performance of the model, but also increase the computation time. A good starting range could be between 100 and 1000, depending on the size of your dataset.\n",
    "\n",
    "max_depth: the maximum depth of each decision tree. A deeper tree can capture more complex relationships in the data, but may also overfit the data. A good starting range could be between 5 and 30, depending on the number of features and the size of your dataset.\n",
    "\n",
    "max_features: the maximum number of features to consider when looking for the best split. A lower number can reduce overfitting, but may also result in a less accurate model. A good starting range could be between 0.1 and 1.0, depending on the number of features in your dataset.\n",
    "\n",
    "min_samples_leaf: the minimum number of samples required to be at a leaf node. A higher number can reduce overfitting, but may also result in a less accurate model. A good starting range could be between 1 and 10, depending on the size of your dataset.\n",
    "\n",
    "min_samples_split: the minimum number of samples required to split an internal node. A higher number can reduce overfitting, but may also result in a less accurate model. A good starting range could be between 2 and 20, depending on the size of your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "36f010d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set hyperparameters\n",
    "KFOLD = 5\n",
    "N_ITER = 100\n",
    "RANDOM_STATE = 42\n",
    "N_JOBS = 1\n",
    "VERBOSE = 4\n",
    "\n",
    "config_bl = { 'n_estimators' : 1000, \"bootstrap\": True, \"criterion\": \"squared_error\", \"random_state\": RANDOM_STATE,}\n",
    "\n",
    "classes  = {\n",
    "    \"class_min\" : GHF_BOUNDS[0], \n",
    "    \"class_max\" : GHF_BOUNDS[1], \n",
    "}\n",
    "\n",
    "split_base = int(X.shape[0]* 0.01) * 2\n",
    "leaf_base  = int(X.shape[0]* 0.01)\n",
    "depth_base = int(np.log2(X.shape[0]))\n",
    "                 \n",
    "\n",
    "search_spaces_dict = {    \n",
    "    'n_estimators' :np.arange(800, 1400, 50),\n",
    "    \"max_depth\": list(range(3, depth_base+10, 1)),  # Additional depths including None for unlimited\n",
    "    \"min_samples_split\": list(range(split_base-4, split_base+6, 1)),  # From 2 to 10 in steps of 2\n",
    "    \"min_samples_leaf\": list(range(leaf_base-2, leaf_base+3)),  # From 1 to 4\n",
    "    \"max_samples\": list(np.arange(0.6, 0.9, 0.1)),  # Extended range of fractions\n",
    "    \"max_features\": list(np.arange(0.6, 0.9, 0.1)),  # Including 'auto', 'sqrt', 'log2'\n",
    "    #\"bootstrap\": [True, False],\n",
    "    #\"max_leaf_nodes\": [None, 5, 10, 15, 20, 30, 40],  # Grow trees with max_leaf_nodes in best-first fashion\n",
    "    #\"criterion\": ['squared_error', 'absolute_error'],  # Measure the quality of a split\n",
    "    #\"min_weight_fraction_leaf\": list(np.arange(0.0, 0.2, 0.01)),  # Minimum weighted fraction of the sum total of weights required to be at a leaf node\n",
    "    #\"min_impurity_decrease\": [0.0, 0.1, 0.2, 0.3],  # A node will be split if this split induces a decrease of the impurity greater than or equal to this value.\n",
    "    #\"ccp_alpha\": [0.0, 0.01, 0.1]  # Complexity parameter used for Minimal Cost-Complexity Pruning.\n",
    "}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "792725d8",
   "metadata": {},
   "source": [
    "### Analysis of the effect of all hyperparameter together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2e5ae85",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-14 23:15:37,212 - INFO - Now tuning.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Aggregating values: 100%|████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 5196.25it/s]\n",
      "Estimating for samples: 100%|██████████████████████████████████████████████████████| 119/119 [00:00<00:00, 4807.34it/s]\n",
      "2024-08-14 23:15:40,078 - INFO - Calculating Quantiles has been terminated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END max_depth=9, max_features=0.7999999999999999, max_samples=0.8999999999999999, min_samples_leaf=4, min_samples_split=12, n_estimators=1000;, score=0.370 total time=   2.7s\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[10], line 36\u001b[0m\n\u001b[0;32m     33\u001b[0m logging\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNow tuning.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     35\u001b[0m \u001b[38;5;66;03m# Fit BAYESIAN_CV\u001b[39;00m\n\u001b[1;32m---> 36\u001b[0m \u001b[43mbayes_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m \n\u001b[0;32m     37\u001b[0m \u001b[38;5;66;03m#bayes_search.fit(X, y) \u001b[39;00m\n\u001b[0;32m     38\u001b[0m \n\u001b[0;32m     39\u001b[0m \n\u001b[0;32m     40\u001b[0m \u001b[38;5;66;03m# Get best parameters and score\u001b[39;00m\n\u001b[0;32m     41\u001b[0m best_params_dict \u001b[38;5;241m=\u001b[39m bayes_search\u001b[38;5;241m.\u001b[39mbest_params_\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\GQ\\lib\\site-packages\\skopt\\searchcv.py:542\u001b[0m, in \u001b[0;36mBayesSearchCV.fit\u001b[1;34m(self, X, y, groups, callback, **fit_params)\u001b[0m\n\u001b[0;32m    535\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcallable\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrefit):\n\u001b[0;32m    536\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    537\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBayesSearchCV doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt support a callable refit, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    538\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt define an implicit score to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    539\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moptimize\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    