cleaning_Luca.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_dfs(dfs: list[pd.DataFrame]):\n",
    "    out_df = dfs[0]\n",
    "\n",
    "    for i in range(1, len(dfs)):\n",
    "        out_df = pd.merge(out_df, dfs[i], how=\"left\", on=[\"Country\", \"Date\"])\n",
    "\n",
    "    return out_df\n",
    "\n",
    "\n",
    "def generate_indications_cols(df: pd.DataFrame, num_ind: int, label: str, filler):\n",
    "    new_cols = pd.DataFrame.from_dict([\n",
    "        {\n",
    "            f\"{label}_ind{indication_num}\": filler\n",
    "                for indication_num in range(1, num_ind+1)\n",
    "        } \n",
    "        for _ in range(len(df))\n",
    "    ], dtype=float)\n",
    "\n",
    "    return pd.concat([ df, new_cols ], axis=1)\n",
    "\n",
    "def fix_date_missing_period(df: pd.DataFrame, col_name=\"Date\"):\n",
    "    month_map = {\n",
    "        \"janv\": \"Jan\",\n",
    "        \"févr\": \"Feb\",\n",
    "        \"mars\": \"Mar\",\n",
    "        \"avr\": \"Apr\",\n",
    "        \"mai\": \"May\",\n",
    "        \"juin\": \"Jun\",\n",
    "        \"juil\": \"Jul\",\n",
    "        \"août\": \"Aug\",\n",
    "        \"sept\": \"Sep\",\n",
    "        \"oct\": \"Oct\",\n",
    "        \"nov\": \"Nov\",\n",
    "        \"déc\": \"Dec\"\n",
    "    }\n",
    "    df[col_name] = df[col_name].apply(lambda x: f\"{month_map[x.split('-')[0]]}-{x.split('-')[1]}\")\n",
    "    df[col_name] = pd.to_datetime(df[col_name], format=\"%b-%y\")\n",
    "    return df\n",
    "\n",
    "\n",
    "def clean_innovix_ex_factory_vol(df: pd.DataFrame):\n",
    "    df.drop(columns=[\"Data type\", \"Unit of measure\"], inplace=True)\n",
    "    df.rename(columns={\"Value\": \"ex_factory_volumes\"}, inplace=True)\n",
    "    return df\n",
    "\n",
    "def clean_innovix_demand_volumes(df: pd.DataFrame, products):\n",
    "    df.drop(index=df.index[df[\"Unit of measure\"] == \"Milligrams\"], inplace=True)\n",
    "    df.drop(columns=[\"Data type\", \"Unit of measure\"], inplace=True)\n",
    "\n",
    "    for prod in products:\n",
    "        df[f\"{prod}_months_of_treatment\"] = 0.0\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        df.loc[i, f\"{row['Product']}_months_of_treatment\"] = row[\"Value\"]\n",
    "\n",
    "    df.drop(columns=[\"Value\"], inplace=True)\n",
    "\n",
    "    country = df.iloc[0][\"Country\"]\n",
    "    df = df.groupby(\"Date\").sum(numeric_only=True)\n",
    "    df[\"Country\"] = country\n",
    "\n",
    "    return df\n",
    "\n",
    "def clean_innovix_activity(df: pd.DataFrame, num_ind: int, products: list[str]):\n",
    "    channel_name_map = {\n",
    "        \"Face to face call\": \"face-to-face-call\", \n",
    "        \"Email\": \"email\", \n",
    "        \"Remote call\": \"remote-call\", \n",
    "        \"Meetings\": \"meetings\"\n",
    "    }\n",
    "    df[\"Channel\"] = df[\"Channel\"].map(channel_name_map)\n",
    "    df[\"Indication\"] = df[\"Indication\"].map(lambda x: f\"ind{x.split(' ')[1]}\")\n",
    "\n",
    "    for prod in products:\n",
    "        for channel in df[\"Channel\"].unique().tolist():\n",
    "            df = generate_indications_cols(df, num_ind, f\"{prod}_{channel}\", 0.0)\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        df.loc[i, f\"{row['Product']}_{row['Channel']}_{row['Indication']}\"] = row[\"Value\"]\n",
    "\n",
    "    df.drop(columns=[\"Data type\", \"Product\", \"Channel\", \"Indication\", \"Value\"], inplace = True)\n",
    "    country = df.iloc[0][\"Country\"]\n",
    "    df = df.groupby(\"Date\").sum(numeric_only=True)\n",
    "    df[\"Country\"] = country\n",
    "\n",
    "    return df\n",
    "\n",
    "def clean_innovix_share_of_voice(df: pd.DataFrame, num_ind: int, products):\n",
    "    df[\"Indication\"] = df[\"Indication\"].map(lambda x: f\"ind{x.split(' ')[1]}\")\n",
    "    for prod in products:\n",
    "        df = generate_indications_cols(df, num_ind, f\"{prod}_share_of_voice\", 0.0)\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        df.loc[i, f\"{row['Product']}_share_of_voice_{row['Indication']}\"] = row[\"Value\"]\n",
    "\n",
    "    df.drop(columns=[\"Data type\", \"Indication\", \"Product\", \"Value\", \"Product\"], inplace=True)\n",
    "    country = df.iloc[0][\"Country\"]\n",
    "    df = df.groupby(\"Date\").sum(numeric_only=True)\n",
    "    df[\"Country\"] = country\n",
    "    return df\n",
    "\n",
    "def clean_innovix_new_patient_share(df: pd.DataFrame, num_ind: int, products):\n",
    "    df[\"Indication\"] = df[\"Indication\"].map(lambda x: f\"ind{x.split(' ')[1]}\")\n",
    "    for prod in products:\n",
    "        df = generate_indications_cols(df, num_ind, f\"{prod}_new_patient_shape\", 0.0)\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        df.loc[i, f\"{row['Product']}_new_patient_shape_{row['Indication']}\"] = row[\"Value\"]\n",
    "\n",
    "    df.drop(columns=[\"Data type\", \"Indication\", \"Product\", \"Sub-Indication\", \"Value\"], inplace=True)\n",
    "    country = df.iloc[0][\"Country\"]\n",
    "    df = df.groupby(\"Date\").sum(numeric_only=True)\n",
    "    df[\"Country\"] = country\n",
    "    return df\n",
    "\n",
    "\n",
    "def clean_innovix_indication_split(df: pd.DataFrame, num_ind: int, products):\n",
    "    df[\"Indication\"] = df[\"Indication\"].map(lambda x: f\"ind{x.split(' ')[1]}\")\n",
    "    for prod in products:\n",
    "        df = generate_indications_cols(df, num_ind, f\"{prod}_indication_split\", 0.0)\n",
    "\n",
    "    for i, row in df.iterrows():\n",
    "        df.loc[i, f\"{row['Product']}_indication_split_{row['Indication']}\"] = row[\"Value\"]\n",
    "\n",
    "    df.drop(columns=[\"Data type\", \"Indication\", \"Product\", \"Sub-Indication\", \"Value\"], inplace=True)\n",
    "    country = df.iloc[0][\"Country\"]\n",
    "    df = df.groupby(\"Date\").sum(numeric_only=True)\n",
    "    df[\"Country\"] = country\n",
    "    return df\n",
    "\n",
    "EXCEL_FILE = \"./INNOVIX_Floresland.xlsx\"\n",
    "\n",
    "df_floresland_ex_factory_vol = pd.read_excel(EXCEL_FILE, sheet_name=\"Ex-Factory volumes\")\n",
    "df_floresland_demand_volumes = pd.read_excel(EXCEL_FILE, sheet_name=\"Demand volumes\")\n",
    "df_floresland_activity = pd.read_excel(EXCEL_FILE, sheet_name=\"Activity\")\n",
    "df_floresland_share_of_voice = pd.read_excel(EXCEL_FILE, sheet_name=\"Share of Voice\")\n",
    "df_floresland_new_patient_share = pd.read_excel(EXCEL_FILE, sheet_name=\"New patient share\")\n",
    "df_floresland_new_patient_share = fix_date_missing_period(df_floresland_new_patient_share)\n",
    "df_floresland_indication_split = pd.read_excel(EXCEL_FILE, sheet_name=\"Indication split\")\n",
    "\n",
    "\n",
    "\n",
    "NUM_IND = max(\n",
    "    max([int(x.split(\" \")[1]) for x in df_floresland_activity[\"Indication\"].unique()]),\n",
    "    max([int(x.split(\" \")[1]) for x in df_floresland_share_of_voice[\"Indication\"].unique()]),\n",
    "    max([int(x.split(\" \")[1]) for x in df_floresland_new_patient_share[\"Indication\"].unique()]),\n",
    "    max([int(x.split(\" \")[1]) for x in df_floresland_indication_split[\"Indication\"].unique()])\n",
    ")\n",
    "PRODUCTS = [\"INNOVIX\", \"YREX\"]\n",
    "\n",
    "\n",
    "df = merge_dfs([\n",
    "    clean_innovix_ex_factory_vol(df_floresland_ex_factory_vol),\n",
    "    clean_innovix_demand_volumes(df_floresland_demand_volumes, products=PRODUCTS),\n",
    "    clean_innovix_activity(df_floresland_activity, NUM_IND, products=PRODUCTS),\n",
    "    clean_innovix_share_of_voice(df_floresland_share_of_voice, NUM_IND, products=PRODUCTS),\n",
    "    clean_innovix_new_patient_share(df_floresland_new_patient_share, NUM_IND, products=PRODUCTS),\n",
    "    clean_innovix_indication_split(df_floresland_indication_split, NUM_IND, products=PRODUCTS)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['optimized tslmx', 'optimized arima', 'univariate xgboost',\n",
       "       'multivariate xgboost', 'optimized ets', 'univariate arima',\n",
       "       'optimized tbats'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Country</th>\n",
       "      <th>Product</th>\n",
       "      <th>Data type</th>\n",
       "      <th>Forecast Algorithm</th>\n",
       "      <th>Data period</th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2024-11-01</td>\n",
       "      <td>4.187574e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2024-12-01</td>\n",
       "      <td>4.227316e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>5.214774e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-02-01</td>\n",
       "      <td>4.646591e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-03-01</td>\n",
       "      <td>4.815716e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-04-01</td>\n",
       "      <td>4.344956e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-05-01</td>\n",
       "      <td>4.406519e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-06-01</td>\n",
       "      <td>4.089633e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>4.361719e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-08-01</td>\n",
       "      <td>4.427256e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-09-01</td>\n",
       "      <td>4.404180e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-10-01</td>\n",
       "      <td>4.386894e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-11-01</td>\n",
       "      <td>4.421644e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Floresland</td>\n",
       "      <td>INNOVIX</td>\n",
       "      <td>Forecast volumes</td>\n",
       "      <td>optimized arima</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>2025-12-01</td>\n",
       "      <td>4.075436e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Product         Data type Forecast Algorithm Data period  \\\n",
       "84  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "85  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "86  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "87  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "88  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "89  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "90  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "91  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "92  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "93  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "94  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "95  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "96  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "97  Floresland  INNOVIX  Forecast volumes    optimized arima     Monthly   \n",
       "\n",
       "         Date         Value  \n",
       "84 2024-11-01  4.187574e+06  \n",
       "85 2024-12-01  4.227316e+06  \n",
       "86 2025-01-01  5.214774e+06  \n",
       "87 2025-02-01  4.646591e+06  \n",
       "88 2025-03-01  4.815716e+06  \n",
       "89 2025-04-01  4.344956e+06  \n",
       "90 2025-05-01  4.406519e+06  \n",
       "91 2025-06-01  4.089633e+06  \n",
       "92 2025-07-01  4.361719e+06  \n",
       "93 2025-08-01  4.427256e+06  \n",
       "94 2025-09-01  4.404180e+06  \n",
       "95 2025-10-01  4.386894e+06  \n",
       "96 2025-11-01  4.421644e+06  \n",
       "97 2025-12-01  4.075436e+06  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_truth = pd.read_excel(EXCEL_FILE, sheet_name=\"Forecast volumes\")\n",
    "display(df_truth[\"Forecast Algorithm\"].unique())\n",
    "\n",
    "algo = \"optimized arima\"\n",
    "\n",
    "df_truth = df_truth[ df_truth[\"Forecast Algorithm\"] == algo ]\n",
    "df_truth = df_truth[ df_truth[\"Data period\"] == \"Monthly\" ]\n",
    "\n",
    "display(df_truth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"Country\", \"Product\"], inplace=True)\n",
    "df = df.sort_values(by=\"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df[\"Date\"] = np.arange(0, len(df[\"Date\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>ex_factory_volumes</th>\n",
       "      <th>INNOVIX_months_of_treatment</th>\n",
       "      <th>YREX_months_of_treatment</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind1</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind2</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind3</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind4</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind5</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind6</th>\n",
       "      <th>...</th>\n",
       "      <th>YREX_indication_split_ind14</th>\n",
       "      <th>YREX_indication_split_ind15</th>\n",
       "      <th>YREX_indication_split_ind16</th>\n",
       "      <th>YREX_indication_split_ind17</th>\n",
       "      <th>YREX_indication_split_ind18</th>\n",
       "      <th>YREX_indication_split_ind19</th>\n",
       "      <th>YREX_indication_split_ind20</th>\n",
       "      <th>YREX_indication_split_ind21</th>\n",
       "      <th>YREX_indication_split_ind22</th>\n",
       "      <th>YREX_indication_split_ind23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3503180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2611020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2952120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2995280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2929660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>3967620</td>\n",
       "      <td>7707.728375</td>\n",
       "      <td>23359.408837</td>\n",
       "      <td>377.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017831</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571180</td>\n",
       "      <td>0.006816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.045567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>4932260</td>\n",
       "      <td>9653.302392</td>\n",
       "      <td>28975.411650</td>\n",
       "      <td>158.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.536082</td>\n",
       "      <td>0.013918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>4063420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>4464140</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>4556560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 326 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date  ex_factory_volumes  INNOVIX_months_of_treatment  \\\n",
       "0      0             3503180                          NaN   \n",
       "1      1             2611020                          NaN   \n",
       "2      2             2952120                          NaN   \n",
       "3      3             2995280                          NaN   \n",
       "4      4             2929660                          NaN   \n",
       "..   ...                 ...                          ...   \n",
       "77    77             3967620                  7707.728375   \n",
       "78    78             4932260                  9653.302392   \n",
       "79    79             4063420                          NaN   \n",
       "80    80             4464140                          NaN   \n",
       "81    81             4556560                          NaN   \n",
       "\n",
       "    YREX_months_of_treatment  INNOVIX_face-to-face-call_ind1  \\\n",
       "0                        NaN                             NaN   \n",
       "1                        NaN                             NaN   \n",
       "2                        NaN                             NaN   \n",
       "3                        NaN                             NaN   \n",
       "4                        NaN                             0.0   \n",
       "..                       ...                             ...   \n",
       "77              23359.408837                           377.0   \n",
       "78              28975.411650                           158.0   \n",
       "79                       NaN                             0.0   \n",
       "80                       NaN                             0.0   \n",
       "81                       NaN                             0.0   \n",
       "\n",
       "    INNOVIX_face-to-face-call_ind2  INNOVIX_face-to-face-call_ind3  \\\n",
       "0                              NaN                             NaN   \n",
       "1                              NaN                             NaN   \n",
       "2                              NaN                             NaN   \n",
       "3                              NaN                             NaN   \n",
       "4                              0.0                             0.0   \n",
       "..                             ...                             ...   \n",
       "77                           168.0                             0.0   \n",
       "78                           287.0                             0.0   \n",
       "79                             0.0                             0.0   \n",
       "80                             0.0                             0.0   \n",
       "81                             0.0                             0.0   \n",
       "\n",
       "    INNOVIX_face-to-face-call_ind4  INNOVIX_face-to-face-call_ind5  \\\n",
       "0                              NaN                             NaN   \n",
       "1                              NaN                             NaN   \n",
       "2                              NaN                             NaN   \n",
       "3                              NaN                             NaN   \n",
       "4                              0.0                             0.0   \n",
       "..                             ...                             ...   \n",
       "77                             0.0                             0.0   \n",
       "78                             0.0                             0.0   \n",
       "79                             0.0                             0.0   \n",
       "80                             0.0                             0.0   \n",
       "81                             0.0                             0.0   \n",
       "\n",
       "    INNOVIX_face-to-face-call_ind6  ...  YREX_indication_split_ind14  \\\n",
       "0                              NaN  ...                          NaN   \n",
       "1                              NaN  ...                          NaN   \n",
       "2                              NaN  ...                          NaN   \n",
       "3                              NaN  ...                          NaN   \n",
       "4                              0.0  ...                          NaN   \n",
       "..                             ...  ...                          ...   \n",
       "77                             0.0  ...                     0.017831   \n",
       "78                             0.0  ...                     0.013297   \n",
       "79                             0.0  ...                          NaN   \n",
       "80                             0.0  ...                          NaN   \n",
       "81                             0.0  ...                          NaN   \n",
       "\n",
       "    YREX_indication_split_ind15  YREX_indication_split_ind16  \\\n",
       "0                           NaN                          NaN   \n",
       "1                           NaN                          NaN   \n",
       "2                           NaN                          NaN   \n",
       "3                           NaN                          NaN   \n",
       "4                           NaN                          NaN   \n",
       "..                          ...                          ...   \n",
       "77                          0.0                          0.0   \n",
       "78                          0.0                          0.0   \n",
       "79                          NaN                          NaN   \n",
       "80                          NaN                          NaN   \n",
       "81                          NaN                          NaN   \n",
       "\n",
       "    YREX_indication_split_ind17  YREX_indication_split_ind18  \\\n",
       "0                           NaN                          NaN   \n",
       "1                           NaN                          NaN   \n",
       "2                           NaN                          NaN   \n",
       "3                           NaN                          NaN   \n",
       "4                           NaN                          NaN   \n",
       "..                          ...                          ...   \n",
       "77                          0.0                          0.0   \n",
       "78                          0.0                          0.0   \n",
       "79                          NaN                          NaN   \n",
       "80                          NaN                          NaN   \n",
       "81                          NaN                          NaN   \n",
       "\n",
       "    YREX_indication_split_ind19  YREX_indication_split_ind20  \\\n",
       "0                           NaN                          NaN   \n",
       "1                           NaN                          NaN   \n",
       "2                           NaN                          NaN   \n",
       "3                           NaN                          NaN   \n",
       "4                           NaN                          NaN   \n",
       "..                          ...                          ...   \n",
       "77                     0.571180                     0.006816   \n",
       "78                     0.536082                     0.013918   \n",
       "79                          NaN                          NaN   \n",