540\u001b[0m     )\n\u001b[1;32m--> 542\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mfit(X\u001b[38;5;241m=\u001b[39mX, y\u001b[38;5;241m=\u001b[39my, groups\u001b[38;5;241m=\u001b[39mgroups, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m    544\u001b[0m \u001b[38;5;66;03m# BaseSearchCV never ranked train scores,\u001b[39;00m\n\u001b[0;32m    545\u001b[0m \u001b[38;5;66;03m# but apparently we used to ship this (back-compat)\u001b[39;00m\n\u001b[0;32m    546\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_train_score:\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1471\u001b[0m     )\n\u001b[0;32m   1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\model_selection\\_search.py:1018\u001b[0m, in \u001b[0;36mBaseSearchCV.fit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m   1012\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_results(\n\u001b[0;32m   1013\u001b[0m         all_candidate_params, n_splits, all_out, all_more_results\n\u001b[0;32m   1014\u001b[0m     )\n\u001b[0;32m   1016\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m results\n\u001b[1;32m-> 1018\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1020\u001b[0m \u001b[38;5;66;03m# multimetric is determined here because in the case of a callable\u001b[39;00m\n\u001b[0;32m   1021\u001b[0m \u001b[38;5;66;03m# self.scoring the return type is only known after calling\u001b[39;00m\n\u001b[0;32m   1022\u001b[0m first_test_score \u001b[38;5;241m=\u001b[39m all_out[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtest_scores\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\GQ\\lib\\site-packages\\skopt\\searchcv.py:599\u001b[0m, in \u001b[0;36mBayesSearchCV._run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m    595\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m n_iter \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    596\u001b[0m     \u001b[38;5;66;03m# when n_iter < n_points points left for evaluation\u001b[39;00m\n\u001b[0;32m    597\u001b[0m     n_points_adjusted \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(n_iter, n_points)\n\u001b[1;32m--> 599\u001b[0m     optim_result, score_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    600\u001b[0m \u001b[43m        \u001b[49m\u001b[43msearch_space\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    601\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    602\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscore_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    603\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevaluate_candidates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    604\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_points\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_points_adjusted\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    605\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    606\u001b[0m     n_iter \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m n_points\n\u001b[0;32m    608\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m eval_callbacks(callbacks, optim_result):\n",
      "File \u001b[1;32m~\\anaconda3\\envs\\GQ\\lib\\site-packages\\skopt\\searchcv.py:453\u001b[0m, in \u001b[0;36mBayesSearchCV._step\u001b[1;34m(self, search_space, optimizer, score_name, evaluate_candidates, n_points)\u001b[0m\n\u001b[0;32m    450\u001b[0m \u001b[38;5;66;03m# make lists into dictionaries\u001b[39;00m\n\u001b[0;32m    451\u001b[0m params_dict \u001b[38;5;241m=\u001b[39m [point_asdict(search_space, p) \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m params]\n\u001b[1;32m--> 453\u001b[0m all_results \u001b[38;5;241m=\u001b[39m \u001b[43mevaluate_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams_dict\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    455\u001b[0m \u001b[38;5;66;03m# if self.scoring is a callable, we have to wait until here\u001b[39;00m\n\u001b[0;32m    456\u001b[0m \u001b[38;5;66;03m# to get the score name\u001b[39;00m\n\u001b[0;32m    457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m score_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\model_selection\\_search.py:964\u001b[0m, in \u001b[0;36mBaseSearchCV.fit.<locals>.evaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    956\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m    957\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\n\u001b[0;32m    958\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitting \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m folds for each of \u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m candidates,\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    959\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m totalling \u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m fits\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[0;32m    960\u001b[0m             n_splits, n_candidates, n_candidates \u001b[38;5;241m*\u001b[39m n_splits\n\u001b[0;32m    961\u001b[0m         )\n\u001b[0;32m    962\u001b[0m     )\n\u001b[1;32m--> 964\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mparallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    965\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_fit_and_score\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    966\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbase_estimator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    967\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    968\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    969\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    970\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    971\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    972\u001b[0m \u001b[43m        \u001b[49m\u001b[43msplit_progress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_splits\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    