       "80                          NaN                          NaN   \n",
       "81                          NaN                          NaN   \n",
       "\n",
       "    YREX_indication_split_ind21  YREX_indication_split_ind22  \\\n",
       "0                           NaN                          NaN   \n",
       "1                           NaN                          NaN   \n",
       "2                           NaN                          NaN   \n",
       "3                           NaN                          NaN   \n",
       "4                           NaN                          NaN   \n",
       "..                          ...                          ...   \n",
       "77                          0.0                          0.0   \n",
       "78                          0.0                          0.0   \n",
       "79                          NaN                          NaN   \n",
       "80                          NaN                          NaN   \n",
       "81                          NaN                          NaN   \n",
       "\n",
       "    YREX_indication_split_ind23  \n",
       "0                           NaN  \n",
       "1                           NaN  \n",
       "2                           NaN  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "..                          ...  \n",
       "77                     0.045567  \n",
       "78                     0.049469  \n",
       "79                          NaN  \n",
       "80                          NaN  \n",
       "81                          NaN  \n",
       "\n",
       "[82 rows x 326 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
    "from sklearn.gaussian_process.kernels import WhiteKernel\n",
    "from sklearn.gaussian_process.kernels import ConstantKernel\n",
    "from sklearn.gaussian_process.kernels import ExpSineSquared\n",
    "from sklearn.gaussian_process.kernels import DotProduct\n",
    "from sklearn.gaussian_process.kernels import Matern\n",
    "\n",
    "\n",
    "# kernel = WhiteKernel(0.1)\n",
    "# kernel += ConstantKernel(1) * RBF(1, (-1e, 1e2))\n",
    "# kernel += ConstantKernel(1) * DotProduct(1)\n",
    "\n",
    "PARAM_RANGES = (1e-5, 1e5)\n",
    "\n",
    "kernel = WhiteKernel(0.1, PARAM_RANGES)\n",
    "kernel += ConstantKernel(1, PARAM_RANGES) * RBF(1, PARAM_RANGES)\n",
    "kernel += ConstantKernel(1, PARAM_RANGES) * DotProduct(1, PARAM_RANGES)\n",
    "\n",
    "\n",
    "# # dff = df.dropna()\n",
    "# dff = df\n",
    "# X = dff.drop(columns=[\"ex_factory_volumes\"])\n",
    "# y = dff[\"ex_factory_volumes\"]\n",
    "\n",
    "\n",
    "# print(\"ex_factory_volumes\" in X.columns, X.shape)\n",
    "\n",
    "# X = X.fillna(X.mean())\n",
    "# # X = pd.DataFrame(X[\"Date\"])\n",
    "\n",
    "# th = 10\n",
    "\n",
    "# gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)\n",
    "# gp.fit(X.iloc[:-th], y.iloc[:-th])\n",
    "\n",
    "# pred, std = gp.predict(X.iloc[-th:], return_std=True)\n",
    "# print( pred )\n",
    "# print( std*1.96 )\n",
    "# print( y.iloc[-th:].tolist() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60/60 [00:20<00:00,  2.97it/s]\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "from tqdm import tqdm\n",
    "\n",
    "\n",
    "\n",
    "# dff = df.dropna()\n",
    "dff = df.set_index([\"Date\"], drop=False)\n",
    "\n",
    "X = dff.drop(columns=[\"ex_factory_volumes\"])\n",
    "y = dff[\"ex_factory_volumes\"]\n",
    "\n",
    "X = X.fillna(X.mean())\n",
    "# X = pd.DataFrame(X[\"Date\"])\n",
    "\n",
    "# th = 10\n",
    "X = X # Assigns X to X\n",
    "y = y # Assigns y to y\n",
    "\n",
    "X = (X-X.mean())/(X.std() + 1e-9)\n",
    "\n",
    "y_mean = y.mean()\n",
    "y_std = y.std()\n",
    "y = (y - y.mean()) / (y.std())\n",
    "\n",
    "\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "\n",
    "    gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=2)\n",
    "    gp.fit(X, y)\n",
    "    stds = []\n",
    "\n",
    "    # Test scenario\n",
    "    new_sample = {\n",
    "        col: np.random.normal(X[col].mean(), X[col].std(), (1,))[0]\n",
    "        for col in X.columns\n",
    "    }\n",
    "    new_sample[\"INNOVIX_months_of_treatment\"] *= (1 - 0.1)\n",
    "    new_sample[\"Date\"] = 81 + 12\n",
    "\n",
    "    sampled_in = pd.DataFrame.from_dict([new_sample])\n",
    "\n",
    "    pred, std = gp.predict(sampled_in, return_std=True)\n",
    "    pred = pred[0]\n",
    "\n",
    "    stds.append(std)\n",
    "\n",
    "    start_time = int(X.iloc[-1][\"Date\"])\n",
    "\n",
    "\n",
    "    \n",
    "    X = pd.concat([ X, pd.DataFrame.from_dict([new_sample]) ])\n",
    "    y = pd.concat([ y, pd.Series([pred]) ])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    for _ in tqdm(range(60)):\n",
    "        if start_time == 81 + 12:\n",
    "            start_time += 1\n",
    "            continue\n",
    "            \n",
    "        gp.fit(X, y)\n",
    "\n",
    "        new_sample = {\n",
    "            col: np.random.normal(X[col].mean(), X[col].std(), (1,))[0]\n",
    "            for col in X.columns\n",
    "        }\n",
    "        new_sample[\"Date\"] = start_time\n",
    "\n",
    "        sampled_in = pd.DataFrame.from_dict([new_sample])\n",
    "\n",
    "        pred, std = gp.predict(sampled_in, return_std=True)\n",
    "        pred = pred[0]\n",
    "\n",
    "        stds.append(std)\n",
    "\n",
    "        X = pd.concat([ X, pd.DataFrame.from_dict([new_sample]) ])\n",
    "        y = pd.concat([ y, pd.Series([pred]) ])\n",
    "\n",
    "        start_time += 1\n",
    "\n",
    "\n",
    "        # print((pd.DataFrame.from_dict([new_sample]).columns))\n",
    "        # print((dff.columns))\n",
    "\n",
    "        # dff.loc[len(dff)] = pd.DataFrame.from_dict([new_sample])\n",
    "\n",
    "\n",
    "        # dff.append(pd.DataFrame.from_dict([new_sample]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10000000.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_denorm = (y * y_std) + y_mean\n",
    "\n",
    "plot_gp(\n",
    "    pred = pd.Series(index=range(len(y_denorm[-len(stds):])), data=y_denorm[-len(stds):].values), \n",
    "    samples = pd.Series(index=range(len(y_denorm[-len(stds):])), data=y_denorm[-len(stds):].values), \n",
    "    std = np.array([x[0] for x in stds]) * y_std,\n",
    "    figsize = (16, 8)\n",
    ")\n",
    "\n",
    "plt.ylim((0, 1e7))\n",
    "# plt.plot(range(len(df_truth[\"Value\"])), df_truth[\"Value\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_gp(target=None, pred=None, std=None, samples=None,\n",
    "        target_samples=None, figsize=None):\n",
    "    plt.figure(figsize=figsize)\n",
    "    if target is not None:\n",
    "        plt.plot(target.index, target, c='black', label='target')\n",
    "    if pred is not None:\n",
    "        plt.scatter(pred.index, pred, c='tab:blue',\n",
    "                label='predictions')\n",
    "    if std is not None:\n",
    "        plt.fill_between(pred.index, pred-1.96*std, pred+1.96*std,\n",
    "                alpha=.3, fc='tab:blue', ec='None',\n",
    "                label='95% C.I.')\n",
    "    # Add scatter plots\n",
    "    if samples is not None:\n",
    "        try:\n",
    "            x = samples.index\n",
    "            y = samples.values\n",
    "        except AttributeError:\n",
    "            x = samples[0]\n",
    "            y = samples[1]\n",
    "        plt.scatter(x, y, color='tab:orange',\n",
    "              label='samples', marker='x')\n",
    "    if target_samples is not None:\n",
    "        try:\n",
    "            x = target_samples.index\n",
    "            y = target_samples.values\n",
    "        except AttributeError:\n",
    "            x = target_samples[0]\n",
    "            y = target_samples[1]\n",
    "        plt.scatter(x, y,\n",
    "                color='black', label='target', s=5)\n",
    "    plt.legend()\n",
    "    plt.grid(':')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/326 [00:00<?, ?it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 32%|███▏      | 104/326 [00:00<00:00, 322.33it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 42%|████▏     | 137/326 [00:09<00:16, 11.52it/s] c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 46%|████▋     | 151/326 [00:13<00:20,  8.58it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 49%|████▉     | 159/326 [00:15<00:22,  7.37it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 50%|█████     | 164/326 [00:16<00:24,  6.70it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 52%|█████▏    | 168/326 [00:17<00:25,  6.17it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 52%|█████▏    | 171/326 [00:18<00:26,  5.76it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 53%|█████▎    | 173/326 [00:19<00:27,  5.49it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 54%|█████▎    | 175/326 [00:19<00:29,  5.19it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 54%|█████▍    | 176/326 [00:20<00:30,  4.99it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 54%|█████▍    | 177/326 [00:20<00:30,  4.82it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 55%|█████▍    | 178/326 [00:20<00:30,  4.78it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 55%|█████▍    | 179/326 [00:20<00:32,  4.56it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 55%|█████▌    | 180/326 [00:21<00:33,  4.40it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 56%|█████▌    | 181/326 [00:21<00:34,  4.21it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 56%|█████▌    | 182/326 [00:21<00:35,  4.09it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 56%|█████▌    | 183/326 [00:22<00:36,  3.91it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 56%|█████▋    | 184/326 [00:22<00:35,  3.95it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 185/326 [00:22<00:35,  3.99it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 186/326 [00:22<00:33,  4.13it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 57%|█████▋    | 187/326 [00:23<00:34,  4.04it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 189/326 [00:23<00:41,  3.28it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 58%|█████▊    | 190/326 [00:24<00:46,  2.92it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 59%|█████▊    | 191/326 [00:24<00:40,  3.37it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 59%|█████▉    | 192/326 [00:24<00:36,  3.64it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 59%|█████▉    | 193/326 [00:24<00:33,  3.92it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 60%|█████▉    | 194/326 [00:25<00:31,  4.20it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 60%|█████▉    | 195/326 [00:25<00:30,  4.23it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 60%|██████    | 196/326 [00:25<00:30,  4.33it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 61%|██████    | 198/326 [00:25<00:31,  4.11it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 61%|██████▏   | 200/326 [00:26<00:34,  3.64it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 201/326 [00:26<00:31,  3.97it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 202/326 [00:26<00:29,  4.22it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 62%|██████▏   | 203/326 [00:27<00:27,  4.43it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 63%|██████▎   | 204/326 [00:27<00:27,  4.46it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 63%|██████▎   | 205/326 [00:27<00:26,  4.56it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 63%|██████▎   | 206/326 [00:27<00:26,  4.52it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 63%|██████▎   | 207/326 [00:28<00:29,  4.10it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 64%|██████▍   | 208/326 [00:28<00:27,  4.25it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 64%|██████▍   | 209/326 [00:28<00:26,  4.49it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 64%|██████▍   | 210/326 [00:28<00:24,  4.77it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 65%|██████▍   | 211/326 [00:29<00:31,  3.62it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 65%|██████▌   | 213/326 [00:29<00:36,  3.06it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 66%|██████▌   | 214/326 [00:30<00:31,  3.51it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 66%|██████▌   | 215/326 [00:30<00:28,  3.85it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 66%|██████▋   | 216/326 [00:30<00:27,  4.04it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 67%|██████▋   | 217/326 [00:30<00:24,  4.42it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 67%|██████▋   | 218/326 [00:30<00:24,  4.49it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 67%|██████▋   | 219/326 [00:31<00:23,  4.57it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 68%|██████▊   | 221/326 [00:31<00:26,  3.92it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 68%|██████▊   | 223/326 [00:32<00:26,  3.83it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 69%|██████▊   | 224/326 [00:32<00:24,  4.15it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 69%|██████▉   | 225/326 [00:32<00:22,  4.45it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 69%|██████▉   | 226/326 [00:32<00:22,  4.48it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 70%|██████▉   | 227/326 [00:32<00:21,  4.69it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 70%|██████▉   | 228/326 [00:33<00:20,  4.69it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 70%|███████   | 229/326 [00:33<00:20,  4.84it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 71%|███████   | 230/326 [00:33<00:23,  4.07it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 71%|███████   | 231/326 [00:33<00:22,  4.21it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 71%|███████   | 232/326 [00:34<00:21,  4.42it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 72%|███████▏  | 234/326 [00:34<00:22,  4.14it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 73%|███████▎  | 237/326 [00:36<00:35,  2.49it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 73%|███████▎  | 238/326 [00:36<00:30,  2.86it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 73%|███████▎  | 239/326 [00:36<00:27,  3.22it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 74%|███████▎  | 240/326 [00:36<00:24,  3.47it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 74%|███████▍  | 241/326 [00:37<00:23,  3.57it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 74%|███████▍  | 242/326 [00:37<00:22,  3.69it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 75%|███████▍  | 243/326 [00:37<00:27,  3.02it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 75%|███████▍  | 244/326 [00:38<00:33,  2.44it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 75%|███████▌  | 245/326 [00:38<00:29,  2.77it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 75%|███████▌  | 246/326 [00:39<00:31,  2.54it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 76%|███████▌  | 247/326 [00:39<00:26,  2.93it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 76%|███████▋  | 249/326 [00:39<00:27,  2.83it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 77%|███████▋  | 250/326 [00:40<00:24,  3.13it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 77%|███████▋  | 251/326 [00:40<00:21,  3.49it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 78%|███████▊  | 253/326 [00:41<00:25,  2.82it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 78%|███████▊  | 254/326 [00:41<00:24,  2.98it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 78%|███████▊  | 255/326 [00:41<00:22,  3.09it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 79%|███████▉  | 257/326 [00:42<00:25,  2.70it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 79%|███████▉  | 258/326 [00:42<00:22,  2.97it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 79%|███████▉  | 259/326 [00:43<00:26,  2.49it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 80%|███████▉  | 260/326 [00:43<00:23,  2.81it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 80%|████████  | 261/326 [00:43<00:21,  2.99it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 80%|████████  | 262/326 [00:44<00:19,  3.30it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 81%|████████  | 263/326 [00:44<00:18,  3.44it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 81%|████████  | 264/326 [00:44<00:22,  2.73it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 81%|████████▏ | 265/326 [00:45<00:20,  3.02it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 82%|████████▏ | 266/326 [00:45<00:21,  2.84it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 82%|████████▏ | 267/326 [00:46<00:23,  2.48it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 82%|████████▏ | 268/326 [00:46<00:20,  2.80it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 83%|████████▎ | 269/326 [00:46<00:23,  2.47it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k2__sigma_0 is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 83%|████████▎ | 270/326 [00:47<00:20,  2.77it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 83%|████████▎ | 271/326 [00:47<00:18,  2.92it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 83%|████████▎ | 272/326 [00:47<00:17,  3.11it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 84%|████████▎ | 273/326 [00:47<00:15,  3.40it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 84%|████████▍ | 274/326 [00:48<00:15,  3.37it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 85%|████████▍ | 276/326 [00:49<00:18,  2.77it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 85%|████████▍ | 277/326 [00:49<00:15,  3.07it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 85%|████████▌ | 278/326 [00:49<00:14,  3.31it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 279/326 [00:49<00:13,  3.53it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 280/326 [00:50<00:15,  2.96it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 86%|████████▌ | 281/326 [00:50<00:14,  3.05it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 87%|████████▋ | 282/326 [00:50<00:14,  2.96it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 87%|████████▋ | 283/326 [00:51<00:12,  3.41it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 87%|████████▋ | 284/326 [00:51<00:11,  3.73it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 87%|████████▋ | 285/326 [00:51<00:10,  3.96it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 88%|████████▊ | 286/326 [00:51<00:09,  4.15it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 88%|████████▊ | 287/326 [00:52<00:09,  4.12it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 88%|████████▊ | 288/326 [00:52<00:08,  4.28it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 89%|████████▊ | 289/326 [00:52<00:09,  4.04it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 89%|████████▉ | 290/326 [00:52<00:09,  3.88it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 89%|████████▉ | 291/326 [00:52<00:08,  4.22it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 90%|████████▉ | 292/326 [00:53<00:08,  3.82it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 90%|████████▉ | 293/326 [00:53<00:08,  4.03it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 90%|█████████ | 294/326 [00:53<00:07,  4.20it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 90%|█████████ | 295/326 [00:54<00:08,  3.72it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 91%|█████████ | 296/326 [00:54<00:07,  3.89it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 91%|█████████ | 297/326 [00:54<00:07,  4.12it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 91%|█████████▏| 298/326 [00:54<00:06,  4.21it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 92%|█████████▏| 299/326 [00:55<00:07,  3.41it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 92%|█████████▏| 300/326 [00:55<00:07,  3.34it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 92%|█████████▏| 301/326 [00:55<00:06,  3.84it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 302/326 [00:55<00:05,  4.13it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 303/326 [00:56<00:06,  3.64it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 93%|█████████▎| 304/326 [00:56<00:06,  3.40it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 94%|█████████▎| 305/326 [00:56<00:06,  3.22it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 94%|█████████▍| 306/326 [00:57<00:05,  3.60it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 94%|█████████▍| 307/326 [00:57<00:05,  3.80it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 94%|█████████▍| 308/326 [00:57<00:05,  3.47it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 95%|█████████▍| 309/326 [00:57<00:04,  3.81it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 95%|█████████▌| 310/326 [00:58<00:04,  3.50it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 95%|█████████▌| 311/326 [00:58<00:03,  3.80it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 96%|█████████▌| 312/326 [00:58<00:03,  3.95it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 96%|█████████▌| 313/326 [00:58<00:03,  4.16it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 96%|█████████▋| 314/326 [00:59<00:02,  4.22it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k2__length_scale is close to the specified upper bound 100000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 97%|█████████▋| 315/326 [00:59<00:02,  3.86it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 97%|█████████▋| 316/326 [00:59<00:02,  4.24it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 97%|█████████▋| 317/326 [00:59<00:02,  4.44it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 98%|█████████▊| 318/326 [01:00<00:01,  4.37it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 98%|█████████▊| 319/326 [01:00<00:01,  4.54it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 98%|█████████▊| 320/326 [01:00<00:01,  4.73it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 98%|█████████▊| 321/326 [01:00<00:01,  4.89it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 99%|█████████▉| 322/326 [01:01<00:01,  3.52it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 99%|█████████▉| 323/326 [01:01<00:00,  3.53it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      " 99%|█████████▉| 324/326 [01:01<00:00,  3.65it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "100%|█████████▉| 325/326 [01:01<00:00,  4.03it/s]c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:419: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified lower bound 1e-09. Decreasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "100%|██████████| 326/326 [01:02<00:00,  5.25it/s]\n"