973\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcandidate_progress\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_candidates\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    974\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_and_score_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    975\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    976\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcand_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43msplit_idx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mproduct\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    977\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcandidate_params\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    978\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplitter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    979\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    980\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    982\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(out) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    983\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    984\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo fits were performed. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    985\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWas the CV iterator empty? \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    986\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWere there no candidates?\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    987\u001b[0m     )\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\parallel.py:74\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     69\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m     70\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     71\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[0;32m     73\u001b[0m )\n\u001b[1;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py:1918\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1916\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[0;32m   1917\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[1;32m-> 1918\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1920\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[0;32m   1921\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[0;32m   1922\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[0;32m   1923\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[0;32m   1924\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[0;32m   1925\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py:1847\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1845\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m-> 1847\u001b[0m res \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1848\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1849\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\parallel.py:136\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    134\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[1;32m--> 136\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\model_selection\\_validation.py:888\u001b[0m, in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, score_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    886\u001b[0m         estimator\u001b[38;5;241m.\u001b[39mfit(X_train, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m    887\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 888\u001b[0m         estimator\u001b[38;5;241m.\u001b[39mfit(X_train, y_train, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params)\n\u001b[0;32m    890\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m    891\u001b[0m     \u001b[38;5;66;03m# Note fit time as time until error\u001b[39;00m\n\u001b[0;32m    892\u001b[0m     fit_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m start_time\n",
      "File \u001b[1;32m~\\Magued\\GHF\\model\\GQ\\GQ\\QRF\\wrapper\\qrf_wrapper.py:70\u001b[0m, in \u001b[0;36mQuantileRegressionForest.fit\u001b[1;34m(self, X_train, y_train, sample_weight)\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m X_train\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y_train\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX_train and y_train must have the same number of samples\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;66;03m# Determine class labels and histogram edges based on class_min and class_max\u001b[39;00m\n\u001b[0;32m     73\u001b[0m min_val \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclass_min \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclass_min \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m y_train\u001b[38;5;241m.\u001b[39mmin())\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1466\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1469\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1470\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1471\u001b[0m     )\n\u001b[0;32m   1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\ensemble\\_forest.py:489\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    478\u001b[0m trees \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    479\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_estimator(append\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, random_state\u001b[38;5;241m=\u001b[39mrandom_state)\n\u001b[0;32m    480\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(n_more_estimators)\n\u001b[0;32m    481\u001b[0m ]\n\u001b[0;32m    483\u001b[0m \u001b[38;5;66;03m# Parallel loop: we prefer the threading backend as the Cython code\u001b[39;00m\n\u001b[0;32m    484\u001b[0m \u001b[38;5;66;03m# for fitting the trees is internally releasing the Python GIL\u001b[39;00m\n\u001b[0;32m    485\u001b[0m \u001b[38;5;66;03m# making threading more efficient than multiprocessing in\u001b[39;00m\n\u001b[0;32m    486\u001b[0m \u001b[38;5;66;03m# that case. However, for joblib 0.12+ we respect any\u001b[39;00m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;66;03m# parallel_backend contexts set at a higher level,\u001b[39;00m\n\u001b[0;32m    488\u001b[0m \u001b[38;5;66;03m# since correctness does not rely on using threads.