     ]
    },
    {
     "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>Date</th>\n",
       "      <th>ex_factory_volumes</th>\n",
       "      <th>INNOVIX_months_of_treatment</th>\n",
       "      <th>YREX_months_of_treatment</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind1</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind2</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind3</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind4</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind5</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind6</th>\n",
       "      <th>...</th>\n",
       "      <th>YREX_indication_split_ind14</th>\n",
       "      <th>YREX_indication_split_ind15</th>\n",
       "      <th>YREX_indication_split_ind16</th>\n",
       "      <th>YREX_indication_split_ind17</th>\n",
       "      <th>YREX_indication_split_ind18</th>\n",
       "      <th>YREX_indication_split_ind19</th>\n",
       "      <th>YREX_indication_split_ind20</th>\n",
       "      <th>YREX_indication_split_ind21</th>\n",
       "      <th>YREX_indication_split_ind22</th>\n",
       "      <th>YREX_indication_split_ind23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>8.200000e+01</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.500000</td>\n",
       "      <td>3.917054e+06</td>\n",
       "      <td>7446.626009</td>\n",
       "      <td>13006.329038</td>\n",
       "      <td>1.580986e+02</td>\n",
       "      <td>204.496963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.715763</td>\n",
       "      <td>0.022359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.042814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.815261</td>\n",
       "      <td>5.189044e+05</td>\n",
       "      <td>1024.907299</td>\n",
       "      <td>9254.033465</td>\n",
       "      <td>1.648592e+02</td>\n",
       "      <td>238.228542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090384</td>\n",
       "      <td>0.012689</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.016581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.611020e+06</td>\n",
       "      <td>6009.244155</td>\n",
       "      <td>-2642.730998</td>\n",
       "      <td>-4.043357e-10</td>\n",
       "      <td>-0.231251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.536082</td>\n",
       "      <td>0.002891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.250000</td>\n",
       "      <td>3.547195e+06</td>\n",
       "      <td>6656.770708</td>\n",
       "      <td>5182.256030</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.643947</td>\n",
       "      <td>0.017278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.039980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.500000</td>\n",
       "      <td>3.834920e+06</td>\n",
       "      <td>7174.118900</td>\n",
       "      <td>13473.133950</td>\n",
       "      <td>1.262500e+02</td>\n",
       "      <td>184.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727664</td>\n",
       "      <td>0.017408</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.750000</td>\n",
       "      <td>4.292570e+06</td>\n",
       "      <td>8190.293917</td>\n",
       "      <td>20827.303031</td>\n",
       "      <td>3.120000e+02</td>\n",
       "      <td>286.250000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.797725</td>\n",
       "      <td>0.026568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>5.303380e+06</td>\n",
       "      <td>10448.201792</td>\n",
       "      <td>28975.411650</td>\n",
       "      <td>5.990000e+02</td>\n",
       "      <td>1278.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017831</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.832286</td>\n",
       "      <td>0.064739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.086929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 326 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  ex_factory_volumes  INNOVIX_months_of_treatment  \\\n",
       "count  82.000000        8.200000e+01                    82.000000   \n",
       "mean   40.500000        3.917054e+06                  7446.626009   \n",
       "std    23.815261        5.189044e+05                  1024.907299   \n",
       "min     0.000000        2.611020e+06                  6009.244155   \n",
       "25%    20.250000        3.547195e+06                  6656.770708   \n",
       "50%    40.500000        3.834920e+06                  7174.118900   \n",
       "75%    60.750000        4.292570e+06                  8190.293917   \n",
       "max    81.000000        5.303380e+06                 10448.201792   \n",
       "\n",
       "       YREX_months_of_treatment  INNOVIX_face-to-face-call_ind1  \\\n",
       "count                 82.000000                    8.200000e+01   \n",
       "mean               13006.329038                    1.580986e+02   \n",
       "std                 9254.033465                    1.648592e+02   \n",
       "min                -2642.730998                   -4.043357e-10   \n",
       "25%                 5182.256030                    0.000000e+00   \n",
       "50%                13473.133950                    1.262500e+02   \n",
       "75%                20827.303031                    3.120000e+02   \n",
       "max                28975.411650                    5.990000e+02   \n",
       "\n",
       "       INNOVIX_face-to-face-call_ind2  INNOVIX_face-to-face-call_ind3  \\\n",
       "count                       82.000000                            82.0   \n",
       "mean                       204.496963                             0.0   \n",
       "std                        238.228542                             0.0   \n",
       "min                         -0.231251                             0.0   \n",
       "25%                          0.000000                             0.0   \n",
       "50%                        184.750000                             0.0   \n",
       "75%                        286.250000                             0.0   \n",
       "max                       1278.000000                             0.0   \n",
       "\n",
       "       INNOVIX_face-to-face-call_ind4  INNOVIX_face-to-face-call_ind5  \\\n",
       "count                            82.0                            82.0   \n",
       "mean                              0.0                             0.0   \n",
       "std                               0.0                             0.0   \n",
       "min                               0.0                             0.0   \n",
       "25%                               0.0                             0.0   \n",
       "50%                               0.0                             0.0   \n",
       "75%                               0.0                             0.0   \n",
       "max                               0.0                             0.0   \n",
       "\n",
       "       INNOVIX_face-to-face-call_ind6  ...  YREX_indication_split_ind14  \\\n",
       "count                            82.0  ...                    82.000000   \n",
       "mean                              0.0  ...                     0.003324   \n",
       "std                               0.0  ...                     0.005165   \n",
       "min                               0.0  ...                     0.000000   \n",
       "25%                               0.0  ...                     0.000000   \n",
       "50%                               0.0  ...                     0.000027   \n",
       "75%                               0.0  ...                     0.006401   \n",
       "max                               0.0  ...                     0.017831   \n",
       "\n",
       "       YREX_indication_split_ind15  YREX_indication_split_ind16  \\\n",
       "count                         82.0                         82.0   \n",
       "mean                           0.0                          0.0   \n",
       "std                            0.0                          0.0   \n",
       "min                            0.0                          0.0   \n",
       "25%                            0.0                          0.0   \n",
       "50%                            0.0                          0.0   \n",
       "75%                            0.0                          0.0   \n",
       "max                            0.0                          0.0   \n",
       "\n",
       "       YREX_indication_split_ind17  YREX_indication_split_ind18  \\\n",
       "count                         82.0                         82.0   \n",
       "mean                           0.0                          0.0   \n",
       "std                            0.0                          0.0   \n",
       "min                            0.0                          0.0   \n",
       "25%                            0.0                          0.0   \n",
       "50%                            0.0                          0.0   \n",
       "75%                            0.0                          0.0   \n",
       "max                            0.0                          0.0   \n",
       "\n",
       "       YREX_indication_split_ind19  YREX_indication_split_ind20  \\\n",
       "count                    82.000000                    82.000000   \n",
       "mean                      0.715763                     0.022359   \n",
       "std                       0.090384                     0.012689   \n",
       "min                       0.536082                     0.002891   \n",
       "25%                       0.643947                     0.017278   \n",
       "50%                       0.727664                     0.017408   \n",
       "75%                       0.797725                     0.026568   \n",
       "max                       0.832286                     0.064739   \n",
       "\n",
       "       YREX_indication_split_ind21  YREX_indication_split_ind22  \\\n",
       "count                         82.0                         82.0   \n",
       "mean                           0.0                          0.0   \n",
       "std                            0.0                          0.0   \n",
       "min                            0.0                          0.0   \n",
       "25%                            0.0                          0.0   \n",
       "50%                            0.0                          0.0   \n",
       "75%                            0.0                          0.0   \n",
       "max                            0.0                          0.0   \n",
       "\n",
       "       YREX_indication_split_ind23  \n",
       "count                    82.000000  \n",
       "mean                      0.042814  \n",
       "std                       0.016581  \n",
       "min                       0.002189  \n",
       "25%                       0.039980  \n",
       "50%                       0.040018  \n",
       "75%                       0.049310  \n",
       "max                       0.086929  \n",
       "\n",
       "[8 rows x 326 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fill_missing_values_with_gp(df):\n",
    "    \"\"\"\n",
    "    Fill missing values in the dataframe using Gaussian Processes.\n",
    "    \"\"\"\n",
    "    for col in tqdm(df.columns):\n",
    "        if df[col].isna().sum() > 0:  # Process columns with missing values\n",
    "            # Extract observed data\n",
    "            observed = df[col].dropna()\n",
    "            X_observed = np.array(observed.index).reshape(-1, 1)  # Reshape index\n",
    "            y_observed = observed.values\n",
    "\n",
    "            # Extract missing data locations\n",
    "            X_missing = np.array(df[df[col].isna()].index).reshape(-1, 1)\n",
    "\n",
    "            # Define Gaussian Process\n",
    "            # kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))\n",
    "            PARAM_RANGES = (1e-9, 1e5)\n",
    "\n",
    "            kernel = WhiteKernel(0.1, PARAM_RANGES)\n",
    "            kernel += ConstantKernel(1, PARAM_RANGES) * RBF(1, PARAM_RANGES)\n",
    "            kernel += ConstantKernel(1, PARAM_RANGES) * DotProduct(1, PARAM_RANGES)\n",
    "            gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10, alpha=1e-2)\n",
    "\n",
    "            # Fit GP to observed data\n",
    "            gp.fit(X_observed, y_observed)\n",
    "\n",
    "            # Predict missing values\n",
    "            y_missing_pred, sigma = gp.predict(X_missing, return_std=True)\n",
    "\n",
    "            # Fill missing values in the original dataframe\n",
    "            df.loc[df[col].isna(), col] = y_missing_pred\n",
    "    return df\n",
    "\n",
    "df_filled = fill_missing_values_with_gp(df)\n",
    "df_filled.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from statsmodels.tsa.vector_ar.var_model import VAR\n",
    "\n",
    "\n",
    "\n",
    "def generate_forecasts(positive_drop: float,positive_columns, negative_drop:float,negative_columns, maxlags: int = 5, drop_duration: int = 5):\n",
    "    # Preprocessing the data\n",
    "    dff = df_filled.set_index([\"Date\"], drop=False)\n",
    "\n",
    "    X = dff.drop(columns=[\"ex_factory_volumes\"])\n",
    "    # Drop from X all the columns that have 0 std\n",
    "    X = X.loc[:, X.std() != 0]\n",
    "    # y = dff[\"ex_factory_volumes\"]\n",
    "    \n",
    "    X_means = X.mean()\n",
    "    X_stds = X.std()\n",
    "\n",
    "    X_means[\"Date\"] = 0\n",
    "    X_stds[\"Date\"] = 1\n",
    "    \n",
    "    X = (X - X_means) / X_stds\n",
    "    \n",
    "    \n",
    "\n",
    "    # y_mean = y.mean()\n",
    "    # y_std = y.std()\n",
    "    # y = (y - y.mean()) / y.std()\n",
    "\n",
    "    # Combine X and y for VAR model\n",
    "    # data = pd.concat([X, y.rename(\"ex_factory_volumes\")], axis=1)\n",
    "    # display(data.describe())\n",
    "    data = X\n",
    "\n",
    "    # Initialize results container\n",
    "    stds = []\n",
    "    start_time = int(X.iloc[-1][\"Date\"])\n",
    "\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "        \n",
    "        model = VAR(data)\n",
    "        results = model.fit(maxlags=maxlags)\n",
    "        \n",
    "        if not results.is_stable:\n",
    "            raise ValueError(\"Model is unstable. Check data or adjust lags.\")\n",
    "        num_dropped = 0\n",
    "        for i in tqdm(range(40)):\n",
    "            forecast_input = data.values[-results.k_ar:]\n",
    "            forecast = results.forecast(y=forecast_input, steps=1)\n",
    "            # print(forecast)            \n",
    "            \n",
    "            # Create the new sample\n",
    "            new_sample = {col: val for col, val in zip(data.columns, forecast[0])}\n",
    "            new_sample[\"Date\"] = start_time\n",
    "            \n",
    "            # Introduce a simulated change at a specific future timestamp (e.g., 100)\n",
    "            # print(start_time, num_dropped)\n",
    "            if start_time >= 81 + 12 and num_dropped <= drop_duration:\n",
    "                print(\"DIO MERDA\")\n",
    "                for positive_column in positive_columns:\n",
    "                    for col in data.columns:\n",
    "                        if col == positive_column:\n",
    "                            de_norm = new_sample[col] * X_stds[positive_column] + X_means[positive_column]\n",
    "                            new_sample[col] = (de_norm * (1 + positive_drop) - X_means[positive_column]) / X_stds[positive_column]  \n",
    "                \n",
    "                for negative_column in negative_columns:\n",
    "                    for col in data.columns:\n",
    "                        if col == negative_column:\n",
    "                            de_norm = new_sample[col] * X_stds[negative_column] + X_means[negative_column]\n",
    "                            new_sample[col] = (de_norm * (1 - negative_drop) - X_means[negative_column]) / X_stds[negative_column]\n",
    "                num_dropped += 1\n",
    "                        # new_sample[col] -= drop_amount  # Simulate a decrease\n",
    "                    # elif col != \"Date\":\n",
    "                    #     new_sample[col] = np.random.normal(data[col].mean(), data[col].std())\n",
    "            \n",
    "            # Append the new sample\n",
    "            data = pd.concat([data, pd.DataFrame.from_dict([new_sample])], ignore_index=True)\n",
    "            \n",
    "            # Update the model\n",
    "            model = VAR(data)\n",
    "            results = model.fit(maxlags=maxlags)\n",
    "            if not results.is_stable:\n",
    "                print(f\"Warning: Model became unstable at iteration {i}.\")\n",
    "            \n",
    "            start_time += 1\n",
    "            \n",
    "    # print(results.params)\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 16/40 [00:00<00:00, 32.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DIO MERDA\n",
      "DIO MERDA\n",
      "DIO MERDA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [00:01<00:00, 27.18it/s]\n",
      " 40%|████      | 16/40 [00:00<00:00, 30.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DIO MERDA\n",
      "DIO MERDA\n",
      "DIO MERDA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [00:01<00:00, 26.25it/s]\n",
      " 40%|████      | 16/40 [00:00<00:00, 30.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DIO MERDA\n",
      "DIO MERDA\n",
      "DIO MERDA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [00:01<00:00, 26.42it/s]\n",
      " 40%|████      | 16/40 [00:00<00:00, 32.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DIO MERDA\n",
      "DIO MERDA\n",
      "DIO MERDA\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 40/40 [00:01<00:00, 26.06it/s]\n"
     ]
    }
   ],
   "source": [
    "datas = []\n",
    "for drop_amount in [0, 0.1, 0.2, 0.3]:\n",
    "    data = generate_forecasts(positive_drop=drop_amount,\n",
    "                                positive_columns=[\"YREX_months_of_treatment\"],\n",
    "                                negative_drop=drop_amount,\n",
    "                                negative_columns=[\"INNOVIX_months_of_treatment\"],\n",
    "                                drop_duration=2, maxlags=15)\n",
    "    # y_denorm = (data[\"ex_factory_volumes\"] * y_std) + y_mean\n",
    "    # y_denorm = (data[\"ex_factory_volumes\"])\n",
    "    \n",
    "    # stds = np.array(stds) * (y_std)\n",
    "    # datas.append((data, stds, y_mean, y_std, results))\n",
    "    datas.append(data)\n",
    "    # plt.plot(y_denorm, label=f\"d:{drop_amount}\")\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k1__noise_level is close to the specified upper bound 1000000000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Luca\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\gaussian_process\\kernels.py:429: ConvergenceWarning: The optimal value found for dimension 0 of parameter k2__k1__constant_value is close to the specified upper bound 1000000000.0. Increasing the bound and calling fit again may find a better value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train = datas[1].iloc[:82]\n",
    "y_train = df_filled[\"ex_factory_volumes\"]\n",
    "X_test = datas[1].iloc[82:]\n",
    "\n",
    "# Define the gaussian process\n",
    "PARAM_RANGES = (1e-9, 1e9)\n",
    "kernel = WhiteKernel(0.1, PARAM_RANGES)\n",
    "kernel += ConstantKernel(1, PARAM_RANGES) * RBF(1, PARAM_RANGES)\n",
    "kernel += ConstantKernel(1, PARAM_RANGES) * DotProduct(1, PARAM_RANGES)\n",
    "\n",
    "gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)\n",
    "\n",
    "# Fit the model\n",
    "gp.fit(X_train, y_train)\n",
    "\n",
    "# Predict the test set\n",
    "pred, std = gp.predict(X_test, return_std=True)\n",
    "\n",
    "# Plot the results\n",
    "plot_gp(pred=pd.Series(index=range(len(pred)), data=pred), std=std, figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f883cd4e20>]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(datas[2][\"YREX_months_of_treatment\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_denorm = (y * y_std) + y_mean\n",
    "data, stds, y_mean, y_std, results = datas[0]\n",
    "y_denorm = data[\"ex_factory_volumes\"] * y_std + y_mean\n",
    "\n",
    "plot_gp(\n",
    "    pred = pd.Series(index=range(len(y_denorm[-len(stds):])), data=y_denorm[-len(stds):].values), \n",
    "    samples = pd.Series(index=range(len(y_denorm[-len(stds):])), data=y_denorm[-len(stds):].values), \n",
    "    std = np.array(stds),\n",
    "    figsize = (16, 8)\n",
    ")\n",
    "\n",
    "# plt.ylim((0, 1e7))\n",
    "# plt.plot(range(len(df_truth[\"Value\"])), df_truth[\"Value\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 10000000.0)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_denorm = (y * y_std) + y_mean\n",