\u001b[39;00m\n\u001b[1;32m--> 489\u001b[0m trees \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    490\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    491\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    492\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprefer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mthreads\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    493\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    494\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_parallel_build_trees\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    495\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    496\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbootstrap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    497\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrees\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    503\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclass_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    504\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_samples_bootstrap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_samples_bootstrap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    505\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    506\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    507\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrees\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    508\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    510\u001b[0m \u001b[38;5;66;03m# Collect newly grown trees\u001b[39;00m\n\u001b[0;32m    511\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_\u001b[38;5;241m.\u001b[39mextend(trees)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\parallel.py:74\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m     69\u001b[0m config \u001b[38;5;241m=\u001b[39m get_config()\n\u001b[0;32m     70\u001b[0m iterable_with_config \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     71\u001b[0m     (_with_config(delayed_func, config), args, kwargs)\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m delayed_func, args, kwargs \u001b[38;5;129;01min\u001b[39;00m iterable\n\u001b[0;32m     73\u001b[0m )\n\u001b[1;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterable_with_config\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py:1918\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1916\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[0;32m   1917\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[1;32m-> 1918\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1920\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[0;32m   1921\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[0;32m   1922\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[0;32m   1923\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[0;32m   1924\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[0;32m   1925\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py:1847\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1845\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1846\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m-> 1847\u001b[0m res \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1848\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1849\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\parallel.py:136\u001b[0m, in \u001b[0;36m_FuncWrapper.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    134\u001b[0m     config \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig):\n\u001b[1;32m--> 136\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\ensemble\\_forest.py:192\u001b[0m, in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap, missing_values_in_feature_mask)\u001b[0m\n\u001b[0;32m    189\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m class_weight \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbalanced_subsample\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    190\u001b[0m         curr_sample_weight \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m compute_sample_weight(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbalanced\u001b[39m\u001b[38;5;124m\"\u001b[39m, y, indices\u001b[38;5;241m=\u001b[39mindices)\n\u001b[1;32m--> 192\u001b[0m     \u001b[43mtree\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    193\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    194\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    195\u001b[0m \u001b[43m        \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurr_sample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    196\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_input\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    198\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    199\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    200\u001b[0m     tree\u001b[38;5;241m.