    "data, stds, y_mean, y_std, results = datas[-1]\n",
    "y_denorm = data[\"ex_factory_volumes\"] * y_std + y_mean\n",
    "\n",
    "plot_gp(\n",
    "    pred = pd.Series(index=range(len(y_denorm[-len(stds):])), data=y_denorm[-len(stds):].values), \n",
    "    samples = pd.Series(index=range(len(y_denorm[-len(stds):])), data=y_denorm[-len(stds):].values), \n",
    "    std = np.array(stds),\n",
    "    figsize = (16, 8)\n",
    ")\n",
    "\n",
    "plt.ylim((0, 1e7))\n",
    "# plt.plot(range(len(df_truth[\"Value\"])), df_truth[\"Value\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6424798697671388\n"
     ]
    }
   ],
   "source": [
    "print(df_filled.corr()[\"ex_factory_volumes\"][\"YREX_months_of_treatment\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>ex_factory_volumes</th>\n",
       "      <th>INNOVIX_months_of_treatment</th>\n",
       "      <th>YREX_months_of_treatment</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind1</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind2</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind3</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind4</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind5</th>\n",
       "      <th>INNOVIX_face-to-face-call_ind6</th>\n",
       "      <th>...</th>\n",
       "      <th>YREX_indication_split_ind14</th>\n",
       "      <th>YREX_indication_split_ind15</th>\n",
       "      <th>YREX_indication_split_ind16</th>\n",
       "      <th>YREX_indication_split_ind17</th>\n",
       "      <th>YREX_indication_split_ind18</th>\n",
       "      <th>YREX_indication_split_ind19</th>\n",
       "      <th>YREX_indication_split_ind20</th>\n",
       "      <th>YREX_indication_split_ind21</th>\n",
       "      <th>YREX_indication_split_ind22</th>\n",
       "      <th>YREX_indication_split_ind23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.606794</td>\n",
       "      <td>0.826575</td>\n",
       "      <td>0.994663</td>\n",
       "      <td>0.639561</td>\n",
       "      <td>0.321288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.687237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.954187</td>\n",
       "      <td>-0.017581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.226815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ex_factory_volumes</th>\n",
       "      <td>0.606794</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800744</td>\n",
       "      <td>0.642480</td>\n",
       "      <td>0.487303</td>\n",
       "      <td>0.393369</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.404448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.590860</td>\n",
       "      <td>-0.125684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.049318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNOVIX_months_of_treatment</th>\n",
       "      <td>0.826575</td>\n",
       "      <td>0.800744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.862735</td>\n",
       "      <td>0.578928</td>\n",
       "      <td>0.279318</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.561562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.798746</td>\n",
       "      <td>-0.081357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.129130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YREX_months_of_treatment</th>\n",
       "      <td>0.994663</td>\n",
       "      <td>0.642480</td>\n",
       "      <td>0.862735</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.639173</td>\n",
       "      <td>0.330800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.953726</td>\n",
       "      <td>-0.004640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.224425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INNOVIX_face-to-face-call_ind1</th>\n",
       "      <td>0.639561</td>\n",
       "      <td>0.487303</td>\n",
       "      <td>0.578928</td>\n",
       "      <td>0.639173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.541943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.597931</td>\n",
       "      <td>0.009304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.198675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YREX_indication_split_ind19</th>\n",
       "      <td>-0.954187</td>\n",
       "      <td>-0.590860</td>\n",
       "      <td>-0.798746</td>\n",
       "      <td>-0.953726</td>\n",
       "      <td>-0.597931</td>\n",
       "      <td>-0.280964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.678357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.236731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YREX_indication_split_ind20</th>\n",
       "      <td>-0.017581</td>\n",
       "      <td>-0.125684</td>\n",
       "      <td>-0.081357</td>\n",
       "      <td>-0.004640</td>\n",
       "      <td>0.009304</td>\n",
       "      <td>0.026916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.342290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.345566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YREX_indication_split_ind21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YREX_indication_split_ind22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YREX_indication_split_ind23</th>\n",
       "      <td>0.226815</td>\n",
       "      <td>-0.049318</td>\n",
       "      <td>0.129130</td>\n",
       "      <td>0.224425</td>\n",
       "      <td>0.198675</td>\n",
       "      <td>-0.214485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.073300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.236731</td>\n",
       "      <td>0.345566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>326 rows × 326 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Date  ex_factory_volumes  \\\n",
       "Date                            1.000000            0.606794   \n",
       "ex_factory_volumes              0.606794            1.000000   \n",
       "INNOVIX_months_of_treatment     0.826575            0.800744   \n",
       "YREX_months_of_treatment        0.994663            0.642480   \n",
       "INNOVIX_face-to-face-call_ind1  0.639561            0.487303   \n",
       "...                                  ...                 ...   \n",
       "YREX_indication_split_ind19    -0.954187           -0.590860   \n",
       "YREX_indication_split_ind20    -0.017581           -0.125684   \n",
       "YREX_indication_split_ind21          NaN                 NaN   \n",
       "YREX_indication_split_ind22          NaN                 NaN   \n",
       "YREX_indication_split_ind23     0.226815           -0.049318   \n",
       "\n",
       "                                INNOVIX_months_of_treatment  \\\n",
       "Date                                               0.826575   \n",
       "ex_factory_volumes                                 0.800744   \n",
       "INNOVIX_months_of_treatment                        1.000000   \n",
       "YREX_months_of_treatment                           0.862735   \n",
       "INNOVIX_face-to-face-call_ind1                     0.578928   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                       -0.798746   \n",
       "YREX_indication_split_ind20                       -0.081357   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                        0.129130   \n",
       "\n",
       "                                YREX_months_of_treatment  \\\n",
       "Date                                            0.994663   \n",
       "ex_factory_volumes                              0.642480   \n",
       "INNOVIX_months_of_treatment                     0.862735   \n",
       "YREX_months_of_treatment                        1.000000   \n",
       "INNOVIX_face-to-face-call_ind1                  0.639173   \n",
       "...                                                  ...   \n",
       "YREX_indication_split_ind19                    -0.953726   \n",
       "YREX_indication_split_ind20                    -0.004640   \n",
       "YREX_indication_split_ind21                          NaN   \n",
       "YREX_indication_split_ind22                          NaN   \n",
       "YREX_indication_split_ind23                     0.224425   \n",
       "\n",
       "                                INNOVIX_face-to-face-call_ind1  \\\n",
       "Date                                                  0.639561   \n",
       "ex_factory_volumes                                    0.487303   \n",
       "INNOVIX_months_of_treatment                           0.578928   \n",
       "YREX_months_of_treatment                              0.639173   \n",
       "INNOVIX_face-to-face-call_ind1                        1.000000   \n",
       "...                                                        ...   \n",
       "YREX_indication_split_ind19                          -0.597931   \n",
       "YREX_indication_split_ind20                           0.009304   \n",
       "YREX_indication_split_ind21                                NaN   \n",
       "YREX_indication_split_ind22                                NaN   \n",
       "YREX_indication_split_ind23                           0.198675   \n",
       "\n",
       "                                INNOVIX_face-to-face-call_ind2  \\\n",
       "Date                                                  0.321288   \n",
       "ex_factory_volumes                                    0.393369   \n",
       "INNOVIX_months_of_treatment                           0.279318   \n",
       "YREX_months_of_treatment                              0.330800   \n",
       "INNOVIX_face-to-face-call_ind1                        0.541943   \n",
       "...                                                        ...   \n",
       "YREX_indication_split_ind19                          -0.280964   \n",
       "YREX_indication_split_ind20                           0.026916   \n",
       "YREX_indication_split_ind21                                NaN   \n",
       "YREX_indication_split_ind22                                NaN   \n",
       "YREX_indication_split_ind23                          -0.214485   \n",
       "\n",
       "                                INNOVIX_face-to-face-call_ind3  \\\n",
       "Date                                                       NaN   \n",
       "ex_factory_volumes                                         NaN   \n",
       "INNOVIX_months_of_treatment                                NaN   \n",
       "YREX_months_of_treatment                                   NaN   \n",
       "INNOVIX_face-to-face-call_ind1                             NaN   \n",
       "...                                                        ...   \n",
       "YREX_indication_split_ind19                                NaN   \n",
       "YREX_indication_split_ind20                                NaN   \n",
       "YREX_indication_split_ind21                                NaN   \n",