\u001b[39m_fit(\n\u001b[0;32m    201\u001b[0m         X,\n\u001b[0;32m    202\u001b[0m         y,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    205\u001b[0m         missing_values_in_feature_mask\u001b[38;5;241m=\u001b[39mmissing_values_in_feature_mask,\n\u001b[0;32m    206\u001b[0m     )\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\tree\\_classes.py:472\u001b[0m, in \u001b[0;36mBaseDecisionTree._fit\u001b[1;34m(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)\u001b[0m\n\u001b[0;32m    461\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    462\u001b[0m     builder \u001b[38;5;241m=\u001b[39m BestFirstTreeBuilder(\n\u001b[0;32m    463\u001b[0m         splitter,\n\u001b[0;32m    464\u001b[0m         min_samples_split,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    469\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmin_impurity_decrease,\n\u001b[0;32m    470\u001b[0m     )\n\u001b[1;32m--> 472\u001b[0m \u001b[43mbuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtree_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmissing_values_in_feature_mask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    474\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_outputs_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m is_classifier(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    475\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_classes_[\u001b[38;5;241m0\u001b[39m]\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "cv_fnct = model_selection.KFold(n_splits=KFOLD, random_state=RANDOM_STATE, shuffle=True)\n",
    "qrt = QuantileRegressionForest(**classes).set_params(**config_bl)\n",
    "\n",
    "\n",
    "\n",
    "scoring_fnct = make_sklearn_compatible_scorer(task_type=\"regression\", \n",
    "                                            metric=\"picp\", greater_is_better=True)\n",
    "\n",
    "\n",
    "###BEYSIANSEARCH\n",
    "\n",
    "# Initialize BaysSearch object   \n",
    "bayes_search = BayesSearchCV(\n",
    "        estimator=qrt,\n",
    "        search_spaces=search_spaces_dict,\n",
    "        n_iter=N_ITER,\n",
    "        return_train_score=False,\n",
    "        random_state=RANDOM_STATE,\n",
    "        scoring=scoring_fnct,\n",
    "        cv=cv_fnct,\n",
    "        verbose=VERBOSE,\n",
    "        n_jobs=N_JOBS,   \n",
    "        #optimizer_kwargs=None,\n",
    "        #fit_params=False,\n",
    "        #n_points=5,\n",
    "        refit=False,\n",
    "        #pre_dispatch='2*n_jobs',\n",
    "        #error_score=1\n",
    ")\n",
    "\n",
    "\n",
    "# Print message to user\n",
    "logging.info(f\"Now tuning.\")\n",
    "\n",
    "# Fit BAYESIAN_CV\n",
    "bayes_search.fit(X, y, sample_weight=weights) \n",
    "#bayes_search.fit(X, y) \n",
    "\n",
    "\n",
    "# Get best parameters and score\n",
    "best_params_dict = bayes_search.best_params_\n",
    "best_score       = bayes_search.best_score_\n",
    "\n",
    "logging.info(f\"Best score: {best_score}\")\n",
    "logging.info(best_params_dict)\n",
    "\n",
    "\n",
    "logging.info('terminated')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e211894b",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame(bayes_search.cv_results_)\n",
    "    \n",
    "# Plotting the results\n",
    "fig, axs = plt.subplots(3, 2, figsize=(14, 10))\n",
    "\n",
    "axs[0, 0].scatter(results['param_n_estimators'], results['mean_test_score'])\n",
    "axs[0, 0].set_xlabel('n_estimators')\n",
    "axs[0, 0].set_ylabel('Mean CV Accuracy')\n",
    "axs[0, 0].set_title('n_estimators vs Mean CV Accuracy')\n",
    "\n",
    "axs[0, 1].scatter(results['param_max_depth'], results['mean_test_score'])\n",
    "axs[0, 1].set_xlabel('max_depth')\n",
    "axs[0, 1].set_ylabel('Mean CV Accuracy')\n",
    "axs[0, 1].set_title('max_depth vs Mean CV Accuracy')\n",
    "\n",
    "\n",
    "axs[1, 0].scatter(results['param_min_samples_split'], results['mean_test_score'])\n",
    "axs[1, 0].set_xlabel('min_samples_split')\n",
    "axs[1, 0].set_ylabel('Mean CV Accuracy')\n",
    "axs[1, 0].set_title('min_samples_split vs Mean CV Accuracy')\n",
    "\n",
    "\n",
    "axs[1, 1].scatter(results['param_min_samples_leaf'], results['mean_test_score'])\n",
    "axs[1, 1].set_xlabel('min_samples_leaf')\n",
    "axs[1, 1].set_ylabel('Mean CV Accuracy')\n",
    "axs[1, 1].set_title('min_samples_leaf vs Mean CV Accuracy')\n",
    "\n",
    "\n",
    "axs[2, 0].scatter(results['param_max_samples'], results['mean_test_score'])\n",
    "axs[2, 0].set_xlabel('max_samples')\n",
    "axs[2, 0].set_ylabel('Mean CV Accuracy')\n",
    "axs[2, 0].set_title('max_samples vs Mean CV Accuracy')\n",
    "\n",
    "axs[2, 1].scatter(results['param_max_features'], results['mean_test_score'])\n",
    "axs[2, 1].set_xlabel('max_features')\n",
    "axs[2, 1].set_ylabel('Mean CV Accuracy')\n",
    "axs[2, 1].set_title('max_features vs Mean CV Accuracy')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f2996f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.table_grid_search(bayes_search, all_ranks=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff7ff59",
   "metadata": {},
   "source": [
    "### saving grid search and hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51e80d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define file paths\n",
    "best_params_path = DIR / 'Hyperparameters' / 'hyperparameters.pkl'\n",
    "grid_results_path = DIR / 'Hyperparameters' / 'tuned_grid.csv'\n",
    "\n",
    "dropped_columns = ['mean_fit_time', 'std_fit_time', 'params', 'mean_score_time', 'std_score_time']\n",
    "\n",
    "# Saving the dictionary using pickle\n",
    "with open(best_params_path, 'wb') as f:\n",
    "    pickle.dump(best_params_dict, f)\n",
    "\n",
    "\n",
    "# Save grid search results to file\n",
    "bayes_search_df = pd.DataFrame(bayes_search.cv_results_)\n",
    "bayes_search_df = bayes_search_df.drop(columns=dropped_columns)\n",
    "bayes_search_df = bayes_search_df.sort_values('rank_test_score', ascending=True).reset_index(drop=True)\n",
    "#flip sign\n",
    "\n",
    "bayes_search_df.to_csv(grid_results_path, index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90b59947",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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