       "YREX_indication_split_ind22                                NaN   \n",
       "YREX_indication_split_ind23                                NaN   \n",
       "\n",
       "                                INNOVIX_face-to-face-call_ind4  \\\n",
       "Date                                                       NaN   \n",
       "ex_factory_volumes                                         NaN   \n",
       "INNOVIX_months_of_treatment                                NaN   \n",
       "YREX_months_of_treatment                                   NaN   \n",
       "INNOVIX_face-to-face-call_ind1                             NaN   \n",
       "...                                                        ...   \n",
       "YREX_indication_split_ind19                                NaN   \n",
       "YREX_indication_split_ind20                                NaN   \n",
       "YREX_indication_split_ind21                                NaN   \n",
       "YREX_indication_split_ind22                                NaN   \n",
       "YREX_indication_split_ind23                                NaN   \n",
       "\n",
       "                                INNOVIX_face-to-face-call_ind5  \\\n",
       "Date                                                       NaN   \n",
       "ex_factory_volumes                                         NaN   \n",
       "INNOVIX_months_of_treatment                                NaN   \n",
       "YREX_months_of_treatment                                   NaN   \n",
       "INNOVIX_face-to-face-call_ind1                             NaN   \n",
       "...                                                        ...   \n",
       "YREX_indication_split_ind19                                NaN   \n",
       "YREX_indication_split_ind20                                NaN   \n",
       "YREX_indication_split_ind21                                NaN   \n",
       "YREX_indication_split_ind22                                NaN   \n",
       "YREX_indication_split_ind23                                NaN   \n",
       "\n",
       "                                INNOVIX_face-to-face-call_ind6  ...  \\\n",
       "Date                                                       NaN  ...   \n",
       "ex_factory_volumes                                         NaN  ...   \n",
       "INNOVIX_months_of_treatment                                NaN  ...   \n",
       "YREX_months_of_treatment                                   NaN  ...   \n",
       "INNOVIX_face-to-face-call_ind1                             NaN  ...   \n",
       "...                                                        ...  ...   \n",
       "YREX_indication_split_ind19                                NaN  ...   \n",
       "YREX_indication_split_ind20                                NaN  ...   \n",
       "YREX_indication_split_ind21                                NaN  ...   \n",
       "YREX_indication_split_ind22                                NaN  ...   \n",
       "YREX_indication_split_ind23                                NaN  ...   \n",
       "\n",
       "                                YREX_indication_split_ind14  \\\n",
       "Date                                               0.687237   \n",
       "ex_factory_volumes                                 0.404448   \n",
       "INNOVIX_months_of_treatment                        0.561562   \n",
       "YREX_months_of_treatment                           0.681648   \n",
       "INNOVIX_face-to-face-call_ind1                     0.560068   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                       -0.678357   \n",
       "YREX_indication_split_ind20                       -0.342290   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                        0.073300   \n",
       "\n",
       "                                YREX_indication_split_ind15  \\\n",
       "Date                                                    NaN   \n",
       "ex_factory_volumes                                      NaN   \n",
       "INNOVIX_months_of_treatment                             NaN   \n",
       "YREX_months_of_treatment                                NaN   \n",
       "INNOVIX_face-to-face-call_ind1                          NaN   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                             NaN   \n",
       "YREX_indication_split_ind20                             NaN   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                             NaN   \n",
       "\n",
       "                                YREX_indication_split_ind16  \\\n",
       "Date                                                    NaN   \n",
       "ex_factory_volumes                                      NaN   \n",
       "INNOVIX_months_of_treatment                             NaN   \n",
       "YREX_months_of_treatment                                NaN   \n",
       "INNOVIX_face-to-face-call_ind1                          NaN   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                             NaN   \n",
       "YREX_indication_split_ind20                             NaN   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                             NaN   \n",
       "\n",
       "                                YREX_indication_split_ind17  \\\n",
       "Date                                                    NaN   \n",
       "ex_factory_volumes                                      NaN   \n",
       "INNOVIX_months_of_treatment                             NaN   \n",
       "YREX_months_of_treatment                                NaN   \n",
       "INNOVIX_face-to-face-call_ind1                          NaN   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                             NaN   \n",
       "YREX_indication_split_ind20                             NaN   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                             NaN   \n",
       "\n",
       "                                YREX_indication_split_ind18  \\\n",
       "Date                                                    NaN   \n",
       "ex_factory_volumes                                      NaN   \n",
       "INNOVIX_months_of_treatment                             NaN   \n",
       "YREX_months_of_treatment                                NaN   \n",
       "INNOVIX_face-to-face-call_ind1                          NaN   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                             NaN   \n",
       "YREX_indication_split_ind20                             NaN   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                             NaN   \n",
       "\n",
       "                                YREX_indication_split_ind19  \\\n",
       "Date                                              -0.954187   \n",
       "ex_factory_volumes                                -0.590860   \n",
       "INNOVIX_months_of_treatment                       -0.798746   \n",
       "YREX_months_of_treatment                          -0.953726   \n",
       "INNOVIX_face-to-face-call_ind1                    -0.597931   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                        1.000000   \n",
       "YREX_indication_split_ind20                        0.026313   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                       -0.236731   \n",
       "\n",
       "                                YREX_indication_split_ind20  \\\n",
       "Date                                              -0.017581   \n",
       "ex_factory_volumes                                -0.125684   \n",
       "INNOVIX_months_of_treatment                       -0.081357   \n",
       "YREX_months_of_treatment                          -0.004640   \n",
       "INNOVIX_face-to-face-call_ind1                     0.009304   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                        0.026313   \n",
       "YREX_indication_split_ind20                        1.000000   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                        0.345566   \n",
       "\n",
       "                                YREX_indication_split_ind21  \\\n",
       "Date                                                    NaN   \n",
       "ex_factory_volumes                                      NaN   \n",
       "INNOVIX_months_of_treatment                             NaN   \n",
       "YREX_months_of_treatment                                NaN   \n",
       "INNOVIX_face-to-face-call_ind1                          NaN   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                             NaN   \n",
       "YREX_indication_split_ind20                             NaN   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                             NaN   \n",
       "\n",
       "                                YREX_indication_split_ind22  \\\n",
       "Date                                                    NaN   \n",
       "ex_factory_volumes                                      NaN   \n",
       "INNOVIX_months_of_treatment                             NaN   \n",
       "YREX_months_of_treatment                                NaN   \n",
       "INNOVIX_face-to-face-call_ind1                          NaN   \n",
       "...                                                     ...   \n",
       "YREX_indication_split_ind19                             NaN   \n",
       "YREX_indication_split_ind20                             NaN   \n",
       "YREX_indication_split_ind21                             NaN   \n",
       "YREX_indication_split_ind22                             NaN   \n",
       "YREX_indication_split_ind23                             NaN   \n",
       "\n",
       "                                YREX_indication_split_ind23  \n",
       "Date                                               0.226815  \n",
       "ex_factory_volumes                                -0.049318  \n",
       "INNOVIX_months_of_treatment                        0.129130  \n",
       "YREX_months_of_treatment                           0.224425  \n",
       "INNOVIX_face-to-face-call_ind1                     0.198675  \n",
       "...                                                     ...  \n",
       "YREX_indication_split_ind19                       -0.236731  \n",
       "YREX_indication_split_ind20                        0.345566  \n",
       "YREX_indication_split_ind21                             NaN  \n",
       "YREX_indication_split_ind22                             NaN  \n",
       "YREX_indication_split_ind23                        1.000000  \n",
       "\n",
       "[326 rows x 326 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filled.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8627349587704716\n"
     ]
    }
   ],
   "source": [
    "print(df_filled.corr()[\"INNOVIX_months_of_treatment\"][\"YREX_months_of_treatment\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}